Applicability of near-infrared spectroscopy for process monitoring in bioethanol production.

Abstract The applicability of near-infrared (NIR) spectroscopy to bioethanol production is investigated. The NIR technique can provide assistance for rapid process monitoring, because organic compounds absorb radiation in the wavelength range 1100–2300 nm. For quantification of a sample's chemical composition, a calibration model is required that relates the measured spectral NIR absorbances to concentrations. For calibration, the concentrations in g/l are determined by the analytical reference method high performance liquid chromatography (HPLC). The calibration models are built and validated for moisture, protein, and starch in the feedstock material, and for glucose, ethanol, glycerol, lactic acid, acetic acid, maltose, fructose, and arabinose in the processed broths. These broths are prepared in laboratory experiments: The ground cereal samples are fermented to alcoholic broths (‘mash’), which are divided into an ethanol fraction and the residual fraction ‘stillage’ by distillation. The NIR technology together with chemometrics proved itself beneficial for fast monitoring of the current state of the bioethanol process, primarily for higher concentrated substances (>1 g/l).

[1]  Roberto Todeschini,et al.  The data analysis handbook , 1994, Data handling in science and technology.

[2]  M Smith,et al.  Near infrared spectroscopy. , 1999, British journal of anaesthesia.

[3]  J. Coello,et al.  Near-infrared spectroscopy in the pharmaceutical industry. , 1998, The Analyst.

[4]  Marcelo Blanco,et al.  NIR spectroscopy: a rapid-response analytical tool , 2002 .

[5]  Peter Filzmoser,et al.  Robust and classical PLS regression compared , 2010 .

[6]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[7]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[8]  Emil W. Ciurczak,et al.  Handbook of Near-Infrared Analysis , 1992 .

[9]  Tormod Næs,et al.  A user-friendly guide to multivariate calibration and classification , 2002 .

[10]  J. Kalivas Two data sets of near infrared spectra , 1997 .

[11]  Tom Fearn,et al.  Practical Nir Spectroscopy With Applications in Food and Beverage Analysis , 1993 .

[12]  Riccardo Leardi,et al.  Genetic algorithms in chemistry. , 2007, Journal of chromatography. A.

[13]  P. Filzmoser,et al.  Repeated double cross validation , 2009 .

[14]  Paul Geladi,et al.  The start and early history of chemometrics: Selected interviews. Part 2 , 1990 .

[15]  Svante Wold,et al.  Chemometrics; what do we mean with it, and what do we want from it? , 1995 .

[16]  Roberto Todeschini,et al.  MobyDigs: software for regression and classification models by genetic algorithms , 2003 .

[17]  Riccardo Leardi,et al.  Nature-Inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks , 2005 .

[18]  B. Liebmann,et al.  Determination of glucose and ethanol in bioethanol production by near infrared spectroscopy and chemometrics. , 2009, Analytica chimica acta.

[19]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .