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Raman OEM applications need consistent spectra to deliver dependable answers. Learn how to achieve >99.5% unit-to-unit agreement using a series of simple corrections.

Making Raman Measurements Reproducible

Biotechnology, the production of chemicals of choice using microorganisms, is an application very well suited to monitoring with Raman spectroscopy. Raman’s high selectivity and low sensitivity to the solvent, water, can be employed using a simple immersion probe setup to monitor and more tightly control these processes.


We used a Wasatch Photonics Raman spectrometer at 785 nm, fiber-coupled to an external laser and an immersion probe inserted into a stirred solution of 50 g/L glucose and 2 g/L yeast. The fermentation was observed over 14 h, with spectra collected every 2 min at 5 s integration time and an average of 20 scans.


We performed a separate multivariate calibration for glucose in the presence of yeast over the concentration range from 0 to 50 g/L glucose.


We correlated the glucose calibration spectra with the concentration using partial least squares (PLS) regression, yielding a prediction error of 0.7 g/L in the presence of yeast and about 0.1 g/L without yeast. The Raman spectra recorded during the fermentation are shown in Figure 1 together with the pure Raman spectra for the main reactant and product, glucose and ethanol.
Applying the PLS glucose model yielded the prediction of the glucose concentration, which showed a slow decrease with a noise level of below 1 g/L glucose.


Raman spectra recorded during glucose fermentation over 14 h, with time (in seconds) indicated by the color, together with the reactant and product spectra. The glucose peaks around 1100 cm

 decrease, while the ethanol peak at 900 cm

 and the overall yeast background increase.

Principal component analysis (PCA) identified the main spectral trends during the process. The first principal component expressed the remaining glucose level, while the second component captured the simultaneous decrease of glucose (spectrum negative in the corresponding loading) and increase of ethanol (positive spectrum in the loading). The corresponding PCA score therefore allowed the progress of the reaction to be summarized in a single coordinate, and to compare batch to batch.


Additional reaction products could be discovered in the higher principal components; due to its specificity, Raman allows identification. For example, the fourth principle component for the fermentation spectra showed the production of dissolved carbon dioxide. Likewise, unwanted side-products could be discovered and identified during the fermentation.


Using an immersion probe and a Wasatch Photonics Raman spectrometer at 785 nm, we continuously monitor the fermentation of glucose with yeast. Despite the significant yeast background, multivariate regression yields the glucose concentration, while principal component analysis projects the spectra onto a reaction progress coordinate and allows deep insight into the details of the fermentation. The same approach could be used for other processes to harness the strength of Raman spectroscopy.


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Raman spectroscopy’s high analytical selectivity and insensitivity to water is well suited for process monitoring in biotechnology. Here we explore the fermentation of glucose, a common feedstock, with a commonly used microorganism, yeast. Applying multivariate tools, we can monitor the main reactants and products with high sensitivity: glucose, ethanol, and carbon dioxide.

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Wasatch Photonics

Articles

Making Raman Measurements Reproducible

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