First step towards a fast analytical method for the determination of Biochemical Methane Potential of solid wastes by near infrared spectroscopy

Bioresource Technology

Volumn 102, Issue 3, 2011, Pages 2280-2288

  Lesteur, M ^a,b,c   Latrille, E ^a   Maurel, V Bellon ^b   Roger, J M ^b   Gonzalez, C ^c   Junqua, G ^c   Steyer, J P ^a  

^a LABORATOIRE DE BIOTECHNOLOGIE DE L ENVIRONNEMENT   (France)

^b UMR ITAP   (France)

^c ECOLE DES MINES D ALÈS   (France)

Author keywords

Anaerobic digestion; Biochemical Methane Potential (BMP); Biogas; Near infrared spectroscopy; Solid wastes

Indexed keywords

ANAEROBIC BIODEGRADABILITY; ANALYTICAL METHOD; BIOCHEMICAL METHANE POTENTIAL; BIOCHEMICAL METHANE POTENTIAL (BMP); FERMENTATION PROCESS; MUNICIPAL SOLID WASTE; NEAR INFRARED; SAMPLE PREPARATION; STANDARD ERROR OF PREDICTION;

BIODEGRADATION; BIOGAS; FERMENTATION; FORECASTING; INFRARED DEVICES; METHANE; NEAR INFRARED SPECTROSCOPY; SOLID WASTES; VALUE ENGINEERING; WASTE MANAGEMENT;

ANAEROBIC DIGESTION;

METHANE;

BIOCHEMISTRY; BIODEGRADATION; DIGESTIBILITY; FERMENTATION; INFRARED SPECTROSCOPY; NEAR INFRARED; SAMPLE PREPARATION; SOLID WASTE; WASTE MANAGEMENT;

ANAEROBIC DIGESTION; ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; BIOCHEMICAL METHANE POTENTIAL TEST; BIODEGRADABILITY; BIOMASS FERMENTATION; CHEMICAL ANALYSIS; CHEMICAL COMPOSITION; CONTROLLED STUDY; INDUSTRIAL WASTE; LIGHT; MUNICIPAL SOLID WASTE; NEAR INFRARED SPECTROSCOPY; PREDICTION; PRIORITY JOURNAL;

ALGORITHMS; METHANE; REFUSE DISPOSAL; SEWAGE; SPECTROSCOPY, NEAR-INFRARED;

EID: 78650801823     PISSN: 09608524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biortech.2010.10.044     Document Type: Article

References (35)

