Citation: CHEN Yue-Yao, XIA Jing-Jing, WEI Yun, XU Wei-Xin, MAO Xin-Ran, CHEN Yue-Fei, MIN Shun-Geng, XIONG Yan-Mei. 近红外光谱法无损检测平谷产大桃品质方法研究[J]. Chinese Journal of Analytical Chemistry, ;2023, 51(3): 454-462. doi: 10.19756/j.issn.0253-3820.221405 shu

近红外光谱法无损检测平谷产大桃品质方法研究

College of Science, China Agricultural University, Beijing 100193, China

Corresponding author: MIN Shun-Geng, XIONG Yan-Mei,
Received Date: 6 August 2022
Revised Date: 13 January 2023

Fund Project: Supported by the National Natural Science Foundation of China (No. 21023048) and the Project of Research and Application of Intelligent Agricultural Inspection Service (No. 2021110041000003).

利用近红外(Near infrared,NIR)漫反射光谱法结合化学计量学定量分析技术开展了平谷产大桃品质的无损检测研究。使用手持式近红外光谱仪采集7个不同品种平谷产大桃的近红外漫反射光谱,采用系统抽样法将其划分为验证集和验证集,结合Savitzky-Golay卷积平滑法(Savitzky-Golay Smoothing,S-G)、标准正态变换法(Standard normal variation,SNV)和多元散射校正法(Multivariate scattering correction,MSC)3种预处理方法以及偏最小二乘回归(Partial least square regression,PLSR)与随机森林(Random forest,RF)两种建模算法,分别建立了大桃中的糖度(Soluble solids content,SSC)、酸度、硬度和水分定量模型。结果表明,非线性RF模型优于线性PLSR模型,对于SSC,一阶导数光谱结合MSC预处理建立的RF模型效果最佳,验证集决定系数(Coefficient of determination,R²)和预测均方根误差(Root mean squared error prediction,RMSEP)分别为0.79和0.77° Brix;对于硬度,一阶导数光谱结合MSC预处理建立的RF模型效果最佳,验证集R²和RMSEP分别为0.90和0.55 N;对于酸度,一阶导数光谱结合MSC预处理建立的RF模型效果最佳,验证集R²和RMSEP分别为0.71和0.18;对于水分,一阶导数光谱结合MSC预处理建立的RF模型效果最佳,验证集R²和RMSEP分别为0.80和0.64%。本研究采用近红外光谱结合化学计量学算法建立的方法可实现平谷大桃品质的快速无损检测。
- Near infrared spectroscopy,
- Peach,
- Nondestructive testing,
- Random forest,
- Quality testing
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