Applications of colorimetric sensors for non-destructive predicting total volatile basic nitrogen (TVB-N) content of Fujian oyster (Crassostrea angulata)

Abstract

Total volatile basic nitrogen (TVB-N) content is one of the critical safety evaluation indexes in seafood. This study investigated the colorimetric sensing array (CSA) technique with two data collection blocks, imaging and visible near-infrared spectroscopy, for non-destructive prediction of TVB-N content in Fujian oysters. Three data fusion strategies coupled with variable selection approaches including variable combination population analysis (VCPA), ant colony optimization, and competitive adaptive reweighted sampling were applied to establish the partial least squares (PLS) model for predicting the TVB-N content of oysters. Better prediction performance was obtained using data fusion of two information blocks than using individual block data. The results showed that the high-level-VCPA strategy showed the best performance with the correlation coefficient of the prediction set (Rp) at 0.9128. This work demonstrates that the use of CSA has the potential for non-destructive monitoring of TVB-N content in Fujian oysters.