A multivariate calibration procedure for the tensammetric determination of detergents

A multivariate calibration procedure based on singular value decomposition (SVD) and the Ho-Kashyap algorithm is used for the tensammetric determination of the cationic detergents Hyamine 1622, benzalkonium chloride (BACl), N-cetyl-N,N,N-trimethylammonium bromide (CTABr) and mixtures of CTABr and BACl. The sensitivity and accuracy depend strongly on the nature of the detergent. Acceptable accuracy is obtained with a two-step calculation procedure in which calibration constants for the total concentration range of interest are used to guide the choice of a more specific set of calibration constants which are valid for a much smaller concentration span. For Hyamine 1622, concentrations in the range 5 × 10−6−2 × 10−4 M could be determined with an accuracy of ± 10−6 M. For CTABr, these numbers were 3 × 10−6−2 × 10−4 M and ± 5 × 10−7 M; for BACl, they were 2 × 10−3−9 × 10−2 g l−1 and ± 1 × 10−3 g l−1. In the mixtures of CTABr and BACl, the accuracies were ± 3 × 10−6 M and × 1 × 10−3 g l−1, respectively

MULTIVARIATE CALIBRATION FOR ICP-AES

The analysis of metals is now a major application area for ICP-AES, however, the technique suffers from both spectral and non-spectral interferences. This thesis details the application of univariate and multivariate calibration methods for the prediction of Pt, Pd, and Rh in acid-digested and of Au, Ag and Pd in fusion-digested autocatalyst samples. Of all the univariate calibration methods investigated matrix matching proved the most accurate method with relative root mean square errors (RRMSEs) for Pt, Pd and Rh of 2.4, 3.7, and 2.4 % for a series of synihelic lest solutions, and 12.0, 2.4, and 8.0 % for autocatalyst samples. In comparison, the multivariate calibration method (PLSl) yielded average relative errors for Pt, Pd, and RJi of 5.8, 3.0, and 3.5 % in the test solutions, and 32.0, 7.5, and 75.0 % in the autocatalyst samples. A variable selection procedure has been developed enabling multivariate models to be built using large parts of the atomic emission spectrum. The first stage identified and removed wavelengths whose PLS regression coefficients were equal to zero. The second stage ranked the remaining wavelengths according to their PLS regression coefficient and estimated standard error ratio. The algorithms were applied to the emission spectra for the determination of Pt, Pd and Rh in a synthetic matrix. For independent test samples variable selection gave RRMSEs of 5.3, 2.5 and 1.7 % for Pt, Pd and Rh respectively compared with 8.3, 7.0 and 3.1 % when using integrated atomic emission lines. Variable selection was then applied for the prediction of Au, Ag and Pd in independent test fusion digests. This resulted in RRMSEs of 74.2, 8.8 and 12.2 % for Au, Ag and Pd respectively which were comparable to those obtained using a more traditional univariate calibration approach. A preliminary study has shown that calibration drift can be corrected using Piecewise Direct Standardisation (PDS). The application of PDS to synthetic test samples analysed 10 days apart resulted in RRMSEs of 4.14, 3.03 and 1.88%, compared to 73.04, 44.39 and 28.06 % without correction, for Pt, Pd, and Rh respectively.The Analytical Innovation Programme, Johnson Matthey Ltd. and Department of Trade and Industr

Multivariate calibration of a water and energy balance model in the spectral domain

The objective of this paper is to explore the possibility of using multiple variables in the calibration of hydrologic models in the spectral domain. A simple water and energy balance model was used, combined with observations of the energy balance and the soil moisture profile. The correlation functions of the model outputs and the observations for the different variables have been calculated after the removal of the diurnal cycle of the energy balance variables. These were transformed to the frequency domain to obtain spectral density functions, which were combined in the calibration algorithm. It has been found that it is best to use the square root of the spectral densities in the parameter estimation. Under these conditions, spectral calibration performs almost equally as well as time domain calibration using least squares differences between observed and simulated time series. Incorporation of the spectral coefficients of the cross-correlation functions did not improve the results of the calibration. Calibration on the correlation functions in the time domain led to worse model performance. When the meteorological forcing and model calibration data are not overlapping in time, spectral calibration has been shown to lead to an acceptable model performance. Overall, the results in this paper suggest that, in case of data scarcity, multivariate spectral calibration can be an attractive tool to estimate model parameters

