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![Figure 4. Illustrative example described by Schwartz and Gelb [65] as...](profile/Agustin-Garcia-Asuero/publication/320116698/figure/fig4/AS:544099302207488@1506734790472/llustrative-example-described-by-Schwartz-and-Gelb-65-as-optimal-Numerical-data-are_Q320.jpg)
Citations
A smartphone-based technique for determining the titration equivalence point from a linear-segment curve was developed for the first time. In this method, a titrant in an increasing microliter-volume was added to a set of sample aliquots containing an indicator covering both sides of the equivalence point. The solutions were subsequently photographed in one shot, in a dark box using a smartphone camera and an illuminating screen of a tablet or light emitting diode lamps arranged below a white acrylic sheet as a light source. After the colors of the solutions were delineated to Red, Green, and Blue (RGB) values, 1/log G was used to construct a plot in which the equivalence point was located at the intersection of the two lines in the region before and after the equivalence point. The technique was successfully applied to the miniaturized titration of sodium chloride injections, showing the good linear relationship of equivalence points to the sodium chloride concentration in the range of 0.4163%–0.9675% w/v (R² of 0.9998). The assay was accurate (% recovery of 98.92–100.52), precise (% relative standard deviation < 1.20), and unaffected by the use of different types of microplates, smartphones, and RGB analysis tools. Additionally, it required no expensive nor complicated equipment and offered the possibility of performing analysis on a single smartphone device when it was used with a mobile application developed to aid data processing and immediate production of reports of analytical results.
Segmentation has vital employment in regression analysis where data have some change point. Traditional estimation methods such as Hudson, D.J.;(1966) and Muggeo, V. M., (2003) have been reviewed. But these methods do not take into account robustness in the presence of outliers values. However, third method was used as rank-based method, where the analysis will be devoted to the ranks of data instead of the data themselves. Our contribution in this paper is to use M-estimator methodology with three distinct weight functions (Huber, Tukey, and Hampel) which has been combined with Muggeo version approach to gain more robustness, Thus we get robust estimates from the change point and regression parameters simultaneously. We call our new estimator as robust Iterative Rewrighted M-estimator:IRWm-method with respect to its own weight function. Our primary interest is to estimate the change point that joins the segments of regression curve, and our secondary interest is to estimate the parameters of segmented regression model. The real data set were used which concerned to bed-loaded transport as dependent variable (y) and discharge explanatory variable (x). The comparison has been conducted by using several criteria to select the most appropriate method for estimating the change point and the regression parameters. The superior results were marked for IRWm-estimator with respect to Tukey weight function.
