Transfer of Near-Infrared Multivariate Calibrations without Standards

Calibration transfer via filter learning

BACKGROUND

Calibration transfer is an essential activity in analytical chemistry in order to avoid a complete recalibration. Currently, the most popular calibration transfer methods, such as piecewise direct standardization and dynamic orthogonal projection, require a certain amount of standard or reference samples to guarantee their effectiveness. To achieve higher efficiency, it is desirable to perform the transfer with as few reference samples as possible.

RESULTS

To this end, we propose a new calibration transfer method by using a calibration database from a master instrument (source domain) and only one spectrum with known properties from a slave instrument (target domain). We first generate a counterpart of this spectrum in the source domain by a multivariate Gaussian kernel. Then, we train a filter to make the response function of the slave instrument equivalent to that of the master instrument. To avoid the need for labels from the target domain, we also propose an unsupervised way to implement our method. Compared with several state-of-the-art methods, the results on one simulated dataset and two real-world datasets demonstrate the effectiveness of our method.

SIGNIFICANCE

Traditionally, the demand for certain amounts of reference samples during calibration transfer is cumbersome. Our approach, which requires only one reference sample, makes the transfer process simple and fast. In addition, we provide an alternative for performing unsupervised calibration transfer. As such, the proposed method is a promising tool for calibration transfer.

An effective moisture interference correction method for maize powder NIR spectra analysis

The detection of maize starch content is of great significance for maize processing industry and near-infrared spectroscopy (NIRS) is an ideal rapid detection technology. However, the interference of moisture in maize is a bottleneck problem that affects the accuracy of NIRS quantitative analysis. In this study, we proposed methods based on external parameter orthogonalization (EPO) combined with wavelength selection algorithms to bring more accurate analytical results. Two groups of maize starch samples with different moisture content distributions were investigated to compare the predictive performance of NIRS models. The results showed that the model built using EPO combined with the synergy interval partial least squares (EPO-siPLS) algorithm exhibited the superior prediction accuracy, whose RMSEP/RMSEPck is improved by 9.7 % compared with that of siPLS model, 25.3 % compared with that of EPO-PLS, and 45.8 % compared with that of the PLS model. This study provides a more accurate and robust new method for rapid detection of maize starch and offers new insights for its application.

Standardization of near infrared spectroscopies via sample spectral correlation equalization

A novel method for near-infrared (NIR) spectroscopy spectra standardization is presented. NIR spectroscopies have been widely used in analytical chemistry, and many methods have been developed for NIR spectra standardization. To establish a robust standardization transformation, most existing methods require spectral data sets from both primal and secondary instruments for 1-1 correspondence validation. However, this limits the usage of standardization methods. This paper investigates an interesting issue, "Can spectra data in sets be arbitrarily order?" and further develops a completely different approach from existing methods in view of statistical signal processing. The key idea is to first compensate for the distortion along the wavelength and intensity of the spectra, and then transfer the second order statistic (2OS) from the primal spectra to the secondary spectra via data sphering and an inverse sphering transform so that the 2OS can be estimated regardless of the sample statistic order. To further demonstrate how the developed method can extend the usage of the NIR spectra standardization, several application-driven experiments on classification and regression are conducted for demonstration, and a comparison to the piecewise direct standardization (PDS) is also studied.

Improved Principal Component Analysis (IPCA): A Novel Method for Quantitative Calibration Transfer between Different Near-Infrared Spectrometers

