使用拉曼光谱和机器学习识别子颗粒的快速,非破坏性和准确的方法
Rui Liu1, Yuanpeng Li2,3, Tinghui Li1,4
1Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.
Journal of food science
|December 10, 2024
概括
这项研究引入了拉曼光谱和机器学习方法来检测果颗粒化,这是一个存储问题. 该技术能够准确地识别颗粒,减少食物浪费和经济损失.
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:
- 类水果的质量受到储存期间果汁袋颗粒化的影响.
- 这种情况给果产业带来了重大挑战,影响了营销能力,并导致经济损失.
研究的目的:
- 开发一种快速,非破坏性和精确的方法来检测类颗粒.
- 利用拉曼光谱和机器学习进行准确的颗粒评估.
主要方法:
- 从颗粒和非颗粒果样本中分析了969个拉曼光谱数据点.
- 逻辑回归,决策树和部分最小方程差异分析的应用.
- 通过主要组件分析,连续投影算法和竞争性自适应重量取样 (CARS) 改进模型.
主要成果:
- 标识了标志性拉曼峰 (1580和1661厘米-1),表明有颗粒,与水,酸和糖分的差异有关.
- 部分最小平方差分分析实现了高精度 (0.997),回忆 (0.994) 和F-分数 (0.996).
- 一个结合的第二导数-CARS-部分最小平方差分分析模型在测试集中显示出100%的准确性.
结论:
- 拟议的拉曼光谱和机器学习方法为评估类水果质量和检测粒度提供了强大而可靠的方法.
- 该技术可用于在加工过程中对果进行线上选,从而最大限度地减少浪费和经济损失.
- 为分类类作物的质量提供技术支持,提高行业标准.
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