A. Gustavo González

Intra-laboratory testing of method accuracy from recovery assays

A revision on intra-laboratory testing of accuracy of analytical methods from recovery assays is given. Procedures based on spiked matrices and spiked samples are presented and discussed.

Multivariate characterisation of beers according to their mineral content

In the present paper, a study on the characterisation of beer samples according to their mineral content has been carried out. Zn, P, B, Mn, Fe, Mg, Al, Sr, Ca, Ba, Na and K were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) in 32 beer samples. Lager, dark and low alcoholic content beers were considered. The analysed elements were considered as chemical descriptors in order to apply pattern recognition procedures, including display and supervised learning methods such as linear discriminant analysis and artificial neural networks.

Pattern recognition procedures for differentiation of Green, Black and Oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry.

Eight metals Zn, Mn, Mg, Cu, Al, Ca, Ba and K selected as chemical descriptors were analysed by inductively coupled plasma atomic emission spectrometry. The metal profile was studied in 48 samples of commercial tea - 26 black, 18 green, and four Oolong teas. Pattern recognition procedures such as display methods (Principal Component analysis and Cluster analysis) and supervised techniques (Linear Discriminant analysis and Artificial Neural Networks trained by back propagation) were applied to attain the differentiation of the three tea classes.

Fatty acid profiles as discriminant parameters for coffee varieties differentiation

The fatty acid contents of coffee lipid extracts have been determined by capillary gas chromatography. Ten fatty acids were considered: myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3), arachidic (C20:0), eicosenoic (C20:1) and behenic acid (C22:0). The analyzed coffee samples belonged to arabica and robusta varieties and were either green or roasted coffee beans. The lipids were Soxhlet extracted from ground coffee beans with hexane, and the fatty acids were determined as their corresponding methyl esters. Fatty acids contents were considered as chemical descriptors to differentiate coffee varieties. Several Pattern Recognition methods, Principal Component Analysis and Linear Discriminant Analysis allowed discrimination between green and roasted arabica and robusta coffees.

Ion-exclusion chromatographic determination of organic acids in vinegars

A new ion-exclusion HPLC procedure for the determination of organic acids in vinegars under isocratic elution was developed. Optimum experimental conditions for pH of the eluent, column temperature and mobile phase flow-rate were estimated from a factorial design. The method was successfully validated and enables the reliable separation of major organic acids in wine vinegars (i.e. tartaric, citric, malic, lactic and acetic acid) in about 25 min. It was applied to the analysis of different vinegar samples.

Differentiation of two Canary DO red wines according to their metal content from inductively coupled plasma optical emission spectrometry and graphite furnace atomic absorption spectrometry by using Probabilistic Neural Networks

The metal content of 54 commercialized wines (30 samples from Tacoronte-Acentejo DO (class T) and 24 Valle de la Orotava DO (class O) wines) was performed by ICP-OES (Al, Ba, Cu, Fe, Mn, Sr, Zn, Ca, K, Na and Mg) and GF-AAS (Ni and Pb). Wine samples were processed by dry ashing followed by solution with 5% nitric acid. Metals were considered as suitable descriptors to differentiate between T and O classes. Supervised learning pattern recognition procedures were applied. Linear discriminant analysis (LDA) led to good results up to about 90% of correct classification. In order to improve the results, another kind of algorithms able to model non-linear separation between classes was considered: Probabilistic Neural Networks. Accordingly, excellent results were obtained, leading to sensitivities and specificities higher than 95% for the two classes.

Evaluation of measurement uncertainty in analytical assays by means of Monte-Carlo simulation.

The main limitations of the Guide to the expression of Uncertainty Measurement (GUM) approach for evaluating the measurement uncertainty of analytical assays are presented and explained. The advantages of using Monte-Carlo simulation against the GUM approach are outlined and discussed and the principle of propagation of distributions is explained. The procedure of Monte-Carlo analysis is illustrated by two case studies. A first simple example quoted from the EURACHEM Guide and dealing with the preparation of a calibration standard is used to present the technique with detail in a step-by-step way. In this case the results obtained by both approaches are very similar. A second example deals with the calibration of mass according to a strong non-linear model. In this case, the Monte-Carlo analysis leads to better results.

Accuracy profiles from uncertainty measurements

Accuracy profiles of chemical assays are introduced and derived from the uncertainty of the analytical result. The calculation of accuracy profiles is based on the estimation of the measurement uncertainty of the analytical assay from validation data. For the sake of illustration, a case study dealing with the spectrofluorimetric determination of quinine in tonic water is explained in detail.

Estimation of pH and autoprotolysis constants in mixtures of aliphatic amides with water: Medium effect on the 4-aminoazobenzene system

Correction factors to the glass electrode and autoprotolysis constants of mixtures of aliphatic amides with water: N,N-dimethylformamide (DMF), N,N-dimethylacetamide (DMA), N-methylformamide (NMF), N-methylacetamide (NMA), formamide (F) and acetamide (A), have been determined. The acidity constants of 4-aminoazobenzene referred to the standard state of the mixtures of these aliphatic amides with water as well as the medium effect on the 4-aminoazobenzene system have been evaluated from spectrophotometric measurements.

Classification of tea samples by their chemical composition using discriminant analysis.

Multivariate analysis in combination with pattern recognition procedures has been applied to samples of green and black tea. Discriminant analysis has been used for classification purposes. Aqueous extract, polyphenols, amino acids, caffeine, theobromine and theophylline were used as chemical descriptors.