Given the labor-consuming nature of model establishment, model transfer has become a considerable topic in the study of near-infrared (NIR) spectroscopy. Recently, many new algorithms have been proposed for the model transfer of spectra collected by the same types of instruments under different situations. However, in a practical scenario, we need to deal with model transfer between different types of instruments. To expand model applicability, we must develop a method that could transfer spectra acquired from different types of NIR spectrometers with different wavenumbers or absorbance. Therefore, in our study, we propose a new methodology based on improved principal component analysis (IPCA) for calibration transfer between different types of spectrometers. We adopted three datasets for method evaluation, including public pharmaceutical tablets (dataset 1), corn data (dataset 2), and the spectra of eight batches of samples acquired from the plasma ethanol precipitation process collected by FT-NIR and MicroNIR spectrometers (dataset 3). In the calibration transfer for public datasets, IPCA displayed comparable results with the classical calibration transfer method using piecewise direct standardization (PDS), indicating its obvious ability to transfer spectra collected from the same types of instruments. However, in the calibration transfer for dataset 3, our proposed IPCA method achieved a successful bi-transfer between the spectra acquired from the benchtop and micro-instruments with/without wavelength region selection. Furthermore, our proposed method enabled improvements in prediction ability rather than the degradation of the models built with original micro spectra. Therefore, our proposed method has no limitations on the spectrum for model transfer between different types of NIR instruments, thus allowing a wide application range, which could provide a supporting technology for the practical application of NIR spectroscopy.

Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer

For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.

Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process

When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

Principal component analysis or kernel principal component analysis based joint spectral subspace method for calibration transfer

To transfer a calibration model in the case where only the master and slave spectra of standardization samples are available, principal component analysis (PCA) and kernel principal component analysis (KPCA) based joint spectral space (termed as JPCA or JKPCA) methods are proposed. As a feature subspace shared by master and slave spectra, the joint spectral subspace in JPCA and JKPCA are the projection of the joint spectral matrix comprising all the spectra of standardization by utilizing PCA and KPCA, respectively. The two corresponding low-dimensional feature matrices for master and slave spectra are extracted from the joint spectral subspace, and then a transfer matrix is estimated based on the least square criterion. In JKPCA, a partial least squares (PLS) model, named the primary model, is constructed using the low-dimensional feature matrix of master calibration spectra, and the model is then used to predict the transferred low-dimensional feature matrix of slave test spectra. Different from JKPCA, JPCA firstly reconstructs master calibration spectra and transferred slave test spectra, respectively. Then the primary model built on the reconstructed version of master calibration spectra is applied to predict the reconstructed version of transferred slave test spectra. A comparative study of the two proposed methods, multiplicative scatter correction (MSC), orthogonal signal correction (OSC), piecewise direct standardization (PDS), canonical correlation analysis based calibration transfer (CCACT), generalized least squares (GLS), slope and bias correction (SBC) and spectral space transformation (SST) is conducted on two datasets. All the statistical results together exhibit that the transfer ability of JKPCA is the best. Except JKPCA, JPCA performs at least comparable with the GLS or SST, and frequently better than the other methods.

Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards

Calibration transfer is an important field for near-infrared (NIR) spectroscopy in practical applications. However, most transfer methods are constructed with standard samples, which are expensive and difficult to obtain. Taking this problem into account, this paper proposes a calibration transfer method based on affine invariance without transfer standards (CTAI). Our method can be utilized to adjust the difference between two instruments by affine transformation. CTAI firstly establishes a partial least squares (PLS) model of the master instrument to obtain score matrices and predicted values of the two instruments, and then the regression coefficients between each of the score vectors and predicted values are computed for the master instrument and the slave instrument, respectively. Next, angles and biases are calculated between the regression coefficients of the master instrument and the corresponding regression coefficients of the slave instrument, respectively. Finally, by introducing affine transformation, new samples are predicted based on the obtained angles and biases. A comparative study between CTAI and the other five methods was conducted, and the performances of these algorithms were tested with two NIR spectral datasets. The obtained experimental results show clearly that, in general CTAI is more robust and can also achieve the best Root Mean Square Error of test sets (RMSEPs). In addition, the results of statistical difference with the Wilcoxon signed rank test show that CTAI is generally better than the others, and at least statistically the same.

PLS Subspace-Based Calibration Transfer for Near-Infrared Spectroscopy Quantitative Analysis

In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results.

