Du Lin, Wang Shujie, Chen Yongyan, Zhu Zhongxu, Sun Hai-Xi, Chiu Tsan-Yu
College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
BGI, Shenzhen, 518083, China.
NPJ Sci Food. 2025 Jun 2;9(1):92. doi: 10.1038/s41538-025-00435-6.
Aroma and precision fermentation converge in exciting ways, enabling the precise production of aromatic compounds. Precision fermentation employs engineered microorganisms to create and refine scents and aromas with high accuracy, allowing for customizable aromas and opening new possibilities for both culinary experiences and consumer products. Structured data on volatile compounds from canned meat and fermented products was compiled to train machine learning (ML) models aimed at predicting volatile compounds and simulating meat aroma in Saccharomyces cerevisiae. We proposed a framework encompassing data generation and preprocessing, feature selection, model construction, and evaluation. Principal Component Analysis ensured data quality control, while embedding-based feature selection identified key volatile compounds. A two-stage model was developed to quantify the importance of volatile compounds and predict meat aroma and the gradient-boosted decision trees (GBDT) model demonstrated optimal performance. Our study guides simulating meat aroma through fermentation, offering a promising approach for plant-based meat flavoring.
香气与精准发酵以令人兴奋的方式融合，实现了芳香化合物的精准生产。精准发酵利用工程微生物高精度地创造和提炼气味与香气，从而实现可定制的香气，并为烹饪体验和消费品开辟了新的可能性。收集了来自罐装肉类和发酵产品的挥发性化合物的结构化数据，用于训练旨在预测挥发性化合物并模拟酿酒酵母中肉类香气的机器学习（ML）模型。我们提出了一个涵盖数据生成与预处理、特征选择、模型构建和评估的框架。主成分分析确保了数据质量控制，而基于嵌入的特征选择则识别出关键挥发性化合物。开发了一个两阶段模型来量化挥发性化合物的重要性并预测肉类香气，梯度提升决策树（GBDT）模型表现出最佳性能。我们的研究为通过发酵模拟肉类香气提供了指导，为植物性肉类调味提供了一种有前景的方法。
