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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.

The feasibility of monitoring the reaction of itaconic acid and 1-butanol by non-invasive acoustic emission measurements has been assessed. A piezoelectric transducer with a resonant mode at 90 kHz was attached to the external wall of a 1 L jacketed glass reactor. Acoustic emission from the oil jacket, stirrer and toluene was insignificant in comparison to that produced by the itaconic acid particles, which was transmitted through the glass walls and heating oil to the transducer. The transducer responded to acoustic emission from itaconic acid up to approximately 300 kHz, with the region around 90 kHz having the highest sensitivity. The effect of particle concentration and size on the acoustic emission generated has also been investigated, with higher concentrations and larger particles giving the greater signals. The detection limit for itaconic acid particles was 14 g dm(-3) of toluene. The effect of 1-butanol concentration and temperature on the progression of reactions was monitored using acoustic emission. It was possible to detect differences in the rate and extent of the reaction under different conditions, and also to identify when a combination of the concentration and/or size of itaconic acid particles had reached a steady state. However, it was not possible to differentiate between changes in particle size and concentration using the resonant transducer.

Abstract Hyperspectral imaging (HSI) can acquire data in two modes: imaging and spectroscopy, revealing the spatially-resolved spectral properties of materials. Traditional HSI processing in the close-range domain primarily focuses on the spectral information with minimal utilisation of the spatial information present in the data. The present work describes a methodology for utilising the spatial information present in HSI data to improve classification modelling over that achievable with spectral information alone. The methodology has been evaluated using near infrared (NIR) HSI data of sixteen green tea products from seven different countries. The methodology involves selecting and sharpening an image plane to enhance the textural details. The textural information is then extracted from the statistical properties of the grey level co-occurrence matrix (GLCM) of the sharpened image plane using a moving window operation. Finally, the textural properties are combined with the spectral information using one of the three different levels of data fusion, i.e. raw data level, feature level and decision level. Raw data-level fusion involved concatenating the spectral and textural data before performing the classification task. The feature-level fusion involved performing principal component analysis (PCA) on spectral and textural information and combining the PC scores obtained prior to performing classification. Decision-level fusion involved a majority voting scheme to enhance the final classification maps. All the classification tasks were performed using multi-class support vector machine (SVM) models. The results showed that combining the textural and spectral information during modelling resulted in improved classification of the sixteen green tea products compared to models built using spectral or textural information alone. Highlights • Green tea products were analysed by near infrared hyperspectral imaging. • Textural information was extracted from the grey level co-occurrence matrix. • Textural properties were fused with near-infrared spectral information. • Data fusion improved the classification accuracy for green tea products. [ABSTRACT FROM AUTHOR]

A method of obtaining particle size and concentration information, from ultrasonic transducer measurements of particle-wall interactioas in a particle laden fluid, is presented. A mathematical model of the flexural vibrations of the vessel wall due to the particle impact is constructed. The key component of this model is the derivation of an analytic expression for the impact force amplitude. An analytic expression for the power spectrum is then obtained that shows its explicit dependency on the system parameters. This spectrum is then integrated over a specific frequency range and a comparison with experimental results is reported. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The scale-up of batch kinetic models was studied by examining the kinetic fitting results of batch esterification reactions completed in 75mL and 5L reactors. Different temperatures, amounts of catalysts, and amounts of initial starting reagents were used to completely characterize the reaction. A custom written Matlab toolbox called GUIPRO was used to fit first-principles kinetic models directly to in-line NIR and Raman spectroscopic data. Second-order kinetic models provided calibration-free estimates of kinetic and thermodynamic reaction parameters, time dependent concentration profiles, and pure component spectra of reagents and product. The estimated kinetic and thermodynamic parameters showed good agreement between small-scale and large-scale reactions. The accuracy of pure component spectra estimates was validated by comparison to collected NIR and Raman pure component spectra. The model estimated product concentrations were also validated by comparison to concentrations measured by off-line GC analysis. Based on the good agreement between kinetic and thermodynamic parameters and comparison between actual and estimated concentration and spectral profiles, it was concluded that the scale-up of batch kinetic models was successful. [Copyright &y& Elsevier]