Spectroscopic determination of industrial oil blends using multivariate calibraton

Thesis (Master)--İzmir Institute of Technology, Chemistry, İzmir, 2009Includes bibliographical references (leaves: 104-106)Text in English; Abstract: Turkish and Englishxv, 106 leavesThis study focuses on the development of multivariate calibration models for the aluminum rolling oil additives and contaminants using Fourier Transform Infrared (FTIR) spectroscopy and a genetic algorithm based inverse least squares (GILS) method. Multivariate calibration models were generated for both synthetic mixtures and real process samples taken from an industrial aluminum production plant. Two different additives and six different suspected contaminants were investigated in the base oil lubricant. Gas chromatography (GC) was used for the analysis of real process samples in order to establish reference values of additives and contaminants in the base rolling oil. FTIR spectra of real samples together with the reference values established with GC analysis were used to generate multivariate calibration models. GC analysis revealed that most of the contaminants gave overlapped chromatograms and therefore only the total contamination was determined with reference GC analysis. On the other hand, FTIR spectroscopy coupled with multivariate calibration was able to resolve overlapping components with synthetic samples. The reference values for both additives and contaminants obtained by GC were compared with the results of the spectroscopic analysis. The multivariate calibration models based on spectroscopic data validated with the real process samples in a period of twelve months, however only a set of 3-month data is given in this thesis. The R2 values between GC and multivariate spectroscopic determinations were around 0.99 indicating a good correlation between the two methods

Regularization Adaption Processes for Multivariate Calibration Maintenance

In the field of chemometrics, an important issue in multivariate calibration is model updating. Model updating is the adaption process in which a model obtained for a given set of samples and measurement conditions (primary) is updated to predict the analyte in new samples and measurement conditions (secondary). The calibration method partial least squares is applied with two new updating approaches. In one approach, only one updated model is obtained to predict the analyte amount in both primary and secondary conditions. The other approach forms two updated models in which one model is used to predict in primary conditions and second model based on the first model is used to predict in secondary conditions. Both approaches are evaluated with near-infrared spectral datasets. Datasets include spectra of soil, corn, olive oil adulterated with sunflower and pharmaceutical tablets. Fusion process and single merits are used to select models. Model selection methods are evaluated based on prediction errors using selected models

Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration

AbstractThe aim of this study was to evaluate near-infrared reflectance spectroscopy (NIR), and multivariate calibration potential as a rapid method to determinate anthocyanin content in intact fruit (açaí and palmitero-juçara). Several multivariate calibration techniques, including partial least squares (PLS), interval partial least squares, genetic algorithm, successive projections algorithm, and net analyte signal were compared and validated by establishing figures of merit. Suitable results were obtained with the PLS model (four latent variables and 5-point smoothing) with a detection limit of 6.2gkg−1, limit of quantification of 20.7gkg−1, accuracy estimated as root mean square error of prediction of 4.8gkg−1, mean selectivity of 0.79gkg−1, sensitivity of 5.04×10−3gkg−1, precision of 27.8gkg−1, and signal-to-noise ratio of 1.04×10−3gkg−1. These results suggest NIR spectroscopy and multivariate calibration can be effectively used to determine anthocyanin content in intact açaí and palmitero-juçara fruit

Multivariate Calibration Domain Adaptation with Unlabeled Data

Multivariate calibration is about modeling the relationship between a substance\u27s chemical profile and its spectrum (here, near-infrared) in order to predict the concentration of new samples with known spectra. However, these new samples are often measured under different conditions than the primary conditions; different instruments, instrument drift, and temperature all affect the measurement conditions. Domain adaptation (DA) methods force the model to ignore these differences in order to generate an accurate model for the new domain (secondary conditions). There are two fundamental DA processes that individual methods can be classified under. One augments a few samples from the secondary domain with chemical reference values (labels) to the primary data and the other augments only secondary spectra (unlabeled data). In this work, we compare two existing labeled DA methods and two existing unlabeled DA methods to two novel labeled methods and a novel unlabeled approach. Since DA methods require selection of hyperparameters, a model selection framework based on model diversity and prediction similarity (MDPS) is applied to the DA methods. Regardless of the DA method, the MDPS process is shown to select models more accurate than the first quartile of all models generated by the DA process in three near-infrared datasets