Oysters are a marine bivalve extensively used as a food product and in medicinal drugs. However, the geog. origin of oysters greatly affects their economic value and quality. In this study, a simple and eco-friendly method is proposed for geog. tracing oysters and determining their bioactive glycogen content using attenuated total reflectance Fourier-transform IR spectroscopy (ATR-FTIR). Three classification algorithms, including partial least squares-discriminant anal. (PLS-DA), orthogonal PLS-DA (OPLS-DA), and least squares support vector machine (LS-SVM), were applied to determine the geog. 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 qual. and quant. models. For qual. anal., 100% classification accuracy was achieved using the PLS-DA, OPLS-DA, and LS-SVM. For quant. anal., the SI-PLS model showed the best predicted results (correlation coefficient of prediction (RP) = 0.96, relative anal. error of prediction (RPDP) = 3.38), indicating its stable and high predictive performance as a new anal. technique for the traceability supervision and quality evaluation of oysters.