机器学习算法在食品气味表征中的应用

Application of Machine Learning Algorithms in Food Odor Characterization

食品气味的客观表征对于食品生产工艺优化及品质评价具有重要意义。然而,食品气味形成机理复杂、成分繁杂,加之气味的评价过程易受环境、心理及感知方式等多种因素的影响,使得在表征食品气味时需要处理大量高维复杂的信息,这要求借助具有强大数据处理能力的工具。伴随人工智能、计算机科学以及大数据技术的快速发展,机器学习具备了强大数据处理能力。因此,使用机器学习表征食品气味已成为可能,此过程的实现主要依赖机器学习算法。本文概述各类机器学习算法在食品气味客观表征中的应用情况,总结使用机器学习算法对食品气味进行表征的优势和待解决的问题,并展望机器学习算法应用于食品气味表征的未来发展方向。

The objective characterization of food odor is important for the optimization of food production process and quality evaluation.However,the complexity of the food odor formation mechanism and its complicated composition,coupled with the fact that the odor evaluation process is susceptible to a variety of factors such as environmental,psychological and perception modes,make it necessary to deal with a large amount of high-dimensional and complex information in the characterization of food odors,which requires the use of tools with powerful data processing capabilities.Along with the rapid development of artificial intelligence,computer science and big data technology,machine learning has powerful data processing capability,it has become possible to characterize food odors using machine learning,and the implementation of this process relies heavily on machine learning algorithms.In this paper,the application of various machine learning methods in food odor characterization was summarized,and the advantages and problems to be solved in using machine learning methods for food odor characterization were pointed out.Finally,the future development direction of machine learning algorithms applied to food odor characterization was foreseen.