Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets

• Published in: Journal of food engineering Vol. 161; pp. 33 - 39
• Main Authors: Cheng, Jun-Hu, Sun, Da-Wen, Pu, Hong-Bin, Chen, Xinghai, Liu, Yelin, Zhang, Hong, Li, Jiang-Lin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2015
• Subjects: Frozen-thawed; Grass carp fish; Imaging spectroscopy; Spectral pre-processing; Spectral pre-processing; Imaging spectroscopy; Frozen-thawed; Grass carp fish
• ISSN: 0260-8774; 1873-5770
• DOI: 10.1016/j.jfoodeng.2015.03.011

•Hyperspectral imaging was used to classify fresh and thawed fish fillets.•Four classifiers were used to build and compare the classification models.•Spectral pre-processing methods were used to enhance the prediction models.•The simplified LS-SVM model with 1ST pre-processing showed best results. The investigation of visible and near infrared hyperspectral imaging (400–1000nm) coupled with classifiers and spectral pre-processing techniques was conducted to discriminate fresh from cold-stored (4°C for 7days) and frozen-thawed (−20°C and −40°C for 30days) grass carp fish fillets. Four classifiers with four spectral pretreatment methods were applied to establish the classification models. Compared with the original models established using the full wavelengths, the classification models with three classifiers of soft independent modeling of class analogy (SIMCA), least squares-support vector machine (LS-SVM) and probabilistic neural network (PNN) in tandem with the first derivative pretreatment showed the best classification performance and the highest correct classification rate (CCR) of 94.29%. In addition, in order to reduce the high dimensionality of hyperspectral images, seven optimal wavelengths were selected by successive projections algorithm (SPA) and used to simplify the classification models. The simplified model obtained by the LS-SVM classifier coupled with the first derivative pre-processing method also presented good prediction accuracy with the CCR of 91.43%. The results demonstrated that the integration of hyperspectral imaging and classifiers analysis had a great potential for on-line detection and was feasible to rapidly and non-invasively discriminate fresh and frozen-thawed fish fillets.