Duan Hong-wei, Zhu Rong-guang, Wang Long, Xu Wei-dong, Ma Ben-xue
Guang Pu Xue Yu Guang Pu Fen Xi. 2016 Apr;36(4):1145-9.
Selection of Regions of interest (ROIs) and subsequent spectral extraction was a key step of non-destructive detection and analysis based on hyperspectral imaging (HSI). For the rapid and accurate detection of mutton pH, the study on the effects of 2 different ROIs on mutton pH models was carried out in the visible-near infrared region of 473～1 000 nm. 2 ROIs methods of Rectangle Regions (RR) and Image Segmentation (IS) were adopted to extract 122 corresponding representative spectra respectively. The influence of different preprocessing methods and ROIs methods on 3 pH models, including stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR), was compared and analyzed. The results indicated that SMLR and PLSR model performance was optimal in 3 models established with spectral data extracted from Rectangle Regions (RR) and Image Segmentation (IS) respectively. As for the SMLR model, corresponding to the RR ROIs method, the correlation coefficient (Rcal) and root mean square error (RMSEC) of calibration set was 0.85 and 0.085 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.82 and 0.097 respectively. As for the PLSR model, corresponding to the IS ROIs method, the correlation coefficient(Rcal) and root mean square error (RMSEC) of calibration set was 0.95 and 0.050 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.91 and 0.071 respectively. By comparing the modeling results of spectral data extracted from 2 ROIs methods, the modeling performances of Image Segmentation (IS) were always better than Rectangle Regions (RR) in all the 3 modeling methods. The study shows that it is feasible to apply hyperspectral imaging technology combined with the ROIs method of Image Segmentation (IS) to accurate, fast and non-destructive detection of mutton pH.
感兴趣区域（ROIs）的选择及随后的光谱提取是基于高光谱成像（HSI）的无损检测与分析的关键步骤。为了快速、准确地检测羊肉pH值，在473～1000nm的可见-近红外区域开展了2种不同感兴趣区域对羊肉pH值模型影响的研究。采用矩形区域（RR）和图像分割（IS）这2种感兴趣区域方法分别提取了122条相应的代表性光谱。比较并分析了不同预处理方法和感兴趣区域方法对3种pH值模型的影响，这3种模型包括逐步多元线性回归（SMLR）、主成分回归（PCR）和偏最小二乘回归（PLSR）。结果表明，分别用从矩形区域（RR）和图像分割（IS）提取的光谱数据建立的3种模型中，SMLR和PLSR模型性能最优。对于SMLR模型，对应RR感兴趣区域方法，校正集的相关系数（Rcal）和均方根误差（RMSEC）分别为0.85和0.085，预测集的相关系数（Rp）和均方根误差（RMSEP）分别为0.82和0.097。对于PLSR模型，对应IS感兴趣区域方法，校正集的相关系数（Rcal）和均方根误差（RMSEC）分别为0.95和0.050，预测集的相关系数（Rp）和均方根误差（RMSEP）分别为0.91和0.071。通过比较从2种感兴趣区域方法提取的光谱数据的建模结果，在所有3种建模方法中，图像分割（IS）的建模性能总是优于矩形区域（RR）。研究表明，将高光谱成像技术与图像分割（IS）感兴趣区域方法相结合应用于羊肉pH值的准确、快速、无损检测是可行的。
