可见-近红外高光谱成像技术对灵武长枣VC含量的无损检测方法

were collected in the wavelength range of 400 to 1 000 nm. Genetic algorithm (GA), successive projection algorithm (SPA)
and competitive adaptive reweighted sampling (CARS) algorithm were used to extract the characteristic wavelengths from
HE Jialin, QIAO Chunyan, LI Dongdong, ZHANG Haihong*, DENG Hong, SHAN Qimei, GAO Kun, MA Rui
(School of Agriculture, Ningxia University, Yinchuan 750021, China)
194 2018, Vol.39, No.06
食品科学
※成分分析
可见-近红外高光谱成像技术对灵武长枣 VC含量的无损检测方法
何嘉琳，乔春燕，李冬冬，张海红*，邓 鸿，单启梅，高 坤，马 瑞
（宁夏大学农学院，宁夏 银川 750021）
摘 要：为探究基于高光谱成像技术预测灵武长枣VC含量的可行性并寻找最佳预测模型。采集100 个长枣样本在 波长400～1 000 nm处的高光谱图像，对光谱数据进行预处理；应用遗传算法（genetic algorithm，GA）、连续投 影算法（successive projection algorithm，SPA）和竞争性正自适应加权（competitive adaptive reweighted sampling， CARS）算法对原始光谱数据提取特征波长；分别建立基于全光谱和特征波长的偏最小二乘（partial least squares regression，PLS）和最小二乘支持向量机（least squares support vector machine，LSSVM）VC含量预测模型。结 果表明，采用标准正态变换预处理算法效果最优，其PLS模型的交叉验证相关系数为0.839 5，交叉验证均方根误 差为16.248 2；利用GA、SPA和CARS从全光谱的125 个波长中分别选取出12、5 个和26 个特征波长；基于CARS 建立的PLS模型效果最优，其Rc、Rp、校正均方根误差、预测均方根误差分别为0.896 2、0.889 2、10.746 2%、 12.145 3%。研究结果表明基于高光谱成像技术对灵武长枣VC含量的无损检测是可行的。 关键词：灵武长枣；VC；可见-近红外高光谱成像；无损检测
methods were used to select 12, 5, and 26 characteristic wavelengths. The PLS model based on CARS method was the best
among the models developed, and its Rc, Rp, RMSEC and RMSEP values were 0.896 2, 0.889 2, 10.746 2%, and 12.145 3%, respectively. These results confirmed the feasibility of using hyperspectral imaging for the non-destructive detection of VC
variate (SNV) transformation was the best preprocessing approach. The cross validation correlation coefficient (Rcv) of the PLS model was 0.839 5, and the root mean square error of cross validation (RMSECV) was 16.248 2. GA, SPA and CARS
பைடு நூலகம்
Abstract: This study aimed to explore the feasibility of predicting the vitamin C (VC) content in “Lingwu Changzao”
jujubes using hyperspectral imaging and to find the best prediction model. Hyperspectral images of 100 jujube samples
the original spectral data. Partial least squares (PLS) and least squares support vector machine (LSSVM) were separately
used to establish VC prediction models based on the full and characteristic spectra. The results showed that standard normal
content in “Lingwu Changzao” jujubes.
Keywords: “Lingwu Changzao” jujubes; VC; visible near infrared hyperspectral imaging; non-destructive testing
Non-Destructive Detection of Vitamin C Content in “Lingwu Changzao” Jujubes (Zizyphus jujuba Mill. cv. Lingwu Changzao) Using Visible Near Infrared Hyperspectral Imaging