波段筛选方法的选取以及随后的光谱特征波段的提取对高光谱模型效果的影响较大。 为了快速准确检测羊肉的pH值, 开展并讨论了利用两种特征波段筛选方法对羊肉pH值高光谱模型的影响研究。 本研究采用二阶导数(2D)、 多元散射校正(MSC)和中心化处理(mean-centering)相结合的方法对所提取纯肌肉部分的代表性光谱进行预处理, 利用联合区间偏最小二乘(siPLS)和联合区间偏最小二乘结合遗传算法(siPLS-GA)对全波段473～1000 nm范围光谱进行特征波段的提取, 并分别建立相对应特征波段范围羊肉pH的PLS预测模型, 同时与全波段的PLS模型效果相比较。 结果表明采用siPLS-GA提取的特征波长建立的PLS模型效果最优, 其选取的特征波长点数为56, 校正集相关系数(Rcal)和均方根误差(RMSEC)分别为0.96和0.043, 预测集相关系数(RP)和均方根误差(RMSEP)分别为0.96和0.048。 siPLS-GA方法既能够减少建模使用的光谱变量, 又可以提高模型精度, 因此利用高光谱图像技术结合siPLS-GA可以实现羊肉pH的特征波段筛选和快速准确检测。

Characteristic bands method selection and subsequent spectral extraction has a great influence on the hyperspectral model performance. For rapid and accurate detection of mutton pH value, the effects of 2 band-selection methods on PLS models of mutton pH based on HSI technique were carried out and discussed. Initially, the preprocessing method of second derivative (2D), multiplicative scatter correction (MSC) and mean-centering together was implemented on the representative spectra of mutton muscle portion. Then, 2 methods of synergy interval partial least square (siPLS) and the combination of synergy interval partial least squares with genetic algorithm (siPLS-GA) were used to extract the characteristic bands in the spectral range of 473～1 000 nm. Finally, 2 PLS models of lamb pH value were established with the corresponding characteristic bands, and were also compared with the effect of full-band PLS model. The results indicated that the effect of siPLS-GA-PLS model was the best. As for the siPLS-GA-PLS model, 56 characteristic wavelength points were chosen, the correlation coefficient(Rcal) and root mean square error(RMSEC) of calibration set was 0.96 and 0.043 respectively, and the correlation coefficient(Rp) and root mean square error(RMSEP) of prediction set was 0.96 and 0.048 respectively. Spectral variables were reduced and model accuracy was improved. It can be concluded that characteristic bands selection and rapid and accurate detection of lamb pH can be achieved using hyperspectral imaging technique combined with siPLS-GA method.