Boosting the Performance of Genetic Algorithms for Variable Selection in Partial Least Squares Spectral Calibrations

A genetic algorithm (GA) for variable selection in partial least squares (PLS) regression that incorporates adaptive boosting to identify informative wavelengths in near-infrared (NIR) spectra has been developed. Three studies demonstrating the advantages of incorporating an adaptive boosting routine into a GA that employs the root mean square error of calibration as its fitness function are highlighted: (1) prediction of hydroxyl number of terpolymers from NIR diffuse reflectance spectra; (2) calibration of acetone from NIR transmission spectra of mixtures of water, acetone, t-butyl alcohol and isopropyl alcohol; and (3) determination of the active pharmaceutical ingredients in drug tablets from NIR diffuse reflectance spectra. The performance of the GA with adaptive boosting to select wavelengths was compared with one without adaptive boosting. For all three NIR data sets, variable selected PLS models developed by a GA with adaptive boosting performed better. Analysis of the wavelengths selected by the GA with adaptive boosting also demonstrate that chemical information indicative of the analyte was captured by the selected wavelengths.

Calibration transfer of a Raman spectroscopic quantification method from at-line to in-line assessment of liquid detergent compositions

The industrial production of liquid detergent compositions entails delicate balance of ingredients and process steps. In order to assure high quality and productivity in the manufacturing line, process analytical technology tools such as Raman spectroscopy are to be implemented. Marked chemical specificity, negligible water interference and high robustness are ascribed to this process analytical technique. Previously, at-line calibration models have been developed for determining the concentration levels of the being studied liquid detergents main ingredients from Raman spectra. A strategy is now proposed to transfer such at-line developed regression models to an in-line set-up, allowing real-time dosing control of the liquid detergent composition under production. To mimic in-line manufacturing conditions, liquid detergent compositions are created in a five-liter vessel with an overhead mixer. Raman spectra are continuously acquired by pumping the detergent under production via plastic tubing towards a Raman superhead probe, which is incorporated into a metal frame with a sapphire window facing the detergent fluid. Two at-line developed partial least squares (PLS) models are aimed at transferring, predicting the concentration of surfactant 1 and polymer 2 in the examined liquid detergent composition. A univariate slope/bias correction (SBC) is investigated, next to three well-acknowledged multivariate transformation methods: direct, piecewise and double-window piecewise direct standardization. Transfer is considered successful when the magnitude of the validation sets root mean square error of prediction (RMSEP) is similar to or smaller than the corresponding at-line prediction error. The transferred model offering the most promising outcome is further subjected to an exhaustive statistical evaluation, in order to appraise the applicability of the suggested calibration transfer method. Interval hypothesis tests are thereby performed for method comparison. It is illustrated that the investigated transfer approach yields satisfactory results, provided that the original at-line calibration model is thoroughly validated. Both SBC transfer models return lower RMSEP values than their corresponding original models. The surfactant 1 assay met all relevant evaluation criteria, demonstrating successful transfer to the in-line set-up. The in-line quantification of polymer 2 levels in the liquid detergent composition could not be statistically validated, due to the poorer performance of the at-line model.

Transfer of multivariate regression models between high-resolution NMR instruments: application to authenticity control of sunflower lecithin

In recent years the number of spectroscopic studies utilizing multivariate techniques and involving different laboratories has been dramatically increased. In this paper the protocol for calibration transfer of partial least square regression model between high-resolution nuclear magnetic resonance (NMR) spectrometers of different frequencies and equipped with different probes was established. As the test system previously published quantitative model to predict the concentration of blended soy species in sunflower lecithin was used. For multivariate modelling piecewise direct standardization (PDS), direct standardization, and hybrid calibration were employed. PDS showed the best performance for estimating lecithin falsification regarding its vegetable origin resulting in a significant decrease in root mean square error of prediction from 5.0 to 7.3% without standardization to 2.9-3.2% for PDS. Acceptable calibration transfer model was obtained by direct standardization, but this standardization approach introduces unfavourable noise to the spectral data. Hybrid calibration is least recommended for high-resolution NMR data. The sensitivity of instrument transfer methods with respect to the type of spectrometer, the number of samples and the subset selection was also discussed. The study showed the necessity of applying a proper standardization procedure in cases when multivariate model has to be applied to the spectra recorded on a secondary NMR spectrometer even with the same magnetic field strength. Copyright © 2016 John Wiley & Sons, Ltd.

