Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes: A review

References

• Dahlquist, E. (2013) Technologies for converting biomass to useful energy: Combustion, gasification, pyrolysis, torrefaction and fermentation. Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Bakeev, K. A. (2010) Process analytical technology: Spectroscopic tools and implementation strategies for the chemical and pharmaceutical industries. Chichester, West Sussex, UK: John Wiley & Sons.
   Google Scholar
• Chen, Z., Lovett, D., and Morris, J. (2011) Process analytical technologies and real time process control a review of some spectroscopic issues and challenges. J. Process Control. 21(10): 1467–1482.
   Web of Science ®Google Scholar
• Pomerantsev, A. L., and Rodionova, O. Y. (2012) Process analytical technology: A critical view of the chemometricians. J. Chemom. 26(6): 299–310.
   Web of Science ®Google Scholar
• Roggo, Y., Chalus, P., Maurer, L., Lema-Martinez, C., Edmond, A., and Jent, N. (2007) A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharm. Biomed. Anal. 44(3 SPEC. ISS.): 683–700.
   PubMed Web of Science ®Google Scholar
• Reich, G. (2005) Near-infrared spectroscopy and imaging: Basic principles and pharmaceutical applications. Adv. Drug Deliv. Rev. 57(8): 1109–1143.
   PubMed Web of Science ®Google Scholar
• Luypaert, J., Massart, D. L., and Vander Heyden, Y. (2007) Near-infrared spectroscopy applications in pharmaceutical analysis. Talanta 72(3): 865–883.
   PubMed Web of Science ®Google Scholar
• Huang, H., Yu, H., Xu, H., and Ying, Y. (2008) Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review. J. Food Eng. 87(3): 303–313.
   Web of Science ®Google Scholar
• Cen, H., and He, Y. (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci. Technol. 18(2): 72–83.
   Web of Science ®Google Scholar
• Jimaré Benito, M. T., Bosch Ojeda, C., and Sanchez Rojas, F. (2008) Process analytical chemistry: Applications of near infrared spectrometry in environmental and food analysis: An overview. Appl. Spectrosc. Rev. 43(5): 452–484.
   Web of Science ®Google Scholar
• Tsuchikawa, S. (2007) A review of recent near infrared research for wood and paper. Appl. Spectrosc. Rev. 42(1): 43–71.
   Web of Science ®Google Scholar
• Tsuchikawa, S., and Schwanninger, M. (2013) A review of recent near-infrared research for wood and paper (part 2). Appl. Spectrosc. Rev. 48(7): 560–587.
   Web of Science ®Google Scholar
• Tsuchikawa, S., and Kobori, H. (2015) A review of recent application of near infrared spectroscopy to wood science and technology. J. Wood Sci. 61(3): 213–220.
   Web of Science ®Google Scholar
• Xu, F., Yu, J., Tesso, T., Dowell, F., and Wang, D. (2013) Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review. Appl. Energy. 104: 801–809.
   Web of Science ®Google Scholar
• Lupoi, J. S., Singh, S., Simmons, B. A., and Henry, R. J. (2014) Assessment of lignocellulosic biomass using analytical spectroscopy: An evolution to high-throughput techniques. Bioenergy Res. 7(1): 1–23.
   Web of Science ®Google Scholar
• Chadwick, D. T., McDonnell, K. P., Brennan, L. P., Fagan, C. C., and Everard, C. D. (2014) Evaluation of infrared techniques for the assessment of biomass and biofuel quality parameters and conversion technology processes: A review. Renew. Sustain. Energy Rev. 30: 672–681.
   Web of Science ®Google Scholar
• Garfield, E. (1994) The Thomson Reuters impact factor. Curr. Contents (20 June): 1–6.
   Google Scholar
• Mañana-Rodríguez, J. (2014) A critical review of SCImago journal & country rank. Res. Eval. 24: 343–354.
   Web of Science ®Google Scholar
• Siesler, H. W., Ozaki, Y., Kawata, S., and Heise, H. M. Near-infrared spectroscopy: Principles, instruments, applications. Weinheim, Germany: WILEY-VCH Verlag GmbH.
   Google Scholar
• Burns, D. A., and Ciurczak, E. W. (2007) Handbook of near-infrared analysis. Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Griffiths, P. R. (2006) Introduction to vibrational spectroscopy. In: Griffiths, P. R. and Chalmers, J. M (Eds.), Handbook of Vibrational Spectroscopy, Vol. 1, pp. 1–11. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Pasquini, C. (2003) Near infrared spectroscopy: Fundamentals, practical aspects and analytical applications. J. Braz. Chem. Soc. 14(2): 198–219.
   Web of Science ®Google Scholar
• Blanco, M., and Villarroya, I. (2002) NIR spectroscopy: A rapid-response analytical tool. TrAC - Trends Anal. Chem. 21(4): 240–250.
   Web of Science ®Google Scholar
• Workman, J., and Weyer, L. (2007) Practical guide and spectral atlas for interpretive near-infrared spectroscopy. Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Schwanninger, M., Rodrigues, J. C., and Fackler, K. (2011) A review of band assignments in near infrared spectra of wood and wood components. J. Near Infrared Spectrosc. 19(5): 287–308.
   Web of Science ®Google Scholar
• Mark, H., and Workman Jr., J. (2010) Chemometrics in spectroscopy. Amsterdam, Netherlands: Academic Press, Elsevier.
   Google Scholar
• Miller, J. N., and Miller, J. C. (2005) Statistics and chemometrics for analytical chemistry. Harlow, UK: Pearson Education.
   Google Scholar
• Tauler, R., Walczak, B., and Brown, S. D. (2009) Comprehensive chemometrics: Chemical and biochemical data analysis. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Gemperline, P. (2006) Practical guide to chemometrics. Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Adams, M. J. (2004) Chemometrics in analytical spectroscopy. Cambridge, UK: Royal Society of Chemistry.
   Google Scholar
• Varmuza, K. (2012) Chemometrics in practical applications. Rijeka, Croatia: InTech.
   Google Scholar
• Kumar, N., Bansal, A., Sarma, G. S., and Rawal, R. K. (2014) Chemometrics tools used in analytical chemistry: An overview. Talanta 123: 186–199.
   PubMed Web of Science ®Google Scholar
• Lavine, B. K., and Workman Jr., J. (2012) Chemometrics. Anal. Chem. 85(2): 705–714.
   PubMed Web of Science ®Google Scholar
• Geladi, P. (2003) Chemometrics in spectroscopy. Part 1. Classical chemometrics. Spectrochim. Acta - Part B At. Spectrosc. 58(5): 767–782.
