VIS-NIR Algılama ve Kenar Bilişim Kullanılarak Sığır Suyuna Karıştırılmış Tavuk Suyunun Spektroskopik Tespiti

Year 2026, Volume: 40 Issue: 1 , 152 - 161 , 28.04.2026

Yeliz Durgun , Sabire Yerlikaya

https://doi.org/10.15316/selcukjafsci.1680808

https://izlik.org/JA37KG34TL

Abstract

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.

Keywords

Arduino Nano 33 BLE , AS7265x sensörü , Düşük maliyetler , Yüksek doğruluk ve hassasiyet

Spectroscopic Detection of Chicken Broth Adulteration in Beef Broth Using VIS-NIR Sensing and Edge Computing

https://izlik.org/JA37KG34TL

Abstract

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.

Keywords

Arduino Nano 33 BLE , AS7265x sensor , Lower costs , High accuracy and sensitivity

References

• Bwambok, D. K., Siraj, N., Macchi, S., Larm, N. E., Baker, G. A., Pérez, R. L., ... & Fakayode, S. O. (2020). QCM sensor arrays, electroanalytical techniques and NIR spectroscopy coupled to multivariate analysis for quality assessment of food products, raw materials, ingredients and foodborne pathogen detection: Challenges and breakthroughs. Sensors, 20(23), 6982.
• Cavdaroglu, C., & Ozen, B. (2023). Applications of UV–visible, fluorescence and mid-infrared spectroscopic methods combined with chemometrics for the authentication of apple vinegar. Foods, 12(6), 1139.
• Chacra, S. A., Sireli, Y., & Cali, U. (2021). A review of worldwide blockchain technology initiatives in the energy sector based on go-to-market strategies. International Journal of Energy Sector Management, 15(6), 1050-1065.
• Cozzolino, D. (2021). The ability of near infrared (NIR) spectroscopy to predict functional properties in foods: Challenges and opportunities. Molecules, 26(22), 6981.
• Cozzolino, D., Zhang, S., Khole, A., Yang, Z., Ingle, P., Beya, M., ... & Hoffman, L. C. (2024). Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts. Journal of Food Science and Technology, 61(5), 950-957.
• Edwards, K., Manley, M., Hoffman, L. C., & Williams, P. J. (2021). Non-destructive spectroscopic and imaging techniques for the detection of processed meat fraud. Foods, 10(2), 448.
• Huang, H., Liu, L., & Ngadi, M. O. (2014). Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors, 14(4), 7248-7276.
• Johnson, R. (2014). Food fraud and economically motivated adulteration of food and food ingredients, CRS Report
• Kamruzzaman, M., Makino, Y., & Oshita, S. (2015). Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review. Analytica chimica acta, 853, 19-29.
• Kitalyi, A. J. (1998). Village chicken production systems in rural Africa: Household food security and gender issues (Vol. 142). Food & Agriculture Org..
• Kurniawati, E., Rohman, A., & Triyana, K. (2014). Analysis of lard in meatball broth using Fourier transform infrared spectroscopy and chemometrics. Meat Science, 96(1), 94-98.
• León, J. B., Sullivan, C. M., & Sehgal, A. R. (2013). The prevalence of phosphorus-containing food additives in top-selling foods in grocery stores. Journal of Renal Nutrition, 23(4), 265-270.
• Lin, M. (2009). A review of traditional and novel detection techniques for melamine and its analogues in foods and animal feed. Frontiers of Chemical Engineering in China, 3(4), 427-435.
• Melendreras, C., Soldado, A., Costa-Fernández, J. M., López, A., Valledor, M., Campo, J. C., & Ferrero, F. (2023). An affordable NIR spectroscopic system for fraud detection in olive oil. Sensors, 23(3), 1728.
• Melendreras, A., Cuadros-Rodríguez, L., & Maroto, A. (2023). A low-cost NIR spectroscopic system for quantifying olive oil adulteration. Talanta, 257, 124365.
• Natarajan, S., & Ponnusamy, V. (2023). Classification of organic and conventional vegetables using machine learning: A case study of brinjal, chili and tomato. Foods, 12(6), 1168.
• Ndlovu, P. F. (2021). Rapid monitoring and quantification of unripe banana flour adulteration using visible‑near infrared spectroscopy (Doctoral dissertation, Doctoral dissertation).
• Pal, J. B., Mistry, A., Bandyopadhyay, D., Biswas, B., & Bhattacharya, S. (2023). Adulteration of milk identification using ionic polymer metal composite as sensor. IEEE Sensors Letters, 7(3), 1-3.
• Porambage, P., Okwuibe, J., Liyanage, M., Ylianttila, M., & Taleb, T. (2018). Survey on multi-access edge computing for internet of things realization. IEEE Communications Surveys & Tutorials, 20(4), 2961-2991.
• Rady, A., & Adedeji, A. (2018). Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat science, 136, 59-67.
• Ray, C. L., Gawenis, J. A., Bylo, M. P., Pescaglia, J., & Greenlief, C. M. (2023). Detection of vegetable oil adulteration in pre-grated bovine hard cheeses via 1H NMR spectroscopy. Molecules, 28(3), 920.
• Spink, J., & Moyer, D. C. (2011). Defining the public health threat of food fraud. Journal of food science, 76(9), R157-R163.
• Su, W. H., He, H. J., & Sun, D. W. (2017). Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: a review. Critical reviews in food science and nutrition, 57(5), 1039-1051.
• Xiao, F., Gu, M., Zhang, Y., Xian, Y., Zheng, Y., Zhang, Y., ... & Wang, D. (2023). Detection of soybean-derived components in dairy products using proofreading enzyme-mediated probe cleavage coupled with ladder-shape melting temperature isothermal amplification (Proofman–LMTIA). Molecules, 28(4), 1685.
• Vyth, E. L., Steenhuis, I. H., Vlot, J. A., Wulp, A., Hogenes, M. G., Looije, D. H., ... & Seidell, J. C. (2010). Actual use of a front-of-pack nutrition logo in the supermarket: consumers’ motives in food choice. Public health nutrition, 13(11), 1882-1889.