Calibration transfer via an extreme learning machine auto-encoder

In order to solve the spectra standardization problem in near-infrared (NIR) spectroscopy, a Transfer via Extreme learning machine Auto-encoder Method (TEAM) has been proposed in this study. A comparative study among TEAM, piecewise direct standardization (PDS), generalized least squares (GLS) and calibration transfer methods based on canonical correlation analysis (CCA) was conducted, and the performances of these algorithms were benchmarked with three spectral datasets: corn, tobacco and pharmaceutical tablet spectra. The results show that TEAM is a stable method and can significantly reduce prediction errors compared with PDS, GLS and CCA. TEAM can also achieve the best RMSEPs in most cases with a small number of calibration sets. TEAM is implemented in Python language and available as an open source package at https://github.com/zmzhang/TEAM.

Feasibility study for transforming spectral and instrumental artifacts for multivariate calibration maintenance

Frequently, a spectral-based multivariate calibration model formed on a particular instrument (primary) needs to predict samples measured on other (secondary) instruments of the same spectral type. This situation is often referred to as calibration maintenance or transfer. A new calibration maintenance approach is developed in this paper using spectral differences between instruments. In conjunction with a sample weighting scheme, spectral differences are piecewise (wavelength window) or full spectrum fitted with modeling terms (correction terms) such as polynomials and derivatives. Results demonstrating the potential usefulness of the new method using a near infrared (NIR) benchmark dataset are presented in this paper. The process does not need a standardization sample set measured in the primary condition. Thus, the new approach is a "hybrid" between the popular methods of extended inverted multiplicative signal correction (EISC) and direct standardization (DS) or piecewise DS (PDS). It is found that prediction errors reduce for samples measured in the secondary condition compared to those based on no calibration transfer. Prediction errors are also comparable to those from a full calibration in the secondary condition. In addition to instrument correction, an extension of the new approach is discussed (but not tested) for predicting new samples changing over time due to new chemical, physical, and environmental measurement conditions including individually or combinations of temperature, sample particle size, and new spectrally responding species.

Calibration diagnostic and updating strategy based on quantitative modeling of near-infrared spectral residuals

Near-infrared spectral residuals are used to develop diagnostic and model updating procedures to enhance the performance of multivariate calibrations.

Near-infrared calibration transfer based on spectral regression

A calibration transfer method for near-infrared (NIR) spectra based on spectral regression is proposed. Spectral regression method can reveal low dimensional manifold structure in high dimensional spectroscopic data and is suitable to transfer the NIR spectra of different instruments. A comparative study of the proposed method and piecewise direct standardization (PDS) for standardization on two benchmark NIR data sets is presented. Experimental results show that spectral regression method outperforms PDS and is quite competitive with PDS with background correction. When the standardization subset has sufficient samples, spectral regression method exhibits excellent performance.

Maintaining the predictive abilities of multivariate calibration models by spectral space transformation