   Web of Science ®Google Scholar
• Geladi, P., Sethson, B., Nyström, J., Lillhonga, T., Lestander, T., and Burger, J. (2004) Chemometrics in spectroscopy: Part 2. Examples. Spectrochim. Acta - Part B At. Spectrosc. 59(9): 1347–1357.
   Web of Science ®Google Scholar
• Rinnan, Å., van den Berg, F., and Engelsen, S. B. (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC - Trends Anal. Chem. 28(10): 1201–1222.
   Web of Science ®Google Scholar
• Panero, P. D. S., Panero, F. D. S., Panero, J. D. S., and da SilVa, H. E. B. (2013) Application of extended multiplicative signal correction to short-wavelength near infrared spectra of moisture in marzipan. J. Data Anal. Inf. Process. 1: 30–34.
   Google Scholar
• Wold, S., Antti, H., Lindgren, F., and Öhman, J. (1998) Orthogonal signal correction of near-infrared spectra. Chemom. Intell. Lab. Syst. 44(1–2): 175–185.
   Web of Science ®Google Scholar
• Blanco, M., Coello, J., Montoliu, I., and Romero, M. A. (2001) Orthogonal signal correction in near infrared calibration. Anal. Chim. Acta 434(1): 125–132.
   Web of Science ®Google Scholar
• Fearn, T., Riccioli, C., Garrido-Varo, A., and Guerrero-Ginel, J. E. (2009) On the geometry of SNV and MSC. Chemom. Intell. Lab. Syst. 96(1): 22–26.
   Web of Science ®Google Scholar
• Czarnecki, M. a. (2015) Resolution enhancement in second-derivative spectra. Appl. Spectrosc. 69(1): 67–74.
   PubMed Web of Science ®Google Scholar
• Rinnan, Å. (2014) Pre-processing in vibrational spectroscopy–when, why and how. Anal. Methods 6(18): 7124–7129.
   Web of Science ®Google Scholar
• Zeaiter, M., Roger, J. M., Bellon-Maurel, V., and Rutledge, D. N. (2004) Robustness of models developed by multivariate calibration. Part I: The assessment of robustness. TrAC - Trends Anal. Chem. 23(2): 157–170.
   Google Scholar
• Zeaiter, M., Roger, J. M., and Bellon-Maurel, V. (2005) Robustness of models developed by multivariate calibration. Part II: The influence of pre-processing methods. TrAC - Trends Anal. Chem. 24(5): 437–445.
   Web of Science ®Google Scholar
• Rajalahti, T., and Kvalheim, O. M. (2011) Multivariate data analysis in pharmaceutics: A tutorial review. Int. J. Pharm. 417(1–2): 280–290.
   PubMed Web of Science ®Google Scholar
• Via, B. K., Zhou, C., Acquah, G., Jiang, W., and Eckhardt, L. (2014) Near infrared spectroscopy calibration for wood chemistry: Which chemometric technique is best for prediction and interpretation? Sensors (Switzerland) 14(8): 13532–13547.
   PubMed Web of Science ®Google Scholar
• Ergon, R. (2002) PLS score-loading correspondence and a bi-orthogonal factorization. J. Chemom. 16(7): 368–373.
   Web of Science ®Google Scholar
• Labbé, N., Lee, S. H., Cho, H. W., Jeong, M. K., and André, N. (2008) Enhanced discrimination and calibration of biomass NIR spectral data using non-linear kernel methods. Bioresour. Technol. 99(17): 8445–8452.
   PubMed Web of Science ®Google Scholar
• Balabin, R. M., Lomakina, E. I., and Safieva, R. Z. (2011) Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy. Fuel 90(5): 2007–2015.
   Web of Science ®Google Scholar
• Balabin, R. M., and Smirnov, S. V. (2012) Interpolation and extrapolation problems of multivariate regression in analytical chemistry: benchmarking the robustness on near-infrared (NIR) spectroscopy data. Analyst 137(7): 1604–1610.
   PubMed Web of Science ®Google Scholar
• Esbensen, K. H., and Geladi, P. (2010) Principles of proper validation: Use and abuse of re-sampling for validation. J. Chemom. 24(3–4): 168–187.
   Web of Science ®Google Scholar
• Petersen, L., Minkkinen, P., and Esbensen, K. H. (2005) Representative sampling for reliable data analysis: Theory of Sampling. Chemom. Intell. Lab. Syst. 77(1–2 SPEC. ISS.): 261–277.
   Web of Science ®Google Scholar
• Westad, F., and Marini, F. (2015) Validation of chemometric models - A tutorial. Anal. Chim. Acta 893: 14–24.
   PubMed Web of Science ®Google Scholar
• De Carvalho Rocha, W. F., Nogueira, R., and Vaz, B. G. (2012) Validation of model of multivariate calibration: An application to the determination of biodiesel blend levels in diesel by near-infrared spectroscopy. J. Chemom. 26(8–9): 456–461.
   Web of Science ®Google Scholar
• Guimaraes, C. C., Simeone, M. L. F., Parrella, R. A. C., and Sena, M. M. (2014) Use of NIRS to predict composition and bioethanol yield from cell wall structural components of sweet sorghum biomass. Microchem. J. 117: 194–201.
   Web of Science ®Google Scholar
• Abrahamsson, C., Johansson, J., Sparen, A., and Lindgren, F. (2003) Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets. Chemom. Intell. Lab. Syst. 69(1–2): 3–12.
   Web of Science ®Google Scholar
• Forina, M., Lanteri, S., Oliveros, M. C. C., and Millan, C. P. (2004) Selection of useful predictors in multivariate calibration. Anal. Bioanal. Chem. 380: 397–418.
   PubMed Web of Science ®Google Scholar
• Xiaobo, Z., Jiewen, Z., Povey, M. J. W., Holmes, M., and Hanpin, M. (2010) Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 667(1–2): 14–32.
   PubMed Web of Science ®Google Scholar
• Balabin, R. M., and Smirnov, S. V. (2011) Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data. Anal. Chim. Acta 692(1–2): 63–72.
   PubMed Web of Science ®Google Scholar
• Mehmood, T., Liland, K. H., Snipen, L., and Sæbø, S. (2012) A review of variable selection methods in partial least squares regression. Chemom. Intell. Lab. Syst. 118: 62–69.
   Web of Science ®Google Scholar
• Kawata, S. (2008) Instrumentation for near-infrared spectroscopy. In Siesler, H. W., Ozaki, Y., Kawata, S., and Heise, H. M. (Eds.), Near-infrared spectroscopy: principles, instruments, applications, pp. 43–73. Weinheim, Germany: WILEY-VCH Verlag GmbH.
