Quantitative analysis of caprolactam in sauce-based food using infrared spectroscopy combined with data fusion strategies

Abstract

In this study, we aimed to quantify caprolactam content in sauce-based food using near-infrared (NIR) and mid-infrared (MIR) spectroscopy, combined with chemometrics and data fusion strategy. Sauce-based food samples with different caprolactam levels were prepared. Partial least squares (PLS) and support vector machine (SVM) regression models were developed based on NIR and MIR spectral data. The high-level fusion model showed the best performance for quantifying caprolactam for sauces, with square of correlation coefficient of prediction (Rp2) in range of 0.9929−0.9990 and root mean square error of prediction (RMSEP) in range of 1.0794–2.9303 mg kg−1. Finally, a comprehensive model of caprolactam content in sauces was established with Rp2 of 0.9961 and RMSEP of 2.1505. These findings indicated that a combination of NIR and MIR spectroscopy as high-level fusion strategy can achieve rapid, non-destructive, and accurate quantification of caprolactam content in sauces and hence can be used for monitoring the safety of food.