Coulometer from a Digitally Controlled Galvanostat with Photometric Endpoint Detection.

In this work, a coulometer was developed from a digitally controlled galvanostat. A simple colorimeter based on a RGB LED was used as a light emitter coupled to light detectors, while light dependent resistance (LDR) and photodiodes have been developed as endpoint detectors. Both hardware and software have been adapted from the original galvanostat design. Regarding the hardware, new electrical signal conditioners (filters and voltage dividers) were included to optimize the working system. The software was developed based on an open source Arduino UNO microcontroller. The different variables that control the titration process are managed by an add-in module for Excel data acquisition software that is freely available. A study of the possible variables that influence the titration process has been carried out. The system was tested with two classical coulometric titrations such as iodometry (thiosulfate, ascorbic acid) and acid/base (potassium acid phthalate as standard). The developed system is versatile as different endpoint color indicators can be employed (starch and phenolphthalein for the investigated reactions). Different experimental arrangements have been studied: the nature of the electrodes (Pt, Ag), type of cells (two separate compartments or a single compartment), and light detectors (LDR, photodiode). The influence of several experimental parameters (both electrical, light, and integration time) was studied and chosen to obtain the best performance of the complete system. Reproducibility results below 1% can be obtained under controlled conditions. In the case of acid/base titrations, the presence of atmospheric carbon dioxide was detected, whose interference was mainly affected by the stirring rate and the titration time.

Supervised pattern recognition procedures for discrimination of whiskeys from gas chromatography/mass spectrometry congener analysis.

The volatile congener analysis of 52 commercialized whiskeys (24 samples of single malt Scotch whiskey, 18 samples of bourbon whiskey, and 10 samples of Irish whiskey) was carried out by gas chromatography/mass spectrometry after liquid-liquid extraction with dichloromethane. Pattern recognition procedures were applied for discrimination of different whiskey categories. Multivariate data analysis includes linear discriminant analysis (LDA), k nearest neighbors (KNN), soft independent modeling of class analogy (SIMCA), procrustes discriminant analysis (PDA), and artificial neural networks techniques involving multilayer perceptrons (MLP) and probabilistic neural networks (PNN). Classification rules were validated by considering the number of false positives (FPs) and false negatives (FNs) of each class associated to the prediction set. Artificial neural networks led to the best results because of their intrinsic nonlinear features. Both techniques, MLP and PNN, gave zero FPs and zero FNs for all of the categories. KNN is a nonparametric method that also provides zero FPs and FNs for every class but only when selecting K = 3 neighbors. PDA produced good results also (zero FPs and FNs always) but only by selecting nine principal components for class modeling. LDA shows a lesser classification performance, because of the building of linear frontiers between classes that does not apply in many real situations. LDA led to one FP for bourbons and one FN for scotches. The worse results were obtained with SIMCA, which gave a higher number of FPs (five for both scotches and bourbons) and FNs (six for scotchs and two for bourbons). The possible cause of these findings is the strong influence of class inhomogeneities on the SIMCA performance. It is remarkable that in any case, all of the methodologies lead to zero FPs and FNs for the Irish whiskeys.