• International Journal of Technology (IJTech)
  • Vol 17, No 2 (2026)

Low-Cost Near-Infrared Spectroscopy for Rapid Prediction of Biodiesel Properties: Acid Value, Density, Viscosity, and Water Content

Low-Cost Near-Infrared Spectroscopy for Rapid Prediction of Biodiesel Properties: Acid Value, Density, Viscosity, and Water Content

Title: Low-Cost Near-Infrared Spectroscopy for Rapid Prediction of Biodiesel Properties: Acid Value, Density, Viscosity, and Water Content
Kittisak Phetpan, Chitwadee Thongphut, Thatchapol Chungcharoen, Warunee Limmun

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Cite this article as:
Phetpan, K., Thongphut, C., Chungcharoen, T., & Limmun, W. (2026). Low-cost near-infrared spectroscopy for rapid prediction of biodiesel properties: Acid value, density, viscosity, and water content. International Journal of Technology, 17 (2), 422–439


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Kittisak Phetpan Department of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus, Chumphon 86160, Thailand
Chitwadee Thongphut Department of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus, Chumphon 86160, Thailand
Thatchapol Chungcharoen Department of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus, Chumphon 86160, Thailand
Warunee Limmun Department of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus, Chumphon 86160, Thailand
Email to Corresponding Author

Abstract
Low-Cost Near-Infrared Spectroscopy for Rapid Prediction of Biodiesel Properties: Acid Value, Density, Viscosity, and Water Content

Partial least squares (PLS) regression, combined with various spectral pre-processing techniques, was employed to compare the performance of two diode array near-infrared (NIR) spectrometers in predicting key biodiesel quality parameters, including acidity, viscosity, density, and water content. An AvaSpec-Mini4096CL NIR spectrometer, operating within the 350–1100 nm wavelength range, was used as the representative shortwave near-infrared (SW-NIR) spectrometer, while a NIRQuest512 spectrometer, covering the 900–1700 nm range, was employed as the longwave near-infrared (LW-NIR) spectrometer. Both spectrometers were equipped with a transflection probe for spectral collection from oil palm-based biodiesel samples. The SW-NIR spectrometer outperformed the LW-NIR spectrometer. The optimal PLS models achieved root mean square errors of prediction (RMSEP) of 0.0037 mg KOH/g for acidity, 0.062 cSt (mm2/s) for viscosity, 2.67 kg/m3 for density, and 59.14 mg/kg for water content, highlighting the potential of compact SW-NIR spectrometers as effective, low-cost tools for rapid biodiesel quality monitoring.

Biodiesel properties; Diode array spectrometer; Multivariate calibration; Near- infrared spectroscopy (NIRS)

References

Abreu, F., Lima, D., Hamú, E., Wolf, C., & Suarez, P. (2004). Utilization of metal complexes as catalysts in the transesterification of Brazilian vegetable oils with different alcohols. Journal of Molecular Catalysis A: Chemical, 209, 29–33. https://doi.org/10.1016/j.molcata.2003.08.003

Balabin, R., Lomakina, E., & Safieva, R. (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, 2007–2015. https://doi.org/10.1016/j.fuel.2010.11.038

Balabin, R., & Safieva, R. (2007). Capabilities of near infrared spectroscopy for the determination of petroleum macromolecule content in aromatic solutions. Journal of Near Infrared Spectroscopy, 15, 343–349. https://doi.org/10.1255/jnirs.749

Balabin, R., & Smirnov, S. (2011). Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data. Analytica Chimica Acta, 692, 63–72. https://doi.org/10.1016/j.aca.2011.03.006

Baptista, P., Felizardo, P., Menezes, J., & Correia, M. (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, 144–151. https://doi.org/10.1016/j.talanta.2008.06.001

Barra, I., Kharbach, M., Qannari, E., Hanafi, M., Cherrah, Y., & Bouklouze, A. (2020). Predicting cetane number in diesel fuels using FTIR spectroscopy and PLS regression. Vibrational Spectroscopy, 111, 103157. https://doi.org/10.1016/j.vibspec.2020.103157

Berchmans, H., & Hirata, S. (2008). Biodiesel production from crude Jatropha curcas L. seed oil with a high content of free fatty acids. Bioresource Technology, 99, 1716–1721. https://doi.org/10.1016/j.biortech.2007.03.051

Bournay, L., Casanave, D., Delfort, B., Hillion, G., & Chodorge, J. (2005). New heterogeneous process for biodiesel production: A way to improve the quality and the value of the crude glycerin produced by biodiesel plants. Catalysis Today, 106, 190–192. https://doi.org/10.1016/j.cattod.2005.07.181

Bukkarapu, K. R., & Krishnasamy, A. (2022). Predicting engine fuel properties of biodiesel and biodiesel–diesel blends using spectroscopy-based approach. Fuel Processing Technology, 230, 107227. https://doi.org/10.1016/j.fuproc.2022.107227

Cunha, C., Luna, A., Oliveira, R., Xavier, G., Paredes, M., & Torres, A. (2017). Predicting the properties of biodiesel and its blends using mid-FTIR spectroscopy and first-order multivariate calibration. Fuel, 204, 185–194. https://doi.org/10.1016/j.fuel.2017.05.057

Cunha, C., Torres, A., & Luna, A. (2020). Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends. Fuel, 261, 116344. https://doi.org/10.1016/j.fuel.2019.116344

Davies, A. (2007). Back to basics: Spectral pre-treatments, derivatives. Tony Davies Column, 19, 32–33.

