Abstract
Aquatic products freshness detection is an important topic in the current issue of food quality and safety. In this paper, we presented an automatic device based on electronic nose for evaluation freshness of Chinese mitten crab. The crabs were stored at 4 °C for nine days. Electronic nose sensor responses of each sensor over the array were collected from the living crab samples in parallel with data from microbiological analysis for total volatile basic nitrogen (TVB-N). Qualitative interpretation of response data was based on sensory evaluation discriminating samples in three quality classes (fresh, semi-fresh, and spoiled). Principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA) and Laplacian Eigenmap (LE) were developed to classify crab samples in the respective quality class with response data. Experiment results indicated that LE outperform other methods and achieve the highest recognition accuracy for crabs with three quality classes.
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Acknowledgments
It is a project supported by the Project of Talent Peak of Six Industries of Jiangsu Province (2014-NY-021) and Qing Lan Project of Jiangsu Province, the Prospective Study in Changshu Institute of Technology (QZ1502).
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© 2016 Springer International Publishing Switzerland
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Zhu, P., Chen, C., Xu, B., Lu, M. (2016). Research on Freshness Detection for Chinese Mitten Crab Based on Machine Olfaction. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_24
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DOI: https://doi.org/10.1007/978-3-319-41009-8_24
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