Influence of Lighting Pattern and Sample Positioning on Detection of Moldy Core Disease in Apples by NIR Spectroscopy

To enhance the precision of detecting moldy core disease in apples, near-infrared (NIR) spectroscopy was employed for quickly and non-destructive detection. The impact of lighting patterns and sample positioning on detection efficacy was investigated, with optical simulation methods being utilized. Discrimination models for moldy core were developed using support vector machines (SVM) and particle swarm optimization-least squares support vector machine (PSO-LSSVM), allowing for the optimal lighting pattern to be determined based on the results of these models. After that, the discrimination models of moldy core in the three sample positionings were developed, and the optimal sample positioning was determined. Finally, interval combination optimization (ICO)-competitive adaptive reweighted sampling (CARS) method was used to screen the feature wavelengths for moldy core under the optimal lighting pattern and sample positioning. The results show that 90° + 180° combined lighting pattern is the optimal lighting pattern for detection of moldy core in apples. The model built by PSO-LSSVM with normalization + Gaussian filter smoothing + detrended fluctuation analysis (NOR + GFS + Detrend) has the best performance; the sensitivity, specificity, and accuracy of the model in prediction set are 93.75%, 100%, and 96.83%, respectively. T1 is the optimal sample positioning under the 90° + 180° combined lighting pattern, and the sensitivity, specificity, and accuracy of the best SVM model are 91.89%, 94.44%, and 93.15%, respectively. After ICO-CARS screening, the number of modeling variables accounts for only 1.6% of the original wavelength variables, effectively simplifying the classification model. This study provides technical support for the rapid non-destructive and high-precision detection of moldy core in apples.