Analysis on physicochemical and sensory qualities of soymilk prepared by various cultivars: Application of fuzzy logic technique

A deep learning-based quantitative prediction model for the processing potentials of soybeans as soymilk raw materials

Current technologies as correlation analysis, regression analysis and classification model, exhibited various limitations in the evaluation of soybean possessing potentials, including single, vague evaluation and failure of quantitative prediction, and thereby hindering more efficient and profitable soymilk industry. To solve this problem, 54 soybean cultivars and their corresponding soymilks were subjected to chemical, textural, and sensory analyses to obtain the soybean physicochemical nature (PN) and the soymilk profit and quality attribute (PQA) datasets. A deep-learning based model was established to quantitatively predict PQA data using PN data. Through 45 rounds of training with the stochastic gradient descent optimization, 9 remaining pairs of PN and PQA data were used for model validation. Results suggested that the overall prediction performance of the model showed significant improvements through iterative training, and the trained model eventually reached satisfying predictions (|relative error| ≤ 20%, standard deviation of relative error ≤ 40%) on 78% key soymilk PQAs. Future model training using big data may facilitate better prediction on soymilk odor qualities.

Effect of Kefir on Soybean Isoflavone Aglycone Content in Soymilk Kefir

Kefir is a traditional fermented milk originating in the Caucasus area and parts of Eastern Europe. In this study, the kefir culture, which is modified upon the addition of lactic acid bacteria (LAB) cells, specifically for soymilk kefir fermentation with the highest capacity of isoflavone biotransformation, was successfully produced, and the metagenomics composition of soymilk or milk fermented using these kefir cultures was investigated. The metagenome analysis showed that the microbiota of kefir in M-K (milk inoculated with kefir), SM-K (equal volumes of soymilk and milk inoculated with kefir), and S-K (pure milk inoculated with kefir) were related to the addition of soymilk or not. Furthermore, the HPLC chromatogram revealed that Guixia 2 (Guangzhou, China) may be a good source of soymilk kefir fermentation due to its high isoflavone aglycone content (90.23 ± 1.26 μg/g in daidzein, 68.20 ± 0.74 μg/g in genistein). Importantly, the starter culture created by adding 1.5 g probiotics (Biostime®, Guangzhou, China) to Chinese kefir showed a significant increase in the levels of isoflavone aglycones (72.07 ± 0.53 μg/g in isoflavone aglycones). These results provided insight into understanding the suitable soybean cultivar and starter cultures, which exhibit promising results of isoflavone biotransformation and flavor promotion during soymilk kefir fermentation.