Machine learning supported ground beef freshness monitoring based on near-infrared and paper chromogenic array

Maintaining freshness and quality is crucial in the meat industry, as lipid oxidation can lead to undesirable odors, flavors, and potential health risks. Traditional methods for assessing meat freshness often involve time-consuming and destructive techniques, highlighting the need for rapid, noninvasive approaches. Recent advancements in spectroscopic and chromogenic sensor array technologies have opened up new avenues for monitoring meat quality parameters, offering the potential for real-time, accurate, and cost-effective solutions. As thiobarbituric acid reactive substances (TBARS) value is a classic indicator of meat lipid oxidation, this study investigated the data fusion of near-infrared spectroscopy (NIR) and paper chromogenic array (PCA) for monitoring ground beef TBARS. A standardized PCA was fabricated by photolithography with nine chemoresponsive dyes. Changes in ground beef volatile organic compounds during storage were captured in the shifts of PCA color patterns. Nippy, an open-source Python module, was used for automated NIR spectra preprocessing. The optimal preprocessing pipeline was found by 10-fold cross-validation in machine learning model development. Among optimized models, partial least square regression showed the best performance in coefficient of determination (R2) of .9477, root mean squared error of prediction of 0.0545 mg malondialdehyde/kg meat, and residual prediction deviation of 4.3717. The promising result of this study indicated the potential for NIR and PCA combinations to monitor TBARS values for ground beef freshness assessment. A real-time and non-destructive meat quality monitoring system was built on the data fusion of paper chromogenic arrays and near-infrared spectroscopy. Fine-tuned machine learning models were developed, and they showed high prediction accuracy on the malondialdehyde level of ground beef during cold storage. image