The concept of sustainable production necessitates the utilization of waste and by‐products as raw materials, the implementation of biotechnological processes, and the introduction of automated real‐time monitoring for efficient use of resources. One example is the biocatalyzed conversion of the reusable by‐product glycerin by acetic acid bacteria to dihydroxyacetone (DHA), which is of great importance to the cosmetic industry. The application of compact spectrometers enables the rapid measurement of samples while simultaneously reducing the consumption of resources and energy. Yet, this approach requires comprehensive data preprocessing and, on occasion, multivariate data analysis. For the process monitoring of the production of DHA, a low‐field 1H nuclear magnetic resonance (NMR) spectrometer was implemented in on‐line mode. Small‐volume samples were taken from a bypass and transferred to the spectrometer by an autosampler. Complete analysis within minutes allowed real‐time process control. To this purpose, reliable automated spectral preprocessing preceded the creation of a univariate model. The model enabled the acquisition of process knowledge from chemical kinetics and facilitated the tracking of both substrate and product concentrations, requiring independent calibration. As a second multivariate approach, principal component analysis was utilized to monitor the process in a semi‐quantitative manner without the necessity for calibration. The results of this study are beneficial for real‐time monitoring applications with the objective of exerting control over the process in question while minimizing expenditure.

中文翻译：

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使用低场 1H NMR 波谱仪监测二羟基丙酮的生物技术生产


可持续生产的概念需要利用废物和副产品作为原材料，实施生物技术流程，并引入自动化实时监控以有效利用资源。一个例子是乙酸菌将可重复使用的副产物甘油生物催化转化为二羟基丙酮 （DHA），这对化妆品行业非常重要。紧凑型光谱仪的应用能够快速测量样品，同时减少资源和能源的消耗。然而，这种方法需要全面的数据预处理，有时还需要多变量数据分析。为了对 DHA 的生产进行过程监测，以在线模式实施了低场 1H 核磁共振 （NMR） 波谱仪。从旁路中取出小体积样品，并通过自动进样器转移到光谱仪中。在几分钟内完成分析，实现实时过程控制。为此，在创建单变量模型之前，进行了可靠的自动光谱预处理。该模型能够从化学动力学中获取过程知识，并有助于跟踪需要独立校准的底物和产品浓度。作为第二种多变量方法，主成分分析用于以半定量方式监测过程，无需校准。这项研究的结果有利于实时监测应用，目的是在最大限度地减少支出的同时对相关过程进行控制。