Detection of volatile fatty acids in anaerobic digestion system by near infrared spectroscopy

The development of renewable energy technologies, such as anaerobic digestion (AD) systems, is critical to the future of climate-adaptive energy systems. Volatile fatty acid (VFA) is one of the most important intermediates during the AD process, with two effects - inhibiting digestion and promoting CH4 production. Existing techniques for measuring VFA levels are restricted by difficulties in real-time detection, tedious experimentation, laborious processes, and high cost. To achieve the goal of low-cost and high-efficiency, we report the feasibility of evaluating acetic acid, propionic acid, and butyric acid in AD systems using NIRS. We found that the best combinations of preprocessing methods and feature selection were Savitzky-Golay smoothing-multivariate scattering correction-genetic algorithm, Savitzky-Golay smoothing-interval Random Frog, and Savitzky-Golay smoothinggenetic algorithm, respectively. For all three models, the correlation coefficients were above 0.90, and the ratio of performance to deviation was above 3.00. Our results show that the near-infrared spectral model, after preprocessing and feature band extraction, has a good quantitative prediction effect on VFA. This provides technical support for the rapid and non-destructive quantitative analysis of VFA content in AD systems, and contributes to the development of renewable energy technologies.