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Moldy Peanut Kernel Identification Using Wavelet Spectral Features Extracted from Hyperspectral Images

  • Xiaotong Qi
  • Jinbao JiangEmail author
  • Ximin Cui
  • Deshuai Yuan
Article
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Abstract

Moldy peanuts may contain aflatoxin, a highly carcinogenic substance that threatens the health of humans and livestock. This study aimed to identify moldy peanuts using hyperspectral measurements and continuous wavelet transform (CWT). Peanuts were allowed to develop mold in a simulation of natural process of fungal infection; detailed hyperspectral images of healthy and moldy peanuts were captured. Based on these spectral data, CWT with separability analysis was conducted, generating a Jeffries–Matusita distance scalogram that summarized the separability of the wavelet power at different wavelengths and the decomposition scales between healthy and moldy peanuts. Using thresholding, five wavelet features (WFs) were isolated to identify moldy peanuts. In addition, seven optimal bands obtained from a successive projection algorithm were compared with the WFs. Partial least squares discrimination analysis (PLS-DA) and support vector machines (SVM) were adopted as classifiers for evaluating the WFs and optimal bands. The results show that according to the WFs, both PLS-DA and SVMs achieved higher overall classification results (at least 96.19% for the test data) than those using optimal bands selected via the successive projection algorithm (SPA). The CWT was found to be a promising method for analyzing the fungal infection of peanuts.

Keywords

Continuous wavelet transform Fungal contamination Partial least squares discrimination analysis Peanuts Support vector machine 

Notes

Acknowledgments

The research was sponsored by National Natural Science Foundation of China (41871341, 41571412), and the hyperspectral imaging system was provided by Sichuan Dualix Spectral Imaging Technology Co., Ltd.

Compliance with Ethical Standards

Conflict of Interest

Xiaotong Qi declares that she has no conflict of interest. Jinbao Jiang declares that he has no conflict of interest. Ximin Cui declares that he has no conflict of interest. Deshuai Yuan declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xiaotong Qi
    • 1
  • Jinbao Jiang
    • 1
    Email author
  • Ximin Cui
    • 1
  • Deshuai Yuan
    • 1
  1. 1.College of Geosciences and Surveying EngineeringChina University of Mining and TechnologyBeijingChina

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