花椒中麻味物质的研究及机器学习方法应用进展

Research Progress on Numbing Substances in Zanthoxylum bungeanum and Application of Machine Learning in Research on Them

本综述对机器学习在花椒中麻味物质的应用进行了总结。市面上不同品种的花椒含有不同的麻味物质且其含量亦不尽相同,同时花椒麻味物质的组成及其含量的传统检测与分析方法有诸多局限性,因此引入机器学习算法为这一领域带来了新的可能性。使用机器学习算法建立花椒品质的预测模型与干燥模型,综合对花椒的感官评价,建立花椒种质资源库,对花椒的遗传育种具有重大意义。本综述系统地回顾了不同花椒品种中麻味分子的组成及其含量,同时分析了机器学习算法在品质预测模型、干燥模型与麻味物质数据分析中的应用情况。通过整合机器学习技术,研究人员能够更深入地了解目前已建立的模型,基于麻味物质为花椒的产量与品质的优化提供支持。

This review summarizes the application of machine learning in research on the numbing substances of Zanthoxylum bungeanum.Different varieties of Z.bungeanum on the market vary in the composition and content of numbing substances.Traditional methods for the detection and analysis of the composition and content of numbing substances in Z.bungeanum have many limitations,so the introduction of machine learning algorithms has brought new possibilities to this field.Applying machine learning for predictive modeling of the quality of Z.bungeanum and modeling of its drying process,the sensory evaluation of Z.bungeanum,and establishing a germplasm bank for Z.bungeanum is of great significance for the genetic breeding of Z.bungeanum.This article systematically reviews the composition and content of numbing substances in different varieties of Z.bungeanum,and analyzes the application of machine learning algorithms in predictive quality modeling and drying modeling of Z.bungeanum and data analysis of its numbing substances.Integration with machine learning technology enables a deeper understanding of the currently established models,which will provide support for the optimization of the yield and quality of Z.bungeanum based on numbing substances.