A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei). (February 2019)

• Record Type: Journal Article
• Authors: Yu, Xinjie; Wang, Jianping; Wen, Shiting; Yang, Jinqiu; Zhang, Fengfeng
• Is Part Of: Biosystems engineering. Volume 178(2019)
• Page Start: 244
• Page End: 255
• Publication Date: 2019-02
• Subjects: Pacific white shrimp -- Hyperspectral imaging -- Total volatile basic nitrogen -- Stacked auto-encoders -- Nondestructive prediction; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2018.11.018
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : 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 RP 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. Highlights: Hyperspectral imaging could nondestructively predict TVB-N in Pacific white shrimp. Spectral features were extracted by deep learning algorithm. LS-SVM was applied to fit spectral features to TVB-N with satisfactory accuracy.