International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Machine Learning Approach to Detect the Adulterants in Turmeric Powder

Abstract

The popular spice turmeric powder is used in many dishes and offers several health advantages. However, it is frequently contaminated with less expensive materials, producing a product of lower quality, and possibly posing health risks to consumers. The initial deficiencies after the economic crisis in Sri Lanka started a black-market for turmeric as prices were increase steeply. Fake powders also made an appearance in the Sri Lankan market. Food adulteration is a frequent crisis which has been a concern over decades now. Thus, it is vital to discover the possibility of the composition of original turmeric powder among the turmeric powders in the market. There is a great possibility of the adulteration of Turmeric in powdered forms and mixing other ingredients with powdered turmeric is easy. Distinguishing these other ingredients mixed powdered turmeric is not easy. Due to the rise in demand in customers, the manufacturers tend to keep up the production but by adulterating turmeric powders using different methods and these powders have several health effects.  This study intends to construct a machine learning-based fraud detection web application that can precisely identify adulterants in turmeric powder samples using microscope pictures to solve this issue. Machine learning has emerged as a powerful tool for detecting adulterants in various food products and has shown promising results in recent years. In this research, we propose a machine learning-based fraud detection web application to detect adulterants in turmeric powder samples using microscope images. The application will utilize transfer learning, specifically the MobileV2Net, to improve the accuracy of adulterant detection.

Country : Sri Lanka

1 Oshani Navodya2 Gayani Silva3 Kavindya Perera4 Harshani Dissanayake5 Narmada Gamage6 Buddhima Attanayake

  1. Department of Computer Systems and Network Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Computer Systems and Network Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Department of Computer Systems and Network Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  6. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 7, July 2023 pp. 153-159

doi.org/10.47001/IRJIET/2023.707024

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