scholarly journals Discrimination of Cultivated Regions of Soybeans (Glycine max) Based on Multivariate Data Analysis of Volatile Metabolite Profiles

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 763
Author(s):  
So-Yeon Kim ◽  
So Young Kim ◽  
Sang Mi Lee ◽  
Do Yup Lee ◽  
Byeung Kon Shin ◽  
...  
Keyword(s):  
Glycine Max ◽  
Data Analysis ◽  
Volatile Compounds ◽  
Multivariate Data Analysis ◽  
Butyl Acetate ◽  
Solid Phase ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Ethyl Hexanoate ◽  
Score Plot

Soybean (Glycine max) is a major crop cultivated in various regions and consumed globally. The formation of volatile compounds in soybeans is influenced by the cultivar as well as environmental factors, such as the climate and soil in the cultivation areas. This study used gas chromatography-mass spectrometry (GC-MS) combined by headspace solid-phase microextraction (HS-SPME) to analyze the volatile compounds of soybeans cultivated in Korea, China, and North America. The multivariate data analysis of partial least square-discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) were then applied to GC-MS data sets. The soybeans could be clearly discriminated according to their geographical origins on the PLS-DA score plot. In particular, 25 volatile compounds, including terpenes (limonene, myrcene), esters (ethyl hexanoate, butyl butanoate, butyl prop-2-enoate, butyl acetate, butyl propanoate), aldehydes (nonanal, heptanal, (E)-hex-2-enal, (E)-hept-2-enal, acetaldehyde) were main contributors to the discrimination of soybeans cultivated in China from those cultivated in other regions in the PLS-DA score plot. On the other hand, 15 volatile compounds, such as 2-ethylhexan-1-ol, 2,5-dimethylhexan-2-ol, octanal, and heptanal, were related to Korean soybeans located on the negative PLS 2 axis, whereas 12 volatile compounds, such as oct-1-en-3-ol, heptan-4-ol, butyl butanoate, and butyl acetate, were responsible for North American soybeans. However, the multivariate statistical analysis (PLS-DA) was not able to clearly distinguish soybeans cultivated in Korea, except for those from the Gyeonggi and Kyeongsangbuk provinces.

scholarly journals Investigation of Volatile Compounds, Microbial Succession, and Their Relation During Spontaneous Fermentation of Petit Manseng

2021 ◽  
Vol 12 ◽  
Author(s):  
Yanqin Ma ◽  
Tian Li ◽  
Xiaoyu Xu ◽  
Yanyu Ji ◽  
Xia Jiang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Volatile Compounds ◽  
Multivariate Data Analysis ◽  
Microbial Population ◽  
Solid Phase ◽  
Multivariate Data ◽  
Aroma Compounds ◽  
Gas Chromatography Mass Spectrometry ◽  
Spontaneous Fermentation ◽  
Sweet Wine

Petit Manseng is widely used for fermenting sweet wine and is popular among younger consumers because of its sweet taste and attractive flavor. To understand the mechanisms underlying spontaneous fermentation of Petit Manseng sweet wine in Xinjiang, the dynamic changes in the microbial population and volatile compounds were investigated through high-throughput sequencing (HTS) and headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) technology, respectively. Moreover, the relationship between the microbial population and volatile compounds was deduced via multivariate data analysis. Candida and Mortierella were dominant genera in Petit Manseng wine during spontaneous fermentation. Many fermentative aroma compounds, including ethyl octanoate, isoamyl acetate, ethyl butyrate, ethyl decanoate, isoamyl alcohol, ethyl laurate, isopropyl acetate, hexanoic acid, and octanoic acid, were noted and found to be responsible for the strong fruity and fatty aroma of Petit Manseng sweet wine. Multivariate data analysis indicated that the predominant microorganisms contributed to the formation of these fermentative aroma compounds. Hannaella and Neomicrosphaeropsis displayed a significantly positive correlation with the 6-methylhept-5-en-2-one produced. The current results provide a reference for producing Petit Manseng sweet wine with desirable characteristics.

