宁夏赤霞珠葡萄水分含量的高光谱无损检测研究

蔡正云; 吴龙国; 王菁; 潘媛; 马建荣; 李梓溢

doi:10.13386/j.issn1002-0306.2017.02.007

宁夏赤霞珠葡萄水分含量的高光谱无损检测研究

1. 宁夏大学农学院
2. 宁夏大学土木与水利工程学院

基金项目:

2014年度国家级大学生创新创业训练计划项目(141074917)；

详细信息

作者简介:
蔡正云(1981-),男,硕士,副教授,主要从事农产品检测方面的研究,E-mail:caizhy@nxu.edu.cn。;

中图分类号: TS255.7;O657.3
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出版历程
- 收稿日期: 2016-07-04

Non-destructive determination of moisture composition in Ningxia wine grapes based on visible near-infrared hyperspectral imaging technique

School of agriculture,Ningxia University
School of Civil and Hydraulic Engineering,Ningxia University

摘要

摘要: 利用可见近红外高光谱成像技术对宁夏赤霞珠葡萄含水量的无损检测进行了初步探讨。通过高光谱成像系统(400~1000 nm)采集了136幅赤霞珠葡萄图像,对原始光谱、平均平滑、高斯滤波、中值滤波、卷积平滑、归一化、多元散射校正、标准正态化、基线校准、去趋势化等预处理的偏最小二乘回归(PLSR)模型进行对比分析;采用主成分分析(PCA)、偏最小二乘回归(PLSR)、连续投影算法(SPA)、竞争性自适应重加权(CARS)方法选择特征波长,建立4种特征波长下的PLSR的葡萄含水量预测模型,优选CARS提取特征波长的方法。在此基础上,对比分析了全波段与特征波长下的MLR、PCR、PLSR的葡萄含水量预测模型。结果表明:采用多元散射校正(MSC)光谱建立的PLSR模型优于原始光谱和其他预处理光谱的PLSR模型;CARS提取特征波长建立的PLSR模型优于多元线性回归(MLR)、主成分回归(PCR)模型,预测集的相关系数(R)和预测均方根误差(RMSEP)分别为0.806、0.144。因此,利用可见近红外高光谱成像技术提取特征波长进行宁夏赤霞珠葡萄含水量的检测是可行的。
- 高光谱成像技术 /
- 酿酒葡萄 /
- 含水量 /
- 无损检测
Abstract: This article summarizes a visible near- infrared( Vis- NIR) hyperspectral imaging technique was investigated for non- destructive determination of moisture composition of cabernet sauvignon grape produced in Helan mountain.The hyperspectral images of grape over the spectral region between 400 nm and 1000 nm were acquired for 136 grape samples and the difference between raw wavelength and pretreatment wavelength( Average Smoothing,Gaussian Filter,Median Filter,Savitzky- Golay Smoothing,Normalize,MSC,SNV,Baseline Correction,Detrending) by partial least- squares regression( PLSR) were obtained.The efficient wavelengths were selected using weighted coefficients of principal component analysis( PCA),weightedβ- coefficients of partial least- squares regression( PLSR),SPA and CARS.Then the 4 characteristics wavelength of PLSR model of the moisture of cabernet sauvignon grape were built and the CARS method of PLSR model was the best. On this basis,the MLR,PCR and PLSR model of raw wavelength and optimal wavelength for the prediction of grape moisture were established.The results suggested that MSC method was considered to the optimal preprocessing method. The characteristic wavelength extracted by CARS method preformed better than MLR and PCR methods,and the correlation coefficient of prediction and the root mean square prediction error( RMSEP) were 0.806,0.144,respectively.Hence,It's feasible to determinate the moisture of cabernet sauvignon grape using these optimal wavelengths by hyperspectral imaging technique.
- hyperspectral imaging technique /
- wine grape /
- moisture content /
- non-destruction

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