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Biorefining and biorefineries are the future of industry and energy. It is still a long way to complete its implementation, but small biorefineries focused mainly on the production of fuels and energy are more and more frequent in rural areas and large areas located near big cities in which, in addition to fuels and energy, various organic substances of high market value are also produced. In order to optimize biogas production and to control methane fermentation processes, fast and accurate identification of carboxylic acid concentrations, including propionic acid as a precursor to acetic acid, is needed. In this study, a process quality control method was developed to evaluate the propionic acid content of an aqueous solution from the fermentation mass. The proposed methodology is based on near infrared spectroscopy with multivariate analysis and stochastic metamodeling with a denoising procedure based on proper orthogonal decomposition (POD). The proposed methodology uses the Bayesian theory, which provides additional information on the magnitude of the correlation between state and control variables. The calibration model was, therefore, constructed by using Gaussian Processes (GP) to predict propionic acid content in the aqueous solution using an NIR-Vis spectrophotometer. The design of the calibration model was based on absorbance spectra and calculation data from selected wavelength ranges from 305 nm to 2210 nm. Measurement data were first denoised and truncated to build a fast and reliable metamodel for precise identification of the acid content of an aqueous solution at a concentration from 0 to 5.66%. The mean estimation error generated by the metamodel does not exceed 0.7%.

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