ORCID Profile
0000-0002-0377-5085
Current Organisations
University of Melbourne
,
Tecnologico de Monterrey
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Photonics and Electro-Optical Engineering (excl. Communications) | Innovation and Technology Management | Colloid and Surface Chemistry | Analytical Chemistry Not Elsewhere Classified | Space maritime and aviation law | Marketing | Plant Biochemistry And Physiology | Electrical and Electronic Engineering | Plant cell and molecular biology | Horticultural Production | Oenology And Viticulture | Agricultural molecular engineering of nucleic acids and proteins | Plant biology | Consumer-Oriented Product or Service Development | Food engineering |
ANZSRC Socio-Economic Objective (SEO)
Beverages (e.g. alcohol, wines, soft drinks, excl. fruit juices) | Grapes | Industrial Instruments | Expanding Knowledge in Engineering | Expanding Knowledge in Technology | Sugar and Confectionery Products
Related Links
Publications
Optimizing the methodology to measure firmness of grape berries (Vitis vinifera L.) during ripening
Publisher: International Society for Horticultural Science (ISHS)
Date: 03-2018
Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine
Publisher: MDPI AG
Date: 25-01-2021
Abstract: Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as “ashy”, “burning rubber”, and “smoked meats”, resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned that their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send s les to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.
Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters
Publisher: MDPI AG
Date: 24-05-2020
DOI: 10.3390/S20102975
Abstract: Increased global temperatures and climatic anomalies, such as heatwaves, as a product of climate change, are impacting the heat stress levels of farm animals. These impacts could have detrimental effects on the milk quality and productivity of dairy cows. This research used four years of data from a robotic dairy farm from 36 cows with similar heat tolerance (Model 1), and all 312 cows from the farm (Model 2). These data consisted of programmed concentrate feed and weight combined with weather parameters to develop supervised machine learning fitting models to predict milk yield, fat and protein content, and actual cow concentrate feed intake. Results showed highly accurate models, which were developed for cows with a similar genetic heat tolerance (Model 1: n = 116, 456 R = 0.87 slope = 0.76) and for all cows (Model 2: n = 665, 836 R = 0.86 slope = 0.74). Furthermore, an artificial intelligence (AI) system was proposed to increase or maintain a targeted level of milk quality by reducing heat stress that could be applied to a conventional dairy farm with minimal technology addition.
Growth and Water Use Efficiency of Capsicum annuum in a Silt Loam Soil Treated Three Years Previously With a Single Compost Application and Repeatedly Dried
Publisher: Informa UK Limited
Date: 02-06-2014
Coffee Label Assessment Using Sensory and Biometric Analysis of Self-Isolating Panelists through Videoconference
Publisher: MDPI AG
Date: 21-01-2021
Abstract: Label concepts, information, logos, figures, and colors of beverages are critical for consumer perception, preference, and purchase intention. This is especially relevant for new beverage products. During social isolation, many sensory laboratories were unable to provide services, making virtual sensory sessions relevant to studying different label concepts and design preferences among consumers. This study proposed a novel virtual sensory system to analyze coffee labels using videoconference, self-reported, and biometric analysis software from video recordings to obtain sensory and emotional responses from 69 participants (power analysis: 1 − β 0.99) using six different label concepts: (i) fun, (ii) bold, (iii) natural, (iv) everyday, (v) classic, and (vi) premium. The results show that the label concept rated as having the highest perceived quality was premium, presenting significant differences (p 0.05) compared to all of the other concepts. The least perceived quality score was attributed to the bold concept due to the confronting aroma lexicon (cheese dip), which is supported by previous studies. Furthermore, even though graphics, colors, and the product name can be considered positive attributes, they do not determine perceived quality or purchase intention, which was found for the bold, everyday, and classic concepts. The findings from this study were as expected and are consistent with those from similar publications related to labels, which shows that the proposed virtual method for sensory sessions and biometrics is reliable. Further technology has been proposed to use this system with multiple participants, which could help beverage companies perform virtual sensory analysis of new products’ labels.
Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality
Publisher: Elsevier BV
Date: 04-2020
Night-time responses to water supply in grapevines (Vitis vinifera L.) under deficit irrigation and partial root-zone drying
Publisher: Elsevier BV
Date: 05-2014
Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers
Publisher: Elsevier BV
Date: 03-2019
DOI: 10.1016/J.PHYSBEH.2018.02.051
Abstract: Traditional sensory tests rely on conscious and self-reported responses from participants. The integration of non-invasive biometric techniques, such as heart rate, body temperature, brainwaves and facial expressions can gather more information from consumers while tasting a product. The main objectives of this study were i) to assess significant differences between beers for all conscious and unconscious responses, ii) to find significant correlations among the different variables from the conscious and unconscious responses and iii) to develop a model to classify beers according to liking using only the unconscious responses. For this study, an integrated camera system with video and infrared thermal imagery (IRTI), coupled with a novel computer application was used. Videos and IRTI were automatically obtained while tasting nine beers to extract biometrics (heart rate, temperature and facial expressions) using computer vision analysis. Additionally, an EEG mobile headset was used to obtain brainwave signals during beer consumption. Consumers assessed foam, color, aroma, mouthfeel, taste, flavor and overall acceptability of beers using a 9-point hedonic scale with results showing a higher acceptability for beers with higher foamability and lower bitterness. i) There were non-significant differences among beers for the emotional and physiological responses, however, significant differences were found for the cognitive and self-reported responses. ii) Results from principal component analysis explained 65% of total data variability and, along with the covariance matrix (p < 0.05), showed that there are correlations between the sensory responses of participants and the biometric data obtained. There was a negative correlation between body temperature and liking of foam height and stability, and a positive correlation between theta signals and bitterness. iii) Artificial neural networks were used to develop three models with high accuracy to classify beers according to level of liking (low and high) of three sensory descriptors: carbonation mouthfeel (82%), flavor (82%) and overall liking (81%). The integration of both sensory and biometric responses for consumer acceptance tests showed to be a reliable tool to be applied to beer tasting to obtain more information from consumers physiology, behavior and cognitive responses.
Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture
Publisher: MDPI AG
Date: 17-10-2022
DOI: 10.3390/S22207898
Abstract: When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies [...]
Rapid Detection of Rice Adulteration Using a Low-Cost Electronic Nose and Machine Learning Modelling
Publisher: MDPI
Date: 11-2022
DOI: 10.3390/ECSA-9-13291
Physiological Responses to Basic Tastes for Sensory Evaluation of Chocolate Using Biometric Techniques
Publisher: MDPI AG
Date: 05-07-2019
DOI: 10.3390/FOODS8070243
Abstract: Facial expressions are in reaction to basic tastes by the response to receptor stimulation. The objective of this study was to assess the autonomic nervous system responses to basic tastes in chocolates and to identify relationships between conscious and unconscious responses from participants. Panelists (n = 45) tasted five chocolates with either salt, citric acid, sugar, or monosodium glutamate, which generated four distinctive basic tastes plus bitter, using dark chocolate. An integrated camera system, coupled with the Bio-Sensory application, was used to capture infrared thermal images, videos, and sensory responses. Outputs were used to assess skin temperature (ST), facial expressions, and heart rate (HR) as physiological responses. Sensory responses and emotions elicited during the chocolate tasting were evaluated using the application. Results showed that the most liked was sweet chocolate (9.01), while the least liked was salty chocolate (3.61). There were significant differences for overall liking (p 0.05) but none for HR (p = 0.75) and ST (p = 0.27). Sweet chocolate was inversely associated with angry, and salty chocolate positively associated with sad. Positive emotion-terms were associated with sweet s les and liking in self-reported responses. Findings of this study may be used to assess novel tastes of chocolate in the industry based on conscious and emotional responses more objectively.
Effects of Imagery as Visual Stimuli on the Physiological and Emotional Responses
Publisher: MDPI AG
Date: 12-06-2019
DOI: 10.3390/J2020015
Abstract: Study of emotions has gained interest in the field of sensory and consumer research. Accurate information can be obtained by studying physiological behavior along with self-reported-responses. The aim was to identify physiological and self-reported-responses towards visual stimuli and predict self-reported-responses using biometrics. Panelists (N = 63) were exposed to 12 images (ten from Geneva Affective PicturE Database (GAPED), two based on common fears) and a questionnaire (Face scale and EsSense). Emotions from facial expressions (FaceReaderTM), heart rate (HR), systolic pressure (SP), diastolic pressure (DP), and skin temperature (ST) were analyzed. Multiple regression analysis was used to predict self-reported-responses based on biometrics. Results showed that physiological along with self-reported responses were able to separate images based on cluster analysis as positive, neutral, or negative according to GAPED classification. Emotional terms with high or low valence were predicted by a general linear regression model using biometrics, while calm, which is in the center of emotion dimensional model, was not predicted. After separating images, positive and neutral categories could predict all emotional terms, while negative predicted Happy, Sad, and Scared. Heart Rate predicted emotions in positive (R2 = 0.52 for Scared) and neutral (R2 = 0.55 for Sad) categories while ST in positive images (R2 = 0.55 for Sad, R2 = 0.45 for Calm).
Automated grapevine cultivar classification based on machine learning using leaf morpho-colorimetry, fractal dimension and near-infrared spectroscopy parameters
Publisher: Elsevier BV
Date: 08-2018
Chemical characterization of aromas in beer and their effect on consumers liking
Publisher: Elsevier BV
Date: 09-2019
DOI: 10.1016/J.FOODCHEM.2019.04.114
Abstract: Identification of volatiles in beer is important for consumers acceptability. In this study, triplicates of 24 beers from three types of fermentation (top/bottom/spontaneous) were analyzed using Gas Chromatograph with Mass-Selective Detector (GC-MSD) employing solid-phase microextraction (SPME). Principal components analysis was conducted for each type of fermentation. Multiple regression analysis, and an artificial neutral network model (ANN) were developed with the peak-areas of 10 volatiles to evaluate redict aroma, flavor and overall liking. There were no hops-derived volatiles in bottom-fermentation beers, but they were present in top and spontaneous. Top and spontaneous had more volatiles than bottom-fermentation. 4-Ethyguaiacol and trans-β-ionone were positive towards aroma, flavor and overall liking. Styrene had a negative effect on aroma, flavor and overall liking. An ANN model with high accuracy (R = 0.98) was obtained to predict aroma, flavor and overall liking. The use of SPME-GC-MSD is an effective method to detect volatiles in beers that contribute to acceptability.
Early Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen–Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling
Publisher: MDPI AG
Date: 09-11-2022
DOI: 10.3390/S22228645
Abstract: The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection before symptomatology appears. This paper presents the use of a low-cost and portable electronic nose coupled with machine learning (ML) models for early disease detection. Several artificial neural network models were developed to predict plant physiological data and classify processing tomato plants and soil s les according to different levels of pathogen inoculum by using e-nose outputs as inputs, plant physiological data, and the level of infection as targets. Results showed that the pattern recognition models based on different infection levels had an overall accuracy of 94.4–96.8% for tomato plants and between 94.81% and 96.22% for soil s les. For the prediction of plant physiological parameters (photosynthesis, stomatal conductance, and transpiration) using regression models or tomato plants, the overall correlation coefficient was 0.97–0.99, with very significant slope values in the range 0.97–1. The performance of all models shows no signs of under or overfitting. It is hence proven accurate and valid to use the electronic nose coupled with ML modeling for effective early disease detection of processing tomatoes and could also be further implemented to monitor other abiotic and biotic stressors.
The use of infrared thermal imaging as a non-destructive screening tool for identifying drought-tolerant lentil genotypes
Publisher: Elsevier BV
Date: 06-2018
DOI: 10.1016/J.PLAPHY.2018.03.005
Abstract: Lentil (Lens culinaris, Medik.) is an important legume crop, which often experience drought stress especially at the flowering and grain filling phenological stages. The availability of efficient and robust screening tools based on relevant non-destructive quantifiable traits would facilitate research on crop improvement for drought tolerance. The objective of this study was to evaluate the drought tolerance of 37 lentil genotypes using infrared thermal imaging (IRTI), drought tolerance parameters and multivariate data analysis. Potted plants were kept in a completely randomized design in a growth chamber with five replicates. Plants were subjected to three different drought treatments: 100, 50 and 20% of field capacity at the onset of reproductive period. The relative drought stress tolerance was determined based on a set of morpho-physiological parameters including non-destructive measures based on IRTI, such as: canopy temperature (Tc), canopy temperature depression (CTD) and crop water stress index (CWSI) during the growing period and destructive measures at harvest, such as: dry root-shoot ratio (RS ratio), relative water content (RWC) and harvest index (HI). The drought tolerance indices used were drought susceptibility index (DSI) and drought tolerance efficiency (DTE). Results showed that drought stress treatments significantly reduced the RWC, HI, CTD and DSI, whereas, the values of Tc, CWSI, RS ratio and DTE significantly increased for all the genotypes. The cluster analysis from morpho-physiological parameters clustered genotypes in three distinctive groups as per the level of drought stress tolerance. The genotypes with higher values of RS ratio, RWC, HI, DTE and CTD and lower values of DSI, Tc and CWSI were identified as drought-tolerant genotypes. Based on this preliminary screening, the genotypes Digger, Cumra, Indianhead, ILL 5588, ILL 6002 and ILL 5582 were identified as promising drought-tolerant genotypes. It can be concluded that the IRTI analysis is a high-throughput constructive screening tool along with RS ratio, RWC, HI and other drought tolerance indices to define the drought stress tolerance variability within lentil plants. These results provide a foundation for future research directed at identifying powerful drought assessment traits using rapid and non-destructive techniques, such as IRTI along with the yield traits, and understanding the biochemical and molecular mechanisms underlying lentil tolerance to drought stress.
Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies
Publisher: Elsevier BV
Date: 04-2016
Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling
Publisher: MDPI AG
Date: 16-03-2022
DOI: 10.3390/S22062303
Abstract: The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked s les with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94–96% 92–97%, respectively) and white wines (96–97% 90–97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate.
A simple field validation of daily transpiration derived from sapflow using a porometer and minimal meteorological data
Publisher: Springer Science and Business Media LLC
Date: 06-2007
Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle
Publisher: MDPI AG
Date: 23-12-2022
DOI: 10.3390/FERMENTATION9010010
Abstract: Due to increased fraud rates through counterfeiting and adulteration of wines, it is important to develop novel non-invasive techniques to assess wine quality and provenance. Assessment of quality traits and provenance of wines is predominantly undertaken with complex chemical analysis and sensory evaluation, which tend to be costly and time-consuming. Therefore, this study aimed to develop a rapid and non-invasive method to assess wine vintages and quality traits using digital technologies. S les from thirteen vintages from Dookie, Victoria, Australia (2000–2021) of Shiraz were analysed using near-infrared spectroscopy (NIR) through unopened bottles to assess the wine chemical fingerprinting. Three highly accurate machine learning (ML) models were developed using the NIR absorbance values as inputs to predict (i) wine vintage (Model 1 97.2%), (ii) intensity of sensory descriptors (Model 2 R = 0.95), and (iii) peak area of volatile aromatic compounds (Model 3 R = 0.88). The proposed method will allow the assessment of provenance and quality traits of wines without the need to open the wine bottle, which may also be used to detect wine fraud and provenance. Furthermore, low-cost NIR devices are available in the market with required spectral range and sensitivity, which can be affordable for winemakers and retailers and can be used with the machine learning models proposed here.
Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling
Publisher: MDPI AG
Date: 26-04-2022
DOI: 10.3390/CHEMOSENSORS10050159
Abstract: The success of the olive oil industry depends on provenance and quality-trait consistency affecting the consumers' acceptability reference and purchase intention. Companies rely on laboratories to analyze s les to assess consistency within the production chain, which may be time-consuming, cost-restrictive, and untimely obtaining results, making the process more reactive than predictive. This study proposed implementing digital technologies using near-infrared spectroscopy (NIR) and a novel low-cost e-nose to assess the level of rancidity and aromas in commercial extra-virgin olive oil. Four different olive oils were spiked with three rancidity levels (N = 17). These s les were evaluated using gas-chromatography-mass-spectroscopy, NIR, and an e-nose. Four machine learning models were developed to classify olive oil types and rancidity (Model 1: NIR inputs Model 2: e-nose inputs) and predict the peak area of 16 aromas (Model 3: NIR Model 4: e-nose inputs). The results showed high accuracies (Models 1–2: 97% and 87% Models 3–4: R = 0.96 and 0.93). These digital technologies may change companies from a reactive to a more predictive production of food/beverages to secure product quality and acceptability.
Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras
Publisher: MDPI AG
Date: 06-11-2020
DOI: 10.3390/S20216334
Abstract: Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting
The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence
Publisher: Cambridge University Press (CUP)
Date: 06-2022
DOI: 10.1017/S1466252321000177
Abstract: Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing strategy since it increases consumer pressure for a more humane transformation in animal treatment. Common visual welfare practices by professionals and veterinarians may be subjective and cost-prohibitive, requiring trained personnel. Recent advances in remote sensing, computer vision, and artificial intelligence (AI) have helped developing new and emerging technologies for livestock biometrics to extract key physiological parameters associated with animal welfare. This review discusses the livestock farming digital transformation by describing (i) biometric techniques for health and welfare assessment, (ii) livestock identification for traceability and (iii) machine and deep learning application in livestock to address complex problems. This review also includes a critical assessment of these topics and research done so far, proposing future steps for the deployment of AI models in commercial farms. Most studies focused on model development without applications or deployment for the industry. Furthermore, reported biometric methods, accuracy, and machine learning approaches presented some inconsistencies that hinder validation. Therefore, it is required to develop more efficient, non-contact and reliable methods based on AI to assess livestock health, welfare, and productivity.
Beer and Consumer Response Using Biometrics: Associations Assessment of Beer Compounds and Elicited Emotions
Publisher: MDPI AG
Date: 22-06-2020
DOI: 10.3390/FOODS9060821
Abstract: Some chemical compounds, especially alcohol, sugars, and alkaloids such as hordenine, have been reported as elicitors of different emotional responses. This preliminary study was based on six commercial beers selected according to their fermentation type, with two beers of each type (spontaneous, bottom, and top). Chemometry and sensory analysis were performed for all s les to determine relationships and patterns between chemical composition and emotional responses from consumers. The results showed that sweeter s les were associated with higher perceived liking by consumers and positive emotions, which corresponded to spontaneous fermentation beers. There was high correlation (R = 0.91 R2 = 0.83) between hordenine and alcohol content. Beers presenting higher concentrations of both, and higher bitterness, were related to negative emotions. Further studies should be conducted, giving more time for emotional response analysis between beer s les, and comparing alcoholic and non-alcoholic beers with similar styles, to separate the effects of alcohol and hordenine. This preliminary study was a first attempt to associate beer compounds with the emotional responses of consumers using non-invasive biometrics.
Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient
Publisher: MDPI AG
Date: 28-01-2015
DOI: 10.3390/S150202860
Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies
Publisher: MDPI AG
Date: 23-09-2021
DOI: 10.3390/S21196354
Abstract: Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice s les based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice s les into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = .01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice s les. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.
Corrigendum to: An analysis of the sensitivity of sap flux to soil and plant variables assessed for an Australian woodland using a soil - plant - atmosphere model
Publisher: CSIRO Publishing
Date: 2009
DOI: 10.1071/FP08114_CO
The use of pre-dawn leaf water potential and MODIS LAI to explore seasonal trends in the phenology of Australian and southern African woodlands and savannas
Publisher: CSIRO Publishing
Date: 2008
DOI: 10.1071/BT08079
Abstract: Trends in global soil moisture are needed to inform models of soil–plant–atmosphere interactions. Predawn leaf water potential (Ψpd), a surrogate for soil moisture and an index of plant water stress, has been routinely collected in Australian forests, woodlands and savannas, but the associated leaf area index (LAI) has seldom been available to enable the preparation of a Ψpd on LAI relationship. Following an analysis of Ψpd and MODIS LAI data from Australian forests, woodlands and savannas, we identified patterns in Ψpd which provide an understanding of the role of soil-moisture status in controlling LAI. In the savanna of northern Australia, the MODIS LAI product had a basal value of 0.96 during the dry season as compared with a mean value of 2.5 for the wet season. The dry season value is equivalent to the LAI of the tree component and corresponds with ground-truthed LAI. Ψpd is lowest (more negative) during the height of the dry season (late October) at −2.5 MPa, and highest (−0.1 MPa) during the wet season (early March). We present two models which predict Ψpd from the MODIS LAI product. These may be useful surrogates for studying trends in soil moisture in highly seasonal climates and may contribute to climate change research.
Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System
Publisher: MDPI AG
Date: 05-2019
Abstract: Artificial neural networks (ANN) have become popular for optimization and prediction of parameters in foods, beverages, agriculture and medicine. For brewing, they have been explored to develop rapid methods to assess product quality and acceptability. Different beers (N = 17) were analyzed in triplicates using a robotic pourer, RoboBEER (University of Melbourne, Melbourne, Australia), to assess 15 color and foam-related parameters using computer-vision. Those s les were tested using sensory analysis for acceptability of carbonation mouthfeel, bitterness, flavor and overall liking with 30 consumers using a 9-point hedonic scale. ANN models were developed using 17 different training algorithms with 15 color and foam-related parameters as inputs and liking of four descriptors obtained from consumers as targets. Each algorithm was tested using five, seven and ten neurons and compared to select the best model based on correlation coefficients, slope and performance (mean squared error (MSE). Bayesian Regularization algorithm with seven neurons presented the best correlation (R = 0.98) and highest performance (MSE = 0.03) with no overfitting. These models may be used as a cost-effective method for fast-screening of beers during processing to assess acceptability more efficiently. The use of RoboBEER, computer-vision algorithms and ANN will allow the implementation of an artificial intelligence system for the brewing industry to assess its effectiveness.
Computer vision and remote sensing to assess physiological responses of cattle to pre-slaughter stress, and its impact on beef quality: A review
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1016/J.MEATSCI.2019.05.007
Abstract: Pre-slaughter stress is well-known to affect meat quality of beef carcasses and methods have been developed to assess this stress. However, development of more practical and less invasive methods are required in order to assess the response of cattle to pre-slaughter stressors, which will potentially also assist with the prediction of beef quality. This review outlines the importance of pre-slaughter stress as well as existing and emerging technologies for quantification of the pre-slaughter stress. The review includes i) indicators of meat quality and how they are affected by pre-slaughter stress in cattle, ii) contact techniques that have been commonly used to measure stress indicators in animals, iii) remotely sensed imagery techniques recently used as non-invasive methods to monitor physiological and behavioural parameters and iv) potential implementation of remotely sensed imagery data to perform contactless assessment of physiological measurements, which could be related to the pre-slaughter stress, as well as to the indicators of beef quality. Relevance to industry, conclusions and recommendations for research are included.
Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms
Publisher: Wiley
Date: 08-08-2018
DOI: 10.1002/JSFA.8506
Abstract: Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam. The ANN method was able to create more accurate models (R The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry.
Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications
Publisher: Elsevier BV
Date: 10-2018
Impact of grapevine exposure to smoke on vine physiology and the composition and sensory properties of wine
Publisher: Springer Science and Business Media LLC
Date: 15-02-2016
Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning
Publisher: MDPI AG
Date: 31-10-2020
DOI: 10.3390/FERMENTATION6040104
Abstract: Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data prediction of consumer acceptability from near-infrared (Model 2 R = 0.90) and e-nose data (Model 3 R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.
Environmental factors and seasonality affect the concentration of rotundone in vitis vinifera L. Cv. shiraz wine
Publisher: Public Library of Science (PLoS)
Date: 15-07-2015
Partial rootzone drying and deficit irrigation increase stomatal sensitivity to vapour pressure deficit in anisohydric grapevines
Publisher: CSIRO Publishing
Date: 2010
DOI: 10.1071/FP09175
Abstract: The aim of this study was to investigate how alternative irrigation strategies affected grapevine (Vitis vinifera L.) stomatal response to atmospheric vapour pressure deficit (VPD). In two sites, application of partial rootzone drying (PRD) at 90–100% of crop evapotranspiration (ETc) increased stomatal sensitivity of Shiraz (Syrah) grapevines to high VPD compared with control vines irrigated with the same amount of water but applied on both sides of the vine. PRD significantly reduced vine water use (ESF) measured as sap flow and in dry conditions increased the depth of water uptake from the soil profile. In both experiments, PRD reduced vine water use by up to 50% at moderate VPD (~3 kPa) compared with control vines irrigated at the same level. In the same vines, the response to PRD applied at 100% ETc and deficit irrigation applied at 65% ETc was the same, increasing stomatal sensitivity to VPD and decreasing sap flow. Hydraulic signalling apparently did not play a role in changing stomatal sensitivity as there was no difference in stem water potentials between any of the treatment (PRD and DI) and control vines. This suggests that a long distance root-based chemical signal such as ABA may be responsible for the changes in stomatal behaviour. Shiraz grapevines have previously been classified as anisohydric-like, but application of PRD and DI increased stomatal closure in response to conditions of high evaporative demand making the vines behave in a more isohydric-like manner.
Livestock Identification Using Deep Learning for Traceability
Publisher: MDPI AG
Date: 28-10-2022
DOI: 10.3390/S22218256
Abstract: Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie c us at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group.
Effects of Three Irrigation Strategies on Gas Exchange Relationships, Plant Water Status, Yield Components and Water Productivity on Grafted Carménère Grapevines
Publisher: Frontiers Media SA
Date: 12-07-2018
Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)
Publisher: MDPI AG
Date: 11-08-2017
DOI: 10.3390/RS9080828
Influence of Label Design and Country of Origin Information in Wines on Consumers’ Visual, Sensory, and Emotional Responses
Publisher: MDPI AG
Date: 10-03-2022
DOI: 10.3390/S22062158
Abstract: This study aimed to evaluate the influence of origin information on Pinot Noir wine labels using eye-tracking and its associations with purchase intent, and hedonic and subconscious emotional responses. Two studies were carried out on untrained university staff and students aged 20–60 years old. Study 1 was conducted to assess consumers’ (n = 55 55% males, and 45% females) self-reported and subconscious responses towards four design labels (with and without New Zealand origin name/script or origin logo) using eye-tracking and video analysis to evaluate emotions of participants. In study 2, participants (n = 72, 56% males, and 44% females) blind-tasted the same wine s le from different labels while recording their self-reported responses. In study 1, no significant differences were found in fixations between origin name/script and origin logo. However, participants paid more attention to the image and the brand name on the wine labels. In study 2, no significant effects on emotional responses were found with or without the origin name/script or logo. Nonetheless, a multiple factor analysis showed either negative or no associations between the baseline (wine with no label) and the s les showing the different labels, even though the taste of the wine s les was the same, which confirmed an influence of the label on the wine appreciation. Among results from studies 1 and 2, origin information affected the purchase intent and hedonic responses marginally. These findings can be used to design wine labels for e-commerce.
Assessment of an automated digital method to estimate leaf area index (LAI) in cherry trees
Publisher: Informa UK Limited
Date: 05-08-2016
Effects of packaging design on sensory liking and willingness to purchase: A study using novel chocolate packaging
Publisher: Elsevier BV
Date: 06-2019
Development of a biosensory computer application to assess physiological and emotional responses from sensory panelists
Publisher: MDPI AG
Date: 05-09-2018
DOI: 10.3390/S18092958
Abstract: In sensory evaluation, there have been many attempts to obtain responses from the autonomic nervous system (ANS) by analyzing heart rate, body temperature, and facial expressions. However, the methods involved tend to be intrusive, which interfere with the consumers’ responses as they are more aware of the measurements. Furthermore, the existing methods to measure different ANS responses are not synchronized among them as they are measured independently. This paper discusses the development of an integrated camera system paired with an Android PC application to assess sensory evaluation and biometric responses simultaneously in the Cloud, such as heart rate, blood pressure, facial expressions, and skin-temperature changes using video and thermal images acquired by the integrated system and analyzed through computer vision algorithms written in Matlab®, and FaceReaderTM. All results can be analyzed through customized codes for multivariate data analysis, based on principal component analysis and cluster analysis. Data collected can be also used for machine-learning modeling based on biometrics as inputs and self-reported data as targets. Based on previous studies using this integrated camera and analysis system, it has shown to be a reliable, accurate, and convenient technique to complement the traditional sensory analysis of both food and nonfood products to obtain more information from consumers and/or trained panelists.
Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data
Publisher: MDPI AG
Date: 27-06-2020
DOI: 10.3390/S20133618
Abstract: Important wine quality traits such as sensory profile and color are the product of complex interactions between the soil, grapevine, the environment, management, and winemaking practices. Artificial intelligence (AI) and specifically machine learning (ML) could offer powerful tools to assess these complex interactions and their patterns through seasons to predict quality traits to winegrowers close to harvest and before winemaking. This study considered nine vintages (2008–2016) using near-infrared spectroscopy (NIR) of wines and corresponding weather and management information as inputs for artificial neural network (ANN) modeling of sensory profiles (Models 1 and 2 respectively). Furthermore, weather and management data were used as inputs to predict the color of wines (Model 3). Results showed high accuracy in the prediction of sensory profiles of vertical wine vintages using NIR (Model 1 R = 0.92 slope = 0.85), while better models were obtained using weather/management data for the prediction of sensory profiles (Model 2 R = 0.98 slope = 0.93) and wine color (Model 3 R = 0.99 slope = 0.98). For all models, there was no indication of overfitting as per ANN specific tests. These models may be used as powerful tools to winegrowers and winemakers close to harvest and before the winemaking process to maintain a determined wine style with high quality and acceptability by consumers.