   Google Scholar
• Griffiths, P. R., and de Haseth, J. A. (2007) Fourier transform infrared spectrometry. 2nd ed. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Smith, B. C. (2011) Fundamentals of Fourier transform infrared spectroscopy. Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Fearn, T. (2001) Standardisation and calibration transfer for near infrared instruments: A review. J. Near Infrared Spectrosc. 9(4): 229–244.
   Web of Science ®Google Scholar
• Feudale, R. N., Woody, N. A., Tan, H., Myles, A. J., Brown, S. D., and Ferré, J. (2002) Transfer of multivariate calibration models: A review. Chemom. Intell. Lab. Syst. 64(2): 181–192.
   Web of Science ®Google Scholar
• Xue, J., Yang, Z., Han, L., and Chen, L. (2014) Study of the influence of NIRS acquisition parameters on the spectral repeatability for on-line measurement of crop straw fuel properties. Fuel 117(Pt. B): 1027–1033.
   Web of Science ®Google Scholar
• He, C., Yang, Z., Chen, L., Huang, G., Liao, N., and Han, L. (2014) Influencing factors of on-line measurement of straw-coal blends using near infrared spectroscopy. Trans. Chinese Soc. Agric. Eng. 30(9): 192–200.
   Google Scholar
• Togashi, D., Alvarez-Jubete, L., Rifai, H., Cama-Moncunill, R., Cruise, P., Sullivan, C., and Cullen, P. J. (2015) Evaluation of diffuse reflectance near infrared fibre optical sensors in measurements for chemical identification and quantification for binary granule blends. J. Near Infrared Spectrosc. 23(3): 133–144.
   Web of Science ®Google Scholar
• Rodionova, O. Y., Balyklova, K. S., Titova, A. V., and Pomerantsev, A. L. (2013) The influence of fiber-probe accessories application on the results of near-infrared (NIR) measurements. Appl. Spectrosc. 67(12): 1401–1407.
   PubMed Web of Science ®Google Scholar
• Andersson, M., Svensson, O., Folestad, S., Josefson, M., and Wahlund, K. G. (2005) NIR spectroscopy on moving solids using a scanning grating spectrometer - Impact on multivariate process analysis. Chemom. Intell. Lab. Syst. 75(1): 1–11.
   Web of Science ®Google Scholar
• Berntsson, O., Danielsson, L. G., and Folestad, S. (2001) Characterization of diffuse reflectance fiber probe sampling on moving solids using a Fourier transform near-infrared spectrometer. Anal. Chim. Acta 431(1): 125–131.
   Web of Science ®Google Scholar
• Stelte, W., Sanadi, A. R., Shang, L., Holm, J. K., Ahrenfeldt, J., and Henriksen, U. B. (2012) Recent developments in biomass pelletization - a review. BioResources 7(3): 4451–4490.
   Web of Science ®Google Scholar
• Lestander, T. a., Johnsson, B., and Grothage, M. (2009) NIR techniques create added values for the pellet and biofuel industry. Bioresour. Technol. 100(4): 1589–1594.
   PubMed Web of Science ®Google Scholar
• Lestander, T. A., Finell, M., Samuelsson, R., Arshadi, M., and Thyrel, M. (2012) Industrial scale biofuel pellet production from blends of unbarked softwood and hardwood stems-the effects of raw material composition and moisture content on pellet quality. Fuel Process. Technol. 95: 73–77.
   Web of Science ®Google Scholar
• Everard, C. D., Fagan, C. C., and McDonnell, K. P. (2012) Visible-near infrared spectral sensing coupled with chemometric analysis as a method for on-line prediction of milled biomass composition pre-pelletising. J. Near Infrared Spectrosc. 20(3): 361–369.
   Web of Science ®Google Scholar
• Gillespie, G. D., Everard, C. D., and McDonnell, K. P. (2015) Prediction of biomass pellet quality indices using near infrared spectroscopy. Energy 80: 582–588.
   Web of Science ®Google Scholar
• Sundaram, J., Kandala, C. V. K., Butts, C. L., and Windham, W. R. (2010) Application of NIR reflectance spectroscopy on determination of moisture content of in-shell peanuts: A non-destructive analysis. Trans. ASABE 53(Mc): 183–189.
   Google Scholar
• Lestander, T. A. (2008) Water absorption thermodynamics in single wood pellets modelled by multivariate near-infrared spectroscopy. Holzforschung. 62(4): 429–434.
   Web of Science ®Google Scholar
• Zhang, L., Xu, C., Charles,  , and Champagne, P. (2010) Overview of recent advances in thermo-chemical conversion of biomass. Energy Convers. Manag. 51(5): 969–982.
   Web of Science ®Google Scholar
• Chen, W. H., Peng, J., and Bi, X. T. (2015) A state-of-the-art review of biomass torrefaction, densification and applications. Renew. Sustain. Energy Rev. 44: 847–866.
   Web of Science ®Google Scholar
• McKendry, P. (2002) Energy production from biomass (part 1): Overview of biomass. Bioresource Tech., 83(1): 37–46.
   Google Scholar
• Jenkins, B., Baxter, L., Miles, T., and Miles, T. (1998) Combustion properties of biomass. Fuel Process. Technol. 54(1–3): 17–46.
   Web of Science ®Google Scholar
• Demirbas, A. (2004) Combustion characteristics of different biomass fuels. Prog. Energy Combust. Sci. 30(2): 219–230.
   Web of Science ®Google Scholar
• Nyström, J., and Dahlquist, E. (2004) Methods for determination of moisture content in woodchips for power plants - A review. Fuel 83(7–8): 773–779.
   Web of Science ®Google Scholar
• Lestander, T. A., and Rhén, C. (2005) Multivariate NIR spectroscopy models for moisture, ash and calorific content in biofuels using bi-orthogonal partial least squares regression. Analyst 130(8): 1182–1189.
   PubMed Web of Science ®Google Scholar
• So, C. L., and Eberhardt, T. L. (2010) Chemical and calorific characterisation of longleaf pine using near infrared spectroscopy. J. Near Infrared Spectrosc. 18(6): 417–423.
   Web of Science ®Google Scholar
• Cooper, P.A., Jeremic, D., Radivojevic, S., Ung, Y. T., and Leblon, B. (2011) Potential of near-infrared spectroscopy to characterize wood products. Can. J. For. Res. 41(11): 2120–2140.
   Web of Science ®Google Scholar
• Fagan, C. C., Everard, C. D., and McDonnell, K. (2011) Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy. Bioresour. Technol. 102(8): 5200–5206.
   PubMed Web of Science ®Google Scholar
• Everard, C. D., McDonnell, K. P., and Fagan, C. C. (2012) Prediction of biomass gross calorific values using visible and near infrared spectroscopy. Biomass and Bioenergy 45: 203–211.
