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

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.