Journal
BIOSYSTEMS ENGINEERING
Volume 178, Issue -, Pages 244-255Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2018.11.018
Keywords
Pacific white shrimp; Hyperspectral imaging; Total volatile basic nitrogen; Stacked auto-encoders; Nondestructive prediction
Funding
- National Natural Science Foundation of China [61802344]
- Zhejiang Provincial Natural Science Foundation of China [LY16F030012, LY15F030016]
- Ningbo Science and Technology Special Project of China [2017C110002]
- Natural Science Foundation of Ningbo [2017A610118]
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Hyperspectral imaging (HSI) technique with spectral range of 900-1700 nm was implemented to predict total volatile basic nitrogen (TVB-N) content in Pacific white shrimp. Successive projections algorithm (SPA) and deep-learning-based stacked auto-encoders (SAEs) algorithm were comparatively used for spectral feature extraction. Least-squares support vector machine (LS-SVM), partial least squares regression (PLSR) and multiple linear regression (MLR) were used for prediction. The results demonstrated that the SAEs-based prediction models (SAEs-LS-SVM, SAEs-MLR and SAEs-PLSR) performed better than either full wavelengths-based or SPA-based prediction models. The SAEs-LS-SVM was considered to be the best model with R-p(2) value of 0.921, RMSEP value of 6.22 mg N [100 g](-1), RPD value of 3.58 and computational time of 3.9 ms for predicting TVB-N in prediction set. The results of this study indicated that SAES has a high potential in the multivariate analysis of hyperspectral images for shrimp quality inspections. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
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