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.

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.

Process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control.

Robust fit-for-purpose multivariate calibration models are of critical importance to on-line/in-line quantitative monitoring of bio-chemicals and pharmaceuticals using spectroscopic instruments. Unlike in off-line assays, the spectroscopic measurements in on-line/in-line real-time applications are almost inevitably subjected to variations in measurement conditions (e.g. temperature) and samples' physical properties (e.g. cell density, particle size, sample compactness), which can invalidate the assumption of a linear relationship between the spectroscopic measurements and the concentrations of the target chemical components. This Biotech Highlight discusses the effects of such variations on spectroscopic measurements, and presents an overview of recent work on modelling and correcting of the detrimental effects of variations in measurement conditions and samples' physical properties.A number of application studies to complex datasets and an industrial plant demonstrate the methodologies and algorithms discussed.

Advanced calibration strategy for in situ quantitative monitoring of phase transition processes in suspensions using FT-Raman spectroscopy.

There is an increasing interest in using Raman spectroscopy to identify polymorphic forms and monitor phase changes in pharmaceutical products for quality control. Compared with other analytical techniques for the identification of polymorphs such as X-ray powder diffractometry and infrared spectroscopy, FT-Raman spectroscopy has the advantages of enabling fast, in situ, and nondestructive measurements of complex systems such as suspension samples. However, for suspension samples, Raman intensities depend on the analyte concentrations as well as the particle size, overall solid content, and homogeneity of the solid phase in the mixtures, which makes quantitative Raman analysis rather difficult. In this contribution, an advanced model has been derived to explicitly account for the confounding effects of a sample's physical properties on Raman intensities. On the basis of this model, a unique calibration strategy called multiplicative effects correction (MEC) was proposed to separate the Raman contributions due to changes in analyte concentration from those caused by the multiplicative confounding effects of the sample's physical properties. MEC has been applied to predict the anhydrate concentrations from in situ FT-Raman measurements made during the crystallization and phase transition processes of citric acid in water. The experimental results show that MEC can effectively correct for the confounding effects of the particle size and overall solid content of the solid phase on Raman intensities and, therefore, provide much more accurate in situ quantitative predictions of anhydrate concentration during crystallization and phase transition processes than traditional PLS calibration methods.

Improving the linearity of spectroscopic data subjected to fluctuations in external variables by the extended loading space standardization.

In process analytical applications, spectral measurements can be subject to changes in process temperature, pressure, flow turbulence, and compactness as well as other external variations. Generally, the variations of external variables influence spectral data in a non-linear manner which leads to the poor predictive ability of bilinear calibration models on raw spectral data. In this contribution, the influence of external variables on spectral data is generally classified into two different modes, multiplicative influential mode and composition-related influential mode. A new chemometric method, termed Extended Loading Space Standardization (ELSS), has been developed to explicitly model these two kinds of influential modes. ELSS was applied to two sets of spectral data with fluctuations in external variables and its performance evaluated and compared with global partial least squares (PLS) models and Loading Space Standardization (LSS). Results show that ELSS can efficiently model the external non-linear effects in both data sets and greatly improve the accuracy of predictions with the mean square error of prediction for test samples being 2-3 times smaller than those of LSS and global PLS.

Extracting chemical information from spectral data with multiplicative light scattering effects by optical path-length estimation and correction.

When analyzing complex mixtures that exhibit sample-to-sample variability using spectroscopic instrumentation, the variation in the optical path length, resulting from the physical variations inherent within the individual samples, will result in significant multiplicative light scattering perturbations. Although a number of algorithms have been proposed to address the effect of multiplicative light scattering, each has associated with it a number of underlying assumptions, which necessitates additional information relating to the spectra being attained. This information is difficult to obtain in practice and frequently is not available. Thus, with a view to removing the need for the attainment of additional information, a new algorithm, optical path-length estimation and correction (OPLEC), is proposed. The methodology is applied to two near-infrared transmittance spectral data sets (powder mixture data and wheat kernel data), and the results are compared with the extended multiplicative signal correction (EMSC) and extended inverted signal correction (EISC) algorithms. Within the study, it is concluded that the EMSC algorithm cannot be applied to the wheat kernel data set due to core information for the implementation of the algorithm not being available, while the analysis of the powder mixture data using EISC resulted in incorrect conclusions being drawn and hence a calibration model whose performance was unacceptable. In contrast, OPLEC was observed to effectively mitigate the detrimental effects of physical light scattering and significantly improve the prediction accuracy of the calibration models for the two spectral data sets investigated without any additional information pertaining to the calibration samples being required.

Enhancing the signal-to-noise ratio of X-ray diffraction profiles by smoothed principal component analysis.

X-ray diffraction is one of the most widely applied methodologies for the in situ analysis of kinetic processes involving crystalline solids. However, due to its relatively high detection limit, it has only limited application in the context of crystallizations from liquids. Methods that can improve the detection limit of X-ray diffraction are therefore highly desirable. Signal processing approaches such as Savitzky-Golay, maximum likelihood, stochastic resonance, and wavelet transforms have been used previously to preprocess X-ray diffraction data. Since all these methods only utilize the frequency information contained in the single X-ray diffraction profile being processed to discriminate between the signals and the noise, they may not successfully identify very weak but important peaks especially when these weak signals are masked by severe noise. Smoothed principal component analysis (SPCA), which takes advantage of both the frequency information and the common variation within a set of profiles, is proposed as a methodology for the preprocessing of the X-ray diffraction data. Two X-ray diffraction data sets are used to demonstrate the effectiveness of the proposed approach. The first was obtained from mannitol-methanol suspensions, and the second data set was generated from slurries of L-glutamic acid (GA) in methanol. The results showed that SPCA can significantly improve the signal-to-noise ratio and hence lower the detection limits (approximately 0.389% g/mL for mannitol-methanol suspensions and 0.4 wt % for beta-form GA in GA-methanol slurries comprising mixtures of both alpha- and beta-forms of GA) thereby providing an important contribution to crystallization process performance monitoring.

Correction of temperature-induced spectral variations by loading space standardization.

With a view to maintaining the validity of multivariate calibration models for chemical processes affected by temperature fluctuations, loading space standardization (LSS) is proposed. Through the application of LSS, multivariate calibration models built at temperatures other than those of the test samples can provide predictions with an accuracy comparable to the results obtained at a constant temperature. Compared with other methods, designed for the same purpose, such as continuous piecewise direct standardization, LSS has the advantages of straightforward implementation and good performance. The methodology was applied to shortwave NIR spectral data sets measured at different temperatures. The results showed that LSS can effectively remove the influence of temperature variations on the spectra and maintain the predictive abilities of the multivariate calibration models.

The relationship between risk analysis and the precautionary principle.

Definitions of the precautionary principle (PP) are reviewed with particular reference to the role of risk assessment. In general, the PP is employed as a means of justifying decisions that are contrary to the conclusions of a formal risk assessment. Even where risk assessment is accepted as part of a precautionary approach, its importance in subsequent decision-making tends to be undermined by application of the PP. The implications for the future of risk assessment-based decisions in areas as diverse as environmental protection and food safety are briefly considered.