基于近红外光谱技术苹果尺寸差异对糖度模型适用性的影响

为消除水果自身尺寸差异对其糖度预测模型的不利影响，进一步提高水果分选模型精度，应用近红外光谱在线检测装置采集不同果径苹果的近红外光谱，对光谱进行多种预处理后，分别建立苹果可溶性固形物的偏最小二乘法模型，再用苹果果径75~85 mm组中的建模集预测苹果果径分别为65~75、85~95 mm组中的预测集样品，最后用果径组65~75、75~85、85~95 mm中的建模集和预测集，分别作为混合苹果尺寸糖度预测模型的建模集和预测集，并利用特征光谱选择算法对模型进行简化，建立苹果糖度通用预测模型。结果显示：与建模集和预测集果径不同时所建立的苹果糖度预测模型最优组相比，其相关系数R_p由0.805提高至0.943，预测集均方根误差值RMSEP由0.778减小至0.480，RPD由0.96增加至3.05，再对建立的通用模型进行简化，可以降低苹果尺寸对苹果糖度模型的影响，提高模型预测性能。

Effects of apple size on applicability of model for predicting content of sugar based on near infrared spectroscopy

The size difference of the fruit itself results in poor robustness and low accuracy of the model for predicting the content of sugar. Eliminating the influence of fruit size differences is of great significance to improve the accuracy of fruit sorting models. The NIR spectra of apples with different fruit diameters were collected by an online NIR spectroscopy detection device， and the partial least squares （PLS） models of the content of sugar in apple （SSC） were established after various pre-processing of the spectra. The modeling sets in the diameter of apple fruit group with 75-85 mm were used to predict the prediction set samples in the diameter of apple fruit group with 65-75 mm and 85-95 mm， respectively. The modeling and prediction sets in the diameter of apple fruit group with 65-75 mm， 75-85 mm， and 85-95 mm were used as the modeling and prediction sets of the mixed size model for predicting the content of sugar in apple， respectively. The model was simplified by using the feature spectral selection algorithm. The correlation coefficient R_p was increased from 0.805 to 0.943， the root mean square error value RMSEP was reduced from 0.778 to 0.480， and the RPD was increased from 0.96 to 3.05 compared with the optimal set of model for predicting the content of sugar in apple established when the modeling set and the prediction set had different fruit diameters. It is indicated that simplifying the general model established can reduce the effects of apple size on the model for predicting the content of sugar in apple and improve the prediction performance of the model.