Detection of Biological CO2 and 1,3-Pentadiene Using Non-refrigerated Low-Cost MWIR Detectors
| Autor: | Germán Vergara, José M. Peinado, J. I. Robla, Belén Diezma, Petra Wrent, María-Isabel de Silóniz, Eva-María Rivas, Pilar Barreiro, Javier Garcia-Hierro, María Maldonado |
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| Přispěvatelé: | Universidad Complutense de Madrid |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Food preservation 030106 microbiology Food spoilage Analytical chemistry Applied Microbiology and Biotechnology Analytical Chemistry 03 medical and health sciences Gas detector Safety Risk Reliability and Quality Detector Sorbate Filter (signal processing) Linear discriminant analysis Food Analysis Yeast 3. Good health Principal component analysis Environmental science Electrónica Biological system Safety Research Food Science Contaminated food |
| Zdroj: | Food Analytical Methods, ISSN 1936-9751, 2016-06, Vol. 9, No. 6 Digital.CSIC. Repositorio Institucional del CSIC instname Archivo Digital UPM Universidad Politécnica de Madrid |
| ISSN: | 1936-976X |
| Popis: | The early detection of spoiling metabolic products in contaminated food is a very important tool to control quality. Some volatile compounds produce unpleasant odours at very low concentrations, making their early detection very challenging. This is the case of 1,3-pentadiene produced by microorganisms through decarboxylation of the preservative sorbate. In this work, we have developed a methodology to use the data produced by a low-cost, compact MWIR (Mid-Wave IR) spectrometry device without moving parts, which is based on a linear array of 128 elements of VPD PbSe coupled to a linear variable filter (LVF) working in the spectral range between 3 and 4.6 μm. This device is able to analyze food headspace gases through dedicated sample presentation setup. This methodology enables the detection of CO and the volatile compound 1,3-pentadiene, as compared to synthetic patrons. Data analysis is based on an automated multidimensional dynamic processing of the MWIR spectra. Principal component and discriminant analysis allow segregating between four yeast strains including producers and no producers. The segregation power is accounted as a measure of the discrimination quality. This work was funded by the project (GR3/14-910644) from Complutense University of Madrid and Santander-Hispano. Research by Eva María Rivas has been supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM). |
| Databáze: | OpenAIRE |
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