| Literature DB >> 24267060 |
Miguel Peris1, Laura Escuder-Gilabert.
Abstract
Fermentation processes are often sensitive to even slight changes of conditions that may result in unacceptable end-product quality. Thus, close follow-up of this type of processes is critical for detecting unfavorable deviations as early as possible in order to save downtime, materials and resources. Nevertheless the use of traditional analytical techniques is often hindered by the need for expensive instrumentation and experienced operators and complex sample preparation. In this sense, one of the most promising ways of developing rapid and relatively inexpensive methods for quality control in fermentation processes is the use of chemical multisensor systems. In this work we present an overview of the most important contributions dealing with the monitoring of fermentation processes using electronic noses and electronic tongues. After a brief description of the fundamentals of both types of devices, the different approaches are critically commented, their strengths and weaknesses being highlighted. Finally, future trends in this field are also mentioned in the last section of the article.Keywords: 4-ethylguaiacol; 4-ethylphenol; 4EG; 4EP; ANN; Biotechnological process; CG; CP; CPE; DFA; Electronic nose; Electronic tongue; FT; Fermentation monitoring; Food analysis; Fourier transform; HPLC; IMS; MIR; MLR; MOS; MOSFET; MS; NIR; PCA; PLS; PLS-DA; SHS; SLDA; SOM; SPME; TDNN; VOCs; artificial neural network; carbon paste electrode; conducting polymer; discriminant factor analysis; gas chromatography; high performance liquid chromatography; ion mobility spectrometry; mass spectrometry; metal oxide semiconductor; metal oxide semiconductor field-effect transistor; mid infrared spectroscopy; multiple linear regression; near infrared spectroscopy; partial least squares; partial least squares-discriminant analysis; principal component analysis; self-organizing map; solid-phase microextraction; static headspace; stepwise linear discriminant analysis; time-delay neural network; volatile organic compounds
Year: 2013 PMID: 24267060 DOI: 10.1016/j.aca.2013.09.048
Source DB: PubMed Journal: Anal Chim Acta ISSN: 0003-2670 Impact factor: 6.558