Cross-cultural effects of food product familiarity on sensory acceptability and non-invasive physiological responses of consumers
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.FOODRES.2018.10.054
Abstract: This research evaluated the effects of product familiarity on the sensory acceptability and physiological responses of consumers toward different food stimuli using two populations (Asian vs. Western). Two studies were conducted: (1) an online questionnaire and (2) a tasting session. For (1), n = 102 (60% Asians and 40% Westerners) evaluated 31 food items visually for familiarity and liking whereas for (2), participants (n = 60 48% Asians and 52% Westerners) evaluated 10 different foods (tortoise jelly, chili slices, beef jerky, dried tofu, Vegemite®, durian cake, octopus chips, chocolate, corn chips, and wasabi coated peas) by tasting for familiarity and liking (visual/aroma/taste/texture/overall). A novel Android® app (Bio-sensory App) was used to capture sensory and non-invasive physiological responses (temperature, heart rate and facial expressions) of consumers. In (1), Asian and Western participants differed in their familiarity scores, visual liking ratings, and the selection of emotion terms for the stimuli. In (2), cultural differences affected familiarity and the liking scores of appearance, aroma, taste and texture of the products. While food stimuli marginally affected the physiological responses of consumers for both cultures, Asian participants elicited higher temperature values compared to those of Westerners. Both studies (1 and 2) showed that familiarity of food products was positively associated to sensory liking for both cultural groups. These findings are useful to understand consumers acceptability based on both sensory and physiological responses.
Novel insights into the mechanism(s) of silicon-induced drought stress tolerance in lentil plants revealed by RNA sequencing analysis
Publisher: Springer Science and Business Media LLC
Date: 17-10-2023
Editorial: Special Issue “Emerging Sensor Technology in Agriculture”
Publisher: MDPI AG
Date: 09-07-2020
DOI: 10.3390/S20143827
Abstract: Research and innovation activities in the area of sensor technology can accelerate the adoption of new and emerging digital tools in the agricultural sector by the implementation of precision farming practices such as remote sensing, operations, and real-time monitoring [...]
Beer Aroma and Quality Traits Assessment Using Artificial Intelligence
Publisher: MDPI AG
Date: 28-05-2020
DOI: 10.3390/FERMENTATION6020056
Abstract: Increasing beer quality demands from consumers have put pressure on brewers to target specific steps within the beer-making process to modify beer styles and quality traits. However, this demands more robust methodologies to assess the final aroma profiles and physicochemical characteristics of beers. This research shows the construction of artificial intelligence (AI) models based on aroma profiles, chemometrics, and chemical fingerprinting using near-infrared spectroscopy (NIR) obtained from 20 commercial beers used as targets. Results showed that machine learning models obtained using NIR from beers as inputs were accurate and robust in the prediction of six important aromas for beer (Model 1 R = 0.91 b = 0.87) and chemometrics (Model 2 R = 0.93 b = 0.90). Additionally, two more accurate models were obtained from robotics (RoboBEER) to obtain the same aroma profiles (Model 3 R = 0.99 b = 1.00) and chemometrics (Model 4 R = 0.98 b = 1.00). Low-cost robotics and sensors coupled with computer vision and machine learning modeling could help brewers in the decision-making process to target specific consumer preferences and to secure higher consumer demands.
Towards a spatial understanding of water use of several land-cover classes: an examination of relationships amongst pre-dawn leaf water potential, vegetation water use, aridity and MODIS LAI
Publisher: Wiley
Date: 2009
DOI: 10.1002/ECO.63
Editorial: Special Issue “Implementation of Digital Technologies on Beverage Fermentation”
Publisher: MDPI AG
Date: 15-03-2022
DOI: 10.3390/FERMENTATION8030127
Abstract: In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products’ quality traits and international competitiveness, especially within the past five years [...]
A simple field validation of daily transpiration derived from sapflow using a porometer and minimal meteorological data
Publisher: Springer Science and Business Media LLC
Date: 30-07-2008
Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence
Publisher: MDPI AG
Date: 15-07-2021
DOI: 10.3390/FERMENTATION7030117
Abstract: Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification ma-chine learning (ML) modelling. Six different ML models were developed Model 1 (M1) and M2 were developed using the NIR absorbance values (100 inputs from 1596–2396 nm) and e-nose (nine sensor readings) as inputs, respectively, to classify the s les into control, low and high concentration of faults. Model 3 (M3) and M4 were based on NIR and M5 and M6 based on the e-nose readings as inputs with 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neu-ron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 95.6%, M2 = 95.3%, M3 = 98.9%, M4 = 98.3%, M5 = 96.8%, and M6 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.
An assessment of the water budget for contrasting vegetation covers associated with waste management
Publisher: Wiley
Date: 30-04-2010
DOI: 10.1002/HYP.7570
Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme
Publisher: MDPI AG
Date: 16-07-2021
DOI: 10.3390/FERMENTATION7030119
Abstract: The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine s les as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.
Analysis of thermochromic label elements and colour transitions using sensory acceptability and eye tracking techniques
Publisher: Elsevier BV
Date: 03-2018
Digital technologies to assess yoghurt quality traits and consumers acceptability
Publisher: Wiley
Date: 10-05-2022
DOI: 10.1002/JSFA.11911
Abstract: Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and the emotional response from participants, removing variability associated with self-reported responses. The present study aimed to measure consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant-based yoghurts. The physiochemical properties of these products were also measured and linked with consumer responses. Six s les (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by n = 62 consumers using a nine-point hedonic scale. Videos from participants were recorded using the Bio-Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color (L, a and b), firmness and near-infrared (NIR) spectroscopy were also measured. Principal component analysis and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was positively related to firmness, yaw head movement and overall liking, which were further associated with the Cookies s le. Two machine learning regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R = 0.98 Model 2: R = 0.99). The presented methods were shown to be highly accurate and reliable with respect to their potential use by the industry to assess yoghurt quality traits and acceptability. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Sensory acceptability, quality and purchase intent of potato chips with reduced salt (NaCl) concentrations
Publisher: Elsevier BV
Date: 03-2019
Novel Contactless Sensors for Food, Beverage and Packaging Evaluation
Publisher: MDPI AG
Date: 26-09-2023
DOI: 10.3390/S23198082
Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms
Publisher: MDPI AG
Date: 07-09-2020
DOI: 10.3390/S20185099
Abstract: Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00% Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a s le collection of grapes for chemical analysis of smoke taint markers.
An analysis of the sensitivity of sap flux to soil and plant variables assessed for an Australian woodland using a soil - plant - atmosphere model
Publisher: CSIRO Publishing
Date: 2008
DOI: 10.1071/FP08114
Abstract: Daily and seasonal patterns of tree water use were measured for the two dominant tree species, Angophora bakeri E.C.Hall (narrow-leaved apple) and Eucalyptus sclerophylla (Blakely) L.A.S. Johnson & Blaxell (scribbly gum), in a temperate, open, evergreen woodland using sap flow sensors, along with information about soil, leaf, tree and micro-climatological variables. The aims of this work were to: (a) validate a soil–plant–atmosphere (SPA) model for the specific site (b) determine the total depth from which water uptake must occur to achieve the observed rates of tree sap flow (c) examine whether the water content of the upper soil profile was a significant determinant of daily rates of sap flow and (d) examine the sensitivity of sap flow to several biotic factors. It was found that: (a) the SPA model was able to accurately replicate the hourly, daily and seasonal patterns of sap flow (b) water uptake must have occurred from depths of up to 3 m (c) sap flow was independent of the water content of the top 80 cm of the soil profile and (d) sap flow was very sensitive to the leaf area of the stand, whole tree hydraulic conductance and the critical water potential of the leaves, but insensitive to stem capacitance and increases in root biomass. These results are important to future studies of the regulation of vegetation water use, landscape-scale behaviour of vegetation, and to water resource managers, because they allow testing of large-scale management options without the need for large-scale manipulations of vegetation cover.
Spatial variability of aroma profiles of cocoa trees obtained through computer vision and machine learning modelling: A cover photography and high spatial remote sensing application
Publisher: MDPI AG
Date: 11-07-2019
DOI: 10.3390/S19143054
Abstract: Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod s les were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82 mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality.
Novel techniques to understand consumer responses towards food products: A review with a focus on meat
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.MEATSCI.2018.06.006
Abstract: Consumer evaluation has been used extensively over the past decades to evaluate acceptability and quality of food products. New methods have been developed to overcome some biases of traditional techniques. Testing with meat is challenging due to the high variability of the meat s les. This review outlines (a) the traditional consumer-based techniques for assessing food, and available methods to link consumer responses to sensory properties (b) qualitative measurements, (c) past and current methods for evaluating meat sensory characteristics and the development of the Meat Standard Australia (MSA) protocol, and (d) psychological and physiological factors affecting consumers. Conclusions from this review are: (1) Traditional consumer testing provides important information regarding acceptability but may miss important unconscious responses of consumers (2) Qualitative methods can obtain more holistic responses from consumers, (3) The Meat Standard Australia (MSA) protocol delivers consistent scores of meat's juiciness/tenderness/flavour and overall acceptability from consumers, and (4) Physiological responses may help to understand the unbiased emotions of consumers.
Rapid measurement of total non-structural carbohydrate concentration in grapevine trunk and leaf tissues using near infrared spectroscopy
Publisher: Elsevier BV
Date: 04-2017
Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods
Publisher: Hindawi Limited
Date: 08-08-2014
DOI: 10.1111/AJGW.12098
Effect of elevated temperature on the onset and rate of mesocarp cell death in berries of Shiraz and Chardonnay and its relationship with berry shrivel
Publisher: Hindawi Limited
Date: 24-01-2013
DOI: 10.1111/AJGW.12010
The post-translational modification landscape of commercial beers
Publisher: Cold Spring Harbor Laboratory
Date: 28-01-2021
DOI: 10.1101/2021.01.27.427706
Abstract: Beer is one of the most popular beverages worldwide. As a product of variable agricultural ingredients and processes, beer has high molecular complexity. We used DIA/SWATH-MS to investigate the proteomic complexity and ersity of 23 commercial Australian beers. While the overall complexity of the beer proteome was modest, with contributions from barley and yeast proteins, we uncovered a very high ersity of post-translational modifications (PTMs), especially proteolysis, glycation, and glycosylation. Proteolysis was widespread throughout barley proteins, but showed clear site-specificity. Oligohexose modifications were common on lysines in barley proteins, consistent with glycation by maltooligosaccharides released from starch during malting or mashing. O -glycosylation consistent with oligomannose was abundant on secreted yeast glycoproteins. We developed and used data analysis pipelines to efficiently extract and quantify site-specific PTMs from SWATH-MS data, and showed incorporating these features into proteomic analyses extended analytical precision. We found that the key differentiator of the beer glyco roteome was the brewery, with beer from independent breweries having a distinct profile to beer from multinational breweries. Within a given brewery, beer styles also had distinct glyco roteomes. Targeting our analyses to beers from a single brewery, Newstead Brewing Co., allowed us to identify beer style-specific features of the glyco roteome. Specifically, we found that proteins in darker beers tended to have low glycation and high proteolysis. Finally, we objectively quantified features of foam formation and stability, and showed that these quality properties correlated with the concentration of abundant surface-active proteins from barley and yeast.