   Web of Science ®Google Scholar
• Huang, C., Han, L., Yang, Z., and Liu, X. (2008) Prediction of heating value of straw by proximate data, and near infrared spectroscopy. Energy Convers. Manag. 49(12): 3433–3438.
   Web of Science ®Google Scholar
• Huang, C., Han, L., Yang, Z., and Liu, X. (2009) Ultimate analysis and heating value prediction of straw by near infrared spectroscopy. Waste Manag. 29(6): 1793–1797.
   PubMed Web of Science ®Google Scholar
• He, C., Chen, L., Yang, Z., Huang, G., Liao, N., and Han, L. (2012) A rapid and accurate method for on-line measurement of straw-coal blends using near infrared spectroscopy. Bioresour. Technol. 110: 314–320.
   PubMed Web of Science ®Google Scholar
• Xue, J., Yang, Z., Han, L., Liu, Y. Y., Liu, Y. Y., and Zhou, C. (2015) On-line measurement of proximates and lignocellulose components of corn stover using NIRS. Appl. Energy 137: 18–25.
   Web of Science ®Google Scholar
• Skvaril, J., Kyprianidis, K., Avelin, A., Odlare, M., and Dahlquist, E. (2015) Utilization of near infrared (NIR) spectrometry for detection of glass in the waste-based fuel. Energy Procedia 75: 734–741.
   Google Scholar
• Lestander, T. a., Rudolfsson, M., Pommer, L., and Nordin, A. (2014) NIR provides excellent predictions of properties of biocoal from torrefaction and pyrolysis of biomass. Green Chem. 16(12): 4906–4913.
   Web of Science ®Google Scholar
• Via, B. K., Adhikari, S., and Taylor, S. (2013) Modeling for proximate analysis and heating value of torrefied biomass with vibration spectroscopy. Bioresour. Technol. 133: 1–8.
   PubMed Web of Science ®Google Scholar
• Rousset, P., Davrieux, F., Macedo, L., and Perré, P. (2011) Characterisation of the torrefaction of beech wood using NIRS: Combined effects of temperature and duration. Biomass and Bioenergy 35(3): 1219–1226.
   Web of Science ®Google Scholar
• Andrade, C., Trugilho, P., Gherardi Hein, P., Lima, J., and Napoli, A. (2012) Near infrared spectroscopy for estimating Eucalyptus charcoal properties. J. Near Infrared Spectrosc. 20(6): 657.
   Web of Science ®Google Scholar
• Yang, H., and Sheng, K. (2012) Characterization of biochar properties affected by different pyrolysis temperatures using visible-near-infrared spectroscopy. ISRN Spectrosc. 2012(2012): 1–7.
   Google Scholar
• Eriksson, D., Weiland, F., Hedman, H., Stenberg, M., Öhrman, O., Lestander, T. A., Bergsten, U., Öhman, M. (2012) Characterization of scots pine stump-root biomass as feed-stock for gasification. Bioresour. Technol. 104: 729–736.
   PubMed Web of Science ®Google Scholar
• Preece, S. L. M., Auvermann, B. W., MacDonald, J. C., and Morgan, C. L. S. (2013) Predicting the heating value of solid manure with visible and near-infrared spectroscopy. Fuel 106: 712–717.
   Web of Science ®Google Scholar
• Posom, J., and Sirisomboon, P. (2015) Evaluation of the moisture content of Jatropha curcas kernels and the heating value of the oil-extracted residue using near-infrared spectroscopy. Biosyst. Eng. 130: 52–59.
   Web of Science ®Google Scholar
• Posom, J., and Sirisomboon, P. (2014) Evaluation of the thermal properties of Jatropha curcas L. kernels using near-infrared spectroscopy. Biosyst. Eng. 125: 45–53.
   Web of Science ®Google Scholar
• Mata Sánchez, J., Pérez Jiménez, J. A., Díaz Villanueva, M. J., Serrano, A., Núñez, N., and López Giménez, J. (2015) Assessment of near infrared spectroscopy for energetic characterization of olive byproducts. Renew. Energy 74: 599–605.
   Web of Science ®Google Scholar
• Jensen, P. D., Hartmann, H., Böhm, T., Temmerman, M., Rabier, F., and Morsing, M. (2006) Moisture content determination in solid biofuels by dielectric and NIR reflection methods. Biomass and Bioenergy 30(11): 935–943.
   Web of Science ®Google Scholar
• Sandberg, J., Fdhila, R. B., Dahlquist, E., and Avelin, A. (2011) Dynamic simulation of fouling in a circulating fluidized biomass-fired boiler. Appl. Energy 88(5): 1813–1824.
   Web of Science ®Google Scholar
• Moros, J., Garrigues, S., and Guardia, M. de la. (2010) Vibrational spectroscopy provides a green tool for multi-component analysis. TrAC - Trends Anal. Chem. 29(7): 578–591.
   Web of Science ®Google Scholar
• Cherubini, F. (2010) The biorefinery concept: Using biomass instead of oil for producing energy and chemicals. Energy Convers. Manag. 51(7): 1412–1421.
   Web of Science ®Google Scholar
• Weiland, P. (2010) Biogas production: Current state and perspectives. Appl. Microbiol. Biotechnol. 85(4): 849–860.
   PubMed Web of Science ®Google Scholar
• Dahlquist, E. (2013) Biomass as energy source: Resources, systems and applications. Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Chen, Y., Cheng, J. J., and Creamer, K. S. (2008) Inhibition of anaerobic digestion process: A review. Bioresource Tech., 99(10): 4044–4064.
   Google Scholar
• Mosier, N., Wyman, C., Dale, B., Elander, R., Lee, Y. Y., Holtzapple, M., and Ladisch, M. (2005) Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Tech., 96(6): 673–686.
   Google Scholar
• Hayes, D. J. M. (2012) Development of near infrared spectroscopy models for the quantitative prediction of the lignocellulosic components of wet Miscanthus samples. Bioresour. Technol. 119: 393–405.
   PubMed Web of Science ®Google Scholar
• Hayes, D. J. M., Hayes, M. H. B., and Leahy, J. J. (2015) Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions. Fuel 150: 261–268.
   Web of Science ®Google Scholar
• Nkansah, K., Dawson-Andoh, B., and Slahor, J. (2010) Rapid characterization of biomass using near infrared spectroscopy coupled with multivariate data analysis: Part 1 yellow-poplar (Liriodendron tulipifera L.). Bioresour. Technol. 101(12): 4570–4576.
   PubMed Web of Science ®Google Scholar
• Lestander, T., Geladi, P., Larsson, S., and Thyrel, M. (2012) Near infrared image analysis for online identification and separation of wood chips with elevated levels of extractives. J. Near Infrared Spectrosc. 20(5): 591.
