scholarly journals Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils

2019 ◽  
Vol 6 (3) ◽  
pp. 190002
Author(s):  
Qi Zhou ◽  
Shaomin Liu ◽  
Ye Liu ◽  
Huanlu Song
Keyword(s):  
Multivariate Analysis ◽  
Electronic Nose ◽  
Corn Oil ◽  
Solid Phase ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Coefficient Method

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction–gas chromatography–mass spectrometry corresponding to 4-methyl-2-pentanol, ( E )-2-hexenal, 1-tridecene, hexyl acetate, ( Z )-3-hexenyl acetate, ( E )-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.

scholarly journals Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 36
Author(s):  
Khushman Taunk ◽  
Priscilla Porto-Figueira ◽  
Jorge A. M. Pereira ◽  
Ravindra Taware ◽  
Nattane Luíza da Costa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fatty Acid Biosynthesis ◽  
Solid Phase ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Control Group ◽  
Potential Candidate ◽  
Metabolic Pathway Analysis

The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups—including naphthalene derivatives, phenols, and organosulphurs—augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.

scholarly journals Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 286
Author(s):  
Thijs T. Wingelaar ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Pieter-Jan A.M. van Ooij ◽  
Rigo Hoencamp ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Electronic Nose ◽  
Oxygen Toxicity ◽  
Partial Least Square ◽  
Least Square ◽  
Sensor Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Exhaled Breath ◽  
Chromatography Mass Spectrometry

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

scholarly journals Chemical Markers to Distinguish the Homo- and Heterozygous Bitter Genotype in Sweet Almond Kernels

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 747
Author(s):  
Stefania Vichi ◽  
Morgana N. Mayer ◽  
Maria G. León-Cárdenas ◽  
Beatriz Quintanilla-Casas ◽  
Alba Tres ◽  
...  
Keyword(s):  
Solid Phase ◽  
Single Gene ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Model ◽  
Gas Chromatography Mass Spectrometry ◽  
Chemical Markers ◽  
Volatile Metabolites ◽  
First Time ◽  
Sweet Almond

Bitterness in almonds is controlled by a single gene (Sk dominant for sweet kernel, sk recessive for bitter kernel) and the proportions of the offspring genotypes (SkSk, Sksk, sksk) depend on the progenitors’ genotype. Currently, the latter is deduced after crossing by recording the phenotype of their descendants through kernel tasting. Chemical markers to early identify parental genotypes related to bitter traits can significantly enhance the efficiency of almond breeding programs. On this basis, volatile metabolites related to almond bitterness were investigated by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry coupled to univariate and multivariate statistics on 244 homo- and heterozygous samples from 42 different cultivars. This study evidenced the association between sweet almonds’ genotype and some volatile metabolites, in particular benzaldehyde, and provided for the first time chemical markers to discriminate between homo- and heterozygous sweet almond genotypes. Furthermore, a multivariate approach based on independent variables was developed to increase the reliability of almond classification. The Partial Least Square-Discriminant Analysis classification model built with selected volatile metabolites that showed discrimination capacity allowed a 98.0% correct classification. The metabolites identified, in particular benzaldehyde, become suitable markers for the early genotype identification in almonds, while a DNA molecular marker is not yet available.

scholarly journals Varietal Authentication of Extra Virgin Olive Oils by Triacylglycerols and Volatiles Analysis

Foods ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 58 ◽  
Author(s):  
Francesca Blasi ◽  
Luna Pollini ◽  
Lina Cossignani
Keyword(s):  
Volatile Compounds ◽  
Solid Phase ◽  
Stereospecific Analysis ◽  
Mass Spectrometry Analysis ◽  
Gas Chromatography Mass Spectrometry ◽  
Virgin Olive Oils ◽  
Linear Discriminant ◽  
Spectrometry Analysis ◽  
Olive Oils ◽  
Organoleptic Characteristics

In recent years, there is an increasing interest in high-quality extra virgin olive oils (EVOOs) produced from local cultivars. They have particular chemical/organoleptic characteristics and are frequently subjected to fraud, whereby the control of quality requires a powerful varietal check. In the present research, triacylglycerols (TAGs) and volatiles have been studied as chemical markers for the authentication of EVOO samples from four Italian varieties of Olea europea (Dolce Agogia, Frantoio, Leccino, and Moraiolo). The monocultivar EVOO samples have been subjected to a chemical–enzymatic chromatographic method in order to perform a stereospecific analysis, an important procedure for the characterization of TAG of food products. The results, combined with chemometric analysis (linear discriminant analysis, LDA), were elaborated in order to classify Italian EVOO monocultivar samples. In accordance with the total and intrapositional fatty acid (FA) composition of TAG fraction, the results were allowed to carry out a varietal discrimination. In addition, volatile compounds were also determined by solid-phase micro-extraction gas chromatography-mass spectrometry analysis. All EVOO samples were correctly classified when TAG stereospecific data and volatile results were elaborated by the LDA procedure, even if volatile compounds showed a higher discriminant power.

