马铃薯黑心病和单薯质量的透射高光谱检测方法

针对单一检测技术不能同时检测马铃薯内外品质的多项指标，采用透射高光谱成像技术并融合光谱和图像信息，对其内部黑心病、质量指标进行检测。通过透射高光谱成像系统获取266个样本高光谱图像（400～1000 nm），并提取光谱和图像二者信息。采用不同变量选择方法对光谱进行变量选择，用9个光谱变量建立检测马铃薯黑心病偏最小二乘判别分析（partial least squares discriminant analysis, PLS-DA）模型与质量偏最小二乘回归（partial least squares, PLS）模型；提取样本透射高光谱图像的面积信息，建立基于光谱-图像的检测马铃薯质量PLS模型。试验结果表明，黑心样本识别率为100%，识别最小黑心面积为1.88 cm2；基于光谱-图像所建立质量检测模型预测效果较好，其预测集相关系数(Rp)为0.99，预测均方根误差(RMSEP）为10.88。结果表明：采用透射高光谱成像技术并融合图像和光谱信息对马铃薯内部黑心病、质量同时进行检测是可行的。

Transmission hyperspectral detection method for weight and black heart of potato

Abstract: Potatos are one of the world's major food crops. It not only has medicinal value and food value, but also has industrial value. The quality of potatos is directly related to their commodity level, benefits, and market competitiveness. Therefore, its quality testing is an important part of potato processing. Currently, common non-destructive testing techniques (near infrared spectroscopy and machine vision technology) are unable to achieve simultaneous detection of a potato's internal and external quality. Transmission hyperspectral imaging technology has some penetrating ability, when the light passes through the agricultural products, spectral and image of hyperspectral imaging data will change according to the differences in their internal characteristics. Therefore, the transmission hyperspectral imaging technology not only can detect the internal quality of agricultural products, but also can detect some external qualities. Since the single detection technology cannot simultaneously detect the internal and external quality of potatoes, the internal black heart and external weight of potatoes are detected by the transmission hyperspectral imaging technology and fusing spectrum and image information. In this study, 266 hyperspectral images (400-1 000 nm) were collected by the transmission hyperspectral imaging system, and then the spectrum and the image information were extracted. Using a Monte Carlo cross-validation method to exclude the data of two abnormal black heart samples, and variable selection methods of uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to do the variable selection for the spectrum of the black heart sample. The eventual adoption of 9 spectral variables were used to establish the detection model of black heart by a partial least squares discriminant analysis (PLS-DA); variable selection methods of competitive adaptive reweighed sampling (CARS) and successive projections algorithm (SPA) were used to do variable selection for a weight testing sample spectrum, the eventual adoption of 9 variables established a detection model of weight testing by partial least-squares regression (PLS); the Area information of transmission hyperspectral image was extracted, which combined with the 9 spectral variables to set up an PLS model for weight detection based on spectral - image information. The research demonstrates that the accurate recognition rate of black heart is 100%, and the minimum shoddy area which could be identified was 1.88 cm2. The performance of the weight detection model based on the spectrum-image (10 variables) is much better than the one based on the spectrum (9 variables), the prediction correlation coefficient (Rp) was 0.99, and the forecast root mean square error (RMSEP) was 10.88. The results indicate that using the transmission hyperspectral imaging technology with the fusion of image and spectrum information to detect potatoes' internal black heart and external weight simultaneously is feasible.