Variable pathlength cell for internal data validation in computer vision

Abstract

Computer vision has emerged as a fast and cost-effective method for reaction monitoring and determination of analytes. However, one of the drawbacks of computer vision in analytical chemistry is the reliability of color data, particularly in data acquired from real-time analyses, which may inhibit quantitative interpretation. This highlights the necessity for an effective validation method for data collected using computer vision. Here, we report a simple yet effective variable pathlength cell design that can help data validation in computer vision by exploiting the linear pathlength–absorbance relationship of the Beer–Lambert law. The performance of this novel variable pathlength cell is evaluated using a wide range of analyte concentrations. This variable pathlength cell design is versatile and can be fabricated using various methodologies and materials. This design, combined with computer vision, is compatible with flow chemistry and holds great potential for integration into automatic, inline quantitative analysis of reactions and analytical chemistry.