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Rapid and Non-destructive Detection of Iron Porphyrin Content in Pork Using Multispectral Imaging Approach

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Abstract

The feasibility of using multispectral imaging (MSI) technique (405–970 nm) for predicting iron porphyrin (IP) content in pork was assessed. Based on nine feature wavelengths identified by successive projections algorithm (SPA), a partial least squares regression (PLSR) calibration model was established, leading to a determination coefficient (R 2) of 0.996 for the prediction set. Visualization of IP content in each pixel of the multispectral images using the prediction model offers an effective way to monitor the distribution and real-time evolution of IP content during the manufacturing process. The results revealed the potentiality of MSI technique combined with PLSR calibration analysis as a rapid and non-destructive evaluation method of IP content for the agri-food industry.

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Acknowledgments

The authors would like to thank the helps of Miss Shuangshuang Wu, Dr. Changhong Liu, Miss Jinxia Liu, and Miss Xiong Xiao in the pretreatment processing of raw pork after purchase. This study is supported by the Specialized Research Fund for the Anhui Province Key Technologies Research & Development Program (1301031033), the National Key Technologies R&D Programme (2012BAD07B01), the Key Project of Anhui Provincial Educational Department (KJ2014ZD26), the Doctoral Program of Higher Education (20120111110024), the National Natural Science Foundation of China (No. 31271893), and the Funds for Huangshan Professorship of Hefei University of Technology (407-037019).

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Conflict of Interest

Fei Ma declares that he has no conflict of interest. Hao Qin declares that he has no conflict of interest. Cunliu Zhou declares that he has no conflict of interest. Xia Wang declares that he has no conflict of interest. Conggui Chen declares that he has no conflict of interest. Lei Zheng declares that he has no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Authors and Affiliations

  1. School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, Anhui Province, China

    Fei Ma, Hao Qin, Cunliu Zhou, Conggui Chen & Lei Zheng

  2. School of Medical Engineering, Hefei University of Technology, Hefei, 230009, Anhui Province, China

    Xia Wang & Lei Zheng

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  1. Fei Ma
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  2. Hao Qin
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  3. Cunliu Zhou
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  4. Xia Wang
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Correspondence to Lei Zheng.

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Ma, F., Qin, H., Zhou, C. et al. Rapid and Non-destructive Detection of Iron Porphyrin Content in Pork Using Multispectral Imaging Approach. Food Anal. Methods 9, 1180–1187 (2016). https://doi.org/10.1007/s12161-015-0298-0

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