A simple and green method for simultaneously determining the geographical origin and glycogen content of oysters using ATR-FTIR and chemometrics

Oysters are a marine bivalve extensively used as a food product and in medicinal drugs. However, the geographical origin of oysters greatly affects their economic value and quality. In this study, a simple and ecofriendly method is proposed for geographically tracing oysters and determining their bioactive glycogen content using attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR). Three classification algorithms, including partial least squares-discriminant analysis (PLS-DA), orthogonal PLS-DA (OPLS-DA), and least squares support vector machine (LS-SVM), were applied to determine the geographical origin of oysters. Simultaneously, three types of PLS algorithms, backward interval PLS (BI-PLS), synergy interval PLS (SI-PLS), and competitive adaptive reweighted sampling PLS (CARS-PLS), were used to evaluate the feasibility of determining the glycogen content of oysters. In addition, five signal preprocessing methods were compared to enhance the prediction performance of the qualitative and quantitative models. For qualitative analysis, 100% classification accuracy was achieved using the PLS-DA, OPLS-DA, and LS-SVM. For quantitative analysis, the SI-PLS model showed the best predicted results (correlation coefficient of prediction (RP) = 0.96, relative analysis error of prediction (RPDP) = 3.38), indicating its stable and high predictive performance as a new analytical technique for the traceability supervision and quality evaluation of oysters.