Qualitative analysis of flavors and fragrances added to tea by using GC–MS

Wang, Jianwu; Hang, Yuanxin; Yan, Tingting; Liang, Jenn‐Tai; Huang, Zhiqiang; Xu, Huilan

doi:10.1002/jssc.201700916

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…For mid-polar SP, large enough RI databases are absent. However, mid-polar SP are widely used in analytical practice [31][32][33][34], and RI prediction for these SP can be used for the GC-MS analysis of volatiles [11]. RI prediction for a wide variety of SP can be used for SP selection for given analytes and for the planning of an experiment.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Matyushin

Sholokhova

Buryak

2021

IJMS

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Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Matyushin

Sholokhova

Buryak

2021

IJMS

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“…This allows the quasimolecular ion and adducts ion information to be obtained for the analytes of interest, which is of importance as the high intensities of quasimolecular ions render them ideal precursor ions for MS/MS, thereby providing additional structural information. As a result, the GC-QqTOF-MS system gains an advantageous edge in the qualitative analysis of unknown compounds, 9 and so it is commonly employed in the analysis of semi-volatile and volatile compounds in foodstuffs 10–12 and traditional herbal medicines. 13–16 In addition, it has been applied in doping controls, 17 and in pesticide 18–20 and oil spill waste analysis.…”

Section: Introductionmentioning

confidence: 99%

Qualitative analysis of Schizonepeta annua (Pall.) Schischk essential oil by gas chromatography–quadrupole time-of-flight mass spectrometry

Bai

Maitusong

Bakri

et al. 2018

Eur J Mass Spectrom (Chichester)

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In this study, a method for the qualitative analysis of small molecular compounds in Schizonepeta annua (Pall.) Schischk essential oil was established based on gas chromatography-quadrupole time-of-flight mass spectrometry. In addition to an automated search of the NIST library, the identification of oxygenated monoterpenes, phenolic esters, and phenolic compounds was achieved by two additional strategies. One strategy involved comparing the relative errors of accurate masses measured for ions in the experimental spectra with those calculated for fragments identified from the NIST database of candidate matches. The second strategy involved combination of the product ion scans and positive chemical ionisation spectra for structural elucidation. Overall, 95.45% of the total essential oil volatile chemical content of Schizonepeta annua (Pall.) Schischk was identified, with phenolic monoterpenes dominating.

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Characterization of the pattern of development of key compounds contributing to aroma quality of chicken meat and their metabolic markers

Wang,

Liu,

Wang

et al. 2024

Animal Research and One Health

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Age has an important effect on the aroma of chicken meat. In this study, we systematically analyzed the patterns of aroma changes with increasing age and the key aroma‐contributing compounds and metabolites that lead to aroma differences with age. Electronic nose (e‐nose) and gas chromatography‐mass spectrometry analyses showed that the overall aroma intensity and the types and levels of volatile aroma compounds increased with age. Eight key aroma‐contributing compounds were identified by GC‐olfactometry (GC‐O) and odor activity value analyses, and their content increased with age. The e‐nose and GC‐O results revealed that 315‐day‐old chickens had the strongest aroma. Thus, taking 315‐day‐old chickens as reference, we found that the contents of key aroma‐contributing compounds and metabolites at 140 days of age were most similar to those at 315 days of age. Due to low feed cost, yellow chickens around 140 days of age were more suitable for marketing in terms of volatile aroma substances. It was found that hexanal, 1‐octen‐3‐ol, and (E,E)‐2,4‐decadienal contributed the most to chicken aroma. Additionally, small peptides were found to be the main types of metabolites responsible for the aroma difference in chickens due to age. Weighted gene co‐expression network analysis identified Ile‐Ser, Ile‐Thr, and Phe‐Ile as metabolic markers of hexanal and 1‐octen‐3‐ol, respectively. Further analysis revealed that Ile‐Ser, Ile‐Thr, and Phe‐Ile may promote the Maillard reaction by acting as substrates on the one hand, and facilitating the uptake of amino acids on the other hand, which in turn increases the contents of hexanal and 1‐octen‐3‐ol.

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Qualitative analysis of flavors and fragrances added to tea by using GC–MS

Cited by 9 publications

References 31 publications

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Qualitative analysis of Schizonepeta annua (Pall.) Schischk essential oil by gas chromatography–quadrupole time-of-flight mass spectrometry

Characterization of the pattern of development of key compounds contributing to aroma quality of chicken meat and their metabolic markers

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