   Web of Science ®Google Scholar
• Philip Ye, X., Liu, L., Hayes, D., Womac, A., Hong, K., and Sokhansanj, S. (2008) Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques. Bioresour. Technol. 99(15): 7323–32.
   PubMed Web of Science ®Google Scholar
• Park, J. I., Liu, L., Philip Ye, X., Jeong, M. K., and Jeong, Y. S. (2012) Improved prediction of biomass composition for switchgrass using reproducing kernel methods with wavelet compressed FT-NIR spectra. Expert Syst. Appl. 39(1): 1555–1564.
   Web of Science ®Google Scholar
• Kelley, S. S., Rowell, R. M., Davis, M., Jurich, C. K., and Ibach, R. (2004) Rapid analysis of the chemical composition of agricultural fibers using near infrared spectroscopy and pyrolysis molecular beam mass spectrometry. Biomass and Bioenergy 27(1): 77–88.
   Web of Science ®Google Scholar
• Lesteur, M., Bellon-Maurel, V., Gonzalez, C., Latrille, E., Roger, J. M., Junqua, G., and Steyer, J. P. (2010) Alternative methods for determining anaerobic biodegradability: A review. Process Biochem. 45(4): 431–440.
   Web of Science ®Google Scholar
• Lesteur, M., Latrille, E., Maurel, V. B., Roger, J. M., Gonzalez, C., Junqua, G., and Steyer, J. P. (2011) First step towards a fast analytical method for the determination of biochemical methane potential of solid wastes by near infrared spectroscopy. Bioresour. Technol. 102(3): 2280–2288.
   PubMed Web of Science ®Google Scholar
• Doublet, J., Boulanger, A., Ponthieux, A., Laroche, C., Poitrenaud, M., and Cacho Rivero, J. A. (2013) Predicting the biochemical methane potential of wide range of organic substrates by near infrared spectroscopy. Bioresour. Technol. 128: 252–258.
   PubMed Web of Science ®Google Scholar
• Triolo, J. M., Ward, A. J., Pedersen, L., Løkke, M. M., Qu, H., and Sommer, S. G. (2014) Near infrared reflectance spectroscopy (NIRS) for rapid determination of biochemical methane potential of plant biomass. Appl. Energy 116: 52–57.
   Web of Science ®Google Scholar
• Godin, B., Mayer, F., Agneessens, R., Gerin, P., Dardenne, P., Delfosse, P., and Delcarte, J. (2015) Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models. Bioresour. Technol. 175: 382–390.
   PubMed Web of Science ®Google Scholar
• Raju, C. S., Ward, A. J., Nielsen, L., and Møller, H. B. (2011) Comparison of near infra-red spectroscopy, neutral detergent fibre assay and in-vitro organic matter digestibility assay for rapid determination of the biochemical methane potential of meadow grasses. Bioresour. Technol. 102(17): 7835–7839.
   PubMed Web of Science ®Google Scholar
• Krongtaew, C., Messner, K., Ters, T., and Fackler, K. (2010) Characterization of key parameters for biotechnological lignocellulose conversion assessed by FT-NIR spectroscopy. Part I: Qualitative analysis of pretreated straw. BioResources 5(4): 2063–2080.
   Web of Science ®Google Scholar
• Krongtaew, C., Messner, K., Ters, T., and Fackler, K. (2010) Characterization of key parameters for biotechnological lignocellulose conversion assessed by Ft-Nir spectroscopy. Part II: quantitative analysis by partial least squares regression. BioResources 5(4): 2081–2096.
   Web of Science ®Google Scholar
• Kandel, T. P., Gislum, R., Jørgensen, U., and Lærke, P. E. (2013) Prediction of biogas yield and its kinetics in reed canary grass using near infrared reflectance spectroscopy and chemometrics. Bioresour. Technol. 146: 282–287.
   PubMed Web of Science ®Google Scholar
• Grieder, C., Mittweg, G., Dhillon, B. S., Montes, J. M., Orsinia, E., and Melchinger, A. E. (2011) Determination of methane fermentation yield and its kinetics by near infrared spectroscopy and chemical composition in maize. J. Near Infrared Spectrosc. 19(6): 463–477.
   Web of Science ®Google Scholar
• Reed, J. P., Devlin, D., Esteves, S. R. R., Dinsdale, R., and Guwy, a. J. (2011) Performance parameter prediction for sewage sludge digesters using reflectance FT-NIR spectroscopy. Water Res. 45(8): 2463–2472.
   PubMed Web of Science ®Google Scholar
• Reed, J. P., Devlin, D., Esteves, S. R. R., Dinsdale, R., and Guwy, A. J. (2013) Integration of NIRS and PCA techniques for the process monitoring of a sewage sludge anaerobic digester. Bioresour. Technol. 133: 398–404.
   PubMed Web of Science ®Google Scholar
• Hotelling, H. (1951) A generalized T test and measure of multivariate dispersion. In Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability. The Regents of the University of California, Berkeley, CA, pp. 23–40.
   Google Scholar
• Jacobi, H. F., Moschner, C. R., and Hartung, E. (2011) Use of near infrared spectroscopy in online-monitoring of feeding substrate quality in anaerobic digestion. Bioresour. Technol. 102(7): 4688–4696.
   PubMed Web of Science ®Google Scholar
• Jacobi, H. F., Ohl, S., Thiessen, E., and Hartung, E. (2012) NIRS-aided monitoring and prediction of biogas yields from maize silage at a full-scale biogas plant applying lumped kinetics. Bioresour. Technol. 103(1): 162–172.
   PubMed Web of Science ®Google Scholar
• Holm-Nielsen, J. B., and Esbensen, K. H. (2011) Monitoring of biogas test plants-a process analytical technology approach. J. Chemom. 25(7): 357–365.
   Web of Science ®Google Scholar
• Holm-Nielsen, J. B., Andree, H., Lindorfer, H., and Esbensen, K. H. (2007) Transflexive embedded near infrared monitoring for key process intermediates in anaerobic digestion/biogas production. J. Near Infrared Spectrosc. 15(2): 123.
   Web of Science ®Google Scholar
• Stockl, A., and Oechsner, H. (2012) Near-infrared spectroscopic online monitoring of process stability in biogas plants. Eng. Life Sci. 12(3): 295–305.
   Web of Science ®Google Scholar
• Lomborg, C. J., Holm-Nielsen, J. B., Oleskowicz-Popiel, P., and Esbensen, K. H. (2009) Near infrared and acoustic chemometrics monitoring of volatile fatty acids and dry matter during co-digestion of manure and maize silage. Bioresour. Technol. 100(5): 1711–1719.
