Research article Special Issues

Fast honey classification using infrared spectrum and machine learning

  • Received: 22 May 2019 Accepted: 07 July 2019 Published: 29 July 2019
  • Honey has been one previous natural food in human history. However, as the supply cannot satisfy the market demand, many incidents of adulterated and fraudulent honey have been reported. In Taiwan, some common adulterated honey and fraudulent honey incidents include (1) mixing honey with fructose, (2) importing cheap honey abroad but labeling them as domestic honey, and (3) labeling cheaper honey (for example, nectar and lychee honey) as high-price honey (for example, longan honey). It is very difficult for consumers to tell the genuineness of the labeling of honey. To protect consumers and honest honey producers, we aim at exploring and developing an efficient and convenient technology that can effectively classify honey. We analyze the infrared spectra of honey samples and apply machine learning technologies to classify honey. The experimental results confirm that this technology can effectively distinguish several main honey types in Taiwan. This technology has the advantages of non-destruction, immediacy, and low manpower. It can serve as an effective tool to fast screen honey products.

    Citation: Hung-Yu Chien, An-Tong Shih, Bo-Shuen Yang, Vincent K. S. Hsiao. Fast honey classification using infrared spectrum and machine learning[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6874-6891. doi: 10.3934/mbe.2019344

    Related Papers:

  • Honey has been one previous natural food in human history. However, as the supply cannot satisfy the market demand, many incidents of adulterated and fraudulent honey have been reported. In Taiwan, some common adulterated honey and fraudulent honey incidents include (1) mixing honey with fructose, (2) importing cheap honey abroad but labeling them as domestic honey, and (3) labeling cheaper honey (for example, nectar and lychee honey) as high-price honey (for example, longan honey). It is very difficult for consumers to tell the genuineness of the labeling of honey. To protect consumers and honest honey producers, we aim at exploring and developing an efficient and convenient technology that can effectively classify honey. We analyze the infrared spectra of honey samples and apply machine learning technologies to classify honey. The experimental results confirm that this technology can effectively distinguish several main honey types in Taiwan. This technology has the advantages of non-destruction, immediacy, and low manpower. It can serve as an effective tool to fast screen honey products.


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  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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