A Novel Colorimetric Sensor Array Coupled Multivariate Calibration Analysis for Predicting Freshness in Chicken Meat: A Comparison of Linear and Nonlinear Regression Algorithms

Abstract

As a source of vital nutrients for the normal functioning of the body, chicken meat plays an important role in promoting good health. This study examines the occurrence of total volatile basic nitrogen (TVB-N) as an index for evaluating freshness, using novel colorimetric sensor arrays (CSA) in combination with linear and nonlinear regression models. Herein, the TVB-N was determined by steam distillation, and the CSA was fabricated via the use of nine chemically responsive dyes. The corresponding dyes utilized, and the emitted volatile organic compounds (VOCs) were found to be correlated. Afterwards, the regression algorithms were applied, assessed, and compared, with the result that a nonlinear model based on competitive adaptive reweighted sampling coupled with support vector machines (CARS-SVM) achieved the best results. Accordingly, the CARS-SVM model provided improved coefficient values (Rc = 0.98 and Rp = 0.92) based on the figures of merit used, as well as root mean square errors (RMSEC = 3.12 and RMSEP = 6.75) and a ratio of performance deviation (RPD) of 2.25. Thus, this study demonstrated that the CSA paired with a nonlinear algorithm (CARS-SVM) could be employed for fast, noninvasive, and sensitive detection of TVB-N concentration in chicken meat as a major indicator of freshness in meat.