Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr). Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared. Results and Discussion: Gas chromatography-ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

Bacterial Diversity, Organic Acid, and Flavor Analysis of Dacha and Ercha Fermented Grains of Fen Flavor Baijiu

Fen flavor Baijiu needs two rounds of fermentation, which will obtain Dacha after initial fermentation and Ercha after secondary fermentation. The quality of Baijiu is closely related to the microbes within fermented grains. However, the bacterial diversity in Dacha and Ercha fermented grains of Fen flavor Baijiu has not been reported. In the present study, the structure and diversity of bacteria communities within fermented grains of Fen flavor Baijiu were analyzed and evaluated using MiSeq platform's HTS with a sequencing target of the V3-V4 region of the 16S rRNA gene. Through the analysis of physical and chemical indexes and electronic senses, the relationship between bacterial flora, organic acid, taste, and aroma in fermented grains was clarified. The results indicated that Lactobacillus was the main bacteria in Dacha, and the mean relative content was 97.53%. The bacteria within Ercha samples were Pseudomonas and Bacillus, mean relative content was 37.16 and 28.02%, respectively. The diversity of bacterial communities in Ercha samples was significantly greater than that in Dacha samples. The correlation between Lactobacillus and organic acids, especially lactic acid, led to the difference between Dacha and Ercha organic acids, which also made the pH value of Dacha lower and the sour taste significantly higher than Ercha. Lactobacillus was significantly positively correlated with a variety of aromas, which made Dacha the response value of aromas higher. In addition, Bacillus had a significant positive correlation with bitterness and aromatic compounds, which led to a higher response value of bitterness in Ercha and made it present an aromatic aroma. This study provides an in-depth analysis of the difference between different stages of Fen flavor Baijiu, and theoretical support for the standard production and improvement in quality of Fen flavor Baijiu in the future.

Pretreatment methods affecting the color, flavor, bioactive compounds, and antioxidant activity of jujube wine

In the case of wine production, the selection of optimal pretreatment methods and starter cultures are the 2 key points before fermentation. In this research, the fresh jujube was separately underwent alcoholic fermentation at 20°C with 3 different pretreatment methods (with peel, without peel, and juice) and 5 different starter cultures, respectively. Color analysis, electronic sense analysis, bioactive compound analysis, and antioxidant activity analysis combined with multivariate statistical analysis were applied to evaluated the effects of pretreatment methods and starter cultures on the overall quality of jujube wine. It was found that both pretreatment methods and starter cultures have effects on the quality of jujube wines, in which pretreatment methods have much more significant effects. The jujube wines fermented with different pretreatment methods were classified clearly by their overall quality, and that of the jujube wines fermented with peel was the best among all, since it can not only enhance the color and flavor quality of the wine, but also maximize the preservation of bioactive compounds and antioxidant activity of jujube for better consumer acceptance. This will provide a theoretical reference and application basis for the quality improvement of jujube wine.