In quantitative on-line/in-line monitoring of chemical and bio-chemical processes using spectroscopic instruments, multivariate calibration models are indispensable for the extraction of chemical information from complex spectroscopic measurements. The development of reliable multivariate calibration models is generally time-consuming and costly. Therefore, once a reliable multivariate calibration model is established, it is expected to be used for an extended period. However, any change in the instrumental response or variations in the measurement conditions can render a multivariate calibration model invalid. In this contribution, a new method, spectral space transformation (SST), has been developed to maintain the predictive abilities of multivariate calibration models when the spectrometer or measurement conditions are altered. SST tries to eliminate the spectral differences induced by the changes in instruments or measurement conditions through the transformation between two spectral spaces spanned by the corresponding spectra of a subset of standardization samples measured on two instruments or under two sets of experimental conditions. The performance of the method has been tested on two data sets comprising NIR and MIR spectra. The experimental results show that SST can achieve satisfactory analyte predictions from spectroscopic measurements subject to spectrometer/probe alteration, when only a few standardization samples are used. Compared with the existing popular methods designed for the same purpose, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS), SST has the advantages of implementation simplicity, wider applicability and better performance in terms of predictive accuracy.

Investigation of chemometric instrumental transfer methods for high-resolution NMR

The implementation of direct standardization (DS), piecewise direct standardization (PDS), and double-window piecewise direct standardization (DWPDS) instrumental transfer techniques for high-resolution (1)H NMR spectral data was explored. The ability to transfer a multivariate calibration model developed for a "master or target" NMR instrument configuration to seven different ("secondary") NMR instrument configurations was measured. Partial least-squares (PLS) calibration of glucose, glycine, and citrate metabolite relative concentrations in model mixtures following mapping of the secondary instrumental configurations using DS, PDS, or DWPDS instrumental transfer allowed the performance of the different transfer methods to be assessed. Results from these studies suggest that DS and PDS transfer techniques produce similar improvements in the error of prediction compared to each other and provide a significant improvement over standard spectral preprocessing techniques including reference deconvolution and spectral binning. The DS instrumental transfer method produced the largest percent improvement in the predictions of concentrations for these model mixtures but, in general, required that additional transfer calibration standards be used. Limitations of the different instrumental transfer methods with respect to sample subset selection are also discussed.

Importance of prediction outlier diagnostics in determining a successful inter-vendor multivariate calibration model transfer

This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.

Construction of universal quantitative models for determination of roxithromycin and erythromycin ethylsuccinate in tablets from different manufacturers using near infrared reflectance spectroscopy

Universal quantitative models using NIR reflectance spectroscopy were developed for the analysis of API contents (active pharmaceutical ingredient) in roxithromycin and erythromycin ethylsuccinate tablets from different manufacturers in China. The two quantitative models were built from 78 batches of roxithromycin samples from 18 different manufacturers with the API content range from 19.5% to 73.9%, and 66 batches erythromycin ethylsuccinate tablets from 36 manufacturers with the API content range from 28.1% to 70.9%. Three different spectrometers were used for model construction in order to have robust and universal models. The root mean square errors of cross validation (RMSECV) and the root mean square errors of prediction (RMSEP) of the model for roxithromycin tablets were 1.84% and 1.45%, respectively. The values of RMSECV and RMSEP of the model for erythromycin ethylsuccinate tablets were 2.31% and 2.16%, respectively. Based on the ICH guidelines and characteristics of NIR spectroscopy, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, precision, robustness and model transferability. Our study has shown that it is feasible to build a universal quantitative model for quick analysis of pharmaceutical products from different manufacturers. Therefore, the NIR method could be used as an effective method for quick, non-destructive inspection of medicines in the distribution channels or open market.

Practical calibration correction method for the maintenance of an on-line near-infrared monitoring system for molten polymers

