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The potential use of colorimetric sensors has received significant attention due to its feasibility for use in various applications. After reacting with a sample, the image of the colorimetric sensor can be captured and converted into digital data using several different color models. The analytical data can then be processed with various chemometric methods. This research study investigated the predictive performance of calibration models established using color models commonly used in analytical chemistry including RGB, CMYK, HSV and CIELAB. A total of eight commercially available colorimetric sensors were used to determine the presence of manganese (Mn2+), copper (Cu2+), iron (Fe2+/Fe3+), nitrate (NO3–), phosphate (PO43–), sulfate (SO42–), as well as total hardness and pH values. As external validation tests, real water samples collected in Chiang Mai, Thailand were used. Based on the resulting data obtained using the synthetic test samples, the color that was most similar to the appearing color of the chemical sensor could offer satisfactory results. However, it was not always the case especially when the strips composed of multiple colorimetric sensors or sensor array were used. When tested with external validation, the predictive performance could be improved using appropriate data preprocessing and, in this research study, a normalization method was recommended to guarantee the accuracy of the calibration models.

In process monitoring, a representative out-of-control class of samples cannot be generated. Here, it is assumed that it is possible to obtain a representative subset of samples from a single 'in-control class' and one class classifiers namely Q and D statistics (respectively the residual distance to the disjoint PC model and the Mahalanobis distance to the centre of the QDA model in the projected PC space), as well as support vector domain description (SVDD) are applied to disjoint PC models of the normal operating conditions (NOC) region, to categorise whether the process is in-control or out-of-control. To define the NOC region, the cumulative relative standard deviation (CRSD) and a test of multivariate normality are described and used as joint criteria. These calculations were based on the application of window principal components analysis (WPCA) which can be used to define a NOC region. The D and Q statistics and SVDD models were calculated for the NOC region and percentage predictive ability (%PA), percentage model stability (%MS) and percentage correctly classified (%CC) obtained to determine the quality of models from 100 training/test set splits. Q, D and SVDD control charts were obtained, and 90% confidence limits set up based on multivariate normality (D and Q) or SVDD D value (which does not require assumptions of normality). We introduce a method for finding an optimal radial basis function for the SVDD model and two new indices of percentage classification index (%CI) and percentage predictive index (%PI) for non-NOC samples are also defined. The methods in this paper are exemplified by a continuous process studied over 105.11 h using online HPLC.