Exploration of Fast Edible Oil Classification Using Infrared Spectrum, Machine Learning, and Chemometrics

Hung Yu Chien, An Tong Shih, Yuh Min Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Your food is your medicine. Edible oils take important parts in people's daily food, and taking good-quality oils plays an important role to the health. However, as the supply cannot satisfy the market demand and some good-quality edible oils are expensive, many incidents of adulterated and fraudulent edible oils have been reported. In Taiwan, some common adulterated edible oils and fraudulent edible oils incidents include (1) mixing good-quality oils with low-quality oils, but labeling the products as high-quality products; (2) importing cheap and low-quality oils abroad, but labeling them as good-quality ones; and (3) fraudulent labeling with wrong ingredients. Even though high-tech laboratories can differentiate the products and identify ingredients, the popular technologies demand high costs in terms of money, time, and man power. The general cannot easily access these technologies and should only depend on occasional reports from the governments or from some trusted institutions. Furthermore, the jurisdiction process takes a long time, and the punishment is relatively light, compared to the illegal interests. It is, therefore, crucial to develop new technologies that can effectively and efficiently differentiate different edible oils or even identifying concerned ingredients in edible oils. Due to dropping prices of infrared spectroradiometers and advances in machine technologies and chemometrics, we would like to integrate these technologies to develop a process that can fast and effectively differentiate different edible oils and even identify suspicious ones. The preliminary experiments show some promising results and potential. We also point out some challenges for future work.

Original languageEnglish
Title of host publication2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138213
DOIs
Publication statusPublished - 2019 Oct
Event10th IEEE International Conference on Awareness Science and Technology, iCAST 2019 - Morioka, Japan
Duration: 2019 Oct 232019 Oct 25

Publication series

Name2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings

Conference

Conference10th IEEE International Conference on Awareness Science and Technology, iCAST 2019
CountryJapan
CityMorioka
Period19-10-2319-10-25

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Artificial Intelligence
  • Computer Science Applications

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