A Mechanistic Investigation of the N-Hydroxyphthalimide Catalyzed Benzylic Oxidation Mediated by Sodium Chlorite.

A detailed investigation into the mechanistic course of N-hydroxyphthalimide catalyzed oxidation of benzylic centers using sodium chlorite as the stoichiometric oxidant is reported. Through a combination of experimental, spectroscopic, and computational techniques, the transformation is interrogated, providing improved reaction conditions and an enhanced understanding of the mechanism. Performing the transformation in the presence of acetic acid or a pH 4.5 buffer leads to extended reaction times but improves the catalyst lifetime, leading to the complete consumption of the starting material. Chlorine dioxide is identified as the active oxidant that is able to oxidize the N-hydroxyphthalimide anion to the phthalimide-N-oxyl radical, the proposed catalytically active species, which is able to abstract a hydrogen atom from the substrate. A second molecule of chlorine dioxide reacts with the resultant radical and, after loss of hypochlorous acid, leads to the observed product. Through a broad variety of techniques including UV/vis, EPR and Raman spectroscopy, isotopic labeling, and the use of radical traps, evidence for the mechanism is presented that is supported through electronic structural calculations.

Optical Screening and Classification of Drug Binding to Proteins in Human Blood Serum.

Protein-drug interactions in the human bloodstream are important factors in applications ranging from drug design, where protein binding influences efficacy and dose delivery, to biomedical diagnostics, where rapid, quantitative measurements could guide optimized treatment regimes. Current measurement approaches use multistep assays, which probe the protein-bound drug fraction indirectly and do not provide fundamental structural or dynamic information about the in vivo protein-drug interaction. We demonstrate that ultrafast 2D-IR spectroscopy can overcome these issues by providing a direct, label-free optical measurement of protein-drug binding in blood serum samples. Four commonly prescribed drugs, known to bind to human serum albumin (HSA), were added to pooled human serum at physiologically relevant concentrations. In each case, spectral changes to the amide I band of the serum sample were observed, consistent with binding to HSA, but were distinct for each of the four drugs. A machine-learning-based classification of the serum samples achieved a total cross-validation prediction accuracy of 92% when differentiating serum-only samples from those with a drug present. Identification on a per-drug basis achieved correct drug identification in 75% of cases. These unique spectroscopic signatures of the drug-protein interaction thus enable the detection and differentiation of drug containing samples and give structural insight into the binding process as well as quantitative information on protein-drug binding. Using currently available instrumentation, the 2D-IR data acquisition required just 1 min and 10 µL of serum per sample, and so these results pave the way to fast, specific, and quantitative measurements of protein-drug binding in vivo with potentially invaluable applications for the development of novel therapies and personalized medicine.

Non-invasive monitoring of the growth of metal-organic frameworks (MOFs) via Raman spectroscopy.

The applicability of Raman spectroscopy for phase discrimination of metal-organic frameworks (MOFs) has been demonstrated with F4_MIL-140A(Ce) and F4_UiO-66(Ce); analogues prepared from the same metal and ligand sources. Each analogue exhibits unique Raman peaks, with significant differences in the low frequency region, which is more sensitive to structural variations. Non-invasive Raman monitoring of F4_MIL-140A(Ce) synthesis indicated evolution of a unique MOF Raman peak with reaction progress; conversion of this Raman signal to extent of crystallisation was in good agreement with reported reaction kinetics determined via a synchrotron diffraction method. Additionally, Raman spectroscopy indicated initial rapid consumption of the nitric acid modulator present in the reaction coinciding with an expected high probability of nucleation. Raman spectroscopy is a promising technique for rapid screening of MOFs and can be used to study the mechanism of their formation in situ with kinetic insight into both the solution and solid phases of the reaction medium.

Detection of paracetamol binding to albumin in blood serum using 2D-IR spectroscopy.

Binding of drugs to blood serum proteins can influence both therapeutic efficacy and toxicity. The ability to measure the concentrations of protein-bound drug molecules quickly and with limited sample preparation could therefore have considerable benefits in biomedical and pharmaceutical applications. Vibrational spectroscopies provide data quickly but are hampered by complex, overlapping protein amide I band profiles and water absorption. Here, we show that two-dimensional infrared (2D-IR) spectroscopy can achieve rapid detection and quantification of paracetamol binding to serum albumin in blood serum at physiologically-relevant levels with no additional sample processing. By measuring changes to the amide I band of serum albumin caused by structural and dynamic impacts of paracetamol binding we show that drug concentrations as low as 7 µM can be detected and that the availability of albumin for paracetamol binding is less than 20% in serum samples, allowing identification of paracetamol levels consistent with a patient overdose.

