Journal
JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION
Volume 13, Issue 3, Pages 2082-2094Publisher
SPRINGER
DOI: 10.1007/s11694-019-00129-0
Keywords
Hyperspectral image; Microbial spoilage; Deep learning; Stacked auto-encoders; Fully-connected neural network; Nondestructive detection method
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Funding
- Ningbo Science and Technology Special Project of China [2017C110002]
- Natural Science Foundation of China [31201446]
- Zhejiang Provincial Natural Science Foundation of China [LY17C190008, LY16F030012, LY15F030016]
- Ningbo Science Foundation of China [2017A610118]
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In this study, deep learning method coupled with near-infrared (NIR) hyperspectral imaging (HSI) technique was used for nondestructively determining total viable count (TVC) of peeled Pacific white shrimp. Firstly, stacked auto-encoders (SAE) was conducted as a big data analytical method to extract 20 deep hyperspectral features from NIR hyperspectral image (900-1700 nm) of peeled shrimp stored at 4 degrees C, and the extracted features were used to predict TVC by fully-connected neural network (FNN). The SAE-FNN method obtained high prediction accuracy for determining TVC, with RP2 = 0.927. Additionally, TVC spatial distribution of peeled shrimp during storage could be visualized via applying the established SAE-FNN model. The results demonstrate that SAE-FNN combined with HSI technique has a potential for non-destructive prediction of TVC in peeled shrimp, which supply a novel method for the hygienic quality and safety inspections of shrimp product.
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