scholarly journals Characterization of α-Glucosidase Inhibitors from Psychotria malayana Jack Leaves Extract Using LC-MS-Based Multivariate Data Analysis and In-Silico Molecular Docking

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 5885
Author(s):  
Tanzina Sharmin Nipun ◽  
Alfi Khatib ◽  
Zalikha Ibrahim ◽  
Qamar Uddin Ahmed ◽  
Irna Elina Redzwan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Data Analysis ◽  
Binding Affinity ◽  
Bioactive Compounds ◽  
In Silico ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Protein Data Bank Code ◽  
Glucosidase Inhibitors

Psychotria malayana Jack has traditionally been used to treat diabetes. Despite its potential, the scientific proof in relation to this plant is still lacking. Thus, the present study aimed to investigate the α-glucosidase inhibitors in P.malayana leaf extracts using a metabolomics approach and to elucidate the ligand–protein interactions through in silico techniques. The plant leaves were extracted with methanol and water at five various ratios (100, 75, 50, 25 and 0% v/v; water–methanol). Each extract was tested for α-glucosidase inhibition, followed by analysis using liquid chromatography tandem to mass spectrometry. The data were further subjected to multivariate data analysis by means of an orthogonal partial least square in order to correlate the chemical profile and the bioactivity. The loading plots revealed that the m/z signals correspond to the activity of α-glucosidase inhibitors, which led to the identification of three putative bioactive compounds, namely 5′-hydroxymethyl-1′-(1, 2, 3, 9-tetrahydro-pyrrolo (2, 1-b) quinazolin-1-yl)-heptan-1′-one (1), α-terpinyl-β-glucoside (2), and machaeridiol-A (3). Molecular docking of the identified inhibitors was performed using Auto Dock Vina software against the crystal structure of Saccharomyces cerevisiae isomaltase (Protein Data Bank code: 3A4A). Four hydrogen bonds were detected in the docked complex, involving several residues, namely ASP352, ARG213, ARG442, GLU277, GLN279, HIE280, and GLU411. Compound 1, 2, and 3 showed binding affinity values of −8.3, −7.6, and −10.0 kcal/mol, respectively, which indicate the good binding ability of the compounds towards the enzyme when compared to that of quercetin, a known α-glucosidase inhibitor. The three identified compounds that showed potential binding affinity towards the enzymatic protein in molecular docking interactions could be the bioactive compounds associated with the traditional use of this plant.

scholarly journals Determination of Stevioside and Rebaudioside A in Stevia rebaudiana Bertoni Leaves Using near Infrared Spectroscopy and Multivariate Data Analysis

2018 ◽  
Vol 18 (4) ◽  
pp. 664 ◽  
Author(s):  
Yohanes Martono ◽  
Suryasatriya Trihandaru ◽  
Ferdy Semuel Rondonuwu
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Cross Validation ◽  
Multivariate Data ◽  
Stevia Rebaudiana ◽  
Partial Least Square ◽  
Steviol Glycosides ◽  
Rebaudioside A ◽  
Wavenumber Region

Rebaudioside A and stevioside are abundant steviol glycoside contained in Stevia rebaudiana leaves. These components are widely used as a natural sweetener. The objective of this study was to develop rapid determination method of stevioside, and rebaudioside A in S. rebaudiana leaves using near infrared trans-reflectance spectroscopy (NIRS) combined with multivariate analysis. The reference method used was HPLC. A prediction model was developed using partial least square (PLS) regression. Calibration parameters were calculated based on a calibration set of various stevioside, rebaudioside A from 23 samples. Performance of PLS model was assessed in term of optimum determination coefficient (R2), and minimum root mean square error of cross-validation (RMSEV). Validation of PLS model was performed using cross-validation and leave one out calibration of PLS component. Rebaudioside A has well PLS model in wavenumber region of 4100–5100 cm-1, and stevioside determination using difference wavenumber region of 4760-5016 cm-1. PLS model for total (sum of stevioside and rebaudioside A content) was exploited in wavenumber region of 4568-4928 cm-1. NIRS in combination with multivariate data analysis of PLSR can be applied as a rapid method for determining rebaudioside A and the total amount of steviol glycosides in S. rebaudiana leaves.

