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机器学习根据拉曼光谱中的胡卜素含量对果成熟度的分类
Ji Loun Tan1, Fazida Hanim Hashim1,2, Jahariah Sampe3
1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
拉曼光谱通过分析皮肤中的胡卜素化合物,准确地确定果的成熟度. 这种非侵入性方法为传统评估提供了可靠的替代方案,确保了最佳的水果质量和产量.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 果的成熟度对味道,香味和营养至关重要,影响农民的产量.
- 传统的成熟度评估不一致,不准确,耗时.
- 果颜色的变化和人类的感知影响了传统方法.
研究的目的:
- 开发一种非侵入性,高效的方法,使用拉曼光谱检测果成熟度.
- 从原始拉曼光谱中提取有机化合物数据.
- 关联胡卜素特性与果成熟程度.
- 评估果成熟度分类的机器学习模型.
主要方法:
- 分析了29种果果谱,其中13个样本代表不成熟,成熟和过度成熟的类别.
- 利用拉曼光谱分析果皮中的有机化合物.
- 应用统计分析和机器学习模型 (SVM,KNN) 用于分类.
主要成果:
- 在果皮 (1,4801,550厘米−1) 中鉴定了胡卜素 (利科,β-胡卜素,黄蛋白,新山丁).
- 证实了胡卜素峰值强度和果成熟度之间的显著相关性 (p < 0.05).
- 在使用SVM和KNN模型对成熟度进行分类时实现了100%的准确性.
结论:
- 拉曼光谱是一种可靠,强大,非侵入性的方法,用于评估果的成熟度.
- 该技术不受外界因素的影响,如光,湿度和噪音.
- 这种方法有望改善果质量控制和收获管理.
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