基于可见-近红外光谱技术的苹果糖度光照位置优化研究

在利用可见-近红外漫透射光谱技术对苹果的可溶性固形物（SSC）检测时，由于卤素灯光照射在苹果上的位置不同，采集到的苹果光谱中所包含的可溶性固形物信息不同，导致模型得出的结果不同；找到一个最好的苹果光照位置有利于得到最佳的可溶性固形物评价模型。利用多模式可调节的光学结构在相同的实验环境和实验条件下采集了购买于同一水果批发商的尺寸相近但照射位置不同的两批苹果的近红外漫透射光谱，探索苹果可溶性固形物模型建立过程中最佳的照射位置从而得到最佳位置的可溶性固形物评价模型。通过对样品进行光谱采集、糖度真值采集并结合化学计量学方法得出最佳的建模位置，照射位置为上部且光谱没有预处理时的偏最小二乘回归（PLS）模型性能为RMSEC为0.288 2，RMSEP为0.343 6，R_c为0.960 6，R_p为0.934 9；照射位置为斜上部且光谱没有预处理的PLS模型性能为RMSEC为0.340 7，RMSEP为0.513 3，R_c为0.931 1，R_p为0.863 6；照射位置为上部且光谱没有预处理的主成分分析回归（PCR）模型性能为RMSEC为0.573 6，RMSEP为0.601 4，R_c为0.842 4，R_p为0.800 7；照射位置为斜上部且光谱没有预处理的PCR模型性能为RMSEC为0.709 2，RMSEP为0.797 4，R_c为0.701 4，R_p为0.670 7，最佳照射位置为苹果上部；进一步地采用多种预处理方法对照射位置为上部的PLS模型进行对比，得到最优模型为MSC-PLS模型，其RMSEC为0.2264 4，RMSEP为0.301 5，R_c为0.966 9，R_p为0.949 9。最后再对相同的46个苹果进行相同的实验操作得到光谱、真值后，代入到建立的MSC-PLS模型中进行外部验证，结果显示外部验证的相关系数为0.930 58，验证均方根误差为0.843 59，验证了建立的MSC-PLS模型的稳定性和可靠性，进一步表明光谱采集位置为苹果上部时的近红外漫透射模型有很好的预测能力，该研究为预测苹果可溶性固形物的检测提供了技术支持。

Study on Optimization of Apple Sugar Degree and Illumination Position Based on Near-Infrared Technology

When using near-infrared diffuse transmission technology to detect SSC of apple, the information of soluble solids in the collected apple spectrum is different due to the different positions of halogen lamp on apple, which will lead to the different performance of the model. Finding the best illumination position of apple is conducive to obtaining the best evaluation model of soluble solids. Using the multi-mode adjustable optical structure, the near-infrared diffuse transmission spectra of two batches of apples purchased from the same fruit wholesaler with similar size but different irradiation positions were collected under the same experimental environment and conditions. The best irradiation position in the process of establishing the apple soluble solid model was studied, and the evaluation model of the best position of soluble solid was obtained. The best modeling position is obtained by spectrum collection, true sugar degree value collection and chemometrics method. When the irradiation position is the upper part and the spectrum is not pretreated, the PLS(Partial Least Square) model performance is RMSEC 0.288 2, RMSEP 0.343 6, R_c 0.960 6 and R_p 0.934 9. The performance of the PLS model with oblique upper irradiation position and no spectral pretreatment is RMSEC 0.340 7, RMSEP 0.513 3, R_c 0.931 1 and R_p 0.863 6. The performance of the PCR (Principle Component Regression) model with upper irradiation position and no spectral pretreatment was RMSEC 0.573 6, RMSEP 0.601 4, R_c 0.842 4 and R_p 0.800 7. The performance of the PCR model with oblique upper irradiation position and no spectral pretreatment was RMSEC 0.709 2, RMSEP 0.797 4, R_c 0.701 4, R_p 0.670 7. The best irradiation position is the upper part of the apple; Further, a variety of pretreatment methods are used to compare the PLS model with the upper irradiation position. The optimal model is the MSC-PLS model. Its model performance is RMSEC 0.226 44, RMSEP 0.301 5, R_c 0.966 9 and R_p 0.949 9. Finally, after the same experimental operation is carried out on the same 46 apples, the spectra and true values are obtained and substituted into the established MSC-PLS (Multiplicative Scatter Correction-Partial Least Square) model for external verification. The results show that the correlation coefficient of external verification is 0.930 58, and the root mean square error of verification is 0.843 59, which verifies the stability and reliability of the established MSC-PLS model. It further shows that the near-infrared diffuse projection model has good prediction ability when the spectral acquisition position is the upper part of the apple. This paper provides technical support for predicting the detection of soluble solids in the apple.