Introduction: In developing countries, food adulteration is a significant issue that can lead to potentially fatal diseases. This work introduces approaches to this problem—the lab-on-achip concept—that use paper-fluidics systems to yield a viable solution that provides a more straightforward, reasonably priced, and portable platform capable of carrying out a wide range of analytical tests. Methods: The device used for assessment was first fabricated by 3D printing of wax on Whatman filter paper of the desired pattern, and the quantities of heavy metals, starch, urea, soap, sodium hydroxide, hydrogen peroxide, lead, cadmium, and zinc in milk samples were assessed via colorimetric detection. The images were processed using the Python application OpenCV Result: The colored product is developed based on the presence of the analyte; once the colored product is produced, the image captured RGB values are extracted, and one may determine the image's overall color distribution, color dominance, and color fluctuations and, hence, coloured reaction products and evaluate the analyte concentration by comparing the relative brightness of the red, green, and blue values. The simple procedure allowed us to detect ~1ppm of milk impurities. Discussion: This study endeavor can facilitate the expansion and advancement of quality confirmation and food safety testing. The results show how reliable and effective the paper-based microfluidic device is for quantitatively assessing adulterants in milk through Python image processing. Future applications of this created paper-based microfluidics device and image processing methods may include the separation of various contaminants in various kinds of samples. Conclusion: This study creates a number of opportunities for further investigation and advancement in the quantitative analysis of paper-based microfluidic devices, as it detected the seven analytes that might offer thorough analytical and diagnostic methods. In order to ensure the paper-based microfluidic device's consistent quality, robustness, and accuracy in practical scenarios, extensive field testing and approval evaluation should be conducted.
Jaiswal et al. (Wed,) studied this question.