Direct determination of tannins in Acacia mearnsii bark using near-infrared spectroscopy

This study investigated the application of near-infrared spectroscopy (NIRS) and multivariate calibration methods to the direct determination of the tannin content in Acacia mearnsii bark to improve control during the tannin extraction process. Eighty-nine bark samples were collected from the industrial plant of an extractive tannin industry. The NIR spectra were acquired using an FT-NIR spectrometer with an integrating sphere, an indium–gallium–arsenic detector in the range 7500–4000 cm−1, a resolution of 16 cm−1 and 32 scans divided in two different ways: (a) in natura samples (no sample processing); and (b) dried and milled samples. Partial least-squares models were developed and different strategies were investigated during pre-processing of the IR spectra. The results of the prediction were compared with those obtained using the reference methodology (NBR 11131) and gave values for the root mean square error of prediction between 2.11 and 2.42% for the dried and milled bark samples and between 2.31 and 2.54% for the in natura samples. These results show that NIRS combined with multivariate calibration methods may be used for the direct determination of the tannin content in Acacia mearnsii bark. The requirement for little sample preparation, a short analysis time, no reagent consumption and, consequently, no waste generation are the main positive characteristics of the proposed method.

[1]  E. Cassel,et al.  Extraction of tannin by Acacia mearnsii with supercritical fluids , 2004 .

[2]  Zou Xiaobo,et al.  Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.

[3]  Aerenton Ferreira Bueno,et al.  Characterization of petroleum using near-infrared spectroscopy: Quantitative modeling for the true boiling point curve and specific gravity , 2007 .

[4]  L. Weyer Near-Infrared Spectroscopy of Organic Substances , 1985 .

[5]  Adilson Ben da Costa,et al.  Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production , 2013, Sensors.

[6]  Roman M. Balabin,et al.  Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data. , 2011, Analytica chimica acta.

[7]  Roger M. Rowell,et al.  Handbook of wood chemistry and wood composites. , 2005 .

[8]  Lorenzo Cerretani,et al.  Application of near (NIR) infrared and mid (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity , 2010 .

[9]  Å. Rinnan,et al.  Determination of weight percent gain in solid wood modified with in situ cured furfuryl alcohol by near-infrared reflectance spectroscopy , 2008 .

[10]  J. Turnbull,et al.  Australian trees and shrubs: species for land rehabilitation and farm planting , 1997 .

[11]  M. Prasad Analysis of Leucaena mimosine, Acacia tannins and total phenols by near infrared reflectance spectroscopy , 1995 .

[12]  L Bravo,et al.  Polyphenols: chemistry, dietary sources, metabolism, and nutritional significance. , 2009, Nutrition reviews.

[13]  Katherine A. Bakeev Process analytical technology : spectroscopic tools and implementation strategies for the chemical and pharmaceutical industries , 2010 .

[14]  Roman M. Balabin,et al.  Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines , 2011 .

[15]  S. Sherry The Black Wattle (Acacia mearnsii De Wild.). , 1971 .

[16]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[17]  J. Mello,et al.  Determinação quantitativa de taninos em três espécies de Stryphnodendron por cromatografia líquida de alta eficiência , 2009 .

[18]  Frans van den Berg,et al.  Review of the most common pre-processing techniques for near-infrared spectra , 2009 .

[19]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[20]  B. J. Miller,et al.  Vibrational overtone spectroscopy of phenol and its deuterated isotopomers. , 2006, The journal of physical chemistry. A.

[21]  Celio Pasquini,et al.  A PLS regression model using NIR spectroscopy for on-line monitoring of the biodiesel production reaction , 2011 .

[22]  OTIMIZAÇÃO DE CALIBRAÇÕES BASEADAS EM ESPECTROSCOPIA NO INFRAVERMELHO PRÓXIMO PARA ESTIMATIVA DE PROPRIEDADES DA MADEIRA DE Eucalyptus , 2010 .

[23]  Ø. Hammer,et al.  PAST: PALEONTOLOGICAL STATISTICAL SOFTWARE PACKAGE FOR EDUCATION AND DATA ANALYSIS , 2001 .