Review of technology advances to assess rice quality traits and consumer perception
Publisher: Elsevier BV
Date: 10-2023
Night-time sap flow is parabolically linked to midday water potential for field-grown almond trees
Publisher: Springer Science and Business Media LLC
Date: 21-03-2013
The influence of apical and basal defoliation on the canopy structure and biochemical composition of Vitis vinifera cv. shiraz grapes and wine
Publisher: Frontiers Media SA
Date: 07-07-2017
Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conducta
Publisher: MDPI AG
Date: 25-06-2020
DOI: 10.3390/S20123596
Abstract: The midday stem water potential (Ψs) and stomatal conductance (gs) have been traditionally used to monitor the water status of cherry trees (Prunus avium L.). Due to the complexity of direct measurement, the use of infrared thermography has been proposed as an alternative. This study compares Ψs and gs against crop water stress indexes (CWSI) calculated from thermal infrared (TIR) data from high-resolution (HR) and low-resolution (LR) cameras for two cherry tree cultivars: ‘Regina’ and ‘Sweetheart’. For this purpose, a water stress–recovery cycle experiment was carried out at the post-harvest period in a commercial drip-irrigated cherry tree orchard under three irrigation treatments based on Ψs levels. The water status of trees was measured weekly using Ψs, gs, and compared to CWSIs, computed from both thermal cameras. Results showed that the accuracy in the estimation of CWSIs was not statistically significant when comparing both cameras for the representation of Ψs and gs in both cultivars. The performance of all evaluated physiological indicators presented similar trends for both cultivars, and the averaged differences between CWSI’s from both cameras were 11 ± 0.27%. However, these CWSI’s were not able to detect differences among irrigation treatments as compared to Ψs and gs.
The use of a portable robotic sparkling wine pourer and image analysis to assess wine quality in a fast and accurate manner
Publisher: International Society for Horticultural Science (ISHS)
Date: 03-2016
Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy
Publisher: International Journal of Agricultural and Biological Engineering (IJABE)
Date: 2019
Infrared Thermal Imaging and Morpho-Physiological Indices Used for Wheat Genotypes Screening under Drought and Heat Stress
Publisher: MDPI AG
Date: 28-11-2022
Abstract: Bread wheat, one of the largest broadacre crops, often experiences various environmental stresses during critical growth stages. Terminal drought and heat stress are the primary causes of wheat yield reduction worldwide. This study aimed to determine the drought and heat stress tolerance level of a group of 46 erse wheat genotypes procured from the Australian Grains Gene Bank, Horsham, VIC Australia. Two separate drought stress (DS) and heat stress (HS) pot experiments were conducted in separate growth chambers. Ten days after complete anthesis, drought (40 ± 3% field capacity for 14 days) and heat stress (36/22 °C for three consecutive days) were induced. A significant genotype × environment interaction was observed and explained by various morpho-physiological traits, including rapid, non-destructive infrared thermal imaging for computational water stress indices. Except for a spike length in DS and harvest index in HS, the analysis of variance showed significant differences for all the recorded traits. Results showed grains per spike, grains weight per spike, spike fertility, delayed flag leaf senescence, and cooler canopy temperature were positively associated with grain yield under DS and HS. The flag leaf senescence and chlorophyll fluorescence were used to measure each genotype’s stay-green phenotype and photosystem II activity after DS and HS. This study identified the top ten best and five lowest-performing genotypes from drought and heat stress experiments based on their overall performance. Results suggest that if heat or drought adaptive traits are brought together in a single genotype, grain yield can be improved further, particularly in a rainfed cropping environment.
Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling
Publisher: MDPI AG
Date: 23-08-2021
DOI: 10.3390/MOLECULES26165108
Abstract: Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine s le (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = −0.93), decanoic acid, ethyl ester (r = −0.94), and octanoic acid, 3-methylbutyl ester (r = −0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.
Improving water use efficiency of vineyards in semi-arid regions. A review
Publisher: Springer Science and Business Media LLC
Date: 31-12-2014
Plant water stress detection based on aerial and terrestrial infrared thermography: a study case from vineyard and olive orchard
Publisher: International Society for Horticultural Science (ISHS)
Date: 03-2016
Latent heat fluxes during two contrasting years from a juvenile plantation established over a waste disposal landscape
Publisher: Elsevier BV
Date: 03-2011
Site-Specific Evaluation of Canopy Resistance Models for Estimating Evapotranspiration over a Drip-Irrigated Potato Crop in Southern Chile under Water-Limited Conditions
Publisher: MDPI AG
Date: 25-06-2022
DOI: 10.3390/W14132041
Abstract: The evapotranspiration (ET) process is an essential component in many agricultural water management systems, and its estimation is even more determinant when crops are grown under water-limited environments. The traditional canopy resistance (rc) approaches were evaluated to simulate potato evapotranspiration (ETcp) using the original Penman–Monteith equation under different irrigation levels. A field study was carried out on a drip-irrigated potato crop (var. Puyehue INIA) located in the Research Center Carillanca (INIA), La Araucanía Region, Chile (38°41′ S, 72°24′ W, 188 m above sea level) during the 2018/2019 and 2019/2020 growing seasons. The different irrigation levels were full irrigation (IL1), 75% of IL1 (IL2), and 60% of IL1 (IL3). The soil water content, morphological, physiological, meteorological, and micrometeorological variables were measured to calculate the different rc approaches and estimate ET for both growing evaluated seasons. The final values of estimated ETcp were compared to the soil water balance method (ETcpWB). The use of histomatous (LA) and hypostomatous (LH) rc approaches are the best alternative to estimate the ETcp on potato crops. The best estimation of ET was found for ETcpLA with an overestimation of 0.6% for IL1, 7.0% for IL2, and 13.0% for IL3, while for ETcpLH with underestimations of 12.0, 11.0 and 31.0% for IL1, IL2, and IL3, respectively. The lowest average values of root mean square error (RMSE), mean absolute error (MAE), and index of agreement (d) were observed for ETcpLA in both IL1 and IL2 conditions, with values of 4.4 and 3.2 mm, 3.2 and 2.5 mm, and 0.82 and 0.87, respectively. More investigation is necessary on the plasticity of the morphological features of potato leaves and canopy geometry, as the stomatal water vapor flowing on the canopy surface could be affected, which is a key factor in the canopy resistance model for accurate ET estimation under soil-water-limited conditions.
A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines’ Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy
Publisher: MDPI AG
Date: 09-06-2020
Abstract: Berry cell death (BCD) is linked to the development of flavors and aromas in berries and wine. The BCD pattern and rate within a growing season start at around 90–100 days after anthesis (DAA), and the rate until harvest depends on environmental factors. This study assessed the BCD effects on berry and wine composition from a boutique commercial vineyard in Victoria, Australia, using fluorescent imaging. Results showed differences in wine sensory profiles from the two blocks studied, mainly related to variations in BCD, due to differences in altitude between blocks. Furthermore, two machine learning (ML) models were constructed using near-infrared spectroscopy (NIR) measurements from full berries as inputs and living tissue (LT) and dead tissue (DT) from berries as targets (Model 1). Model 2 was developed using Brix, LT, DT from the east and west sides of canopies as inputs and using 19 sensory descriptors from wines as targets. High correlation and performances were achieved for both models without signs of overfitting (R = 0.94 and R = 0.80, respectively). These models could be used for decision-making purposes as an objective and comprehensive berry maturity assessment obtained in a non-destructive, accurate, and in a real-time fashion close to harvest, to secure specific wine styles.
Soluble Protein and Amino Acid Content Affects the Foam Quality of Sparkling Wine
Publisher: American Chemical Society (ACS)
Date: 04-10-2017
Abstract: Proteins and amino acids are known to influence the foam characteristics of sparkling wines. However, it is unclear to what extent they promote foam formation and/or stability. This study aimed to investigate the effect of protein content and amino acid composition, measured via the bicinchoninic acid assay and high-performance liquid chromatography, respectively, on the foaming properties of 28 sparkling white wines, made by different production methods. Foam volume and stability were determined using a robotic pourer and computer vision algorithms. Modifications were applied to the protein determination method involving the use of yeast invertase as a standard in order to improve quantification accuracy. The protein content was found to be significantly correlated to parameters representative of foam stability, as were the amino acids arginine, asparagine, histidine, and tyrosine. Additionally, the production method was found to influence the foam collar height, which favored foaming in Méthode Traditionnelle wines over other those made by production methods. Understanding the contributions of key wine constituents to the visual and mouthfeel parameters of sparkling wine will enable more efficient production of high-quality wines.
Responses of leaf night transpiration to drought stress in Vitis vinifera L.
Publisher: Elsevier BV
Date: 02-2013
The Effect of Soundwaves on Foamability Properties and Sensory of Beers with a Machine Learning Modeling Approach
Publisher: MDPI AG
Date: 26-07-2018
Abstract: The use of ultrasounds has been implemented to increase yeast viability, de-foaming, and cavitation in foods and beverages. However, the application of low frequency audible sound to decrease bubble size and improve foamability has not been explored. In this study, three treatments using India Pale Ale beers were tested, which include (1) a control, (2) the application of audible sound during fermentation, and (3) the application of audible sound during natural carbonation. Five different audible frequencies (20 Hz, 30 Hz, 45 Hz, 55 Hz, and 75 Hz) were applied daily for one minute each (starting from the lowest frequency) during fermentation (11 days, treatment 2) and carbonation (22 days, treatment 3). S les were measured in triplicates using the RoboBEER to assess color and foam-related parameters. A trained panel (n = 10) evaluated the intensity of sensory descriptors. Results showed that s les with sonication treatment had significant differences in the number of small bubbles, alcohol, and viscosity compared to the control. Furthermore, except for foam texture, foam height, and viscosity, there were non-significant differences in the intensity of any sensory descriptor, according to the rating from the trained sensory panel. The use of soundwaves is a potential treatment for brewing to improve beer quality by increasing the number of small bubbles and foamability without disrupting yeast or modifying the aroma and flavor profile.