   PubMed Web of Science ®Google Scholar
• Luck, S., Büge, G., Plettenberg, H., and Hoffmann, M. (2010) Near-infrared spectroscopy for process control and optimization of biogas plants. Eng. Life Sci. 10(6): 537–543.
   Web of Science ®Google Scholar
• Wolf, D., von Canstein, H., and Schröder, C. (2011) Optimisation of biogas production by infrared spectroscopy-based process control. J. Nat. Gas Sci. Eng. 3(5): 625–632.
   Web of Science ®Google Scholar
• Ward, A. J., Bruni, E., Lykkegaard, M. K., Feilberg, A., Adamsen, A. P. S., Jensen, A. P., and Poulsen, A. K. (2011) Real time monitoring of a biogas digester with gas chromatography, near-infrared spectroscopy, and membrane-inlet mass spectrometry. Bioresour. Technol. 102(5): 4098–4103.
   PubMed Web of Science ®Google Scholar
• Castillo, R. D. P., Baeza, J., Rubilar, J., Rivera, Á., and Freer, J. (2012) Infrared spectroscopy as alternative to wet chemical analysis to characterize Eucalyptus globulus pulps and predict their ethanol yield for a simultaneous saccharification and fermentation process. Appl. Biochem. Biotechnol. 168(7): 2028–2042.
   PubMed Web of Science ®Google Scholar
• Hou, S., and Li, L. (2011) Rapid characterization of woody biomass digestibility and chemical composition using near-infrared spectroscopy. J. Integr. Plant Biol. 53(2): 166–175.
   PubMed Web of Science ®Google Scholar
• Monrroy, M., Garcia, J. R., Troncoso, E., and Freer, J. (2014) Fourier transformed near infrared (FT-NIR) spectroscopy for the estimation of parameters in pretreated lignocellulosic materials for bioethanol production. J. Chem. Technol. Biotechnol. 90(7): 1281–1289.
   Web of Science ®Google Scholar
• Horikawa, Y., Imai, M., Kanai, K., Imai, T., Watanabe, T., Takabe, K., Kobayashi, Y. and Sugiyama, J. (2015) Line monitoring by near-infrared chemometric technique for potential ethanol production from hydrothermally treated Eucalyptus globulus. Biochem. Eng. J. 97: 65–72.
   Web of Science ®Google Scholar
• Castillo, R. D. P., Parra, C., Troncoso, E., Franco, H., Peña, S., and Freer, J. (2014) NIR spectroscopy applied to the characterization and selection of pre-treated materials from multiple lignocellulosic resources for bioethanol production. J. Chil. Chem. Soc. 59(1): 2347–2352.
   Web of Science ®Google Scholar
• Horikawa, Y., Imai, T., Takada, R., Watanabe, T., Takabe, K., Kobayashi, Y., and Sugiyama, J. (2011) Near-infrared chemometric approach to exhaustive analysis of rice straw pretreated for bioethanol conversion. Appl. Biochem. Biotechnol. 164(2): 194–203.
   PubMed Web of Science ®Google Scholar
• Hattori, T., Murakami, S., Mukai, M., Yamada, T., Hirochika, H., Ike, M., Tokuyasu, K., Suzuki, S., Sakamoto M. and Umezawa T. (2012) Rapid analysis of transgenic rice straw using near-infrared spectroscopy. Plant Biotechnol. 29(4): 359–366.
   Web of Science ®Google Scholar
• Horikawa, Y., Imai, T., Takada, R., Watanabe, T., Takabe, K., Kobayashi, Y., and Sugiyama, J. (2012) Chemometric analysis with near-infrared spectroscopy for chemically pretreated Erianthus toward efficient bioethanol production. Appl. Biochem. Biotechnol. 166(3): 711–721.
   PubMed Web of Science ®Google Scholar
• Lindedam, J., Bruun, S., De Martini, J., Jørgensen, H., Felby, C., Yang, B., Wyman, C. E. and Magid, J. (2010) Near infrared spectroscopy as a screening tool for sugar release and chemical composition of wheat straw. J. Biobased Mater. Bioenergy. 4(4): 378–383.
   Web of Science ®Google Scholar
• Bruun, S., Jensen, J. W., Magid, J., Lindedam, J., and Engelsen, S. B. (2010) Prediction of the degradability and ash content of wheat straw from different cultivars using near infrared spectroscopy. Ind. Crops Prod. 31(2): 321–326.
   Web of Science ®Google Scholar
• Lomborg, C. J., Thomsen, M. H., Jensen, E. S., and Esbensen, K. H. (2010) Power plant intake quantification of wheat straw composition for 2nd generation bioethanol optimization - A near infrared spectroscopy (NIRS) feasibility study. Bioresour. Technol. 101(4): 1199–1205.
   PubMed Web of Science ®Google Scholar
• Jiang, H., Liu, G., Mei, C., Yu, S., Xiao, X., and Ding, Y. (2012) Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm. Spectrochim. Acta - Part A Mol. Biomol. Spectrosc. 97: 277–283.
   PubMed Web of Science ®Google Scholar
• Vogel, K. P., Dien, B. S., Jung, H. G., Casler, M. D., Masterson, S. D., and Mitchell, R. B. (2011) Quantifying actual and theoretical ethanol yields for switchgrass strains using NIRS analyses. Bioenergy Res. 4(2): 96–110.
   Web of Science ®Google Scholar
• Magana, C., Núñez-Sánchez, N., Fernández-Cabanás, V. M., García, P., Serrano, A., and Pérez-Marín, D. (2011) Direct prediction of bioethanol yield in sugar beet pulp using near infrared spectroscopy. Bioresour. Technol. 102(20): 9542–9549.
   PubMed Web of Science ®Google Scholar
• Pohl, F., and Senn, T. (2011) A rapid and sensitive method for the evaluation of cereal grains in bioethanol production using near infrared reflectance spectroscopy. Bioresour. Technol. 102(3): 2834–2841.
   PubMed Web of Science ®Google Scholar
• Liu, L., Ye, X. P., Womac, A. R., and Sokhansanj, S. (2010) Variability of biomass chemical composition and rapid analysis using FT-NIR techniques. Carbohydr. Polym. 81(4): 820–829.
   Web of Science ®Google Scholar
• Liebmann, B., Friedl, A., and Varmuza, K. (2009) Determination of glucose and ethanol in bioethanol production by near infrared spectroscopy and chemometrics. Anal. Chim. Acta 642(1–2): 171–178.
   PubMed Web of Science ®Google Scholar
• Liebmann, B., Friedl, A., and Varmuza, K. (2010) Applicability of near-infrared spectroscopy for process monitoring in bioethanol production. Biochem. Eng. J. 52(2–3): 187–193.
