Evaluation of freshness in freshwater fish based on near infrared reflectance spectroscopy and chemometrics

Abstract

Freshness is the primary quality attribute of bighead carp (Aristichthys nobilis). Determining freshness in bighead carp with near infrared reflectance spectroscopy (NIRS) combined with an appropriate multivariate regression method has potential and is relevant to the fish food industry, but it has been insufficiently exploited. In this study, a total of 150 samples were scanned in a reflectance mode covering the spectral range of 1000–1799 nm to determine freshness, which included measurement of pH, total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS), and ATP-related compounds (K value). Prediction models of freshness parameters were developed and then validated using partial least-squares regression (PLSR) associated with a competitive adaptive reweighted sampling (CARS) algorithm and optimal pre-processing methods. This resulted in satisfactory coefficients of prediction (Rp) of 0.945, 0.932, 0.954, 0.807 and root mean square error of prediction (RMSEP) of 0.081, 2.099, 0.107, and 6.509 for pH, TVB-N, TBA, and K value, respectively. The overall results demonstrated the feasibility of using NIRS to determine freshness in fish flesh.