Detecting Liquid Food Adulteration with Mobile Hyperspectral Analysis based on RGB Camera

Abstract

Liquid adulteration poses critical societal challenges by endangering public health and causing economic losses. Existing methods on mobile devices rely solely on vibration-based solutions, which face practical limitations due to requirements of specific container materials/structures and vibrational contact. In this paper, we present LiquidLens, a practical, non-contact mobile system for detecting liquid adulteration based on RGB images of liquid surfaces. Inspired by hyperspectral reconstruction, we aim to analyze liquid food's hyperspectral properties from RGB data for effective adulteration detection. To achieve this, we address three major challenges: First, the specular reflections of liquid, which act as a mirror image of surrounding obstacles, distort spectral measurements. Second, the camera's spectral range is restricted to the visible domain, which lacks access to vital information in the invisible bands. Third, the target liquid spectrum is entangled with uncertain ambient illumination, rendering it uninterpretable. To overcome these challenges, we first exploit the camera flash as visible light source and validate its spectral consistency across mobile devices. Next, we design a specular noise suppression technique to cancel the specular noise. Then, we design an RGB-based VIS-NIR spectral recovery (RVN-SR) framework to overcome the spectral range limitation. Besides, a cross-spectral training scheme is designed to train the RVN-SR model in the flash domain, compensating for the lack of NIR radiation. We deploy and validate LiquidLens across multiple smartphones. Experimental results show robust detection accuracy of 96.2% for 8 honey adulteration types and 94.0% for 5 wine adulteration types.