Dai Jinhui, Li Weicheng, Dong Gaifang
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, China.
Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010011, China.
Foods. 2024 Jun 21;13(13):1958. doi: 10.3390/foods13131958.
In the global food industry, fermented dairy products are valued for their unique flavors and nutrients. is crucial in developing these flavors during fermentation. Meeting diverse consumer flavor preferences requires the careful selection of fermentation agents. Traditional assessment methods are slow, costly, and subjective. Although electronic-nose and -tongue technologies provide objective assessments, they are mostly limited to laboratory environments. Therefore, this study developed a model to predict the electronic sensory characteristics of fermented milk. This model is based on the genomic data of , using the DBO (Dung Beetle Optimizer) optimization algorithm combined with 10 different machine learning methods. The research results show that the combination of the DBO optimization algorithm and multi-round feature selection with a ridge regression model significantly improved the performance of the model. In the 10-fold cross-validation, the R values of all the electronic sensory phenotypes exceeded 0.895, indicating an excellent performance. In addition, a deep analysis of the electronic sensory data revealed an important phenomenon: the correlation between the electronic sensory phenotypes is positively related to the number of features jointly selected. Generally, a higher correlation among the electronic sensory phenotypes corresponds to a greater number of features being jointly selected. Specifically, phenotypes with high correlations exhibit from 2 to 60 times more jointly selected features than those with low correlations. This suggests that our feature selection strategy effectively identifies the key features impacting multiple phenotypes, likely originating from their regulation by similar biological pathways or metabolic processes. Overall, this study proposes a more efficient and cost-effective method for predicting the electronic sensory characteristics of milk fermented by . It helps to screen and optimize fermenting agents with desirable flavor characteristics, thereby driving innovation and development in the dairy industry and enhancing the product quality and market competitiveness.
在全球食品工业中，发酵乳制品因其独特的风味和营养成分而受到重视。[此处原文缺失关键信息]在发酵过程中对形成这些风味至关重要。满足消费者多样化的风味偏好需要仔细选择发酵剂。传统的评估方法缓慢、成本高且主观。尽管电子鼻和电子舌技术能提供客观评估，但大多局限于实验室环境。因此，本研究开发了一个模型来预测发酵乳的电子感官特性。该模型基于[此处原文缺失关键信息]的基因组数据，使用蜣螂优化算法（DBO）结合10种不同的机器学习方法。研究结果表明，DBO优化算法与岭回归模型的多轮特征选择相结合显著提高了模型的性能。在10折交叉验证中，所有电子感官表型的R值均超过0.895，表明性能优异。此外，对电子感官数据的深入分析揭示了一个重要现象：电子感官表型之间的相关性与共同选择的特征数量呈正相关。一般来说，电子感官表型之间的相关性越高，共同选择的特征数量就越多。具体而言，高相关性的表型共同选择的特征比低相关性的表型多2至60倍。这表明我们的特征选择策略有效地识别了影响多种表型的关键特征，这些特征可能源于相似的生物途径或代谢过程的调控。总体而言，本研究提出了一种更高效、更具成本效益的方法来预测[此处原文缺失关键信息]发酵乳的电子感官特性。它有助于筛选和优化具有理想风味特征的发酵剂，从而推动乳制品行业的创新与发展，提高产品质量和市场竞争力。