The present study has investigated a practical calibration correction method for an on-line monitoring system for molten polymers using a near-infrared (NIR) spectrometer. A partial least squares (PLS) calibration model for the ethylene (C2) content in melt polypropylene (PP) was developed for the investigation of changes in the performance of the on-line system before and after maintenance necessitated by the relocation. The predicted values for the C2 content from the spectra measured after maintenance by using the calibration model developed from the spectra collected before maintenance showed that there were some differences between the spectra obtained by the NIR spectrometer system before and after maintenance. The loadings from factor analysis suggested that the main cause for the differences in the system performance before and after maintenance was wavenumber shifts in the NIR spectra of PP in the melt state. Six popular standardization or calibration transfer methods (direct standardization (DS), piecewise direct standardization (PDS), additive correction (AD), multiplicative correction (MP), slope and bias (SB), and difference spectrum with interpolation (DSI)) were evaluated for the calibration correction of the on-line NIR monitoring system. However, the results of the evaluation showed that these standardization methods need more than two samples to obtain the high accuracy for the nonlinearity contained in the spectra set. From the standpoint of practical calibration in a real plant, the acceptable number of samples for the calibration is one or two. Moreover, recalibration using transferred spectra is not preferable because of the traceability for a calibration model. As a practical solution for a calibration correction in a real plant, a method considering wavenumber shift and path-length correction has been proposed in this study. The predicted results for the C2 content in the melt-state PP from the spectra measured after maintenance by using the proposed method have shown that the proposed method is useful for calibration correction in a real plant in spite of using only one sample.

Simultaneous resolution of overlapping peaks in high-performance liquid chromatography and micellar electrokinetic chromatography with diode array detection using augmented iterative target transformation factor analysis

In this paper, augmentation has been applied to data matrices, which originate from hyphenated methods that share the same mode of detection, but use different separation methods, HPLC-DAD and MEKC-DAD. A novel method, wavelength shift eigenstructure tracking (WET), has been proposed for the alignment between the wavelength scale of both detectors. WET proves to be suitable for the detection as well as correction of wavelength shift between both detectors. After correction of the wavelength scale, data obtained on both systems have been augmented and submitted to iterative target transformation factor analysis. Augmented curve resolution provides significantly better estimates of the chromatographic and electrophoretic profiles and spectra than the use of non-augmented curve resolution on HPLC and MEKC data separately. It is particularly useful when the pure fraction of a chromatographic peak is less than 0.10. Finally, the relative weight of MEKC versus HPLC in augmentation may be increased using intensity and noise normalisation. However, since noise normalisation and its accompanying decrease in signal-to-noise ratio leads to a loss of information, and, since intensity normalisation may cause a failure of the augmented curve resolution algorithm, benefits and drawbacks of normalisation should be weighed on a case-by-case basis.

Evaluation of basic algorithms for transferring quantitative multivariate calibrations between scanning grating and FT NIR spectrometers

A key issue in near infrared spectroscopy (NIR) is the possibility to use calibrations generated on one instrument for predictions on others. A number of methods for calibration transfer have been proposed, but which method to choose is typically not straightforward. An evaluation of a number of methods for transferring quantitative calibrations between different instruments was carried out on near infrared diffuse-reflectance data from a pharmaceutical formulation. Six instruments were included in the study, five of which were scanning grating instruments, both with and without fibre-optic probe configuration, and one of which was a Fourier-transform instrument, equipped with a fibre-optic probe. The results show that it is possible to transfer calibrations between different instruments, provided that a structured procedure is used. Simple techniques for calibration transfer, such as slope/bias correction on the predicted results, as well as standard normal variate transformation and local centring of the raw spectra, gave considerably lower prediction errors on transfer than did standardisation with a certified diffuse-reflectance standard, or direct transfer without any transfer function. Notably, including more than one instrument in the calibration also improved the prediction ability of the models on calibration transfer. No significant differences in wavelength scale were found when a certified diffuse-reflectance wavelength standard was measured on the instruments studied. Nor did simulated wavelength scale differences below +/-0.3 nm cause any significant change in the prediction errors.

Comparison of methods for transfer of calibration models in near-infared spectroscopy: a case study based on correcting path length differences using fiber-optic transmittance probes in in-line near-infrared spectroscopy

This article addresses problems related to transfer of calibration models due to variations in distance between the transmittance fiber-optic probes. The data have been generated using a mixture design and measured at five different probe distances. A number of techniques reported in the literature have been compared. These include multiplicative scatter correction (MSC), path length correction (PLC), finite impulse response (FIR), orthogonal signal correction (OSC), piecewise direct standardization (PDS), and robust calibration. The quality of the predictions was expressed in terms of root mean square error of prediction (RMSEP). Robust calibration gave good calibration transfer results, while the other methods did not give acceptable results.