   Web of Science ®Google Scholar
• Hongqiang, L., and Hongzhang, C. (2008) Near-infrared spectroscopy with a fiber-optic probe for state variables determination in solid-state fermentation. Process Biochem. 43(5): 511–516.
   Web of Science ®Google Scholar
• Jiang, H., Liu, G., Mei, C., Yu, S., Xiao, X., and Ding, Y. (2012) Rapid determination of pH in solid-state fermentation of wheat straw by FT-NIR spectroscopy and efficient wavelengths selection. Anal. Bioanal. Chem. 404(2): 603–611.
   PubMed Web of Science ®Google Scholar
• Jiang, H., Liu, G., Xiao, X., Mei, C., Ding, Y., and Yu, S. (2012) Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description. Microchem. J. 102: 68–74.
   Web of Science ®Google Scholar
• Jiang, H., Zhang, H., Chen, Q., Mei, C., and Liu, G. (2015) Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS. Spectrochim. Acta - Part A Mol. Biomol. Spectrosc. 149: 1–7.
   PubMed Web of Science ®Google Scholar
• Jiang, H., and Chen, Q. (2014) Development of electronic nose and near infrared spectroscopy analysis techniques to monitor the critical time in SSF process of feed protein. Sensors (Basel). 14(10): 19441–19456.
   PubMed Web of Science ®Google Scholar
• Wang, J., and Zhang, Y. (2012) Speech characteristic signal recognition based on BP_AdaBoost. In: H. Tan (Ed.), Technology for Education and Learning, pp. 259–264. Berlin, Heidelberg, Germany: Springer.
   Google Scholar
• Wen, Z., Yu, X., Tu, S.-T., Yan, J., and Dahlquist, E. (2010) Biodiesel production from waste cooking oil catalyzed by TiO2-MgO mixed oxides. Bioresour. Technol. 101(24): 9570–9576.
   PubMed Web of Science ®Google Scholar
• Zhang, W. B. (2012) Review on analysis of biodiesel with infrared spectroscopy. Renew. Sustain. Energy Rev. 16(8): 6048–6058.
   Web of Science ®Google Scholar
• Azizian, H., and Kramer, J. K. (2005) A rapid method for the quantification of fatty acids in fats and oils with emphasis on trans fatty acids using Fourier Transform near infrared spectroscopy (FT-NIR). Lipids 40(8): 855–867.
   PubMed Web of Science ®Google Scholar
• Baptista, P., Felizardo, P., Menezes, J. C., and Neiva Correia, M. J. (2008) Monitoring the quality of oils for biodiesel production using multivariate near infrared spectroscopy models. J Near Infrared Spectrosc. 16: 445–454.
   Web of Science ®Google Scholar
• Adewale, P., Mba, O., Dumont, M. J., Ngadi, M., and Cocciardi, R. (2014) Determination of the iodine value and the free fatty acid content of waste animal fat blends using FT-NIR. Vib. Spectrosc. 72: 72–78.
   Web of Science ®Google Scholar
• Moreira, S. A., Sarraguca, J., Saraiva, D. F., Carvalho, R., and Lopes, J. A. (2015) Optimization of NIR spectroscopy based PLSR models for critical properties of vegetable oils used in biodiesel production. Fuel 150: 697–704.
   Web of Science ®Google Scholar
• Richard, R., Li, Y., Dubreuil, B., Thiebaud-Roux, S., and Prat, L. (2011) On-line monitoring of the transesterification reaction between triglycerides and ethanol using near infrared spectroscopy combined with gas chromatography. Bioresour. Technol. 102(12): 6702–6709.
   PubMed Web of Science ®Google Scholar
• Richard, R., Dubreuil, B., Thiebaud-Roux, S., and Prat, L. (2013) On-line monitoring of the transesterification reaction carried out in microreactors using near infrared spectroscopy. Fuel 104: 318–325.
   Web of Science ®Google Scholar
• Killner, M. H. M., Rohwedder, J. J. R., and Pasquini, C. (2011) A PLS regression model using NIR spectroscopy for on-line monitoring of the biodiesel production reaction. Fuel 90(11): 3268–3273.
   Web of Science ®Google Scholar
• De Lima, S. M., Silva, B. F. A., Pontes, D. V., Pereira, C. F., Stragevitch, L., and Pimentel, M. F. (2014) In-line monitoring of the transesterification reactions for biodiesel production using NIR spectroscopy. Fuel 115: 46–53.
   Web of Science ®Google Scholar
• Pinzi, S., Alonso, F., García Olmo, J., and Dorado, M. P. (2012) Near infrared reflectance spectroscopy and multivariate analysis to monitor reaction products during biodiesel production. Fuel 92(1): 354–359.
   Web of Science ®Google Scholar
• Zhang, Y., Dubé, M. A., McLean, D. D. L., and Kates, M. (2003) Biodiesel production from waste cooking oil: 1. Process design and technological assessment. Bioresource Tech., 89(1): 1–16.
   Google Scholar
• Al-Zuhair, S., Dowaidar, A., and Kamal, H. (2009) Dynamic modeling of biodiesel production from simulated waste cooking oil using immobilized lipase. Biochem. Eng. J. 44(2–3): 256–262.
   Web of Science ®Google Scholar
• Tapasvi, D., Wiesenborn, D., and Gustafson, C. (2005) Process model for biodiesel production from various feedstocks. Trans. ASAE 48(6): 2215–2221.
   Google Scholar
• Baptista, P., Felizardo, P., Menezes, J. C., and Neiva Correia, M. J. (2008) Multivariate near infrared spectroscopy models for predicting the iodine value, CFPP, kinematic viscosity at 40°C and density at 15°C of biodiesel. Talanta 77(1): 144–151.
   PubMed Web of Science ®Google Scholar
• Balabin, R. M., and Safieva, R. Z. (2011) Near-infrared (NIR) spectroscopy for biodiesel analysis: Fractional composition, iodine value, and cold filter plugging point from one vibrational spectrum. Energy and Fuels 25(5): 2373–2382.
   Web of Science ®Google Scholar
• Coronado, M., Yuan, W., Wang, D., and Dowell, F. E. (2009) Predicting the concentration and specific gravity of biodiesel-diesel blends using near-infrared spectroscopy. Appl. Eng. Agric. 25(2): 217–221.
   Web of Science ®Google Scholar
• Canha, N., Felizardo, P., Menezes, J. C., and Joana Neiva Correia, M. (2012) Multivariate near infrared spectroscopy models for predicting the oxidative stability of biodiesel: Effect of antioxidants addition. Fuel 97: 352–357.
   Web of Science ®Google Scholar
• Vieira, F. S., and Pasquini, C. (2014) Determination of the oxidative stability of biodiesel using near infrared emission spectroscopy. Fuel 117(PARTB): 1004–1009.