Within-Vineyard, Within-Vine, and Within-Bunch Variability of the Rotundone Concentration in Berries of Vitis vinifera L. cv. Shiraz
Publisher: American Chemical Society (ACS)
Date: 24-04-2015
Abstract: This study characterizes the environmental factors driving rotundone concentrations in grape berries by quantifying rotundone variability and correlating it with viticultural parameters. Dissection of the vineyard into distinct zones (on the basis of vigor, electrical soil conductivity, and slope), vine into orientations to sun (shaded/unshaded), and grape bunches into sectors (upper and lower and front and back) shows the influence of vine vigor, sunlight, and temperature. Occurrence of the highest rotundone concentration was observed in shaded bunch sectors and vines and from higher vigor vines in the southern-facing areas of the vineyard. The highest concentration of rotundone is consistently found at the top and in shaded sectors of bunches, and this correlates to lower grape surface temperatures. Modeling showed that berry temperature exceeding 25 °C negatively affects the rotundone concentration in Shiraz. Both natural and artificial shading modulated the grape surface and air temperature at the bunch zone and increased the rotundone concentration, without affecting other grape berry quality parameters. Thus, temperature and possibly sunlight interception are the main determinants of rotundone in grape berries. Vineyard topography, vine vigor, vine row, and grape bunch orientation influence the level of berry shading and can, therefore, adjust bunch surface and zone temperatures and influence the berry rotundone concentration.
The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies
Publisher: MDPI AG
Date: 13-01-2023
DOI: 10.3390/FERMENTATION9010068
Abstract: Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.
Evaluation of compensated heat-pulse velocity method to determine vine transpiration using combined measurements of eddy covariance system and microlysimeters
Publisher: Elsevier BV
Date: 06-2012
Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment
Publisher: MDPI AG
Date: 20-04-2022
Abstract: In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries [...]
Editorial: Special Issue “Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems”
Publisher: MDPI AG
Date: 24-09-2021
DOI: 10.3390/S21196383
Abstract: Artificial intelligence (AI), together with robotics, sensors, sensor networks, internet of things (IoT) and machine/deep learning modeling, has reached the forefront towards the goal of increased efficiency in a multitude of application and purpose [...]
Regulatory Role of Silicon on Photosynthesis, Gas-exchange and Yield Related Traits of Drought-Stressed Lentil Plants
Publisher: Springer Science and Business Media LLC
Date: 03-05-2023
Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery
Publisher: MDPI AG
Date: 04-2021
DOI: 10.3390/IJGI10040211
Abstract: There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) res led at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.
VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine
Publisher: MDPI AG
Date: 23-04-2016
DOI: 10.3390/S16040585
Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis
Publisher: MDPI AG
Date: 17-11-2021
DOI: 10.3390/S21227641
Abstract: New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company providers and software for data acquisition and analysis makes their practical application difficult for research and the industry. This study proposed a prototype integration between eye tracking and emotional biometrics using the BioSensory computer application for three s le labels: Stevia, Potato chips, and Spaghetti. Multivariate data analyses are presented, showing the integrative analysis approach of the proposed prototype system. Further studies can be conducted with this system and integrating other biometrics available, such as physiological response with heart rate, blood, pressure, and temperature changes analyzed while focusing on different label components or packaging features. By maximizing data extraction from various components of packaging and labels, smart predictive systems can also be implemented, such as machine learning to assess liking and other parameters of interest from the whole package and specific components.
Feasibility study on the use of Near Infrared spectroscopy to measure water status of almond trees
Publisher: International Society for Horticultural Science (ISHS)
Date: 10-2018
Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence
Publisher: MDPI AG
Date: 14-10-2021
DOI: 10.3390/S21206844
Abstract: New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow’s heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day−1), cow milk productivity (kg-milk-cow−1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow s les obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.
Development of a robotic and computer vision method to assess foam quality in sparkling wines
Publisher: Elsevier BV
Date: 2017
Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers
Publisher: Wiley
Date: 30-03-2018
Abstract: Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of sensory descriptors in beer using the physical measurements of color and foam-related parameters. A robotic pourer (RoboBEER), was used to obtain 15 color and foam-related parameters from 22 different commercial beer s les. A sensory session using quantitative descriptive analysis (QDA This paper is a novel approach for food science using machine modeling techniques that could contribute significantly to rapid screenings of food and brewage products for the food industry and the implementation of Artificial Intelligence (AI). The use of RoboBEER to assess beer quality showed to be a reliable, objective, accurate, and less time-consuming method to predict sensory descriptors compared to trained sensory panels. Hence, this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line for industry applications.
Seasonal variation of night-time sap flow of a young olive orchard: The unconsidered process for evapotranspiration estimations
Publisher: International Society for Horticultural Science (ISHS)
Date: 03-2016
Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard
Publisher: MDPI AG
Date: 14-07-2021
DOI: 10.3390/RS13142775
Abstract: Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing c aigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.
Effects of grapevine (Vitis vinifera L.) water status on water consumption, vegetative growth and grape quality: An irrigation scheduling application to achieve regulated deficit irrigation
Publisher: Elsevier BV
Date: 07-2010
Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems
Publisher: Elsevier BV
Date: 12-2022
A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting
Publisher: MDPI AG
Date: 21-07-2020
DOI: 10.3390/FERMENTATION6030073
Abstract: The development of digital tools based on artificial intelligence can produce affordable and accurate methodologies to assess quality traits and sensory analysis of beers. These new and emerging technologies can also assess new products in a near real-time fashion through virtual simulations before the brewing process. This research was based on the development of specific digital tools (four models) to assess quality traits and sensory profiles of beers produced using sonication and traditional brewing techniques. Results showed that models developed using supervised machine learning (ML) regression algorithms based on near-infrared spectroscopy (NIR) were highly accurate in the estimation of physicochemical parameters (Model 1 R = 0.94 b = 0.91). Outputs from Model 1 were then used as inputs to obtain estimations of the intensity of sensory descriptors (Model 2 R = 0.99 b = 0.98), liking of sensory attributes (Model 3 R = 0.97 b = 0.99), and the classification of fermentation treatments using supervised classification ML algorithms (Model 4 96% accuracy). These new digital tools can aid craft brewing companies for product development at lower costs and maintain specific quality traits and sensory profiles, creating original styles of beers to get positioned in the market.
Elevated temperature and water stress accelerate mesocarp cell death and shrivelling, and decouple sensory traits in Shiraz berries
Publisher: Springer Science and Business Media LLC
Date: 24-05-2013
Canopy architecture assessment of cherry trees by cover photography based on variable light extinction coefficient modelled using artificial neural networks
Publisher: International Society for Horticultural Science (ISHS)
Date: 02-2019
Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling
Publisher: MDPI AG
Date: 03-05-2020
Abstract: Foam-related parameters are associated with beer quality and dependent, among others, on the protein content. This study aimed to develop a machine learning (ML) model to predict the pattern and presence of 54 proteins. Triplicates of 24 beer s les were analyzed through proteomics. Furthermore, s les were analyzed using the RoboBEER to evaluate 15 physical parameters (color, foam, and bubbles), and a portable near-infrared (NIR) device. Proteins were grouped according to their molecular weight (MW), and a matrix was developed to assess only the significant correlations (p 0.05) with the physical parameters. Two ML models were developed using the NIR (Model 1), and RoboBEER (Model 2) data as inputs to predict the relative quantification of 54 proteins. Proteins in the 0–20 kDa group were negatively correlated with the maximum volume of foam (MaxVol r = −0.57) and total lifetime of foam (TLTF r = −0.58), while those within 20–40 kDa had a positive correlation with MaxVol (r = 0.47) and TLTF (r = 0.47). Model 1 was not as accurate (testing r = 0.68 overall r = 0.89) as Model 2 (testing r = 0.90 overall r = 0.93), which may serve as a reliable and affordable method to incorporate the relative quantification of important proteins to explain beer quality.
Development of emotion lexicons to describe chocolate using the Check-All-That-Apply (CATA) methodology across Asian and Western groups
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.FOODRES.2018.10.001
Abstract: Emotion-based terms selected by Asians and Westerners were analyzed to develop lexicons associated with chocolate consumption. Hence, an online-based questionnaire (Study 1: N = 206 51% Asians, 49% Westerners) and a chocolate (milk and dark) tasting session (Study 2: N = 75 52% Asians, 48% westerners) were conducted to assess emotion terms related to chocolate consumption using Check-All-That-Apply methodology. Emotional satisfaction was the main reason for chocolate consumption. Furthermore, selection of emotional terms was different between cultures and gender. For both studies (1 and 2), flavor of chocolate was the most important factor that determined purchase intention. For Study 2, milk and dark chocolate evoked different emotion terms for participants. The lexicon developed for milk chocolate had similar emotion terms compared to the Study 1 lexicon (online). Developing an emotion lexicon using an online survey could provide a reduced lexicon compared to lexicons generated during the tasting session and can be used as a fast-screening method to develop simplified emotion lexicons due to its similarity to the tasting lexicon. Newly developed lexicons from this study can be applied to sensory consumer tests of chocolate.
Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery
Publisher: International Journal of Agricultural and Biological Engineering (IJABE)
Date: 2019
Bubbles, Foam Formation, Stability and Consumer Perception of Carbonated Drinks: A Review of Current, New and Emerging Technologies for Rapid Assessment and Control
Publisher: MDPI AG
Date: 20-11-2019
DOI: 10.3390/FOODS8120596
Abstract: Quality control, mainly focused on the assessment of bubble and foam-related parameters, is critical in carbonated beverages, due to their relationship with the chemical components as well as their influence on sensory characteristics such as aroma release, mouthfeel, and perception of tastes and aromas. Consumer assessment and acceptability of carbonated beverages are mainly based on carbonation, foam, and bubbles, as a flat carbonated beverage is usually perceived as low quality. This review focuses on three beverages: beer, sparkling water, and sparkling wine. It explains the characteristics of foam and bubble formation, and the traditional methods, as well as emerging technologies based on robotics and computer vision, to assess bubble and foam-related parameters. Furthermore, it explores the most common methods and the use of advanced techniques using an artificial intelligence approach to assess sensory descriptors both for descriptive analysis and consumers’ acceptability. Emerging technologies, based on the combination of robotics, computer vision, and machine learning as an approach to artificial intelligence, have been developed and applied for the assessment of beer and, to a lesser extent, sparkling wine. This, has the objective of assessing the final products quality using more reliable, accurate, affordable, and less time-consuming methods. However, despite carbonated water being an important product, due to its increasing consumption, more research needs to focus on exploring more efficient, repeatable, and accurate methods to assess carbonation and bubble size, distribution and dynamics.
Protocol: optimising hydroponic growth systems for nutritional and physiological analysis of Arabidopsis thaliana and other plants
Publisher: Springer Science and Business Media LLC
Date: 05-02-2013
Abstract: Hydroponic growth systems are a convenient platform for studying whole plant physiology. However, we found through trialling systems as they are described in the literature that our experiments were frequently confounded by factors that affected plant growth, including algal contamination and hypoxia. We also found the way in which the plants were grown made them poorly amenable to a number of common physiological assays. The drivers for the development of this hydroponic system were: 1) the exclusion of light from the growth solution 2) to simplify the handling of in idual plants, and 3) the growth of the plant to allow easy implementation of multiple assays. These aims were all met by the use of pierced lids of black microcentrifuge tubes. Seed was germinated on a lid filled with an agar-containing germination media immersed in the same solution. Following germination, the liquid growth media was exchanged with the experimental solution, and after 14-21 days seedlings were transferred to larger tanks with aerated solution where they remained until experimentation. We provide details of the protocol including composition of the basal growth solution, and separate solutions with altered calcium, magnesium, potassium or sodium supply whilst maintaining the activity of the majority of other ions. We demonstrate the adaptability of this system for: gas exchange measurement on single leaves and whole plants qRT-PCR to probe the transcriptional response of roots or shoots to altered nutrient composition in the growth solution (we demonstrate this using high and low calcium supply) producing highly competent mesophyll protoplasts and, accelerating the screening of Arabidopsis transformants. This system is also ideal for manipulating plants for micropipette techniques such as electrophysiology or SiCSA. We present an optimised plant hydroponic culture system that can be quickly and cheaply constructed, and produces plants with similar growth kinetics to soil-grown plants, but with the advantage of being a versatile platform for a myriad of physiological and molecular biological measurements on all plant tissues at all developmental stages. We present ‘tips and tricks’ for the easy adoption of this hydroponic culture system.
Effect of incorporated or mulched compost on leaf nutrient concentrations and performance of Vitis vinifera cv. Merlot
Publisher: Springer Science and Business Media LLC
Date: 2013
Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages
Publisher: MDPI AG
Date: 11-2019
Abstract: Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.
Silicon improves seed germination and alleviates drought stress in lentil crops by regulating osmolytes, hydrolytic enzymes and antioxidant defense system
Publisher: Elsevier BV
Date: 10-2017
Distribution of rotundone and possible translocation of related compounds amongst grapevine tissues in vitis vinifera L. cv. shiraz
Publisher: Frontiers Media SA
Date: 21-06-2016
Images and chocolate stimuli affect physiological and affective responses of consumers: A cross-cultural study
Publisher: Elsevier BV
Date: 04-2018
Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
Publisher: MDPI AG
Date: 19-04-2022
Abstract: Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7% Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95 Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98 Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
Novel Method to Conduct Remote Sensory Sessions and Biometrics during Isolation
Publisher: MDPI
Date: 13-10-2021
Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle
Publisher: MDPI AG
Date: 06-12-2019
DOI: 10.3390/ANI9121089
Abstract: Precision livestock farming has emerged with the aim of providing detailed information to detect and reduce problems related to animal management. This study aimed to develop and validate computer vision techniques to track required features of cattle face and to remotely assess eye temperature, ear-base temperature, respiration rate, and heart rate in cattle. Ten dairy cows were recorded during six handling procedures across two consecutive days using thermal infrared cameras and RGB (red, green, blue) video cameras. Simultaneously, core body temperature, respiration rate and heart rate were measured using more conventional ‘invasive’ methods to be compared with the data obtained with the proposed algorithms. The feature tracking algorithm, developed to improve image processing, showed an accuracy between 92% and 95% when tracking different areas of the face of cows. The results of this study also show correlation coefficients up to 0.99 between temperature measures obtained invasively and those obtained remotely, with the highest values achieved when the analysis was performed within in idual cows. In the case of respiration rate, a positive correlation (r = 0.87) was found between visual observations and the analysis of non-radiometric infrared videos. Low to high correlation coefficients were found between the heart rates (0.09–0.99) obtained from attached monitors and from the proposed method. Furthermore, camera location and the area analysed appear to have a relevant impact on the performance of the proposed techniques. This study shows positive outcomes from the proposed computer vision techniques when measuring physiological parameters. Further research is needed to automate and improve these techniques to measure physiological changes in farm animals considering their in idual characteristics.
Non-destructive measurement of grapevine water potential using near infrared spectroscopy
Publisher: Hindawi Limited
Date: 28-12-2011
Consumer rejection threshold, acceptability rates, physicochemical properties, and shelf-life of strawberry-flavored yogurts with reductions of sugar
Publisher: Wiley
Date: 28-02-2020
DOI: 10.1002/JSFA.10333
Novel Digital Technologies to Assess Smoke Taint in Wine Using Non-Invasive Chemical Fingerprinting, a Low-Cost Electronic Nose, and Artificial Intelligence
Publisher: MDPI
Date: 13-10-2021
A novel analysis of grapevine berry tissue demonstrates a variety-dependent correlation between tissue vitality and berry shrivel
Publisher: Hindawi Limited
Date: 06-2010
Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
Publisher: MDPI AG
Date: 09-11-2022
DOI: 10.3390/S22228655
Abstract: Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on s les and paired with machine learning (ML) algorithms. For these purposes, the s les were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice’s weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the s le distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice s les using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose R = 0.94–0.98) and non-invasive measurement through the packaging (NIR R = 0.95–0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.
An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remot
Publisher: CSIRO Publishing
Date: 2008
DOI: 10.1071/FP08045
Abstract: Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371 R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468 R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.
Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling
Publisher: MDPI
Date: 13-10-2021
Quality Traits of Sourdough Bread Obtained by Novel Digital Technologies and Machine Learning Modelling
Publisher: MDPI AG
Date: 07-10-2022
DOI: 10.3390/FERMENTATION8100516
Abstract: Sourdough bread (SB) has increased popularity due to health benefits and higher interest in artisan breadmaking due to social isolation during the COVID-19 pandemic. However, quality traits and consumer assessment are still limited to complex laboratory analysis and sensory trials. In this research, new and emerging digital technologies were tested to assess quality traits of SB made from six different flour sources. The results showed that machine learning (ML) models developed to classify the type of wheat used for flours (targets) from near-infrared (NIR) spectroscopy data (Model 1) and a low-cost electronic nose (Model 2) as inputs rendered highly accurate and precise models (96.3% and 99.4%, respectively). Furthermore, ML regression models based on the same inputs for NIR (Model 3) and e-nose (Model 4) were developed to automatically assess 16 volatile aromatic compounds (targets) using GC-MS as ground-truth. To reiterate, models with high accuracy and performance were obtained with correlation (R), determination coefficients (R2), and slope (b) of R = 0.97 R2 = 0.94 and b = 0.99 for Model 3 and R = 0.99 R2 = 0.99 and b = 0.99 for Model 4. The development of low-cost instrumentation and sensors could make possible the accessibility of hardware and software to the industry and artisan breadmakers to assess quality traits and consistency of SB.
Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling
Publisher: MDPI AG
Date: 04-09-2021
DOI: 10.3390/S21175948
Abstract: Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5–99.3% for NIR and between 94.2–99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all s les as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.
Non-Invasive Biometrics and Machine Learning Modeling to Obtain Sensory and Emotional Responses from Panelists during Entomophagy
Publisher: MDPI AG
Date: 09-07-2020
DOI: 10.3390/FOODS9070903
Abstract: Insect-based food products offer a more sustainable and environmentally friendly source of protein compared to plant and animal proteins. Entomophagy is less familiar for Non-Asian cultural backgrounds and is associated with emotions such as disgust and anger, which is the basis of neophobia towards these products. Tradicional sensory evaluation may offer some insights about the liking, visual, aroma, and tasting appreciation, and purchase intention of insect-based food products. However, more robust methods are required to assess these complex interactions with the emotional and subconscious responses related to cultural background. This study focused on the sensory and biometric responses of consumers towards insect-based food snacks and machine learning modeling. Results showed higher liking and emotional responses for those s les containing insects as ingredients (not visible) and with no insects. A lower liking and negative emotional responses were related to s les showing the insects. Artificial neural network models to assess liking based on biometric responses showed high accuracy for different cultures ( %). A general model for all cultures with an 89% accuracy was also achieved.
Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach
Publisher: MDPI AG
Date: 08-09-2020
DOI: 10.3390/S20185108
Abstract: Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C no smoke treatment) and (v) control with in-canopy misting (CM no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 s les into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2 R = 0.98 R2 = 0.95 b = 0.97) in berries at harvest (Model 3 R = 0.99 R2 = 0.97 b = 0.96) in wines (Model 4 R = 0.99 R2 = 0.98 b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5 R = 0.98 R2 = 0.96 b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires.
Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach
Publisher: MDPI AG
Date: 30-07-2019
DOI: 10.3390/S19153335
Abstract: Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposes a non-invasive/in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS).