Delfino, J., Pereira, T., Viegas, H., Marques, E., Ferreira, A., Zhang, L., Zhang, J., & Marques, A. (2018). A simple and fast method to determine water content in biodiesel by electrochemical impedance spectroscopy. Talanta, 179, 753–759. https://doi.org/10.1016/j.talanta.2017.11.053

Fearn, T. (2000). On orthogonal signal correction. Chemometrics and Intelligent Laboratory Systems, 50, 47–52. https://doi.org/10.1016/S0169-7439(99)00045-3

Felizardo, P., Baptista, P., Menezes, J., & Correia, M. (2007). Multivariate near infrared spectroscopy models for predicting methanol and water content in biodiesel. Analytica Chimica Acta, 595, 107–113. https://doi.org/10.1016/j.aca.2007.02.050

Feng, X., Yu, C., Shu, Z., Liu, X., Yan, W., Zheng, Q., Sheng, K., & He, Y. (2018). Rapid and non-destructive measurement of biofuel pellet quality indices based on two-dimensional near infrared spectroscopic imaging. Fuel, 228, 197–205. https://doi.org/10.1016/j.fuel.2018.04.149

Hoang, A. (2021). Prediction of the density and viscosity of biodiesel and the influence of biodiesel properties on a diesel engine fuel supply system. Journal of Marine Engineering and Technology, 20, 299–311. https://doi.org/10.1080/20464177.2018.1532734

Hradecká, I., Vráblík, A., Fratczak, J., Sharkov, N., ?erný, R., & Hönig, V. (2023). Near-infrared spectroscopy as a tool for simultaneous determination of diesel fuel improvers. ACS Omega, 8, 4038–4045. https://doi.org/10.1021/acsomega.2c06845

Knothe, G. (2005). Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters. Fuel Processing Technology, 86(10), 1059–1070. https://doi.org/10.1016/j.fuproc.2004.11.002

Kumbhar, V., Pandey, A., Sonawane, R., El-Shafay, S., Panchal, H., & Chamkha, A. (2022). Statistical analysis on prediction of biodiesel properties from its fatty acid composition. Case Studies in Thermal Engineering, 30, 101775. https://doi.org/10.1016/j.csite.2022.101775

Kuriyama, K., Kaba, Y., Saitoh, H., Bannu, M., Manago, N., Harayama, Y., Osa, K., Yamamoto, M., & Kuze, H. (2011). Visible and near-infrared differential optical absorption spectroscopy (DOAS) for the measurement of nitrogen dioxide, carbon dioxide and water vapor. International Journal of Technology, 2(2), 94–101. https://doi.org/10.14716/ijtech.v2i2.54

Laref, R., Ahmadou, D., Losson, E., & Siadat, M. (2017). Orthogonal signal correction to improve stability regression model in gas sensor systems. Journal of Sensors, 1–8. https://doi.org/10.1155/2017/9851406

Liu, X., Piao, X., Wang, Y., Zhu, S., & He, H. (2008). Calcium methoxide as a solid base catalyst for the transesterification of soybean oil to biodiesel with methanol. Fuel, 87, 1076–1082. https://doi.org/10.1016/j.fuel.2007.05.059

Meher, L., Sagar, D., & Naik, S. (2006). Technical aspects of biodiesel production by transesterification—a review. Renewable and Sustainable Energy Reviews, 10, 248–268. https://doi.org/10.1016/j.rser.2004.09.002

Mishra, P., Gupta, A., Kumar, A., & Bose, A. (2020). Methanol and petrol blended alternate fuel for future sustainable engine: A performance and emission analysis. Measurement, 155, 107519. https://doi.org/10.1016/j.measurement.2020.107519

Mishra, P., Marini, F., Biancolillo, A., & Roger, J. (2021). Improved prediction of fuel properties with near-infrared spectroscopy using a complementary sequential fusion of scatter correction techniques. Talanta, 223, 121693. https://doi.org/10.1016/j.talanta.2020.121693

