Analyzing TVB-N in snakehead by Bayesian-optimized 1D-CNN using molecular vibrational spectroscopic techniques: Near-infrared and Raman spectroscopy

Abstract

Total volatile basic nitrogen (TVB-N) is one of the key indicators for assessing fish freshness. This research employed near-infrared (NIR) and Raman spectroscopy methods to detect the TVB-N content in snakehead fillets. We extracted feature variables associated with TVB-N from NIR and Raman spectroscopy using Variable Crossover Point Arithmetic - Improved Reduced-Input Vector (VCPA-IRIV). Using these features, we established partial least squares (PLS) and One-dimensional Convolutional Neural Network (1D-CNN) models. Subsequently, data fusion strategies were employed to predict the TVB-N content. Notably, feature-level fusion in conjunction with Bayesian-optimized 1D-CNN, reached the best results, as evidenced by calibration and predictive correlation coefficients of 0.9677 and 0.9676 for TVB-N. These findings underscore the effectiveness of both NIR and Raman spectroscopy in evaluating fish freshness. The fusion of these two vibrational spectroscopy techniques enables a more rapid, efficient and comprehensive quantification of fish freshness.