The Effect of Sonication on Bubble Size and Sensory Perception of Carbonated Water to Improve Quality and Consumer Acceptability
Publisher: MDPI AG
Date: 12-09-2019
Abstract: Bubbles are important for carbonated beverage quality since smaller bubbles contribute to higher acceptability. Therefore, the effects and acceptability of the application of audible sound in carbonated water were studied using three brands and applying five frequencies for one minute each in ascending order. Six s les, two from each brand, were used for treatments: (i) control and (ii) sonication. Physicochemical measurements consisted of total dissolved solids (TDS), electric conductivity (EC), pH, bubble size, and bubble size distribution. A sensory session (N = 30) was conducted using the Bio-Sensory application to assess acceptability and emotions using self-reported and biometric responses. Statistical analysis included: ANOVA (α = 0.05) and principal component analysis (PCA) for quantitative data and Cochran Q test with pairwise comparisons (p 0.05) for self-reported emotion responses. Results showed that the sonication effect for the s le with higher TDS, EC, and pH (SPS) reduced bubble size by 46%, while in those with lowest TDS, EC, and pH (IceS) caused an increase of 158% compared to the control. For s les with intermediate values (NuS), there were non-significant differences (p 0.05) compared to the control. Acceptability was higher for s les with sonication for the three brands. Emotional self-reported responses were more positive for s les with sonication, showing significant differences (p 0.05) for emotions such as “happy” and “pleased” during both sound and visual assessments. From PCA, a positive relationship between bubble size and liking of bubbles was found as well as for the number of medium bubbles and happy facial expression. The audible sound generated by ubiquitous sound systems may potentially be used by the industry, applying it to the bottled product to modify bubble size and improve quality and acceptability of carbonated beverages.
Computational water stress indices obtained from thermal image analysis of grapevine canopies
Publisher: Springer Science and Business Media LLC
Date: 22-08-2012
Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management
Publisher: Elsevier BV
Date: 04-2018
Root biomass distribution and soil properties of an open woodland on a duplex soil
Publisher: Springer Science and Business Media LLC
Date: 17-06-2009
Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application
Publisher: MDPI AG
Date: 28-04-2019
DOI: 10.20944/PREPRINTS201904.0316.V1
Abstract: Cocoa is an important commodity crop not only to produce one of the most complex products such as chocolate from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod s les were collected, fermented, dried and grinded to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm and the aroma profile considering six main aromas as targets. The ANN model rendered high accuracy (R = 0.82 MSE = 0.09) with no overfitting. The model was then applied to a satellite image from the whole cocoa field studied to produce canopy vigor and aroma profile maps up to the tree-by-tree scale. The tool developed could aid significantly the canopy management practices in cocoa trees that have a direct effect on cocoa quality.
Non-contact heart rate and blood pressure estimations from video analysis and machine learning modelling applied to food sensory responses: A case study for chocolate
Publisher: MDPI AG
Date: 03-06-2018
DOI: 10.3390/S18061802
Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity
Publisher: MDPI AG
Date: 12-03-2021
DOI: 10.3390/S21062016
Abstract: Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee s les with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the s les into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98% Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products.
Comparison data of common and abundant terpenes at different grape development stages in Shiraz wine grapes
Publisher: Elsevier BV
Date: 09-2016
Effects of Different Beer Compounds on Biometrically Assessed Emotional Responses in Consumers
Publisher: MDPI AG
Date: 09-03-2023
DOI: 10.3390/FERMENTATION9030269
Abstract: The study of emotional responses from consumers toward beer products is an important digital tool to obtain novel information about the acceptability of beers and their optimal physicochemical composition. This research proposed the use of biometrics to assess emotional responses from Mexican beer consumers while tasting top- and bottom-fermented s les. Furthermore, a novel emotional validation assessment using proven evoking images for neutral, negative, and positive emotions was proposed. The results showed that emotional responses obtained from self-reported emoticons and biometrics are correlated to the specific emotions evoked by the visual, aroma, and taste aspects of beers. Consumers preferred bottom-fermentation beers and disliked the wheat-based and higher-bitterness s les. Chemical compounds and concentrations were in accordance to previously reported research for similar beer styles. However, the levels of hordenine were not high enough to evoke positive emotions in the biometric assessment, which opens additional research opportunities to assess higher concentrations of this alkaloid to increase the happiness perception of low or non-alcoholic beers.
Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle
Publisher: MDPI AG
Date: 04-01-2021
DOI: 10.3390/S21010295
Abstract: Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities’ green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016–2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.
Latent heat flux over a furrow-irrigated tomato crop using Penman–Monteith equation with a variable surface canopy resistance
Publisher: Elsevier BV
Date: 04-2006
Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.FOODRES.2016.08.045
Abstract: There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.
Relationship between α- and β-galactosidase activities and firmness loss during the development of Vitis vinifera L. grape berries
Publisher: International Society for Horticultural Science (ISHS)
Date: 03-2018
Modeling pinot noir aroma profiles based on weather and water management information using machine learning algorithms: A vertical vintage analysis using artificial intelligence
Publisher: MDPI AG
Date: 30-12-2020
DOI: 10.3390/FOODS9010033
Abstract: Wine aroma profiles are determinant for the specific style and quality characteristics of final wines. These are dependent on the seasonality, mainly weather conditions, such as solar exposure and temperatures and water management strategies from veraison to harvest. This paper presents machine learning modeling strategies using weather and water management information from a Pinot noir vineyard from 2008 to 2016 vintages as inputs and aroma profiles from wines from the same vintages assessed using gas chromatography and chemometric analyses of wines as targets. The results showed that artificial neural network (ANN) models rendered the high accuracy in the prediction of aroma profiles (Model 1 R = 0.99) and chemometric wine parameters (Model 2 R = 0.94) with no indication of overfitting. These models could offer powerful tools to winemakers to assess the aroma profiles of wines before winemaking, which could help adjust some techniques to maintain/increase the quality of wines or wine styles that are characteristic of specific vineyards or regions. These models can be modified for different cultivars and regions by including more data from vertical vintages to implement artificial intelligence in winemaking.
Consumer acceptability, eye fixation, and physiological responses: A study of novel and familiar chocolate packaging designs using eye-tracking devices
Publisher: MDPI AG
Date: 12-07-2019
DOI: 10.3390/FOODS8070253
Abstract: Eye fixations on packaging elements are not necessarily correlated to consumer attention or positive emotions towards those elements. This study aimed to assess links between the emotional responses of consumers and the eye fixations on areas of interest (AOI) of different chocolate packaging designs using eye trackers. Sixty participants were exposed to six novel and six familiar (commercial) chocolate packaging concepts on tablet PC screens. Analysis of variance (ANOVA) and multivariate analysis were performed on eye tracking, facial expressions, and self-reported responses. The results showed that there were significant positive correlations between liking and familiarity in commercially available concepts (r = 0.88), whereas, with novel concepts, there were no significant correlations. Overall, the total number of fixations on the familiar packaging was positively correlated (r = 0.78) with positive emotions elicited in people using the FaceReader™ (Happy), while they were not correlated with any emotion for the novel packaging. Fixations on a specific AOI were not linked to positive emotions, since, in some cases, they were related to negative emotions elicited in people or not even associated with any emotion. These findings can be used by package designers to better understand the link between the emotional responses of consumers and their eye fixation patterns for specific AOI.
Terpene evolution during the development of Vitis vinifera L. cv. Shiraz grapes
Publisher: Elsevier BV
Date: 08-2016
DOI: 10.1016/J.FOODCHEM.2016.02.125
Abstract: The flavour of wine is derived, in part, from the flavour compounds present in the grape, which change as the grapes accumulate sugar and ripen. Grape berry terpene concentrations may vary at different stages of berry development. This study aimed to investigate terpene evolution in grape berries from four weeks post-flowering to maturity. Grape bunches were s led at fortnightly intervals over two vintages (2012-13 and 2013-14). In total, five monoterpenoids, 24 sesquiterpenes, and four norisoprenoids were detected in grape s les. The highest concentrations of total monoterpenoids, total sesquiterpenes, and total norisoprenoids in grapes were all observed at pre-veraison. Terpenes derived from the same biosynthetic pathway had a similar production pattern during berry development. Terpenes in grapes at harvest might not necessarily be synthesised at post-veraison, since the compounds or their precursors may already exist in grapes at pre-veraison, with the veraison to harvest period functioning to convert these precursors into final products.
D-tagatose as a sucrose substitute and its effect on the physico-chemical properties and acceptability of strawberry-flavored yogurt
Publisher: MDPI AG
Date: 12-07-2019
DOI: 10.3390/FOODS8070256
Abstract: Sugar not only provides the desirable sweetness but its reduction can also alter the physico-chemical properties of foods. The objective of this study was to evaluate the effects of tagatose as a sugar substitute on selected physico-chemical properties and sensory acceptability of strawberry-flavored yogurts. Six yogurt s les with decreasing concentrations of sucrose (8.50 to 1.70 g/100 g) and increasing concentrations of tagatose (0.00 to 9.24 g/100 g) were evaluated. Physico-chemical tests (pH, lactic acid (%), °Brix, water-holding capacity (WHC), viscosity, and color) were conducted to examine the quality and shelf-life of yogurts during 28 days of storage at 4 °C. An acceptability test (n = 55) was conducted to evaluate the sensory characteristics of yogurts. Sucrose reductions by the replacement of up to 80% tagatose showed marginal effects on the selected physico-chemical properties however, the loss of red color (a*) and increase in yellowness (b*) of the tagatose-substituted s les were significant. Strawberry yogurts with tagatose replacements had similar acceptability scores for all attributes. Sucrose reduction showed a positive effect on the purchase intent of the strawberry yogurts (an increase of 3–30%). These findings can be used to understand the effects of tagatose/sucrose formulations on the acceptability and physico-chemical properties of yogurts.
Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling
Publisher: MDPI AG
Date: 03-11-2021
DOI: 10.3390/S21217312
Abstract: Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87 b = 0.82 M2: R = 0.98 b = 0.93 M3: R = 0.99 b = 0.99 M4: R = 0.99 b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV).
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