selcuk j agr food sci Selcuk Journal of Agriculture and Food Sciences 2458-8377 2458-8377 Selcuk University 10.15316/selcukjafsci.1680808 Food Engineering Food Technology Gıda Mühendisliği Gıda Teknolojileri VIS-NIR Algılama ve Kenar Bilişim Kullanılarak Sığır Suyuna Karıştırılmış Tavuk Suyunun Spektroskopik Tespiti Spectroscopic Detection of Chicken Broth Adulteration in Beef Broth Using VIS-NIR Sensing and Edge Computing https://orcid.org/0000-0003-3834-5533 Durgun Yeliz GAZIOSMANPASA UNIVERSITY https://orcid.org/0000-0001-9842-5848 Yerlikaya Sabire KARAMANOĞLU MEHMETBEY ÜNİVERSİTESİ 04 28 2026 40 1 152 161 04 21 2025 02 16 2026 Copyright © 1991, Selcuk Journal of Agriculture and Food Sciences 1991 Selcuk Journal of Agriculture and Food Sciences

Gıdalarda hile, hızlı ve kesin tespit yöntemlerinin uygulanmasını gerektiren acil bir durum olmaya devam ediyor. Bu araştırma, VIS-NIR spektroskopisi ve Edge Computing kullanarak sığır suyundaki tavuk suyu sahteciliğini tespit etmek için bir yöntem sunuyor. AS7265x Akıllı Spektral Sensörler ve Arduino Nano 33 BLE'den yararlanan sistem, 410 nm ila 940 nm arasındaki spektral tepkileri yakalar. Sahteciliği tespit etmek için çok önemli olan 560 nm ve 585 nm dalga boylarında özellikle belirgin farklılıklar gözlemlenmiştir. ANOVA testleri de dahil olmak üzere istatistiksel analizler, bu bulguları bu dalga boylarında sıfıra yakın p değerleriyle doğrulayarak yöntemin etkinliğini vurgulamıştır. Sistem, gelecekte daha gelişmiş tespitler için potansiyel uyarlanabilirliğe sahip uygun maliyetli bir çözüm sunarken, birincil katkısı, gıda güvenliğini ve tüketiciyi korumayı ele almaktır.

Food adulteration in meat-based broths remains a major concern for consumer safety, requiring rapid and reliable analytical approaches. This study presents a low-cost and real-time detection system for identifying chicken broth adulteration in beef broth using VIS–NIR spectroscopy integrated with edge computing. Beef and chicken carcasses were processed under controlled conditions, and a total of 60 samples were prepared to obtain four broth mixtures: 0%, 25%, 50%, and 100% chicken broth (n = 15 per group). Spectral measurements were acquired using the AS7265x smart spectral sensor, covering 18 wavelengths between 410–940 nm, positioned at a fixed 3 cm distance from the sample under controlled illumination. The Arduino Nano 33 BLE processed spectral data locally (edge computing) and executed a real-time decision algorithm based on wavelength-specific intensity changes.Significant spectral differences were observed between the four mixture groups, particularly at 560 nm and 585 nm. One-way ANOVA confirmed highly significant effects of adulteration level on spectral intensity (p < 0.001), and Tukey HSD revealed clear pairwise separation between all groups. Confidence interval analysis further demonstrated that 560 nm and 585 nm provided the most discriminative response ranges. The system achieved consistent detection performance across 40 experimental trials, demonstrating high reliability.The proposed approach offers a rapid, non-destructive, and cost-effective method for broth authentication while enabling on-device, real-time decision-making. These findings highlight the potential of combining VIS–NIR sensing with edge computing for practical and scalable food fraud detection applications.

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