ORCID Profile
0000-0002-1207-0212
Current Organisation
University of Tasmania
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Publisher: Springer Science and Business Media LLC
Date: 31-10-2019
Publisher: Elsevier BV
Date: 11-2020
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1AY00886B
Abstract: Utilising a smartphone-based miniaturized Raman spectrometer and machine learning for the fast identification and discrimination of adulterated essential oils.
Publisher: Elsevier BV
Date: 05-2022
DOI: 10.1016/J.BIORTECH.2022.127041
Abstract: Generation of specific xylooligosaccharides (XOS) is attractive to the pharmaceutical and food industries due to the importance of their structure upon their application. This study used chemometrics to develop a comprehensive computational modelling set to predict the parameters maximising the generation of the desired XOS during enzymatic hydrolysis. The evaluated parameters included pH, temperature, substrate concentration, enzyme dosage and reaction time. A Box-Behnken design was combined with response surface methodology to develop the models. High-performance anion-exchange chromatography coupled with triple-quadrupole mass spectrometry (HPAEC-QqQ-MS) allowed the identification of 22 XOS within beechwood xylan hydrolysates. These data were used to validate the developed models and demonstrated their accuracy in predicting the parameters maximising the generation of the desired XOS. The maximum yields for X2-X6 were 314.2 ± 1.2, 76.6 ± 4.5, 38.4 ± 0.4, 17.8 ± 0.7, and 5.3 ± 0.2 mg/g xylan, respectively. These values map closely to the model predicted values 311.7, 92.6, 43.0, 16.3, and 4.9 mg/g xylan, respectively.
Publisher: Elsevier BV
Date: 02-2022
DOI: 10.1016/J.JCHROMB.2021.123093
Abstract: Essential oils have been used for centuries for their preservative properties. An ex le is ylang-ylang Cananga odorata [Lam.] Hook. f. & Thomson essential oil, which exists in four different distillation grades, where the fraction with the longest distillation time has the highest radical scavenging activity (RSA). Gas chromatography mass spectrometry (GC-MS) followed by multivariate statistical analysis is a powerful approach for determination of RSA. Herein the performance of such multivariate statistical analysis using three data sets derived from gas chromatography mass spectrometry (GC-MS) analysis, is compared to that achieved using two direct and fast spectroscopic techniques, for the prediction of RSA using partial least squares (PLS) regression analysis. The three GC-MS data sets were, 'full chemical composition', 'total chromatogram average mass spectra (TCAMS)' and 'segment average mass spectra (SAMS)', whilst two spectroscopic techniques, namely attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Raman spectroscopy, provided the spectroscopic data sets for comparison. PLS models created using ATR-FTIR and 'full chemical composition' data sets provided the lowest relative error of prediction (REP) and mean error of prediction (MEP) in validation, whilst in independent test sets, the PLS models created using ATR-FTIR and SAMS data sets delivered the lowest REP and MEP. The three GC-MS derived data sets were further compared for value in determination of compounds contributing to the RSA. PLS regression analysis of the full chemical composition data set revealed that germacrene D and (E,E)-α-farnesene were the major contributors to the RSA, whilst average mass spectrum based data sets, TCAMS and SAMS, also highlighted eugenol as another contributor to the RSA.
Publisher: Elsevier BV
Date: 05-2020
DOI: 10.1016/J.CHROMA.2020.460853
Abstract: Analyses of the complex essential oil s les using gas chromatography hyphenated with mass spectrometry (GC-MS) generate large three-way data arrays. Processing such large data sets and extracting meaningful information in the metabolic studies of natural products requires application of multivariate statistical techniques (MSTs). From the GC-MS raw data several different input data sets for the MSTs can be created, including total chromatogram average mass spectra (TCAMS), segmented average mass spectra (SAMS) and chemical composition. Herein, we compared the performance of MSTs on average mass spectrum based data sets, TCAMS and SAMS, against chemical composition and attenuated total reflectance - Fourier transformation infrared (ATR-FTIR) spectroscopy in the evaluation of quality of ylang-ylang essential oils, based on their grade, geographical origin and chemical composition, using principal component analysis (PCA), partial least squares regression (PLS) and discriminatory analysis (PLS-DA). PCA based on TCAMS, SAMS and chemical composition showed clear trends amongst the s les based on increase in grade (distillation time). PLS-DA applied to TCAMS, SAMS and ATR-FTIR discriminated between all geographical origins. Predicted relative abundances of the 18 most important compounds, using PLS regression models on TCAMS, SAMS and ATR-FTIR, were successfully applied to ylang-ylang essential oil quality assessment based on comparison with the ISO 3063:2004 standard, where the SAMS data set showed superior performance, compared to other data sets.
Publisher: Elsevier BV
Date: 02-2020
DOI: 10.1016/J.TALANTA.2019.120471
Abstract: Differences in chemical profiles of various essential oils (EOs) come from the fact that each plant species and chemotype has a distinctive secondary metabolism. Therefore, these differences can be used as the chemical markers for EO classification and determination of their quality. Herein, the Random Forests (RF) machine learning algorithm was applied to the classification of 20 different EOs. From three-way raw gas chromatography - mass spectra data, total chromatogram average mass spectra (TCAMS) and segment average mass spectra (SAMS) were created. TCAMS was generated by averaging response of each m/z over the whole chromatogram and SAMS by averaging the response of each fragment across a certain time segment within the chromatogram. The RF model was applied to the two data sets and optimised through the evaluation of pre-processed data, number of trees, and number of variables used in each node split. The performance of the model was evaluated through a cross-validation process, repeated 50 times by iding the whole s le set into training and validation subsets. The calculated average out-of-bag error (OOBE), over 50 different training TCAMS data sets was 3.22 ± 1.29%, while for SAMS it was found to be 2.28 ± 1.33%. The minimal number of variables necessary for EO classification was determined by a nested cross-validation process. The amount of reduced variables in each step was 10%. It was shown that the TCAMS data set with 6 variables had similar prediction power as the SAMS with 30 variables. OOBE for classification of 20 EOs was 2.89 ± 1.44% and 3.70 ± 1.73%, for TCAMS and SAMS, respectively. Proximity between s les was used to evaluate their qualities. S les with greater intra-class proximity had good similarity, while the lower ones indicated greater variations in the chemical profiles. The SAMS data set showed superior potential for quality assurance, compared with TCAMS.
Publisher: Royal Society of Chemistry (RSC)
Date: 2020
DOI: 10.1039/D0AN00918K
Abstract: This work provides the p K a at the biorelevant temperature of 37 °C for a set of compounds proposed as internal standards for the internal standard capillary electrophoresis method. The method is applied to p K a determination of polyprotic drugs.
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 08-2021
Publisher: Springer Science and Business Media LLC
Date: 12-11-2018
No related grants have been discovered for Leo Lebanov.