Measuring proteins in H2O using 2D-IR spectroscopy: pre-processing steps and applications toward a protein library.

The ability of two-dimensional infrared (2D-IR) spectroscopy to measure the amide I band of proteins in H2O rather than D2O-based solvents by evading the interfering water signals has enabled in vivo studies of proteins under physiological conditions and in biofluids. Future exploitation of 2D-IR in analytical settings, from diagnostics to protein screening, will, however, require comparisons between multiple datasets, necessitating control of data collection protocols to minimize measurement-to-measurement inconsistencies. Inspired by analytical spectroscopy applications in other disciplines, we describe a workflow for pre-processing 2D-IR data that aims to simplify spectral cross-comparisons. Our approach exploits the thermal water signal that is collected simultaneously with, but is temporally separated from the amide I response to guide custom baseline correction and spectral normalization strategies before combining them with Principal Component noise reduction tools. Case studies show that application of elements of the pre-processing workflow to previously published data enables improvements in quantification accuracy and detection limits. We subsequently apply the complete workflow in a new pilot study, testing the ability of a prototype library of 2D-IR spectra to quantify the four major protein constituents of blood serum in a single, label-free measurement. These advances show progress toward the robust data handling strategies that will be necessary for future applications of 2D-IR to pharmaceutical or biomedical problems.

Reaction Monitoring Using SABRE-Hyperpolarized Benchtop (1 T) NMR Spectroscopy.

The conversion of [IrCl(COD)(IMes)] (COD = cis, cis-1,5-cyclooctadiene, IMes = 1,3-bis(2,4,6-trimethyl-phenyl)imidazole-2-ylidene) in the presence of an excess of para-hydrogen ( p-H2) and a substrate (4-aminopyridine (4-AP) or 4-methylpyridine (4-MP)) into [Ir(H)2(IMes)(substrate)3]Cl is monitored by 1H NMR spectroscopy using a benchtop (1 T) spectrometer in conjunction with the p-H2-based hyperpolarization technique signal amplification by reversible exchange (SABRE). A series of single-shot 1H NMR measurements are used to monitor the chemical changes that take place in solution through the lifetime of the hyperpolarized response. Non-hyperpolarized high-field 1H NMR control measurements were also undertaken to confirm that the observed time-dependent changes relate directly to the underlying chemical evolution. The formation of [Ir(H)2(IMes)(substrate)3]Cl is further linked to the hydrogen isotope exchange (HIE) reaction, which leads to the incorporation of deuterium into the ortho positions of 4-AP, where the source of deuterium is the solvent, methanol- d4. Comparable reaction monitoring results are achieved at both high-field (9.4 T) and low-field (1 T). It is notable that the low sensitivity of the benchtop (1 T) NMR enables the use of protio solvents, which when used here allows the effects of catalyst formation and substrate deuteration to be separated. Collectively, these methods illustrate how low-cost low-field NMR measurements provide unique insight into a complex catalytic process through a combination of hyperpolarization and relaxation data.

SABRE hyperpolarization enables high-sensitivity 1H and 13C benchtop NMR spectroscopy.

Benchtop NMR spectrometers operating with low magnetic fields of 1-2 T at sub-ppm resolution show great promise as analytical platforms that can be used outside the traditional laboratory environment for industrial process monitoring. One current limitation that reduces the uptake of benchtop NMR is associated with the detection fields' reduced sensitivity. Here we demonstrate how para-hydrogen (p-H2) based signal amplification by reversible exchange (SABRE), a simple to achieve hyperpolarization technique, enhances agent detectability within the environment of a benchtop (1 T) NMR spectrometer so that informative 1H and 13C NMR spectra can be readily recorded for low-concentration analytes. SABRE-derived 1H NMR signal enhancements of up to 17 000-fold, corresponding to 1H polarization levels of P = 5.9%, were achieved for 26 mM pyridine in d4-methanol in a matter of seconds. Comparable enhancement levels can be achieved in both deuterated and protio solvents but now the SABRE-enhanced analyte signals dominate due to the comparatively weak thermally-polarized solvent response. The SABRE approach also enables the acquisition of 13C NMR spectra of analytes at natural isotopic abundance in a single scan as evidenced by hyperpolarized 13C NMR spectra of tens of millimolar concentrations of 4-methylpyridine. Now the associated signal enhancement factors are up to 45 500 fold (P = 4.0%) and achieved in just 15 s. Integration of an automated SABRE polarization system with the benchtop NMR spectrometer framework produces renewable and reproducible NMR signal enhancements that can be exploited for the collection of multi-dimensional NMR spectra, exemplified here by a SABRE-enhanced 2D COSY NMR spectrum.

