Comparative chemometric analysis for classification of acids and bases via a colorimetric sensor array

With the increasing availability of digital imaging devices, colorimetric sensor arrays are rapidly becoming a simple, yet effective tool for the identification and quantification of various analytes. Colorimetric arrays utilize colorimetric data from many colorimetric sensors, with the multidimensional nature of the resulting data necessitating the use of chemometric analysis. Herein, an 8 sensor colorimetric array was used to analyze select acid and basic samples (0.5 - 10M) to determine which chemometric methods are best suited for classification quantification of analytes within clusters. PCA, HCA, and LDA were used to visualize the data set. All three methods showed well-separated clusters for each of the acid or base analytes and moderate separation between analyte concentrations, indicating that the sensor array can be used to identify and quantify samples. Furthermore, PCA could be used to determine which sensors showed the most effective analyte identification. LDA, KNN, and HQI were used for identification of analyte and concentration. HQI and KNN could be used to correctly identify the analytes in all cases, while LDA correctly identified 95 of 96 analytes correctly. Additional studies demonstrated that controlling for solvent and image effects was unnecessary for all chemometric methods utilized in this study. Data from a pH-sensitive colorimetric sensor array was processed using a variety of chemometric methods to determine the best method for acid and base classification, including based on concentration. Colorimetric data were analyzed with principal component analysis (PCA), hierarchical cluster analysis (HCA), k nearest neighbor analysis (KNN), linear discriminant analysis (LDA), and hit quality index (HQI). PCA, LDA, and HCA allowed trends in the data to be visualized, while LDA, KNN, and HQI identified analytes with >90% accuracy.