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花椒中麻味物质的研究及机器学习方法应用进展

花匠小妙招
2025-01-03 22:49

摘要： 本综述将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的局限性，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的应用。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。

关键词: 机器学习, 花椒, 麻味物质, 模型

Abstract: This review summarizes the application of machine learning in the hemp flavor substances of Zanthoxylum bungeanum. Different varieties of Zanthoxylum bungeanum on the market contain different hemp flavor substances and their contents are also different. At the same time, the traditional detection and analysis methods of the composition and content of Zanthoxylum bungeanum hemp flavor substances have many limitations, so the introduction of machine learning algorithm has brought new possibilities to this field. It is of great significance for the genetic breeding of Zanthoxylum bungeanum to establish the prediction model and drying model of Zanthoxylum bungeanum quality, comprehensively evaluate the sensory evaluation of Zanthoxylum bungeanum, and establish the Germplasm Resource Bank of Zanthoxylum bungeanum using machine learning algorithms. This review systematically reviewed the composition and content of hemp flavor molecules in different breeds of Zanthoxylum bungeanum, and analyzed the application of machine learning algorithms in quality prediction model, drying model and data analysis of hemp flavor substances. By integrating machine learning technology, researchers can have a deeper understanding of the currently established models, and provide support for the optimization of the yield and quality of Zanthoxylum bungeanum based on hemp flavor substances.

Key words: machine learning, Zanthoxylum bungeanum, numb-tasted substance, model

中图分类号:

TS264