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机器学习模型根据其地理原产地标签分类石竹 (Lentinula edodes)

Raquel Rodríguez-Fernández¹, Ángela Fernández-Gómez¹, Juan C Mejuto¹

¹Departamento de Química Física, Facultade de Ciencias, Universidade de Vigo, 32004 Ourense, Spain.

Foods (Basel, Switzerland)

|September 14, 2024

概括

机器学习准确地识别了石竹的起源. 诸如随机森林和支持矢量机器之类的算法可以区分韩国和中国的石竹 (Lentinula edodes),确保产品的真实性.

科学领域:

农业科学农业科学
计算生物学计算生物学
食品科学食品科学食品科学

背景情况:

石竹 (Lentinula edodes) 的消费量在全球范围内升,排名全球第二.
由于其营养和健康益处,消费者越来越多地要求对石竹的地理来源进行透明化.
区分石竹的原产地对于市场完整性和消费者信任至关重要.

研究的目的:

开发和评估用于确定在韩国消费的石竹的地理来源的机器学习算法.
根据稳定同位素数据 (δ13C, δ15N, δ18O, δ34S) 评估模型的预测准确性.

主要方法:

利用文献报告的实验数据,包括稳定同位素比和原产地信息.
开发并验证了机器学习模型:随机森林和支持矢量机器.
测试模型的数据集分为两个和三个来源组 (韩国,中国,中国注射的粉块).

主要成果:

随机森林在三个原产地类别中实现了高精度 (0.940) 和kappa (0.908).
支持向量机在两个类别 (韩国与中国包括粉块) 中表现出卓越的性能 (精度为0.988,kappa为0.975).
随机森林还在不同的两类分类 (韩国和中国,不包括粉块) 中表现出色 (准确率为0.952),测试阶段准确率可接受 (0.839-0.964).

结论:

机器学习算法是识别石竹地理来源的有效工具.
开发的模型显示出强大的预测能力,适用于现实世界的应用.
这项研究支持在石竹市场增强可追溯性和真实性验证.

关键词:

机器学习模型机器学习模型原产地标签的原产地标签.料粉块可以阻止料粉块.稳定的同位素比率是稳定的.

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