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This paper introduces MobiChem, a low-cost, portable, practical, and ubiquitous smartphone-based toolkit for fruit monitoring. The key idea is to leverage the light emitted from a smartphone’s screen and front camera, coupled with a custom-built screen cover, to perform comprehensive hyperspectral analysis on targeted objects. Specifically, we designed a zero-powered screen cover that selectively filters wavelengths essential for hyperspectral sensing. We then incorporate a CNN-based algorithm and a novel ranking-based learning technique that manipulates the latent space to classify maturity stages and characterize their chemical and physical factors. To demonstrate MobiChem’s feasibility, robustness, and practicality, we showcase its application in tomato, banana, and avocado sensing. Our system examines the maturity, chlorophyll, lycopene content, free sugar levels, and firmness, enabling various dietary assessments and food safety applications. Experimental results using 117 tomatoes, 98 bananas, and 73 avocados show MobiChem achieved 95.67% accuracy in chlorophyll concentration measurement, 98.76% for lycopene detection, 93.53% for sugar concentrations analysis, and 91.34% average accuracy in classifying maturity (96.64% for tomato, 86.37% for banana, and 91.03% for avocado).