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比较可见和近红外"点"光谱和高光谱成像技术,以可视化果坚度的变化

Zhenjie Wang¹, Fangchen Ding¹, Yan Ge²

¹College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|April 30, 2024

概括

可见和近红外高光谱成像 (Vis-NIR-HSI) 和光谱学 (Vis-NIR) 准确地预测了果的坚度,特别是在高内变性样本中. 与最小正方形支向量机 (LS-SVM) 模型相结合的Vis-NIR-HSI模型有效地可视化了度变化.

科学领域:

农业科学农业科学
频谱学是一种光谱学.
数据科学数据科学数据科学

背景情况:

果的坚度是影响消费者接受和存储潜力的关键品质特征.
个别果的坚度可以表现出显著的变化,影响预测模型的准确性.
需要快速和非破坏性的方法来评估果坚度的变化.

研究的目的:

为了比较Vis-NIR光谱和Vis-NIR-HSI用于预测果坚硬性的性能.
开发模型来可视化不同品种的果坚度变化.
为了研究果实内部变化的影响,坚固度预测的准确性.

主要方法:

在三种果品种上应用可见和近红外 (Vis-NIR) 光谱和高光谱成像 (Vis-NIR-HSI).
开发最小正方形支向量机 (LS-SVM) 模型,用于稳固性预测.
使用变化系数 (CV) 和R_c2值,分析果坚硬度预测的准确性.

主要成果:

与Vis-NIR光谱法相比,Vis-NIR-HSI模型显示出优越的坚性预测性能 (R_c2>0.84).
高内变性样本 (CV > 10%) 提高了果坚度预测的准确性.
与Vis-NIR-HSI相结合的LS-SVM模型成功地可视化了果坚度的空间变化.

结论:

视-NIR-HSI是一种高效的技术,用于非破坏性的果坚度评估.
纳入具有高内部变量的样本可以提高坚固性预测模型的稳定性.
开发的Vis-NIR-HSI-LS-SVM方法为可视化和管理果坚质量提供了有价值的工具.

关键词:

果公司的坚定不移.超光谱成像技术的使用.内部变量的变化.最小正方形支持向量机器.可见光和近红外光谱学.视觉化的可视化

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