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Enhanced Specificity for Detection of Frauds by Fusion of Multi-class and One-Class Partial Least Squares Discriminant Analysis: Geographical Origins of Chinese Shiitake Mushroom

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Abstract

Both multi-class and one-class discrimination analyses (DAs) have been widely used for tracing the geographical origins of Protected Designation of Origin (PDO) foods. However, due to the complexity of potential non-PDO frauds, both methods have encountered some problems. Because multi-class DA tries to classify two or more predefined classes, its classification results will be unreliable when it is used to predict a new object from an untrained class. One-class DA is developed using only the information concerning one-class objects, so they cannot necessarily ensure the model specificity for detection of various food frauds. In this work, a new chemometric strategy was proposed by fusion of multi-class and one-class DA to trace the geographical origin of a Chinese dried shiitake mushroom with PDO. The PDO shiitake objects (n = 161) and non-PDO objects (n = 264) from five other main producing areas were analyzed using near-infrared spectroscopy. The classification performance of multi-class DA, one-class DA, and model fusion was compared. With second-order derivative (D2) spectra, model fusion obtained a high sensitivity (0.944) and specificity (0.968). Model comparison indicates that fusion of multi-class and one-class DA can enhance the specificity for detecting various non-PDO foods with little loss of model sensitivity.

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Acknowledgments

This work was financially supported by the General and Youth Projects of National Natural Science of China (Grant Nos. 21476270, 21276006, and 21205145). Lu Xu is also grateful to the financial support from the Open Research Program (No. GCTKF2014007) of State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology (Zhejiang University of Technology) and the Research Fund for the Doctoral Program of Tongren University (No. trxyDH1501).

Compliance with Ethical Standards

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

Conflict of Interest

Lu Xu declares that he has no conflict of interest. Hai-Yan Fu declares that she has no conflict of interest. Tian-Ming Yang declares that he has no conflict of interest. Yuan-Bin She declares that he has no conflict of interest. He-Dong Li declares that he has no conflict of interest. Chen-Bo Cai declares that he has no conflict of interest. Li-Juan Chen declares that she has no conflict of interest.

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Authors and Affiliations

  1. College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, People’s Republic China

    Lu Xu & Yuan-Bin She

  2. College of Material and Chemical Engineering, Tongren University, Tongren, 554300, Guizhou, People’s Republic China

    Lu Xu & Li-Juan Chen

  3. College of Pharmacy, South-Central University for Nationalities, Wuhan, 430074, People’s Republic China

    Hai-Yan Fu, Tian-Ming Yang & He-Dong Li

  4. College of Chemistry and Life Science, Chuxiong Normal University, Chuxiong, 675000, People’s Republic China

    Chen-Bo Cai

Authors
  1. Lu Xu
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  2. Hai-Yan Fu
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  3. Tian-Ming Yang
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  4. He-Dong Li
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Correspondence to Lu Xu or Yuan-Bin She.

Additional information

Lu Xu and Hai-Yan Fu equally contributed to this work.

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Xu, L., Fu, HY., Yang, TM. et al. Enhanced Specificity for Detection of Frauds by Fusion of Multi-class and One-Class Partial Least Squares Discriminant Analysis: Geographical Origins of Chinese Shiitake Mushroom. Food Anal. Methods 9, 451–458 (2016). https://doi.org/10.1007/s12161-015-0213-8

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