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Xinyang Maojian (XYMJ) is one of the premium green teas and originates from Xinyang, which is the northernmost green tea production area in China. The special geographic location, environmental conditions, and manufacturing process contribute to the unique flavor and rich nutrition of XYMJ green tea. Aroma is an important quality indicator in XYMJ green tea. In order to illustrate the aroma of XYMJ green tea, the key odorants in XYMJ green tea and their dynamic changes during the manufacturing processes were analyzed by headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). A total of 73 volatile compounds of six different chemical classes were identified in the processed XYMJ green tea samples, and the manufacturing processes resulted in the losses of total volatile compounds. Among the identified volatile compounds, twenty-four aroma-active compounds, such as trans-nerolidol, geranylacetone, nonanal, (+)-δ-cadinene, linalool, (Z)-jasmone, cis-3-hexenyl butyrate, cis-3-hexenyl hexanoate, methyl jasmonate, and β-ocimene, were identified as the key odorants of XYMJ green tea based on odor activity value (OAV). The key odorants are mainly volatile terpenes (VTs) and fatty acid-derived volatiles (FADVs). Except for (+)-δ-cadinene, copaene, cis-β-farnesene, (Z,E)-α-farnesene and phytol acetate, the key odorants significantly decreased after fixing. The principal coordinate analysis (PCoA) and the hierarchical cluster analysis (HCA) analyses suggested that fixing was the most important manufacturing process for the aroma formation of XYMJ green tea. These findings of this study provide meaningful information for the manufacturing and quality control of XYMJ green tea. [ABSTRACT FROM AUTHOR]

Xinyang Maojian (XYMJ) green tea is one of the top ten teas in China, and the consumers prefer spring tea due to its umami taste and pleasurable aroma. However, the knowledge about temporal variation of the volatile compounds in XYMJ green teas harvested during different seasons is very limited. In the present work, the main non-volatile compounds that endowed the taste and volatile compounds responsible for the aroma in XYMJ green teas harvested during the spring and autumn seasons were determined. The average contents of free amino acids (FAA) were significantly higher and gradually declined in the spring teas, whereas the caffeine was significantly lower and gradually increased in the spring teas. A total of 39 volatile compounds of six chemical classes were detected in XYMJ green teas, and they displayed various change trends during the spring and autumn seasons, among which 15 volatile compounds were identified as the key odorants based on odor activity value (OAV). The highest OAV of 2195.05 was calculated for the violet-like smelling trans-β-ionone followed by decanal, nonanal, dimethyl sulfide, linalool, geraniol and naphthalene. The OAVs of geraniol, (Z)-3-hexenyl hexanoate, heptanal, benzaldehyde and hexanal in XYMJ spring teas were higher than XYMJ autumn teas. The hierarchical clustering analysis indicated that XYMJ green teas were divided into three clusters and the quality of XYMJ green teas changed greatly within spring season. Harvest season is a crucial factor affecting the flavor quality of XYMJ green teas. [ABSTRACT FROM AUTHOR]

The flowers of Elaeagnus angustifolia L. have been used as a homologous variety in China, whose quality seriously relies on the compositions during the flowering period. Unfortunately, studies on the variations of volatile compounds during the flowering season are rarely reported. Herein, a gas chromatography-mass spectrometry-based untargeted metabolomic methodology was proposed for the comprehensive analysis of volatile compounds in E. angustifolia flowers to classify various flowering stages. Samples from four flowering stages were collected, including the initial bloom stage, pre-full bloom stage (70–80% of flowers), full bloom stage, and ending of the bloom stage. Simultaneous distillation extraction was used for the extraction of volatile compounds in the flowers, which was then analyzed by a newly developed chemometric data analysis tool, autoGCMSDataAnal. An advantage of the developed methodology is that compounds can be accurately screened and identified. Finally, 59 compounds that showed significant difference among four flowering stages were screened and 31 compounds were identified. Sample clustering results from principal component analysis and hierarchical clustering analysis suggested that flowers from the pre-full bloom stage and full bloom stage may be more suitable when used as raw materials for industrial products. [ABSTRACT FROM AUTHOR]

BACKGROUND Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head‐space solid phase microextraction approach and gas chromatography–mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas. RESULTS: Thirty‐one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k‐nearest neighbor (k‐NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross‐validation prediction abilities (84.6%) was obtained for the PCA–k‐NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION: The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

• For the first time, GC×GC-TOFMS was used to classify strong aroma-type baijiu (SAB) from different regions. • The SAB samples from two different basins in China were clearly separated in PCA score plots. • The predictive ability of the PLS-DA model was excellent, correctly classifying 100% of the unknown SAB samples. • Twenty-three potential markers were identified using standard compounds. • The HCA result showed that the selected markers offered strong discrimination power. A metabolomics strategy was developed to differentiate strong aroma-type baijiu (SAB) (distilled liquor) from the Sichuan basin (SCB) and Yangtze-Huaihe River Basin (YHRB) through liquid–liquid extraction coupled with GC×GC-TOFMS. PCA effectively separated the samples from these two regions. The PLS-DA training model was excellent, with explained variation and predictive capability values of 0.988 and 0.982, respectively. As a result, the model demonstrated its ability to perfectly differentiate all the unknown SAB samples. Twenty-nine potential markers were located by variable importance in projection values, and twenty-four of them were identified and quantitated. Discrimination ability is closely correlated to the characteristic flavor compounds, such as acid, esters, furans, alcohols, sulfides and pyrazine. Most of the marker compounds were less abundant in the SCB samples than in the YHRB samples. The quantitated markers were further processed using hierarchical cluster analysis for targeted analysis, indicating that the markers had great discrimination power to differentiate the SAB samples. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]