为实现苹果可溶性固形物(SSC)的便携式快速检测, 利用环形光纤探头和微型光谱仪搭建便携式苹果可溶性固形物光谱采集系统, 结合无信息变量消除(UVE)、 遗传算法(GA)、 竞争性自适应加权(CARS)算法筛选基于偏最小二乘(PLS)的苹果可溶性固形物的近红外光谱特征波长。 另外, 采用反向区间最小二乘支持向量机(BiLS-SVM)和GA算法优选基于LS-SVM的特征波长变量, 分别建立所选特征波长和全波段的PLS模型和LS-SVM模型。 试验结果表明, 经过GA-CARS算法从全波段1 512个波长中筛选出的50个特征波长建立的PLS模型效果最好, 其预测相关系数和预测均方根误差分别为0.962和0.403°Brix。 利用该检测装置结合GA-CARS筛选的特征波长, 可有效简化苹果可溶性固形物近红外便携式检测模型并提高模型的预测精度, 为进一步构建便携式苹果可溶性固形物检测设备奠定了基础。

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were proposed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS-SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.