Detection of physical hazards from fruit processed products using hyperspectral imaging and prediction based on PLS-DA and logistic regression machine learning models

The current study aims to investigate a prediction model from reflection values and the spectral angle mapper (SAM) obtained from hyperspectral imaging (HSI) technology for the detection of three types of foreign materials – a branch, a knife, and rubber – that are associated with the various fruit-processed products. The study found that the maximum and minimum reflection values in the 900 to 1700 nm range for juices (apple, grape, tomato) and jams (strawberry, peach, tomato) differed depending on the presence or absence of physical hazards. The presence of physical hazards was also confirmed by the color difference in the SAM image. The partial least squares discriminant analysis model (PLS-DA) and logistic regression implemented on the Jupyter Notebook platform through the Anaconda prompt provided accuracy, F1 score, specificity, and sensitivity based on the confusion matrix. The maximum value was 100.0 %, while the minimum value was 97.6 % of the result of the PLS-DA modeling in the testing set. Logistic regression modeling also had a similar result: the maximum value was 100.0 %, while the minimum value was 97.8 % in the testing set. The sensitivity value in the testing set, which is a meaningful result for detecting physical hazards, was a maximum of 100.0 % for both PLS-DA and logistic regression. Results from the current study suggest that the reflection value and SAM data obtained through hyperspectral imaging could build a big data platform for early determination of physical hazards during agricultural product processing.