Moghaddam, H., Tamiji, Z., Lakeh, M., Khoshayand, M., & Mahmoodi, M. (2022). Multivariate analysis of food fraud: A review of NIR-based instruments in tandem with chemometrics. Journal of Food Composition and Analysis, 107, 104343. https://doi.org/10.1016/j.jfca.2021.104343

Monteiro, M., Ambrozin, A., Santos, M., Boffo, E., Pereira-Filho, E., Lião, L., & Ferreira, A. (2009). Evaluation of biodiesel–diesel blends quality using ¹H NMR and chemometrics. Talanta, 78, 660–664. https://doi.org/10.1016/j.talanta.2008.12.026

Moreira, S., Sarraguça, J., Saraiva, D., Carvalho, R., & Lopes, J. (2015). Optimization of NIR spectroscopy-based PLSR models for critical properties of vegetable oils used in biodiesel production. Fuel, 150, 697–704. https://doi.org/10.1016/j.fuel.2015.02.082

Naes, T., Isaksson, T., Fearn, T., & Davies, T. (2002). A user-friendly guide to multivariate calibration and classification. NIR Publications.

Naghipour, M., Ling, L. S., & Connie, T. (2024). A review of AI techniques in fruit detection and classification: Analyzing data, features and AI models used in agricultural industry. International Journal of Technology, 15(3), 585–596. https://doi.org/10.14716/ijtech.v15i3.6404

Oliveira, F., Brandao, C., Ramalho, H., Costa, L., Suarez, P., & Rubim, J. (2007). Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near-infrared spectrometry and FT-Raman spectroscopy. Analytica Chimica Acta, 587, 194–199. https://doi.org/10.1016/j.aca.2007.01.045

Otto, M. (2017). Chemometrics: Statistics and computer application in analytical chemistry (3rd ed.). Wiley-VCH.

Phetpan, K., Udompetaikul, V., & Sirisomboon, P. (2018). An online visible and near-infrared spectroscopic technique for the real-time evaluation of the soluble solids content of sugarcane billets on an elevator conveyor. Computers and Electronics in Agriculture, 154, 406–466. https://doi.org/10.1016/j.compag.2018.09.033

Puttipipatkajorn, A., Terdwongworakul, A., Puttipipatkajorn, A., Kulmutiwat, S., Sangwanangkul, P., & Cheepsomsong, T. (2023). Indirect prediction of dry matter in durian pulp with combined features using miniature NIR spectrophotometer. IEEE Access, 11, 84810–84821. https://doi.org/10.1109/ACCESS.2023.3303020

Rashid, U., & Anwar, F. (2008). Production of biodiesel through optimized alkaline-catalyzed transesterification of rapeseed oil. Fuel, 87, 265–273. https://doi.org/10.1016/j.fuel.2007.05.003

Ruttanadech, N., Phetpan, K., Srisang, N., Srisang, S., Chungcharoen, T., Limmun, W., Youryon, P., & Kongtragoul, P. (2023). Rapid and accurate classification of Aspergillus ochraceous contamination in robusta green coffee bean through near-infrared spectral analysis using machine learning. Food Control, 145, 109446. https://doi.org/10.1016/j.foodcont.2022.109446

Sajjadi, B., Asaithambi, P., Raman, A., & Ibrahim, S. (2017). Hybrid neuro-fuzzy methods for estimation of ultrasound and mechanically stirring influences on biodiesel synthesis through transesterification. Measurement, 103, 62–76. https://doi.org/10.1016/j.measurement.2017.01.044

Sarin, R., Sharma, M., Sinharay, S., & Malhotra, R. (2007). Jatropha–palm biodiesel blends: An optimum mix for Asia. Fuel, 86, 1365–1371. https://doi.org/10.1016/j.fuel.2006.11.040

Udompetaikul, V., Phetpan, K., & Sirisomboon, P. (2021). Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor. Measurement, 167, 107898. https://doi.org/10.1016/j.measurement.2020.107898

Wahyono, Y., Hadiyanto, Budihardjo, M. A., Hariyono, Y., & Baihaqi, R. A. (2022). Multi-feedstock biodiesel production from a blend of five oils through transesterification with variation of moles ratio of oil:methanol. International Journal of Technology, 13(3), 606–618. https://doi.org/10.14716/ijtech.v13i3.4804

Walsh, K. B., Blasco, J., Zude-Sasse, M., & Sun, X. (2020). Visible-NIR point spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biology and Technology, 168, 111246. https://doi.org/10.1016/j.postharvbio.2020.111246

Williams, P. (2019). Near-infrared technology: Getting the best out of light. African Sun Media.

Yang, H., Griffiths, P., & Tate, J. (2003). Comparison of partial least squares regression and multi-layer neural networks for quantification of nonlinear systems and application to gas phase Fourier transform infrared spectra. Analytica Chimica Acta, 498, 125–136. https://doi.org/10.1016/S0003-2670(03)00726-8