{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:53:17Z","timestamp":1761745997446,"version":"3.38.0"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,9,16]]},"abstract":"<jats:p>Adulteration in different spices is an emerging challenge in human civilization. It is commonly detected using different analytical and instrumental techniques. Despite good accuracy and precision many of such techniques are limited by their high processing time, skilled manpower requirement, expensive machinery and portability factor. Computer vision methodology driven by powerful convolutional neural network (CNN) architectures can be a possible way to address those limitations. This paper presents a CNN driven computer vision model which can detect cornstarch adulteration in turmeric powder along with the degree of adulteration. The model has been optimized using binary genetic algorithm (BGA) for improved performance and consistency. The experimentations presented in this paper were conducted with an in-house database prepared for 4 levels of adulteration and found to provide about 98% overall accuracy. The less expensive and faster detection capability of the model along with its mobility makes this proposal a promising addition to the existing spice adulteration screening methods.<\/jats:p>","DOI":"10.3233\/idt-240656","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T15:33:30Z","timestamp":1725377610000},"page":"1955-1964","source":"Crossref","is-referenced-by-count":2,"title":["Identifying the degree of cornstarch adulteration in turmeric powder using optimized convolutional neural network"],"prefix":"10.1177","volume":"18","author":[{"given":"Dipankar","family":"Mandal","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Future Institute of Engineering and Management, Kolkata, India"}]},{"given":"Debashis","family":"Nandi","sequence":"additional","affiliation":[{"name":"Department of Printing Engineering, Jadavpur University, Kolkata, India"}]},{"given":"Bipan","family":"Tudu","sequence":"additional","affiliation":[{"name":"Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India"}]},{"given":"Arpitam","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Department of Printing Engineering, Jadavpur University, Kolkata, India"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-240656_ref1","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.foodcont.2016.07.015","article-title":"The economics of a food fraud incident \u2013 Case studies and examples including Melamine in Wheat Gluten","volume":"71","author":"Moyer","year":"2017","journal-title":"Food Control."},{"key":"10.3233\/IDT-240656_ref3","doi-asserted-by":"crossref","first-page":"109093","DOI":"10.1016\/j.foodcont.2022.109093","article-title":"The detection and determination of adulterants in turmeric using fourier-transform infrared (FTIR) spectroscopy coupled to chemometric analysis and 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