## 1 Introduction  
As a significant source of human food, aquatic products are highly favored by consumers due to their abundant nutritional value and delightful taste [1]. Oysters, often referred to as the \"milk of the sea\", are particularly rich in unsaturated fatty acids, proteins, vitamins, minerals, and notably, taurine [2; 3; 4; 5]. However, oysters are prone to spoilage and deterioration during transportation and storage due to their high-water content, delicate muscle tissue, active endogenous enzymes, and microbial activity. These factors can lead to a decline in quality, a reduction in nutritional value, and even the production of harmful substances that raise food safety concerns [6; 7; 8]. Therefore, it is essential to assess the quality of oysters throughout the processing flow, with freshness being the most critical indicator of oyster quality [9]. Under the influence of microorganisms and enzymes, water products can generate volatile organic compounds (VOCs), the composition and concentration of which can serve as indicators of decay [10]. Gas chromatography-mass spectrometry (GC-MS) is commonly employed for detecting volatile gases in water products due to its advantages, including minimal sample requirements and high sensitivity, which enable the identification of specific compounds [11; 12; 13; 14]. However, this conventional analytical method is time-consuming, complex, and requires skilled personnel for operation. The electronic nose serves as an effective monitoring method, capable of comprehensively capturing and analyzing the overall characteristics and potential features of tested samples through the construction of a sensor array with specific identification capabilities and an advanced pattern recognition system. [PERSON] et al. [15] utilized seven metal oxide gas sensors to develop an electronic nose for monitoring fish volatiles, complementing this with total viable count (TPC) and total volatile basic nitrogen (TVBN) detection methods. When modeling was performed using Principal Component Analysis (PCA) and a multilayer perceptron neural network (MLPNN), the accuracy of the test data reached 96.87%. Similarly, [PERSON] et al. [16] conducted various auxiliary experiments and employed multiple recognition algorithms to construct the Shrimp-Nose model utilizing six metal oxide gas sensors under Fourier-transform infrared (FTIR) experimental conditions. Based on PCA, k-nearest neighbors (KNN), random forest (RF), and softmax regression algorithms for analysis, the softmax regression algorithm achieved decision accuracies of 96.29% for unfrozen samples and 95.73% for frozen samples. [PERSON] et al. [17] employed a greater number of sensors to develop a model with enhanced accuracy. They utilized an electronic nose equipped with eight MOS sensors to differentiate among three types of meat: chicken, pork, and beef. This was complemented by gas chromatography-mass spectrometry (GC-MS) and analytical methods such as Principal Component Analysis (PCA) and deep neural networks (DNNs). Their final model achieved an accuracy of 96.90%. [PERSON] et al. [18] also increased the number of sensors, algorithms, and feature vectors to create a monitoring platform with ten gas sensors for assessing the freshness of fish meal. In conjunction with solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) experiments, they employed various classification methods, including multilayer perceptron neural networks (MLPNNs), random forests (RFs), k-nearest neighbors (KNN), support vector classification (SVC), and Bayesian classifiers. Their findings indicated that the random forest method yielded the highest accuracy, achieving a classification accuracy of 90.7% when using integral values as feature vectors. [PERSON] et al. [19] investigated the freshness of refrigerated tilapia by utilizing a gas sensor array in combination with a convolutional neural network (CNN) model, which achieved a prediction accuracy of 92.31% after optimization. [PERSON] et al.