scholarly journals A multi internal standard calibration approach for the automated and robust analysis of wine volatiles by headspace Solid Phase Microextraction and further Gas Chromatography-Mass Spectrometry

1970 ◽  
pp. 34
Author(s):  
P. Herrero ◽  
J. Zapata ◽  
J. Cacho ◽  
Vicente Ferreira
Keyword(s):  
Volatile Compounds ◽  
Solid Phase Microextraction ◽  
Solid Phase ◽  
Relative Weight ◽  
Experimental Parameter ◽  
Internal Standard ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Volatile Constituents

Head space solid phase microextraction (HS-SPME) is a solvent-free technique that allows an almost complete automatization and getting amazing sensitivities. The hidden risk of SPME lies in the fact that as the amount of analyte extracted is very low; it is extremely sensitive to any experimental parameter that may affect the liquid-gas and gas-solid distribution coefficients. Our aims are to measure the relative weight of these factors on the lack of accuracy, and to design a robust calibration system able to avoid or limit their effects.For the first goal, synthetic but real-like wines containing a fixed amount of selected analytes (70) and variable amounts of ethanol, non-volatile constituents and major volatile constituents were prepared following a 3-Factor complete Factorial design. The study of the relevance of the Factors carried out by analysis of variance (ANOVA) and by Principal Component Analysis revealed that the levels of major volatile constituents affected the extraction of most analytes, while ethanol and matrix affected particularly low volatile compounds. Lipophilic esters are most influenced by major volatile compounds, while acids, phenols and lactones are affected by the non-volatile matrix.13 different internal standard compounds belonging to different chemical classes were used in the calibration experiment. This was similar to the aforementioned experiment, but including as well 5 different concentration levels. In 29 out of 65 cases, a single internal standard provided a robust calibration guaranteeing an accuracy better than 10%, while in others a Partial Least Square Regression analysis was run in order to find a model able to provide maxima accuracy. Satisfactory models in terms of precision, linearity and recovery could be built for 30 other compounds, so that the method can quantify up to 59 relevant wine volatile compounds.

scholarly journals Characterization of α-Glucosidase Inhibitors from Clinacanthus nutans Lindau Leaves by Gas Chromatography-Mass Spectrometry-Based Metabolomics and Molecular Docking Simulation

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2402 ◽  
Author(s):  
Suganya Murugesu ◽  
Zalikha Ibrahim ◽  
Qamar-Uddin Ahmed ◽  
Nik-Idris Nik Yusoff ◽  
Bisha-Fathamah Uzir ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Molecular Docking ◽  
Acid Methyl Ester ◽  
Docking Simulation ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Glucosidase Inhibitors ◽  
Clinacanthus Nutans

Background: Clinacanthus nutans (C. nutans) is an Acanthaceae herbal shrub traditionally consumed to treat various diseases including diabetes in Malaysia. This study was designed to evaluate the α-glucosidase inhibitory activity of C. nutans leaves extracts, and to identify the metabolites responsible for the bioactivity. Methods: Crude extract obtained from the dried leaves using 80% methanolic solution was further partitioned using different polarity solvents. The resultant extracts were investigated for their α-glucosidase inhibitory potential followed by metabolites profiling using the gas chromatography tandem with mass spectrometry (GC-MS). Results: Multivariate data analysis was developed by correlating the bioactivity, and GC-MS data generated a suitable partial least square (PLS) model resulting in 11 bioactive compounds, namely, palmitic acid, phytol, hexadecanoic acid (methyl ester), 1-monopalmitin, stigmast-5-ene, pentadecanoic acid, heptadecanoic acid, 1-linolenoylglycerol, glycerol monostearate, alpha-tocospiro B, and stigmasterol. In-silico study via molecular docking was carried out using the crystal structure Saccharomyces cerevisiae isomaltase (PDB code: 3A4A). Interactions between the inhibitors and the protein were predicted involving residues, namely LYS156, THR310, PRO312, LEU313, GLU411, and ASN415 with hydrogen bond, while PHE314 and ARG315 with hydrophobic bonding. Conclusion: The study provides informative data on the potential α-glucosidase inhibitors identified in C. nutans leaves, indicating the plant’s therapeutic effect to manage hyperglycemia.

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