by Keyword: PLS

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Fernandez, L., Martin-Gomez, A., Mar Contreras, M., Padilla, M., Marco, S., Arce, L., (2017). Ham quality evaluation assisted by gas chromatography ion mobility spectrometry IEEE Conference Publications ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) , IEEE (Montreal, Canada) , 1-3

In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig's diet (either nourished with feed or with acorns). For this purpose, we have acquired a dataset composed of 53 samples of Iberian ham from different food providers using a commercial GC-IMS (FlavourSpec, from G.A.S. Dortmund, Germany). Intensive signal pre-processing for GC-IMS was applied to the raw data. This dataset was employed to create four Partial Least Squares Discriminant Analysis (PLSDA) models corresponding to different train/test partitions of the dataset. Nearly perfect classification rates (above 91 %) were obtained for each partition of the dataset, denoting the high power of GC-IMS to characterize food samples.

Keywords: Classification, Food Science, GC-IMS, Ham quality, PLSDA

Udina, S., Carmona, M., Pardo, A., Calaza, C., Santander, J., Fonseca, L., Marco, S., (2012). A micromachined thermoelectric sensor for natural gas analysis: Multivariate calibration results Sensors and Actuators B: Chemical 166-167, 338-348

The potential use of a micromachined thermopile based sensor device for analyzing natural gas is explored. The sensor consists of a thermally isolated hotplate which is heated by the application of a sequence of programmed voltages to an integrated heater. Once the hotplate reaches a stationary temperature, the thermopile provides a signal proportional to the hotplate temperature. These signals are processed in order to determine different natural gas properties. Sensor response is mainly dependent on the thermal conductivity of the surrounding gas at different temperatures. Seven predicted properties (normal density, Superior Heating Value, Wobbe index and the concentrations of methane, ethane, carbon dioxide and nitrogen) are calibrated against sensor signals by using multivariate regression, in particular Partial Least Squares. Experimental data have been used for calibration and validation. Results show property prediction capability with reasonable accuracy except for prediction of carbon dioxide concentration. A detailed uncertainty analysis is provided to better understand the metrological limits of the system. These results imply for the first time the possibility of designing unprecedented low-cost natural gas analyzers. The concept may be extended to other constrained gas mixtures (e.g. of a known number of components) to enable low-cost multicomponent gas analyzers.

Keywords: Gas sensor, Natural gas, MEMS, Superior Heating Value, density, PLS