A novel free PhotoMetrix UVC is proposed for both the operation of a universal serial bus video camera (UVC) and multivariate image analysis, allowing a full solution for point-of-use analysis. A UVC was placed in an open-source 3D-printed chamber illuminated by a white light-emitting diode (LED) with controlled intensity of light. The digital images captured were converted into red, green, and blue (RGB) histograms, and regression models were used within the app. As a proof-of-concept, four adulterants in raw milk samples were determined. The coefficient of determination (R2Cal) for all models was higher than 0.99, and no significant differences (p < 0.05) between the measured and predicted values were identified. The root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) were satisfactory, with values less than 0.1 and 0.7 g L-1, respectively. The recoveries ranged from 90 to 120% in spiked milk samples, and partial least square (PLS) models showed root mean square error of prediction (RMSEP) of 0.28, 0.33, 0.48 and 0.39 g L-1 for chloride, hypochlorite, hydrogen peroxide and starch, respectively. The PhotoMetrix UVC app was feasible for the colorimetric chemical analysis using a smartphone improving the applicability, mobility, and usability.