Oliver Tomic

A calibration method for handling the temporal drift of solid state gas-sensors

The reproducibility of chemical sensors is an issue that has to be handled in order to apply sensor-array based instruments for quality control purposes on a routine basis in the laboratory. So far, this problem can only be handled by applying mathematical algorithms to describe the temporal variation of the sensor signal. Sensor drift in a commercial solid state based sensor array device has been investigated to develop a drift compensation algorithm in order to handle the sensor drift. The calibration method uses drift compensation algorithms based on curve fitting of the temporal variation of the sensor signal of calibration samples. This procedure eliminates sensor drift within a single measurement sequence and over several sequences (days, months). It is also demonstrated that the algorithm preserves real features in the data structure of real samples that have been measured. However, this requires calibration samples that are highly correlated in sensor response with the real samples to be analysed in order to make a proper drift correction.

Characterization of selected South African young cultivar wines using FTMIR Spectroscopy, Gas chromatography, and multivariate data analysis

The powerful combination of analytical chemistry and chemometrics and its application to wine analysis provide a way to gain knowledge and insight into the inherent chemical composition of wine and to objectively distinguish between wines. Extensive research programs are focused on the chemical characterization of wine to establish industry benchmarks and authentication systems. The aim of this study was to investigate the volatile composition and mid-infrared spectroscopic profiles of South African young cultivar wines with chemometrics to identify compositional trends and to distinguish between the different cultivars. Data were generated by gas chromatography and FTMIR spectroscopy and investigated by using analysis of variance (ANOVA), principal component analysis (PCA), and linear discriminant analysis (LDA). Significant differences were found in the volatile composition of the cultivar wines, with marked similarities in the composition of Pinotage wines and white wines, specifically for 2-phenylethanol, butyric acid, ethyl acetate, isoamyl acetate, isoamyl alcohol, and isobutyric acid. Of the 26 compounds that were analyzed, 14 had odor activity values of >1. The volatile composition and FTMIR spectra both contributed to the differentiation between the cultivar wines. The best discrimination model between the white wines was based on FTMIR spectra (98.3% correct classification), whereas a combination of spectra and volatile compounds (86.8% correct classification) was best to discriminate between the red wine cultivars.

Standardization methods for handling instrument related signal shift in gas-sensor array measurement data

Abstract This paper describes two different approaches that attempt to solve the problem of signal shift between measurements acquired with gas-sensor array systems of identical construction. Both methods provide standardization models that can be used to compensate such instrument related signal shifts by postprocessing of the measurement data. The first approach is a straightforward univariate direct standardization method, based on linear regression, where unique shift compensation models are created for each sensor. The other approach is a multivariate method, based on partial least squares regression, which can be used to design shift compensation models for the whole gas-sensor array. Both methods effectively removed signal shift after being applied on measurement data acquired with five commercial instruments of identical configuration with quartz micro balance (QMB) sensor arrays.

Rapid analysis and information fusion for quantification of intramuscular fat content

Production cost effectiveness and the customers demand for high quality are main concerns of todays food industry. These goals can be achieved by combining various advanced measurement techniques that allow reliable and rapid analysis of both, produc- tion process, raw materials and end product. This re- quires the development of methods that can be ante- grated directly in the production process, handling an enormous amount of measurement data and extract- ing complex features on-line, that are necessary for advanced quality control. Quantification of intramus- cular fat content in beef, using information from both autoflourescence spectra and autoflourescence images, is an example for a method that could be used in this way. The food industry now faces the challenge of im- plementing such methods in their production processes and finally drawing benefits from the great potential in- formation fusion in combination with rapid analysis provides.