机器学习技术在食品风味分析中的研究进展

Progress in Research on Machine Learning for Studies on Food Flavor Analysis

风味特征是影响消费者对食物的偏好和购买欲的重要因素,在食品生产以及食品科学研究领域发挥重要作用。传统的食品风味测定方法是基于实验的感官评价、仪器分析或二者相结合。随着计算机技术的快速发展,具有高预测能力和准确性的机器学习模型已被广泛用于食物风味的分析和预测,有效克服了传统方法在食品风味评价上耗时且不能处理大量数据的局限性。本文综述了食品风味分析技术、机器学习技术在食品风味研究中的最新进展,介绍了常用的食品风味分析技术和机器学习算法,系统阐述了机器学习在食品风味物质高通量筛选、食品风味感知及风味品质控制等方面的应用,并对机器学习在食品风味分析和预测中存在的问题和未来研究趋势进行了展望,为机器学习技术在预测风味形成机制、合成理想风味化合物和控制食品品质等方面提供理论参考。

The flavor profile is an important factor that affects consumer preference and purchase intention and plays a vital role in food production as well as in food science research.Traditional methods for determining food flavor are based on sensory evaluation,instrumental analysis,or a combination of the two.With the rapid development of computer technology,machine learning technology with high predictive ability and accuracy has been widely used for food flavor analysis and prediction.This method overcomes the limitations of traditional methods in food flavor evaluation,which are timeconsuming and cannot be used to process large quantities of data.This review discusses the latest progress in food flavor analysis technology and machine learning technology in food flavor research and presents commonly used food flavor analysis techniques and machine learning algorithms.Based on an overview of common machine learning models,this review systematically summarizes the application of machine learning in the high-throughput screening of food flavor substances,flavor perception,and flavor quality control.The challenges and future research trends in the application of machine learning to food flavor analysis and prediction are summarized and discussed.The purpose of this review is to provide theoretical references for machine learning technology to predict flavor formation mechanisms,synthesize desirable flavor compounds,and control food quality.