This study explores the possibility of predicting the soluble solids content (SSC), firmness and the presence of internal browning disorders in ‘Rocha’ pear (Pyrus communis L.) using a single VIS-NIR spectroscopic measurement in semi-transmittance mode. The spectroscopic measurement setup was developed to mimic real world conditions and takes into account geometry and technical requirements of a commercial fruit sorting optical module. The randomness of the fruit position during the spectra acquisition was simulated by sampling each fruit on four sides. Calibration models for internal quality properties were built using individual and/or average side spectra. The results show that models using the spectrum of each side as an individual sample only under-perform slightly relatively to the models based on spectra averages, which are common in the laboratory but very difficult to implement on an automated grading line. The performance of PLS, SVM and Ridge Regression models was compared for the prediction of SSC and firmness. Multiple types of spectra pre-processing were computed and the best combination of model and pre-processing method identified. The lowest RMSEP results for SSC and firmness were 0.7% (R² = 0.71) and 7.66 N (R² = 0.68) respectively, achieved using SVM on data pre-processed with Standard Normal Variate corrected 2nd derivative. For the internal disorder detection (browning), a classification benchmark composed by five different models (PLS-LDA, PCA-Logistic Regression, PCA-Extremely Randomized Trees, Extremely Randomized Trees and SVC) was implemented. PLS-LDA applied to the raw spectra presented the highest sensitivity, 76%. The results confirm that simultaneously achieving viable firmness and SSC predictions and internal disorder detection levels in pears is possible using a single VIS-NIR spectral measurement.

这项研究探索了预测'Rocha'梨（Pyrus communis）中的可溶性固形物含量（SSC），硬度和内部褐变障碍的可能性L.）在半透射模式下使用单个VIS-NIR光谱测量。光谱测量装置的开发是为了模拟现实环境，并考虑了商业水果分拣光学模块的几何形状和技术要求。通过在四个侧面对每个水果进行采样，可以模拟光谱采集过程中水果位置的随机性。内部质量特性的校准模型是使用单个和/或平均副光谱建立的。结果表明，使用每侧光谱作为单个样本的模型相对于基于光谱平均值的模型，其性能略有逊色，后者在实验室中很常见，但很难在自动分级生产线上实施。PLS的性能，比较了SVM和Ridge回归模型对SSC和硬度的预测。计算了多种类型的光谱预处理，并确定了模型和预处理方法的最佳组合。RMSEP最低的SSC和硬度为0.7％（R ²  = 0.71）和7.66 N（R ²  = 0.68），分别使用SVM在标准正态变量校正的二阶导数预处理的数据上实现。对于内部疾病检测（褐变），实施了由五个不同模型（PLS-LDA，PCA-Logistic回归，PCA-极端随机树，极端随机树和SVC）组成的分类基准。应用于原始光谱的PLS-LDA表现出最高的灵敏度，为76％。结果证实，使用单个VIS-NIR光谱测量，可以同时实现梨中的牢固性和SSC预测以及内部疾病检测水平。