方位和品质对南疆果品空间特性光谱影响及反演方法研究

高光谱无损检测技术在果品定量无损检测中应用广泛，以冬枣、红提、香梨三种果品空间特性光谱为研究目标，探索空间特性光谱的影响因素和反演方法，为提高户外果品无损检测精度提供了一种新思路。分别提取三种果品的光谱库并计算空间特性光谱，依次使用马氏距离、浓度残差等预处理方法以及竞争性自适应权重取样算法选取特征波长，将处理后的三种果品空间特性光谱分别与品质（糖度、水分）和方位（探测角、方位角、相位角）建模，建模结果如下：三种果品（按照冬枣、红提、香梨的顺序）与糖分模型的相关系数r分别为：0.853 3，0.822 7和0.913 3；水分模型的相关系数r分别为：0.741 3，0.784 7和0.891 3；探测角模型相关系数r分别为：0.985 6，0.992 7和0.974 7；方位角模型相关系数r分别为：0.941 8，0.910 5和0.936 9；相位角模型相关系数r分别为：0.960 9，0.957 0和0.956 3。可以看出，不同果品方位模型相关性都明显高于品质模型相关性，因此方位因素是影响空间特性光谱的主要原因。使用Roujean模型和Walthall模型分别对不同方位的空间特性光谱进行反演，反演结果如下：使用Roujean模型反演三种果品（按照冬枣、红提、香梨的顺序）空间特性光谱时R2分别为0.934 4，0.928 1和0.830 6；r分别为0.990 2，0.983 9和0.969 1；RMSEP分别为0.030 9，0.048 7和0.062 7；平均模型误差分别为7.27%，11.02%和8.61%。使用Walthall模型描述不同果品空间特性光谱时R2分别为0.943 3，0.859 7和0.839 0；r分别为0.991 8，0.971 8和0.970 2；RMSEP分别为0.036 6，0.066 1和0.068 7；平均模型误差分别为6.19%，15.40%和7.84%。可以看出，Roujean模型可以很好的描述冬枣和红提的空间特性光谱，也可以较好的描述香梨空间特性光谱；Walthall模型可以很好的描述冬枣空间特性光谱，也可以较好的描述红提和香梨空间特性光谱。综上所述，在今后试验中可以使用Roujean模型反演红提和香梨的空间特性光谱，使用Walthall模型反演冬枣的空间特性光谱，进而提高户外果品户外果品无损检测精度。

Effects of Orientation and Quality on Spatial Spectrum Characteristics of Fruits in Southern Xinjiang

Hyperspectral nondestructive testing technology is widely used in quantitative nondestructive testing of fruit. In this paper, the spatial characteristic spectra of jujube, grape and pear are taken as the research objectives, and the influencing factors and inversion methods of spatial characteristic spectra are explored, which provides a new idea for improving the accuracy of outdoor fruit nondestructive testing. The spectral library of three kinds of fruits was extracted, and the spatial characteristic spectra were calculated. The characteristic wavelengths were selected by Mahalanobis distance, concentration residual and competitive adaptive weight sampling algorithm. Model characteristic spectra of three kinds of fruits after pretreatment with quality parameters and positional parameters respectively.The modeling results are as follows: In the sugar model, the R of jujube, grape and pear were 0.853 3, 0.822 7 and 0.913 3 respectively; In the water model, the R were 0.741 3, 0.784 7 and 0.891 3 respectively; In the detection angle model, the R were 0.985 6, 0.992 7 and 0.974 7 respectively; In the azimuth angle model, the R were 0.941 8, 0.910 5 and 0.936 9 respectively; In the phase angle model, the R were 0.960 9, 0.957 0 and 0.956 3 respectively. In summary, the correlation of different fruit positional models was significantly higher than quality models. Therefore, the positional factor is the main reason affecting the characteristic spectrum. Therefore, the roujean model and waltall model are used to invert the characteristic spatial spectrum of different directions. The inversion results are as follows: roujean model is used when retrieving the spatial characteristic spectra of three kinds of fruits (in the order of jujube, grape and pear), R2 is 0.934 4, 0.928 1 and 0.830 6 respectively; R is 0.990 2, 0.983 9 and 0.969 1 respectively; RMSEP is 0.030 9, 0.048 7 and 0.062 7 respectively; the average model error is 7.27%, 11.02% and 8.61% respectively. The results showed that R2 was 0.943 3, 0.859 7, 0.839 0; R was 0.991 8, 0.971 8, 0.970 2; RMSEP was 0.036 6, 0.066 1, 0.068 7; the average model error was 6.19%, 15.40%, 7.84%. It can be seen that roujean model can well describe the characteristic spatial spectrum of jujube and grape, and also can better describe the characteristic spatial spectrum of pear; waltall model can well describe the characteristic spatial spectrum of jujube, and also can better describe the characteristic spatial spectrum of grape and pear. In conclusion, roujean model can be used to invert the characteristic spatial spectrum of grape and pear, and waltall model can be used to invert the characteristic spatial spectrum of jujube to improve the accuracy of outdoor fruit nondestructive testing.