基于高光谱成像的枇杷果实品质检测

Detection of Loquat Fruit Quality Based on Hyperspectral Imaging

采用高光谱成像技术(HSI)在可见/近红外(363~1026 nm)区域检测枇杷果实的可溶性固形物(SSC)和硬度,并判断其成熟度,以实现枇杷果实品质的无损检测和分级分选.利用蒙特卡洛法(MC)剔除异常样本,基于联合X-Y距离(SPXY)进行建模集和预测集样本的划分,再采用竞争性自适应权重采样算法(CARS)和连续投影算法(SPA)选取特征波长,与全波段光谱(FS)比较,分别建立偏最小二乘回归(PLSR)模型.结果显示,CARS-PLSR模型更优,CARS提取的SSC特征波长和硬度特征波长分别占总波长的8.52%和5.36%,枇杷果实中SSC和硬度的建模集相关系数R_(c)分别为0.9817,0.9707,预测集相关系数R_(p)分别为0.9185,0.7423,说明CARS能有效地对光谱进行降维,简化了数据处理过程.枇杷果实SSC和硬度的变化与果实成熟度显著相关,建立判别偏最小二乘法(DPLS)成熟度预测模型,预测集总识别准确率为89.29%.由此说明,高光谱成像技术可对枇杷品质进行有效检测,为枇杷果实的无损检测和分级分选提供了理论依据.

Hyperspectral imaging technology was used to detect the soluble solid content(SSC),firmness and maturity of loquat fruits in visible and near infrared(363~1026 nm)region,in order to achieve nondestructive testing and sorting for loquat fruit.Abnormal samples were eliminated by using Monte Carlo(MC)method and the remaining samples were divided into calibration set and prediction set based on joint X-Y distances(SPXY).Then the competitive adaptive reweighted sampling(CARS)and successive projections algorithm(SPA)were used to select characteristic wavelengths,and compared with full spectrum(FS),the partial least squares regression(PLSR)models were established,respectively.The results showed that the CARS-PLSR models were better.The characteristic wavelengths of SSC and firmness extracted by CARS accounted for 8.52%and 5.36%of the total wavelength,respectively.The correlation coefficients R_(c)of calibration set for SSC and firmness of loquat fruit were 0.9817 and 0.9707,and the correlation coefficients R_(p)of prediction set were 0.9185 and 0.7423,respectively,which indicated that CARS effectively reduced the dimension of spectrum and simplified the data processing.There was a significant correlation between the changes of SSC and firmness of loquat fruit with fruit maturity.The discriminant partial least squares(DPLS)model of maturity was established and the total recognition accuracy of prediction set was 89.29%.Therefore,hyperspectral imaging technology can effectively detect the quality and maturity of loquat fruit,also can provide a theoretical basis for nondestructive rapid detection,sorting and grading of loquat fruit.