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Background: To perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages – including its inherent quantitative nature – Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible. Results: We present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites – glucose, acetate, alanine, phenylalanine and betaine – were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71–7.76 mM in real samples. Significance: The proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.
Forsberg et al. (Wed,) studied this question.