   Web of Science ®Google Scholar
• Alves, J. C. L., and Poppi, R. J. (2013) Biodiesel content determination in diesel fuel blends using near infrared (NIR) spectroscopy and support vector machines (SVM). Talanta 104: 155–161.
   PubMed Web of Science ®Google Scholar
• Alves, J. C. L., and Poppi, R. J. (2013) Simultaneous determination of hydrocarbon renewable diesel, biodiesel and petroleum diesel contents in diesel fuel blends using near infrared (NIR) spectroscopy and chemometrics. Analyst 138(21): 6477–6487.
   PubMed Web of Science ®Google Scholar
• Alves, J. C. L., and Poppi, R. J. (2016) Quantification of conventional and advanced biofuels contents in diesel fuel blends using near-infrared spectroscopy and multivariate calibration. Fuel 165: 379–388.
   Web of Science ®Google Scholar
• Filgueiras, P. R., Alves, J. C. L., and Poppi, R. J. (2014) Quantification of animal fat biodiesel in soybean biodiesel and B20 diesel blends using near infrared spectroscopy and synergy interval support vector regression. Talanta 119: 582–589.
   PubMed Web of Science ®Google Scholar
• Felizardo, P., Baptista, P., Menezes, J. C., and Correia, M. J. N. (2007) Multivariate near infrared spectroscopy models for predicting methanol and water content in biodiesel. Anal. Chim. Acta 595(1–2 SPEC. ISS.): 107–113.
   PubMed Web of Science ®Google Scholar
• Bampi, M., Scheer, A. D. P., and De Castilhos, F. (2013) Application of near infrared spectroscopy to predict the average droplet size and water content in biodiesel emulsions. Fuel 113: 546–552.
   Web of Science ®Google Scholar
• Paiva, E. M., Rohwedder, J. J. R., Pasquini, C., Pimentel, M. F., and Pereira, C. F. (2015) Quantification of biodiesel and adulteration with vegetable oils in diesel/biodiesel blends using portable near-infrared spectrometer. Fuel 160: 57–63.
   Web of Science ®Google Scholar
• Balabin, R. M., and Safieva, R. Z. (2011) Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data. Anal. Chim. Acta 689(2): 190–197.
   PubMed Web of Science ®Google Scholar
• Veras, G., Gomes, A. D. A., Da Silva, A. C., De Brito, A. L. B., De Almeida, P. B. A., and De Medeiros, E. P. (2010) Classification of biodiesel using NIR spectrometry and multivariate techniques. Talanta 83(2): 565–568.
   PubMed Web of Science ®Google Scholar
• Insausti, M., Gomes, A. A., Cruz, F. V., Pistonesi, M. F., Araujo, M. C. U., Galvão, R. K. H., Pereira, C. F. and Band, B. S. (2012) Screening analysis of biodiesel feedstock using UV-vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm. Talanta 97: 579–583.
   PubMed Web of Science ®Google Scholar
• Pontes, M. J. C., Pereira, C. F., Pimentel, M. F., Vasconcelos, F. V. C., and Silva, A. G. B. (2011) Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification. Talanta 85(4): 2159–2165.
   PubMed Web of Science ®Google Scholar
• Silva, G. W. B., Gomes, A. A., Da Silva, P., Costa, G. B., Fernandes, D. D. S., Fontes, M. M., and Veras, G. (2012) Biodiesel/diesel blends classification with respect to base oil using NIR spectrometry and chemometrics tools. JAOCS, J. Am. Oil Chem. Soc. 89(7): 1165–1171.
   Web of Science ®Google Scholar
• de Oliveira, I. K., de Carvalho Rocha, W. F., and Poppi, R. J. (2009) Application of near infrared spectroscopy and multivariate control charts for monitoring biodiesel blends. Anal. Chim. Acta 642(1–2): 217–221.
   PubMed Web of Science ®Google Scholar
• De Oliveira, R. R., De Lima, K. M. G., De Juan, A., and Tauler, R. (2014) Application of correlation constrained multivariate curve resolution alternating least-squares methods for determination of compounds of interest in biodiesel blends using NIR and UV-visible spectroscopic data. Talanta 125: 233–241.
   PubMed Web of Science ®Google Scholar
• de Lira, L. F. B., de Albuquerque, M. S., Pacheco, J. G. A., Fonseca, T. M., Cavalcanti, E. H., de, S., Stragevitch, L., and Pimentel, M. F. (2010) Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel. Microchem. J. 96(1): 126–131.
   Web of Science ®Google Scholar
• Zhang, W., Yuan, W., Zhang, X., and Coronado, M. (2012) Predicting the dynamic and kinematic viscosities of biodiesel-diesel blends using mid- and near-infrared spectroscopy. Appl. Energy 98: 122–127.
   Web of Science ®Google Scholar
• de Vasconcelos, F. V. C., de Souza, P. F. B., Pimentel, M. F., Pontes, M. J. C., and Pereira, C. F. (2012) Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils. Anal. Chim. Acta 716: 101–107.
   PubMed Web of Science ®Google Scholar
• Gaydou, V., Kister, J., and Dupuy, N. (2011) Evaluation of multiblock NIR/MIR PLS predictive models to detect adulteration of diesel/biodiesel blends by vegetal oil. Chemom. Intell. Lab. Syst. 106(2): 190–197.
   Web of Science ®Google Scholar
• Oliveira, J. S., Montalvão, R., Daher, L., Suarez, P. A. Z., and Rubim, J. C. (2006) Determination of methyl ester contents in biodiesel blends by FTIR-ATR and FTNIR spectroscopies. Talanta 69(5): 1278–1284.
   PubMed Web of Science ®Google Scholar
• Fernandes, D. D. S., Gomes, A. A., Costa, G. B., Da Silva, G. W. B. D., and Véras, G. (2011) Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection. Talanta 87(1): 30–34.
   PubMed Web of Science ®Google Scholar
• Pilar Dorado, M., Pinzi, S., De Haro, A., Font, R., and Garcia-Olmo, J. (2011) Visible and NIR Spectroscopy to assess biodiesel quality: Determination of alcohol and glycerol traces. Fuel 90(6): 2321–2325.
   Web of Science ®Google Scholar
• Oliveira, F. C. C., Brandao, C. R., Ramalho, H. F., da Costa, L. A., Suarez, P. A., and Rubim, J. C. (2007) Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy. Anal. Chim. Acta 587(2): 194–199.
   PubMed Web of Science ®Google Scholar
• Santos, V. O., Oliveira, F. C. C., Lima, D. G., Petry, A. C., Garcia, E., Suarez, P. A. Z., and Rubim, J. C. (2005) A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis. Anal. Chim. Acta 547(2): 188–196.
   Web of Science ®Google Scholar