Effect of machine learning techniques to detect Listeria monocytogenes in Queso fresco using shortwave-infrared imaging

Abstract

Queso fresco (QF) is a type of soft, fresh cheese, often prone to post-processing Listeria monocytogenes (LM) contamination. In this study, we evaluated the potential of shortwave infrared (SWIR) imaging to detect LM in QF. About 10 g of QF was surface inoculated with three different strains of LM, such that the final population was approximately 1.0 log₁₀ CFU/g, 2.0 log₁₀ CFU/g, and 3.0 log₁₀ CFU/g. Following image acquisition, statistical features namely mean reflectance, standard deviation of reflectance, skewness, and kurtosis were used to develop classification models. A trend of decrease in mean reflectance with increase in LM population was observed. Three types of classification (binary, population-wise, and population-strain-wise) were performed by four supervised machine learning (ML) algorithms - Logistic regression (LR), Random Forest (RF), Support vector machine (SVM), and k-Nearest neighbor (kNN). RF outperformed binary and population-wise classifications with an accuracy of 100 %. In binary classification, followed by RF, SVM and kNN exhibited an accuracy of 94 % and 92 % respectively. In population-wise classification, SVM and kNN had classification accuracies in the range of 85–88 %. Among the ML models, LR resulted in poor accuracies across all three classifications. Strain-wise classification did not yield reliable accuracies, implying the overlap in genetic similarities. This study demonstrates that SWIR imaging along with chemometrics can be a prospective tool for real-time detection and (or) quantification of LM in fresh cheeses like QF. This approach will likely be a novel safety assessment tool in cheese industry with the potential to enhance product safety and consumer confidence in consumption of fresh cheeses.