NON-DESTRUCTIVE DETECTION OF MOLD IN MAIZE USING NEAR-INFRARED SPECTRAL FINGERPRINTING

Mold contamination of stored maize can cause significant economic losses, and it is crucial to effectively classify maize kernels without destroying their original structure. But existing studies have found it difficult to distinguish moldy maize. In this paper, a method for non-destructive detection of mold in maize using near-infrared spectral fingerprinting is proposed. The spectral raw data are initially acquired using a handheld near-infrared spectrometer. To enhance the signal quality, preprocessing is conducted, and a classification model is developed for full-band spectral data. In order to further optimize the model and enhance the classification accuracy, the feature wavelengths were extracted from the spectral data with effective preprocessing techniques in the full-band model. Finally, the maize kernel mold classification model is constructed. The classification accuracy of SG+SNV-SVM-ISFLA model can reach up to 97.22%, and the accuracy for the identification of asymptomatic moldy maize is 96.30%, which can realize the accurate grading of moldy maize and can well distinguish asymptomatic moldy maize. This work may significantly control the spread of molds in the food industry while improving storage economics and safety.