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Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat

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Abstract

Springiness is an important quality characteristic of chicken meat, related with muscle structure and contents of biochemical components, and has great influence on eating quality of chicken meat. Traditional methods for springiness evaluation including manual inspection and instrumental measurement are tedious, time-consuming, and destructive. This study implemented a smart and promising nondestructive technique, i.e., hyperspectral imaging, for rapid prediction of springiness of fresh chicken meat. Hyperspectral images of tested samples with different springiness levels were acquired, and their spectral data were extracted in the spectral range of 400–1,000 nm. Two calibration methods, namely, partial least squares regression (PLSR) and artificial neural network (ANN), were respectively used to correlate the extracted spectra of chicken meat samples with the reference springiness values estimated by a twice-compression method. Successful projections algorithm (SPA) as a popular wavelength selection tool was applied, and ten optimal wavelengths (416, 458, 581, 637, 696, 722, 740, 754, 773, and 973 nm) were finally selected. Based on the selected optimal wavelengths, optimized SPA-PLSR and SPA-ANN model were established, respectively. By comparing with the results of two optimized models, the SPA-PLSR model showed better prediction results with high correlation coefficient (R p) of 0.84 and low root mean square error by prediction (RMSEP) of 0.159. Finally, an image processing algorithm was developed to transfer the SPA-PLSR model to each pixel in chicken meat for visualizing their springiness distribution. The results from the current study indicated that hyperspectral imaging could be a rapid and nondestructive tool for prediction of springiness of chicken meat.

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Acknowledgments

The authors gratefully acknowledge the Guangdong Province Government (China) for its support through the program “Leading Talent of Guangdong Province (Da-Wen Sun).” This research was also supported by the National Key Technologies R&D Program (2014BAD08B09). Special thanks to Nannan Wang from South China University of Technology for her kind suggestions.

Conflict of Interest

Zhenjie Xiong declares that he has no conflict of interest. Da-Wen Sun declares that he has no conflict of interest. Qiong Dai declares that she has no conflict of interest. Zhong Han declares that he has no conflict of interest. Xin-An Zeng declares that he has no conflict of interest. Lu Wang declares that she has no conflict of interest. This article does not contain any studies with human or animal subjects.

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  1. College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Zhenjie Xiong, Da-Wen Sun, Qiong Dai, Zhong Han, Xin-An Zeng & Lu Wang

  2. Food Refrigeration and Computerised Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland

    Da-Wen Sun

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  1. Zhenjie Xiong
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Xiong, Z., Sun, DW., Dai, Q. et al. Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat. Food Anal. Methods 8, 380–391 (2015). https://doi.org/10.1007/s12161-014-9853-3

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