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 smoothing-genetic 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.

厌氧消化(AD) 系统等可再生能源技术的发展对于气候适应性能源系统的未来至关重要。挥发性脂肪酸（VFA）是AD过程中最重要的中间体之一，具有抑制消化和促进CH ₄产生两种作用。现有的 VFA 水平测量技术受到实时检测困难、实验繁琐、过程费力和成本高的限制。为了实现低成本和高效率的目标，我们报告了在 AD 系统中使用评估乙酸、丙酸和丁酸的可行性近红外光谱。我们发现预处理方法和特征选择的最佳组合分别是 Savitzky-Golay 平滑-多元散射校正-遗传算法、Savitzky-Golay 平滑-区间随机青蛙算法和 Savitzky-Golay 平滑-遗传算法。对于所有三个模型，相关系数都在 0.90 以上，性能与偏差的比率在 3.00 以上。我们的结果表明，近红外光谱模型经过预处理和特征波段提取后，对VFA具有良好的定量预测效果。这为AD系统中VFA含量的快速、无损定量分析提供了技术支持，有助于可再生能源技术的发展。