Exploration of the flavor diversity of oolong teas: A comprehensive analysis using metabolomics, quantification techniques, and sensory evaluation.

The taste profile of oolong tea is intricately shaped by origins and roasting. Herein, a comprehensive approach integrating non-targeted metabolomics, quantitative analysis and sensory evaluations was employed to analyze the taste profile of oolong tea. 25 selected representative oolong teas, including Southern Fujian (MN), Northern Fujian (MB), and Taiwan (TW), were meticulously were classified into SX-RG-DD, GS, and TGY based on the chemical taste phenotypes. A total of 314 non-volatile compounds were identified, among which 87 and 77 compounds, including catechin, theaflavins, flavonoids and amino acids were screened as critical taste metabolites responsible for regions and roasting degree, respectively. The reduction of bitter and astringent, coupled with the enhancement of umami, sweet and sweet aftertaste exhibited a correlation with the decrease in (-)-epigallocatechin (EGC), (-)-epicatechin (EC), (-)-epicatechin gallate (ECG) and (-)-epigallocatechin gallate (EGCG), while the increase in catechin (C). These findings provide insights for further research on optimizing tea quality and refining processing techniques.

TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning.

In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.

Elucidation of the Impact of Steaming on the Key Odorants of Jinhua Dry-Cured Ham Using the Sensomics Approach.

Jinhua dry-cured ham (JDH) is a traditional fermented meat product favored by Chinese consumers. In this paper, the impact of steaming on the key odorants of JDH was investigated using the sensomics approach. Compounds with odor activity values (OAV) ≥1 were re-engineered in a triglyceride matrix to imitate the odor profiles of both raw and steamed JDHs. The aroma-active compounds were then confirmed by recombination and omission tests using triangle tests. The odor profiles of raw and steamed JDHs were obtained by quantitative descriptive analysis to compare the differences between the original and recombined models. The results showed that pentanal, hexanal, dimethyl trisulfide, (E,E)-2,4-decadienal, (E)-2-heptenal, furaneol, 3-methylbutanoic acid, 1-octen-3-one, and methional influenced the overall raw JDH odor significantly. Furaneol was first reported as a key compound that provides a caramel smell to the raw JDH. Apart from (E)-2-heptenal, dimethyl trisulfide, furaneol, 3-methylbutanoic acid, and methional, the remaining three compounds including 2-furfurylthiol, benzeneacetaldehyde, and phenylethyl alcohol showed a significant influence on the odor profile of steamed JDH. The statistical analysis of the odor profiles showed an 80.0% similarity between the recombination raw JDH and the real raw JDH, and a 76.3% similarity between the model and the real steamed JDH.