scholarly journals Exploring the Mystery of the Sweetness of Baijiu by Sensory Evaluation, Compositional Analysis and Multivariate Data Analysis

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2843
Author(s):  
Yulu Sun ◽  
Yue Ma ◽  
Shuang Chen ◽  
Yan Xu ◽  
Ke Tang
Keyword(s):  
Data Analysis ◽  
Correlation Analysis ◽  
Volatile Compounds ◽  
Sensory Evaluation ◽  
Multivariate Data Analysis ◽  
Compositional Analysis ◽  
Volatile Composition ◽  
Ethyl Hexanoate ◽  
Quality Marker ◽  
Additional Testing

Sweetness is an important Baijiu quality marker, but there is limited research on it. In this study, the main contributors to Baijiu sweetness were identified by “sensomics” combined with “flavoromics”. A total of 43 volatile compounds (mostly esters) were found that appeared to contribute to Baijiu sweetness, through sensory-guided fractionation and compositional analysis. Correlation analysis between the volatile composition and perceived sweetness of 18 Baijiu samples with different sweet intensities identified 14 potential contributors. Additional testing verified that combining the 14 compounds reproduced Baijiu sweetness exactly, and omission testing identified ethyl hexanoate, hexyl hexanoate and ethyl 3-methylbutanoate as the major contributors to Baijiu sweetness. These findings not only broadened our understanding of Baijiu sweetness, but also highlighted the major contribution of volatile compounds to sweetness perception, knowledge which may facilitate future flavor modification of a wide variety of foods and beverages.

scholarly journals A Comprehensive Characterisation of Volatile and Fatty Acid Profiles of Legume Seeds

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 651 ◽  
Author(s):  
Khrisanapant ◽  
Kebede ◽  
Leong ◽  
Oey
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Unsaturated Fatty Acids ◽  
Solid Phase ◽  
Multivariate Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Fatty Acid Profiles ◽  
Legume Seeds

Legumes are rich in unsaturated fatty acids, which make them susceptible to (non) enzymatic oxidations leading to undesirable odour formation. This study aimed to characterise the volatile and fatty acid profiles of eleven types of legumes using headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) and GC coupled with a flame ionisation detector (GC-FID), respectively. Volatile aldehydes, alcohols, ketones, esters, terpenes and hydrocarbons were the chemical groups identified across all the legumes. The lipids comprised palmitic, stearic, oleic, linoleic and α-linolenic acids, with unsaturated fatty acids comprising at least 66.1% to 85.3% of the total lipids for the legumes studied. Multivariate data analysis was used to compare volatile and fatty acid profiles between legumes, which allow discriminant compounds pertinent to specific legumes to be identified. Results showed that soybean, chickpea and lentil had distinct volatile and fatty acid profiles, with discriminating volatiles including lactone, ester and ketone, respectively. While all three Phaseolus cultivars shared similar volatile profiles, 3-methyl-1-butanol was found to be the only volatile differentiating them against the other eight legumes. Overall, this is the first time a multivariate data analysis has been used to characterise the volatile and fatty acid profiles across different legume seeds, while also identifying discriminating compounds specific for certain legume species. Such information can contribute to the creation of legume-based ingredients with specific volatile characteristics while reducing undesirable odours, or potentially inform relevant breeding programs.

scholarly journals Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1294
Author(s):  
Alberto González-Mohino ◽  
Trinidad Pérez-Palacios ◽  
Teresa Antequera ◽  
Jorge Ruiz-Carrascal ◽  
Lary Souza Olegario ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Least Square ◽  
K Nearest Neighbor ◽  
Fermented Sausage ◽  
Ripening Process ◽  
Dry Fermented Sausage

This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950–1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square–discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.

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