Transfer of Multivariate Calibrations between Four Near-Infrared Spectrometers Using Orthogonal Signal Correction

The transfer of partial least squares (PLS) calibration models among four near-infrared spectrometers was investigated for the quantitative analysis of thermoset resin polymers. A comparative study of second derivatives, multiplicative scatter correction, finite impulse response filtering, slope and bias correction, model updating (MU), and orthogonal signal correction (OSC) was conducted to determine which processing methods achieved model transferability. It is shown that OSC and MU were superior to the other calibration transfer methods, leading to very robust PLS models with enhanced predictive ability. It is also shown that the transfer results obtained with OSC were not significantly different from those obtained with model updating.

Calibration standardization algorithm for partial least-squares regression: application to the determination of physiological levels of glucose by near-infrared spectroscopy

Calibration standardization methodology for near-infrared (near-IR) spectroscopy is described for updating a partial least-squares calibration model to take into account changes in instrumental response. The guided model reoptimization (GMR) algorithm uses a transfer set of eight samples to characterize the new response and a database of previously acquired spectra used to develop the original calibration model. The samples in the transfer set need not have been measured under the old instrumental conditions, making the algorithm compatible with samples that change over time. The spectra comprising the transfer set are used to guide an iterative optimization procedure that (1) finds an optimal subset of samples from the original database to use in computing the updated model and (2) finds an optimal set of weights to apply to the spectral resolution elements in order to minimize the effects of instrumental changes on the computed model. The optimization relies on an alternating grid search and stepwise addition/deletion steps. The algorithm is evaluated through the use of combination region near-IR spectra to determine physiological levels of glucose in a synthetic biological matrix containing bovine serum albumin and triacetin in phosphate buffer. The ability to update a calibration to account for changes in the response of a Fourier transform spectrometer over four to six years is examined in this study. Separate spectral databases collected in 1994 and 1996 are used with a transfer set and separate test set of spectra collected in 2000. With the 1994 database, the standardization algorithm achieves a standard error of prediction (SEP) of 0.69 mM for the 2000 test set. This compares favorably to SEP values > 2 mM when the original 1994 calibration model is used without standardization. A similar improvement in the prediction performance of the 2000 test set is obtained after standardization with the 1996 database (SEP = 0.70 mM).

Calibration transfer algorithm for automated qualitative analysis by passive Fourier transform infrared spectrometry

The automated qualitative analysis of passive Fourier transform infrared (FT-IR) remote sensing data is made difficult by the presence in the data of background and instrument-specific variation. For data collected with a single instrument, variation in the data arises from changes in the infrared background radiance, changes in the atmospheric composition within the field-of-view of the spectrometer, and changes in the instrument response function arising from temperature variation in the spectrometer. When more than one spectrometer is used, the variation in detector responses and phase signatures between instruments serves to complicate further the task of implementing an automated processing algorithm for detecting the signature of a target compound. In this work, a combination of signal processing and pattern recognition methodology is applied directly to the interferogram data collected by the FT-IR spectrometer to implement an automated compound detection procedure that is independent of background and instrument-specific variation. The key to this algorithm is the use of highly attenuating digital filters to isolate in the interferogram the frequencies associated with an analyte absorption or emission band while suppressing information at other frequencies. For the test compounds, acetone and sulfur hexafluoride, it is demonstrated that when this digital filtering procedure is coupled with either piecewise linear discriminant analysis or a back-propagation neural network, an automated detection algorithm can be developed with data from a primary instrument and then subsequently used to predict the presence of analyte signatures in data collected with a secondary spectrometer. Correct classification rates in excess of 92% are obtained for both compounds when the algorithm is applied to data collected with the secondary instrument.