Intelligent Evaluation and Dynamic Prediction of Oysters Freshness with Electronic Nose Non-Destructive Monitoring and Machine Learning

Abstract

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

Keywords: GA-BP; electronic nose; gas sensor; intelligent evaluation; non-destructive monitoring; oyster freshness.

Figure 1

Fresh oyster quality change mechanism during the preservation process.

Figure 2

Comprehensive framework for monitoring, traceability, and freshness prediction in the oyster cold chain system.

Figure 3

Architecture of the electronic nose monitoring equipment. (a) Schematic representation of the testing system; (b) actual images of the odor monitoring system; (c) test chamber with constant temperature.

Figure 4

Testing for the volatile gas components of oysters.

Figure 5

Gas quality model modeling flow chart.

Figure 6

Signal response from the ten sensors of the e-nose system: (a) sensor output voltages of oyster samples stored at 4 °C from day 0 to day 9; (b) sensor output voltages of oysters samples stored at 12 °C from day 0 to day 7; (c) sensor output voltages of oyster samples stored at 20 °C from hour 0 to hour 72; (d) sensor output voltages of oyster samples stored at 28 °C from hour 0 to hour 48.

Figure 7

GC-MS spectra of dimethyl sulfide standard and oysters; (a) TIC of dimethyl sulfide standard; (b) MS spectrum of peak 1; (c) TIC of oyster sample stored at 28 °C for 48 h; (d) MS spectrum of peak 2.

Figure 8

Changes in TVBN, hardness, sensory score and total number of colonies with storage time (in days and hours): (a) preservation at 4 °C from day 0 to day 9; (b) preservation at 12 °C from day 0 to day 7; (c) preservation at 20 °C from hour 0 to hour 72; (d) preservation at 28 °C from hour 0 to hour 48.

Figure 9

Correlation coefficients between oyster quality indicators and the deadline of decay: (a) preservation at 4 °C from day 0 to day 9; (b) preservation at 12 °C from day 0 to day 7; (c) preservation at 20 °C from hour 0 to hour 72; (d) preservation at 28 °C from hour 0 to hour 48.

Figure 10

Cluster analysis: (a) preservation at 4 °C from day 0 to day 9; (b) preservation at 12 °C from day 0 to day 7; (c) preservation at 20 °C from hour 0 to hour 72; (d) preservation at 28 °C from hour 0 to hour 48.

Figure 11

Principal component analysis (PCA) of a gas sensor array. (a) Preservation at 4 °C from day 0 to day 9; (b) preservation at 12 °C from day 0 to day 7; (c) preservation at 20 °C from hour 0 to hour 72; (d) preservation at 28 °C from hour 0 to hour 48.

Figure 12

GA-BP neural network model prediction results and confusion matrix analysis stored at 4 °C. (a) GA-BP neural network model prediction results; (b) confusion matrix analysis.

Figure 13

GA-BP neural network model prediction results and confusion matrix analysis stored at 12 °C. (a) GA-BP neural network model prediction results; (b) confusion matrix analysis.

Figure 14

GA-BP neural network model prediction results and confusion matrix analysis stored at 20 °C. (a) GA-BP neural network model prediction results; (b) confusion matrix analysis.

Figure 15

GA-BP neural network model prediction results and confusion matrix analysis stored at 28 °C. (a) GA-BP neural network model prediction results; (b) confusion matrix analysis.