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Appl. Sci. 2018, 8(2), 256; https://doi.org/10.3390/app8020256

Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets

1
College of Engineering, China Agricultural University, Beijing 100083, China
2
Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
3
State Environmental Protection Engineering Center for Mercury Pollution Prevention and Control, and CAS Key Laboratory for Biological Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Received: 2 January 2018 / Revised: 25 January 2018 / Accepted: 28 January 2018 / Published: 9 February 2018
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Abstract

Cooking loss (CL) is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI) technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast fillets were acquired by the system operating in the visible and near-infrared (VNIR, 400–1000 nm) range. Mean spectra were extracted from regions of interest (ROIs) determined by pure muscle tissue pixels. CL was firstly measured by calculating the weight loss in cooking, and then fillets were grouped into high-CL and low-CL according to the threshold of 20%. The classification methods partial least square-discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM) were applied, respectively, to determine the optimal spectral calibration strategy. Results showed that the PLS-DA model developed using the data, that is, first-order derivative (Der1) of VNIR full spectra, performed best with correct classification rates (CCRs) of 0.90 and 0.79 for the calibration and prediction sets, respectively. Furthermore, to simplify the optimal PLS-DA model and make it practical, effective wavelengths were individually selected using uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS). Through performance comparison, the CARS-PLS-DA combination was identified as the optimal method and the PLS-DA model built with 18 informative wavelengths selected by CARS resulted in good CCRs of 0.86 and 0.79. Finally, classification maps were created by predicting CL categories of each pixel in the VNIR hyperspectral images using the CARS-PLS-DA model, and the general CL categories of fillets were readily discernible. The overall results were encouraging and showed the promising potential of the VNIR HSI technique for classifying fresh broiler breast fillets into different CL categories. View Full-Text
Keywords: cooking loss; broiler breast fillet; hyperspectral imaging; VNIR; chemometrics cooking loss; broiler breast fillet; hyperspectral imaging; VNIR; chemometrics
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Jiang, H.; Wang, W.; Zhuang, H.; Yoon, S.; Li, Y.; Yang, Y. Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets. Appl. Sci. 2018, 8, 256.

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