Quantification of hyperpolarisation efficiency in SABRE and SABRE-Relay enhanced NMR spectroscopy.

para-Hydrogen (p-H2) induced polarisation (PHIP) is an increasingly popular method for sensitivity enhancement in NMR spectroscopy. Its growing popularity is due in part to the introduction of the signal amplification by reversible exchange (SABRE) method that generates renewable hyperpolarisation in target analytes in seconds. A key benefit of PHIP and SABRE is that p-H2 can be relatively easily and cheaply produced, with costs increasing with the desired level of p-H2 purity. In this work, the efficiency of the SABRE polarisation transfer is explored by measuring the level of analyte hyperpolarisation as a function of the level of p-H2 enrichment. A linear relationship was found between p-H2 enrichment and analyte 1H hyperpolarisation for a range of molecules, polarisation transfer catalysts, NMR detection fields and for both the SABRE and SABRE-Relay transfer mechanisms over the range 29-99% p-H2 purity. The gradient of these linear relationships were related to a simple theoretical model to define an overall efficiency parameter, E, that quantifies the net fraction of the available p-H2 polarisation that is transferred to the target analyte. We find that the efficiency of SABRE is independent of the NMR detection field and exceeds E = 20% for methyl-4,6-d2-nicotinate when using a previously optimised catalyst system. For the SABRE-Relay transfer mechanism, efficiencies of up to E = 1% were found for 1H polarisation of 1-propanol, when ammonia was used as the polarisation carrier.

A simple hand-held magnet array for efficient and reproducible SABRE hyperpolarisation using manual sample shaking.

Signal amplification by reversible exchange (SABRE) is a hyperpolarisation technique that catalytically transfers nuclear polarisation from parahydrogen, the singlet nuclear isomer of H2 , to a substrate in solution. The SABRE exchange reaction is carried out in a polarisation transfer field (PTF) of tens of gauss before transfer to a stronger magnetic field for nuclear magnetic resonance (NMR) detection. In the simplest implementation, polarisation transfer is achieved by shaking the sample in the stray field of a superconducting NMR magnet. Although convenient, this method suffers from limited reproducibility and cannot be used with NMR spectrometers that do not have appreciable stray fields, such as benchtop instruments. Here, we use a simple hand-held permanent magnet array to provide the necessary PTF during sample shaking. We find that the use of this array provides a 25% increase in SABRE enhancement over the stray field approach, while also providing improved reproducibility. Arrays with a range of PTFs were tested, and the PTF-dependent SABRE enhancements were found to be in excellent agreement with comparable experiments carried out using an automated flow system where an electromagnet is used to generate the PTF. We anticipate that this approach will improve the efficiency and reproducibility of SABRE experiments carried out using manual shaking and will be particularly useful for benchtop NMR, where a suitable stray field is not readily accessible. The ability to construct arrays with a range of PTFs will also enable the rapid optimisation of SABRE enhancement as function of PTF for new substrate and catalyst systems.

Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris.

BACKGROUND: Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles. RESULTS: This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements. CONCLUSIONS: The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.

Comparison of the determination of a low-concentration active ingredient in pharmaceutical tablets by backscatter and transmission Raman spectrometry.

A total of 383 tablets of a pharmaceutical product were analyzed by backscatter and transmission Raman spectrometry to determine the concentration of an active pharmaceutical ingredient (API), chlorpheniramine maleate, at the 2% m/m (4 mg) level. As the exact composition of the tablets was unknown, external calibration samples were prepared from chlorpheniramine maleate and microcrystalline cellulose (Avicel) of different particle size. The API peak at 1594 cm(-1) in the second derivative Raman spectra was used to generate linear calibration models. The API concentration predicted using backscatter Raman measurements was relatively insensitive to the particle size of Avicel. With transmission, however, particle size effects were greater and accurate prediction of the API content was only possible when the photon propagation properties of the calibration and sample tablets were matched. Good agreement was obtained with HPLC analysis when matched calibration tablets were used for both modes. When the calibration and sample tablets are not chemically matched, spectral normalization based on calculation of relative intensities cannot be used to reduce the effects of differences in physical properties. The main conclusion is that although better for whole tablet analysis, transmission Raman is more sensitive to differences in the photon propagation properties of the calibration and sample tablets.

