This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation. 

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis.

In this study, we performed a comparative analysis of fungal communities and cultivable yeast isolates in rice wine from the Xiaogan and Dazhou regions using Illumina MiSeq high-throughput sequencing technology and traditional culture-dependent methods. And we further explored the correlation between the sensory quality and fungal communities in rice wine. The results demonstrated no significant difference (P > 0.05) in the abundance and diversity of fungal communities and cultivable yeast isolates between Xiaogan and Dazhou rice wine. The dominant fungal genera were Rhizopus and Saccharomyces, with Saccharomyces cerevisiae accounting for the greatest proportion of yeast isolates. The rice wine samples could be divided into two types based on the composition of fungal communities. Type 1 mainly contained Rhizopus and Saccharomyces, while type 2 mainly contained Rhizopus. Properties such as lightness, blueness-yellowness, methane levels, ethanol levels, organic sulfide content, and soluble solids content accounted for the main differences between the two types of rice wine. In addition, Candida and Clavispora showed a significant positive correlation (P < 0.05) with the aromatic substances in rice wine. The findings of the study enrich our understanding of traditional rice wine, providing meaningful information for the improvement and development of the traditional rice wine industry. 

Fungal communities and their correlation with the sensory quality of rice wine from the Xiaogan and Dazhou regions in China

Simultaneous inoculation of non-Saccharomyces cerevisiae during the alcoholic fermentation process has been found to be an effective strategy for enhancing wine flavor. This study aimed to investigate the effect of Torulaspora delbrueckii NCUF305.2 on the flavor of navel orange original brandy (NOOB) using E-nose combined with HS-SPME-GC-MS. The results showed a significant increase (p < 0.05) in the sensitivity of NOOB to W5C, W3C, W1S, and W3S sensors by mixed fermentation (MF). Esters in NOOB increased by 4.13%, while higher alcohols increased by 21.93% (p < 0.001), terpenes and others increased by 52.07% and 40.99% (p < 0.01), respectively. Notably, several important volatile compounds with relative odor activity values above 10 showed an increase. Sensory analysis revealed that a more pronounced citrus-like flavor and higher overall appearance scores were found in MF than in pure fermentation (PF). These findings offer valuable theoretical guidance for enhancing the quality of fruit brandies. 

Evaluation of the effect of Torulaspora delbrueckii on important volatile compounds in navel orange original brandy using E-nose combined with HS-SPME-GC-MS

Green tea is a non-fermented tea with flavor and polyphenols. Aroma is one of the important quality indicators of tea. Fermented green tea wine can solve the problem of low-grade tea, which has more bitterness and less aroma. In this study, Camellia sinensis var. pubilimba Hung T. Chang (Kaishan white tea 2) was screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) to benzyl alcohol and phenethyl alcohol presenting a fruity aroma, dimethyl sulfide presenting a green tea aroma, and rich tea polyphenols with contents of 2.08, 2.43, 12.26 and 3.72%, respectively. The optimal fermentation conditions for green tea wine were determined univariately as 1.5% yeast addition, 30 °Brix initial sugar, and fermentation temperature of 25 °C. The electronic sensory assessment showed that the saltiness, richness and umami were more prominent in green tea wine, while the response values of bitterness, astringency and aftertaste-A were lower. The order of aroma contribution can be seen as W1S &gt; W5S &gt; W2S &gt; W2W &gt; W1W &gt; W3S &gt; W6S. Kaisan white tea 2 gives green tea wine a clear tea aroma. This study provides better technical and theoretical strategies for the comprehensive quality assessment and control of fermented green tea wine quality. 

Characteristic Aroma Screening among Green Tea Varieties and Electronic Sensory Evaluation of Green Tea Wine

Microbial Fermentation in Fermented Tea Beverages: Transforming Flavor and Enhancing Bioactivity

Wine is an alcoholic beverage, consisting of several compounds in various ranges of concentrations. Wine quality is usually assessed by a sensory panel of trained personnel. Electronic tongues (e-tongues) and electronic noses (e-noses) have been established in recent years to assess the quality of beverages and foods. Response surface and electronic analysis tools were used to examine the quality of black tea wine. The results indicated the optimum initial sugar level (25 °Brix), yeast addition (0.5%), and fermentation temperature (25 °C) for Golden Peony black tea wine. The black tea wine produced under these conditions with 14.0% vol alcohol has as an orange-red color, full wine and tea flavor, and mild and mellow taste. The sourness of the wine was most affected by fermentation factors—yeast addition, fermentation temperature, and initial sugar level. Alcohols, aldehydes, ketones, and alkanes contributed to most of the volatile components under the influence of yeast addition and fermentation temperature. In contrast, nitrogen oxides, aromatics, and organic sulfides contributed under the influence of the initial sugar level. This study provided a facilitated strategy for obtaining the optimum black tea wine fermentation process through electronic nose and tongue-based techniques. The analysis of wines requires new technologies able to detect various different compounds simultaneously, providing worldwide information about the sample instead of information about specific compounds.

Characterization of the Pure Black Tea Wine Fermentation Process by Electronic Nose and Tongue-Based Techniques with Nutritional Characteristics

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Characterization of the Pure Black Tea Wine Fermentation Process by Electronic Nose and Tongue-Based Techniques with Nutritional Characteristics