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Ensuring Turmeric Authenticity: A Web-Based AI Approach for Detecting Rice Flour Adulteration

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Turmeric powder is widely used for its culinary and medicinal properties. However, its adulteration with substances similar to the properties of pure turmeric powder poses significant concerns regarding quality, authenticity, and consumer health. This study aimed to develop an artificial intelligence (AI)-based tool for detecting rice flour adulteration in turmeric powder using color values and to further validate the findings through chemical, sensory, and microscopic analyses. Pure turmeric powder was mixed with rice flour (0.1%, 0.5%, 1%, 2.5%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, and 100% (w/w)) to create a series of rice flour adulterated turmeric powder samples. The L* , a* , and b* color values were measured and used for the development of the AI tool. Starch iodine complex formation was employed by the iodine test, while sensory evaluation assessed consumer perception to changes in color due to rice flour adulteration in turmeric powder. Microscopic image analysis was utilized to differentiate rice flour adulterated turmeric powder from pure turmeric powder based on the presence of rice flour granules. The results of the iodine test showed its suitability for detecting rice flour adulteration in turmeric powder on a laboratory scale. The AI-based tool enabled rapid and accessible adulteration detection. The findings of this study contribute to enhancing food quality assurance and promoting consumer health by addressing common adulteration practices in turmeric powder.   Received: 30 September 2025 | Revised: 22 January 2026 | Accepted: 25 February 2026   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement Data are available from the corresponding author upon reasonable request.   Author Contribution Statement Manhari Palliyaguruge: Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Thilina Abekoon: Formal analysis, Data curation, Writing – review & editing. Hirushan Sajindra: Software, Formal analysis, Data curation, Writing – review & editing. Rasanjali Samarakoon: Conceptualization, Validation, Supervision, Project administration. Namal Rathnayake: Validation, Data curation. Upaka Rathnayake: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration.
Title: Ensuring Turmeric Authenticity: A Web-Based AI Approach for Detecting Rice Flour Adulteration
Description:
Turmeric powder is widely used for its culinary and medicinal properties.
However, its adulteration with substances similar to the properties of pure turmeric powder poses significant concerns regarding quality, authenticity, and consumer health.
This study aimed to develop an artificial intelligence (AI)-based tool for detecting rice flour adulteration in turmeric powder using color values and to further validate the findings through chemical, sensory, and microscopic analyses.
Pure turmeric powder was mixed with rice flour (0.
1%, 0.
5%, 1%, 2.
5%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, and 100% (w/w)) to create a series of rice flour adulterated turmeric powder samples.
The L* , a* , and b* color values were measured and used for the development of the AI tool.
Starch iodine complex formation was employed by the iodine test, while sensory evaluation assessed consumer perception to changes in color due to rice flour adulteration in turmeric powder.
Microscopic image analysis was utilized to differentiate rice flour adulterated turmeric powder from pure turmeric powder based on the presence of rice flour granules.
 The results of the iodine test showed its suitability for detecting rice flour adulteration in turmeric powder on a laboratory scale.
The AI-based tool enabled rapid and accessible adulteration detection.
The findings of this study contribute to enhancing food quality assurance and promoting consumer health by addressing common adulteration practices in turmeric powder.
  Received: 30 September 2025 | Revised: 22 January 2026 | Accepted: 25 February 2026   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
  Data Availability Statement Data are available from the corresponding author upon reasonable request.
  Author Contribution Statement Manhari Palliyaguruge: Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization.
Thilina Abekoon: Formal analysis, Data curation, Writing – review & editing.
Hirushan Sajindra: Software, Formal analysis, Data curation, Writing – review & editing.
Rasanjali Samarakoon: Conceptualization, Validation, Supervision, Project administration.
Namal Rathnayake: Validation, Data curation.
Upaka Rathnayake: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration.

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