Monitoring the freshness of pork during storage via near-infrared spectroscopy based on colorimetric sensor array coupled with efficient multivariable calibration

Abstract

The quality of pork meat is vital since it is one of the most essential sources of proteins and other nutrients. Near-infrared spectroscopy via colorimetric sensor array (NIRS-CSA) technology as a novel approach combined with multivariate calibrations was proposed to quantitatively evaluate total volatile basic nitrogen (TVB-N) as an indicator of freshness in pork. Using nine chemoselective dyes, the CSA was initially fabricated. The synergy interval-partial least squares (Si-PLS) was applied to select optimum variable intervals. Thereafter, different variable selection algorithms were executed, evaluated and compared. By utilizing 61 variables (7.73% of the Si-PLS variables), the synergy interval- competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) model yielded preeminent performance with Rp = 0.9850, RMSEP = 0.7148, and RPD = 6.00. Therefore, this study discovered that combining NIRS-CSA with Si-CARS-PLS as efficient variable selection algorithm could be employed as a fast and cheap strategy for assessing the freshness of pork meat during storage. • The colorimetric sensor array was fabricated using nine chemically sensitive dyes. • TVB-N was satisfactorily measured and predicted by the NIRS-CSA. • The different variables selection based PLS improved modeling performances. • The Si-CARS-PLS model was optimum for TVB-N prediction.