Effect of particle properties of powders on the generation and transmission of Raman scattering.

Transmission Raman measurements of a 1 mm thick sulfur-containing disk were made at different positions as it was moved through 4 mm of aspirin (150-212 µm) or microcrystalline cellulose (Avicel) of different size ranges (<38, 53-106, and 150-212 µm). The transmission Raman intensity of the sulfur interlayer at 218 cm(-1) was lower when the disk was placed at the top or bottom of the powder bed, compared to positions within the bed and the difference between the sulfur intensity at the outer and inner positions increased with Avicel particle size. Also, the positional intensity difference was smaller for needle-shaped aspirin than for granular Avicel of the same size. The attenuation coefficients for the propagation of the exciting laser and transmitted Raman photons through the individual powders were the same but decreased as the particle size of Avicel increased; also, the attenuation coefficients for propagation through 150-212 µm aspirin were almost half of those through similar sized Avicel particles. The study has demonstrated that particulate size and type affect transmitted Raman intensities and, consequently, such factors need to be considered in the analysis of powders, especially if particle properties vary between the samples.

Quantitative analysis of powder mixtures by Raman spectrometry: the influence of particle size and its correction.

Particle size distribution and compactness have significant confounding effects on Raman signals of powder mixtures, which cannot be effectively modeled or corrected by traditional multivariate linear calibration methods such as partial least-squares (PLS), and therefore greatly deteriorate the predictive abilities of Raman calibration models for powder mixtures. The ability to obtain directly quantitative information from Raman signals of powder mixtures with varying particle size distribution and compactness is, therefore, of considerable interest. In this study, an advanced quantitative Raman calibration model was developed to explicitly account for the confounding effects of particle size distribution and compactness on Raman signals of powder mixtures. Under the theoretical guidance of the proposed Raman calibration model, an advanced dual calibration strategy was adopted to separate the Raman contributions caused by the changes in mass fractions of the constituents in powder mixtures from those induced by the variations in the physical properties of samples, and hence achieve accurate quantitative determination for powder mixture samples. The proposed Raman calibration model was applied to the quantitative analysis of backscatter Raman measurements of a proof-of-concept model system of powder mixtures consisting of barium nitrate and potassium chromate. The average relative prediction error of prediction obtained by the proposed Raman calibration model was less than one-third of the corresponding value of the best performing PLS model for mass fractions of barium nitrate in powder mixtures with variations in particle size distribution, as well as compactness.

Validity of particle size analysis techniques for measurement of the attrition that occurs during vacuum agitated powder drying of needle-shaped particles.

Analysis of needle-shaped particles of cellobiose octaacetate (COA) obtained from vacuum agitated drying experiments was performed using three particle size analysis techniques: laser diffraction (LD), focused beam reflectance measurements (FBRM) and dynamic image analysis. Comparative measurements were also made for various size fractions of granular particles of microcrystalline cellulose. The study demonstrated that the light scattering particle size methods (LD and FBRM) can be used qualitatively to study the attrition that occurs during drying of needle-shaped particles, however, for full quantitative analysis, image analysis is required. The algorithm used in analysis of LD data assumes the scattering particles are spherical regardless of the actual shape of the particles under evaluation. FBRM measures a chord length distribution (CLD) rather than the particle size distribution (PSD), which in the case of needles is weighted towards the needle width rather than their length. Dynamic image analysis allowed evaluation of the particles based on attributes of the needles such as length (e.g. the maximum Feret diameter) or width (e.g. the minimum Feret diameter) and as such, was the most informative of the techniques for the analysis of attrition that occurred during drying.

Calibration model transfer in mid-infrared process analysis with in situ attenuated total reflectance immersion probes.

Process applications of mid-infrared (MIR) spectrometry may involve replacement of the spectrometer and/or measurement probe, which generally requires a calibration transfer method to maintain the accuracy of analysis. In this study, direct standardisation (DS), piecewise direct standardisation (PDS) and spectral space transformation (SST) were compared for analysis of ternary mixtures of acetone, ethanol and ethyl acetate. Three calibration transfer examples were considered: changing the spectrometer, multiplexing two probes to a spectrometer, and changing the diameter of the attenuated total reflectance (ATR) probe (as might be required when scaling up from lab to process analysis). In each case, DS, PDS and SST improved the accuracy of prediction for the test samples, analysed on a secondary spectrometer-probe combination, using a calibration model developed on the primary system. When the probe diameter was changed, a scaling step was incorporated into SST to compensate for the change in absorbance caused by the difference in ATR crystal size. SST had some advantages over DS and PDS: DS was sensitive to the choice of standardisation samples, and PDS required optimisation of the window size parameter (which also required an extra standardisation sample). SST only required a single parameter to be chosen: the number of principal components, which can be set equal to the number of standardisation samples when a low number of standards (n < 7) are used, which is preferred to minimise the time required to transfer the calibration model.

