Front Cover: Headspace‐solid phase microextraction and GC‐MS followed by multivariate data analysis to study the effect of hop processing type and dry hopping time on the aromatic profile of top‐fermented beers

2019 ◽  
Vol 2 (7) ◽  
Author(s):  
Duan Ceola ◽  
Ricardo Dagnoni Huelsmann ◽  
José Augusto Da‐Col ◽  
Edmar Martendal
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Solid Phase Microextraction ◽  
Solid Phase ◽  
Multivariate Data ◽  
Front Cover ◽  
Headspace Solid Phase Microextraction

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 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 Innovative Combination of Dispersive Solid Phase Extraction Followed by NIR-Detection and Multivariate Data Analysis for Prediction of Total Polyphenolic Content

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4807
Author(s):  
Christoph Kappacher ◽  
Markus Neurauter ◽  
Matthias Rainer ◽  
Günther K. Bonn ◽  
Christian W. Huck
Keyword(s):  
Data Analysis ◽  
Solid Phase Extraction ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Solid Phase ◽  
Multivariate Data ◽  
Phase Extraction ◽  
Dispersive Solid Phase Extraction ◽  
Polyphenolic Content ◽  
Total Polyphenolic Content

Recently polyphenols attracted great interest in the field of food and nutrition as well as in the pharmaceutical and cosmetics industries due to their health benefits through antioxidative behavior in the human body. However, because of the high number of compounds characterized as phenols and their structural diversity, quantification of polyphenols turns out to be a highly complex task. Although, a wide variety of analytical methods are used for the determination of total polyphenolic content, they are all found to be lacking in a variety of different tasks, such as their limits of detection and quantification, repeatability, accuracy and specificity. For this reason, a novel approach combining the advantages of solid phase purification, near infrared analysis and multivariate data analysis was investigated for the prediction of total polyphenolic content, suitable for a wide range of sample matrices. Dispersive solid phase extraction was performed and optimized using polyvinylpyrrolidone as sorbent, known to selectively bind polyphenols. Near-infrared detection of adsorbed polyphenols was carried out subsequently. Furthermore, the method was in-house validated, examining selectivity, repeatability and accuracy, working range, as well as multivariate limit of detection and limit of quantification, comparing it with two routinely used methods—namely, Folin–Ciocalteu photometric assay and Löwenthal titration. The novel established method was applied for the prediction of total polyphenolic content in tea and wine samples.

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