基于可见/近红外光谱和变量优选的南水梨糖度在线检测  

On-line detection of sugar content of Nanshui pears by Vis/NIR spectroscopy and variable selection methods

作　　者：孙通 江水泉 

机构地区：[1]江苏楷益智能科技有限公司,江苏无锡214174

出　　处：《食品与机械》2016年第3期69-72,共4页Food and Machinery

基　　金：无锡市农业科技支撑项目(编号:CLE02N1507)

摘　　要：利用可见/近红外光谱技术对南水梨糖度进行在线检测研究。南水梨样本以0.3m/s速度传输,并采用USB4000光谱仪在470~1 150nm波段范围内采集南水梨样本的光谱。然后,利用3种变量选择方法对波长变量进行筛选,应用偏最小二乘（PLS）方法分别建立南水梨糖度的在线预测模型,并分析预测模型性能的优劣。结果表明：可见/近红外光谱技术结合变量选择方法在线检测南水梨的糖度是可行的;竞争自适应重加权采样（CARS）方法优于无信息变量消除（UVE）及连续投影算法（SPA）;CARS方法可以有效简化预测模型并提高预测模型的性能;南水梨全光谱PLS及CARS—PLS糖度预测模型的预测集相关系数和预测均方根误差（RMSEP）分别为0.940,0.951和0.467%,0.420%。Sugar content （SC） is one of the important internal qualities of Nanshui pears. In this research, Visible/near infrared （Vis/NIR） spectroscopy was used to detect SC of Nanshui pears on-line. Trans- mission speed of Nanshui pears was 0. 3 m/s, and USB4000 spec trometer was used to acquire the spectra of Nanshui pear samples in the wavelength range of 470--1 150 nm. Then three variable selection methods were used to select sensitive wavelength variables, and partial least squares （PLS） was used to develop calibration models of SC for Nanshui pears, also performance of calibration models was compared. The results indicate that Vis/NIR spectroscopy combined with variable selection method is feasible for on-line detection o{ SC for Nanshui pears. Competitive adaptive reweighted sampling （CARS） method is superior to uninformative variable elimination （UVE） and successive projections algorithm （SPA） methods. CARS method can simplify calibration model and improve performance of calibration model. The correlation coefficients in prediction and root mean square errors of prediction （RMSEPs） of full PLS and CARS-- PLS models of SC for Nanshui pears are 0. 940,0. 951 and 0. 467%, 0. 420%0, respectively.

关 键 词：可见/近红外光谱 变量选择 糖度 南水梨 

分 类 号：TS255.7[轻工技术与工程—农产品加工及贮藏工程]