使用手持拉曼光谱技术对梨的成熟度进行分类:通过机器学习和重新采样技术解决数据不平衡问题
In-Hwan Lee1, Zhengao Li2, Luyao Ma3
1Department of Food Science and Technology, College of Agricultural Sciences, Oregon State University, Corvallis, OR 97331, USA.
这项研究使用机器学习和拉曼光谱来非破坏性地评估梨的成熟度,有助于减少食物浪费. 开发的方法准确地分类成熟度,提供了一个实用的现场解决方案.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 计算机科学 计算机科学
背景情况:
- 食品浪费是一个重大的全球问题,由低效的质量评估方法加剧.
- 目前用于确定水果成熟度的方法,如破坏性测试和视觉检查,通常是不准确和劳动密集型的.
研究的目的:
- 开发一种非破坏性的机器学习辅助方法,使用手持拉曼光谱来分类梨的成熟度.
- 提供快速准确的现场评估工具,以尽量减少食物浪费.
主要方法:
- 从梨中收集了1274个拉曼光谱,将它们与坚度和内部质量数据相关联.
- 开发和比较机器学习模型,包括一个两层一维卷积神经网络 (1D-CNN).
- 研究了合成少数人过量采样技术对不平衡数据集的影响.
主要成果:
- 在原来的不平衡数据集上,双层1D-CNN实现了高性能 (ROC-AUC为0.831).
- 拉曼光谱显示,在梨成熟过程中,与叶绿素和氨酸水平相关的特征性变化.
- 过量采样技术改善了传统模型 (SVM,随机森林),但1D-CNN在原始数据上仍然优越.
结论:
- 机器学习辅助的拉曼光谱为评估梨成熟度提供了一种可行的非破坏性方法.
- 这项技术有可能通过准确的现场质量控制来显著减少食品浪费.
- 这项研究表明,1D-CNN对成熟度分类的有效性,即使样本大小不平衡.
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