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Temperature Correction of Spectra to Improve Solute Concentration Monitoring by In Situ Ultraviolet and Mid-Infrared Spectrometries toward Isothermal Local Model Performance
journal contributionposted on 2022-11-04, 14:36 authored by Magdalene W. S. Chong, Thomas McGlone, Ching Yee Chai, Naomi E. B. Briggs, Cameron J. Brown, Francesca Perciballi, Jaclyn Dunn, Andrew J. Parrott, Paul Dallin, John Andrews, Alison Nordon, Alastair J. Florence
Changes in temperature can significantly affect spectroscopic-based methods for in situ monitoring of processes. As varying temperature is inherent to many processes, associated temperature effects on spectra are unavoidable, which can hinder solute concentration determination. Ultraviolet (UV) and mid-infrared (IR) data were acquired for l-ascorbic acid (LAA) in MeCN/H2O (80:20 w/w) at different concentrations and temperatures. For both techniques, global partial least squares (PLS) models for prediction of LAA concentration constructed without preprocessing of the spectra required a high number of latent variables to account for the effects of temperature on the spectra (root mean square error of cross validation (RMSECV) of 0.18 and 0.16 g/100 g solvent, for UV and IR datasets, respectively). The PLS models constructed on the first derivative spectra required fewer latent variables, yielding variable results in accuracy (RMSECV of 0.23 and 0.06 g/100 g solvent, respectively). Corresponding isothermal local models constructed indicated improved model performance that required fewer latent variables in the absence of temperature effects (RMSECV of 0.01 and 0.04 g/100 g solvent, respectively). Temperature correction of the spectral data via loading space standardization (LSS) enabled the construction of global models using the same number of latent variables as the corresponding local model, which exhibited comparable model performance (RMSECV of 0.06 and 0.04 g/100 g solvent, respectively). The additional chemometric effort required for LSS is justified if prediction of solute concentration is required for in situ monitoring and control of cooling crystallization with an accuracy and precision approaching that attainable using an isothermal local model. However, the model performance with minimal preprocessing may be sufficient, for example, in the early phase development of a cooling crystallization process, where high accuracy is not always required. UV and IR spectrometries were used to determine solubility diagrams for LAA in MeCN/H2O (80:20 w/w), which were found to be accurate compared to those obtained using the traditional techniques of transmittance and gravimetric measurement. For both UV and IR spectrometries, solubility values obtained from models with LSS temperature correction were in better agreement with those determined gravimetrically. In this first example of the application of LSS to UV spectra, significant improvement in the predicted solute concentration is achieved with the additional chemometric effort. There is no extra experimental burden associated with the use of LSS if a structured approach is employed to acquire calibration data that account for both temperature and concentration.
yielding variable resultssignificantly affect spectroscopicminimal preprocessing mayearly phase developmentdetermine solubility diagramsadditional chemometric effortisothermal local modelcorresponding local modelsolubility values obtained100 g solventcooling crystallization processacquire calibration data2 subglobal models usingpredicted solute concentrationpls models constructedassociated temperature effectslss temperature correctioncooling crystallizationobtained usingtemperature correctionmodel performancesolute concentrationattainable using16 g04 gvarying temperaturetemperature effectsstructured approachsitu monitoringsignificant improvementrespectively ).precision approachinglatent variablesgravimetric measurementdifferent concentrationsdetermined gravimetricallycross validationbetter agreementbased methodsascorbic acidaccurate compared