Arefi Arman, Sturm Barbara, Babor Majharulislam, Horf Michael, Hoffmann Thomas, Höhne Marina, Friedrich Kathleen, Schroedter Linda, Venus Joachim, Olszewska-Widdrat Agata
Department of Systems Process Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth Allee 100, 14469, Potsdam, Germany.
Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth Allee 100, 14469, Potsdam, Germany.
Heliyon. 2024 Oct 1;10(19):e38791. doi: 10.1016/j.heliyon.2024.e38791. eCollection 2024 Oct 15.
As concerns about the environmental impacts of biowaste disposal increase, lactic acid bacterial fermentation is becoming increasingly popular. Current academic research is aimed at the process optimization by developing digital bioreactors. The primary focus is to develop a digital model mimicking the biochemical reactions. In the light of this, this paper intended to build a digital model of biochemical reactions during the fermentation process of both glucose and biowaste substrates, including white pasta and organic municipal waste. For this purpose, near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were used to collect spectral information during the fermentation process. Next, the samples were analyzed by High Pressure Liquid Chromatography (HPLC) to measure their glucose, fructose, arabinose, xylose, disaccharide, lactic acid, and acetic acid contents. The results showed that learning algorithms trained on MIR spectra accurately estimated the biochemical reactions for both glucose and biowaste substrates. For the glucose substrate, the results showed R-squared of 0.97 and RMSE of 4.69 g/L for glucose, and R-squared of 0.98 and RMSE of 2.74 g/L for lactic acid. In the case of biowaste substrate, estimations included glucose (R-squared = 0.97, RMSE = 4.69 g/L), fructose (R-squared = 0.88, RMSE = 1.47 g/L), arabinose (R-squared = 0.98, RMSE = 0.55 g/L), xylose (R-squared = 0.93, RMSE = 1.11 g/L), disaccharide (R-squared = 0.90, RMSE = 0.55 g/L), total sugar (R-squared = 0.98, RMSE = 3.79 g/L), lactic acid (R-squared = 0.98, RMSE = 2.74 g/L), and acetic acid (R-squared = 0.97, RMSE = 0.36 g/L). Regarding NIR spectral data, the predictive models were accurate when the substrate was glucose, however, they failed to accurately estimate the chemical reactions in the case of biowaste substrate. The findings of this study can be used to fulfill the requirements for a continuous fermentation process with the objective of maximizing lactic acid production.
随着对生物废料处理所产生环境影响的担忧日益增加，乳酸菌发酵越来越受欢迎。当前的学术研究旨在通过开发数字生物反应器来优化这一过程。主要重点是开发一个模拟生化反应的数字模型。有鉴于此，本文旨在构建一个葡萄糖和生物废料底物（包括白面食和城市有机垃圾）发酵过程中生化反应的数字模型。为此，在发酵过程中使用近红外（NIR）和中红外（MIR）光谱技术来收集光谱信息。接下来，通过高压液相色谱（HPLC）对样品进行分析，以测量其葡萄糖、果糖、阿拉伯糖、木糖、二糖、乳酸和乙酸含量。结果表明，基于MIR光谱训练的学习算法能够准确估计葡萄糖和生物废料底物的生化反应。对于葡萄糖底物，结果显示葡萄糖的决定系数（R²）为0.97，均方根误差（RMSE）为4.69 g/L，乳酸的决定系数为0.98，均方根误差为2.74 g/L。对于生物废料底物，估计值包括葡萄糖（决定系数 = 0.97，均方根误差 = 4.69 g/L）、果糖（决定系数 = 0.88，均方根误差 = 1.47 g/L）、阿拉伯糖（决定系数 = 0.98，均方根误差 = 0.55 g/L）、木糖（决定系数 = 0.93，均方根误差 = 1.11 g/L）、二糖（决定系数 = 0.90，均方根误差 = 0.55 g/L）、总糖（决定系数 = 0.98，均方根误差 = 3.79 g/L）、乳酸（决定系数 = 0.98，均方根误差 = 2.74 g/L）和乙酸（决定系数 = 0.97，均方根误差 = 0.36 g/L）。关于近红外光谱数据，当底物为葡萄糖时，预测模型是准确的，然而，在生物废料底物的情况下，它们未能准确估计化学反应。本研究的结果可用于满足连续发酵过程的要求，以实现乳酸产量最大化的目标。