Temperature Correction of Spectra to Improve Solute Concentration Monitoring by In Situ Ultraviolet and Mid-Infrared Spectrometries toward Isothermal Local Model Performance.

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.

Improved method for kinetic studies in microreactors using flow manipulation and noninvasive Raman spectrometry.

A novel method has been devised to derive kinetic information about reactions in microfluidic systems. Advantages have been demonstrated over conventional procedures for a Knoevenagel condensation reaction in terms of the time required to obtain the data (fivefold reduction) and the efficient use of reagents (tenfold reduction). The procedure is based on a step change from a low (e.g., 0.6 µL min(-1)) to a high (e.g., 14 µL min(-1)) flow rate and real-time noninvasive Raman measurements at the end of the flow line, which allows location-specific information to be obtained without the need to move the measurement probe along the microreactor channel. To validate the method, values of the effective reaction order n were obtained employing two different experimental methodologies. Using these values of n, rate constants k were calculated and compared. The values of k derived from the proposed method at 10 and 40 °C were 0.0356 ± 0.0008 mol(-0.3) dm(0.9) s(-1) (n = 1.3) and 0.24 ± 0.018 mol(-0.1) dm(0.3) s(-1) (n = 1.1), respectively, whereas the values obtained using a more laborious conventional methodology were 0.0335 ± 0.0032 mol(-0.4) dm(1.2) s(-1) (n = 1.4) at 10 °C and 0.244 ± 0.032 mol(-0.3) dm(0.9) s(-1) (n = 1.3) at 40 °C. The new approach is not limited to analysis by Raman spectrometry and can be used with different techniques that can be incorporated into the end of the flow path to provide rapid measurements.

Calibration of multiplexed fiber-optic spectroscopy.

Large-scale commercial bioprocesses that manufacture biopharmaceutical products such as monoclonal antibodies generally involve multiple bioreactors operated in parallel. Spectra recorded during in situ monitoring of multiple bioreactors by multiplexed fiber-optic spectroscopies contain not only spectral information of the chemical constituents but also contributions resulting from differences in the optical properties of the probes. Spectra with variations induced by probe differences cannot be efficiently modeled by the commonly used multivariate linear calibration models or effectively removed by popular empirical preprocessing methods. In this study, for the first time, a calibration model is proposed for the analysis of complex spectral data sets arising from multiplexed probes. In the proposed calibration model, the spectral variations introduced by probe differences are explicitly modeled by introducing a multiplicative parameter for each optical probe, and then their detrimental effects are effectively mitigated through a "dual calibration" strategy. The performance of the proposed multiplex calibration model has been tested on two multiplexed spectral data sets (i.e., MIR data of ternary mixtures and NIR data of bioprocesses). Experimental results suggest that the proposed calibration model can effectively mitigate the detrimental effects of probe differences and hence provide much more accurate predictions than commonly used multivariate linear calibration models (such as PLS) with and without empirical data preprocessing methods such as orthogonal signal correction, standard normal variate, or multiplicative signal correction.

Studies of particle drying using non-invasive Raman spectrometry and particle size analysis.

The evaporation of methanol from needle-shaped particles of cellobiose octaacetate (COA) has been studied directly in a jacketed vacuum drier using in situ measurements by Raman spectrometry. A design of experiments (DoE) approach was used to investigate the effects of three parameters (method of agitation, % solvent loss on drying and jacket temperature), with the intention of minimising the drying time and extent of particle attrition. Drying curves based on Raman signals for methanol and COA in the spectra of the wet particles indicated the end of drying and revealed three stages in the drying process that could be used to monitor the progress of solvent removal in real time. Off-line particle size measurements based on laser diffraction were made to obtain information on the extent of attrition, to compare with the trends revealed by the Raman drying curves. The study demonstrated that non-invasive Raman spectrometry can be used to study the progress of drying during agitation of particles in a vacuum drier, allowing optimisation of operating conditions to minimise attrition and reduce drying times. Although a correlation between particle size and off-line Raman measurements of COA was demonstrated, it was not possible to derive equivalent information from the in situ Raman spectra owing to the greater effects of particle motion or bulk density variations of the particles in the drier.

Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models.

The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.