1. ADEME, 2009. French National Household Waste Characterization Survey. French Environment and Energy Management Agency (ADEME).
2. Andres S., Calleja A., Lopez S., Gonzalez J.S., Rodriguez P.L., Giraldez F.J. Prediction of gas production kinetic parameters of forages by chemical composition and near infrared reflectance spectroscopy. Anim. feed sci. technol. 2005, 123:487-499.
3. Angelidaki I., Alves M., Bolzonella D., Borzacconi L., Campos J.L., Guwy A.J., Kalyuzhnyi S., Jenicek P., van Lier J.B. Defining the biomethane potential (BMP) of solid organic wastes and energy crops: a proposed protocol for batch assays. Water Sci. Technol. 2009, 59:927-934.
4. APHA Standard methods for the examination of water and wastewater 1992, American Public Health Association, Washington, D.C. 18th ed.
5. Barnes R., Dhanoa M., Lister J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 1989, 43:772-777.
6. BellonMaurel V., Dubois J. Near-infrared hyperspectral imaging in food and agricultural science. Infrared and Raman Spectroscopic Imaging 2009, 259-376. Wiley-VCH, Allemagne. R. Salzer, H.W. Siesler (Eds.).
7. Chauchard F., Cogdill R., Roussel S., Roger J.M., Bellon-Maurel V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometr. intell. lab. syst. 2004, 71:141-150.
8. Dardenne P. Some considerations about NIR spectroscopy: closing speech at NIR-2009. J. Near Infrared Spectrosc. 2010, 21(1):8-14.
9. Davidsson A., Gruvberger C., Christensen T.H., Hansen T.L., Jansen J.l.C. Methane yield in source-sorted organic fraction of municipal solid waste. Waste Manage. 2007, 27:406-414.
10. De Boever J.L., Cottyn B.G., De Brabander D.L., Vanacker J.M., Boucqué C.V. Prediction of the feeding value of grass silages by chemical parameters, in vitro digestibility and near-infrared reflectance spectroscopy. Anim. feed sci. technol. 1996, 60:103-115.
11. Faber N.M., Schreutelkamp F.H., Vedder H.W. Estimation of prediction uncertainty for a multivariate calibration model. Spectrosc. Eur. 2004, 16:17-20.
12. Feller C., Jeanson P., Giummelly P., Bonaly P. Comparaison de différentes méthodes d'hydrolyse acide en vue du dosage des glucides totaux dans les sols. Sci. sol 1991, 29:13-22.
13. Gunaseelan V.N. Regression models of ultimate methane yields of fruits and vegetable solid wastes, sorghum and napiergrass on chemical composition. Bioresour. Technol. 2007, 98:1270-1277.
14. Gunaseelan V.N. Predicting ultimate methane yields of Jatropha curcus and Morus indica from their chemical composition. Bioresour. Technol. 2009, 100:3426-3429.
15. Hansen T.L., Schmidt J.E., Angelidaki I., Marca E., Jansen J.l.C., Mosbaek H., Christensen T.H. Method for determination of methane potentials of solid organic waste. Waste Manage. 2004, 24:393-400.
16. Jacobi H.F., Moschner C.R., Hartung E. Use of near infrared spectroscopy in monitoring of volatile fatty acids in anaerobic digestion. Water Sci. Technol. 2009, 2:339-346.
17. Lesteur M., Bellon-Maurel V., Gonzalez C., Latrille E., Roger J.M., Junqua G., Steyer J.P. Alternative methods for determining anaerobic biodegradability: a review. Process Biochem. 2010, 45:431-440.
18. Leyva A., Quintana A., Sánchez M., Rodríguez E.N., Cremata J., Sánchez J.C. Rapid and sensitive anthrone-sulfuric acid assay in microplate format to quantify carbohydrate in biopharmaceutical products: method development and validation. Biologicals 2008, 36:134-141.
19. Mottet, A., François, E., Latrille, E., Steyer, J.P., Déléris, S., Vedrenne, F., Carrère, H., 2010. Estimating anaerobic biodegradability indicators for waste activated sludge. Chem. Eng. J. Article in press. doi: 10.1016/j.cej.2010.03.059.
20. Piccolo A., Zena A., Conte P. A comparison of acid hydrolyses for the determination of carbohydrate content in soils. Commun. Soil Sci. Plant Anal. 1996, 27:2909-2915.
21. Ras M., Girbal-Neuhauser E., Paul E., Spérandio M., Lefebvre D. Protein extraction from activated sludge: an analytical approach. Water Res. 2008, 42:1867-1878.
22. Rodriguez C., Hiligsmann S., Ongena M., Charlier R., Thonart P. Development of an enzymatic assay for the determination of cellulose bioavailability in municipal solid waste. Biodegradation 2005, 16:415-422.
23. Savitsky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36:1627-1639.
24. Scaglia B., Confalonieri R., D'Imporzano G., Adani F. Estimating biogas production of biologically treated municipal solid waste. Bioresour. Technol. 2010, 101:945-952.
25. Schievano A., Scaglia B., D'Imporzano G., Malagutti L., Gozzi A., Adani F. Prediction of biogas potentials using quick laboratory analyses: upgrading previous models for application to heterogeneous organic matrices. Bioresour. Technol. 2009, 100:5777-5782.
26. Stuth J., Jama A., Tolleson D. Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field crops res. 2003, 84:45-56.
27. Van Der Voet H. Pseudo-degrees of freedom for complex predictive models: the example of partial least square. J. Chemometrics 1999, 13:195-208.
28. Van Soest P.J., Robertson J.B., Lewis B.A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 1991, 74:3583-3597.
29. VIM - International Vocabulary of Metrology - Basic and General Concepts and Associated Terms. JCGM/WG 2 Document N318, 3rd ed., Final Draft 2006-08-01.
30. Viscarra Rossel R.A., Walvoort D.J.J., McBratney A.B., Janik L.J., Skjemstad J.O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006, 131:59-75.
31. Widmer-Girod C., Staub C. Measurement uncertainty: to be defined in forensic toxicology. Ann. Toxicol. Anal. 2004, 16:215-219.
32. Williams P.C., Sobering D.C. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J. Near Infrared Spectrosc. 1993, 1:25-32.
33. Williams P., Noris K. Near-Infrared Technology in the Agricultural and Food Industries 2001, Amer Assn of Cereal Chemists, St. Paul, Minnesota, USA. 2nd Ed.
34. Zeaiter M., Roger J.M., Bellon-Maurel V. Robustness of models developed by multivariate calibration. Part II: the influence of pre-processing methods. TrAC, Trends Anal. Chem. 2005, 24:437-445.
35. Zhang L., Garcia-Munoz S. A comparison of different methods to estimate prediction uncertainty using Partial Least Squares (PLS): a practitioner's perspective. Chemometr. Intell. Lab. Syst. 2009, 97:152-158.