Transfer of multivariate calibration models: a review

Feudale, Robert N.; Woody, Nathaniel A.; Tan, Hao; Myles, Anthony J.; Brown, Steven D.; Ferré, Joan

doi:10.1016/s0169-7439(02)00085-0

Cited by 461 publications

(353 citation statements)

References 80 publications

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“…Considerable effort is put into the construction of robust models since they are meant to be used for extended periods of time. Problems can arise, however, when the samples to be predicted are measured on a different instrument or under significantly different environmental conditions from those used to collect the data from which the model was constructed (Feudale et al, 2002). Ideally, models should be able to be applied without adjustment for a given product on every instrument.…”

Section: Resultsmentioning

confidence: 99%

Off-line and on-line measurements of drug-loaded hot-melt extruded films using Raman spectroscopy

Tumuluri

Kemper

Lewis

et al. 2008

International Journal of Pharmaceutics

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Section: Resultsmentioning

confidence: 99%

Off-line and on-line measurements of drug-loaded hot-melt extruded films using Raman spectroscopy

Tumuluri

Kemper

Lewis

et al. 2008

International Journal of Pharmaceutics

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“…There are several reasons to implement transference of models (standardization), which include the following examples: (a) need to transport a calibration model previously built on the first instrument to another; (b) changes in the instrument over time; (c) variation between samples from different production batches; and (d) situations when necessary sample treatments such as extraction or clean-up steps are carried out [86]. The latter case was observed with different extraction techniques such as solid-phase extraction (SPE) [59,60,64] and solid-phase microextraction (SPME) [61].…”

Section: Standardizationmentioning

confidence: 99%

Chemometric strategies for enhancing the chromatographic methodologies with second-order data analysis of compounds when peaks are overlapped

Goicoechea

Culzoni

García

et al. 2011

Talanta

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This overview covers the different chemometric strategies linked to chromatographic methodologies that have been used and presented in the recent literature to cope with problems related to incomplete separation, the presence of unexpected components in the sample, matrix effect and changes in the analytical signal due to pre-treatment of sample.Among the different chemometric strategies it focuses on pre-treatment of data to correct background and time shift of chromatographic peaks and the use of second-order algorithms to cope with overlapping peaks from analytes or from analytes and interferences in liquid chromatography coupled to diode array, fast-scanning fluorescence spectroscopy and mass spectrometry detectors.Finally the review presents the strategies used to deal with changes in the analytical response as result of matrix effect in liquid and gas chromatography, as well as the use of standardization strategies to correct modifications in the analytical signal as a consequence of sample pre-treatment in liquid chromatography.

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“…The transfer samples were selected from the calibration set according to the classical Kennard-Stone algorithm. 1,24 This algorithm is aimed at covering the sample space in a uniform manner by maximizing the Euclidean distances between the spectra of the selected samples. The effect of varying the number N trans of transfer samples from 2 to 15 was investigated.…”

Section: Data Setsmentioning

confidence: 99%

“…Moreover, calibration transfer may have to be performed if the instrument employed for data acquisition is not the same as the one used for building the calibration model. 1 The methods for calibration transfer can be divided in two basic approaches, namely: (i) Standardization of the model coefficients, instrumental responses, or predicted values. Examples of this approach include the univariate standardization method of Shenk and Westerhaus, 2 Slope/ Bias Correction (SBC), 3 Direct Standardization (DS) and Piecewise Direct Standardization (PDS), 4 among many others.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate calibration transfer employing variable selection and subagging

Martins¹,

Galvão²,

Pimentel³

2010

J. Braz. Chem. Soc.

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Neste artigo, é proposta uma nova técnica para transferência de calibração, que combina o Algoritmo das Projeções Sucessivas (APS) para seleção de variáveis robustas com a técnica de sub-amostragem e agregação de modelos conhecida como subagging. A técnica proposta tem por objetivo construir modelos de Regressão Linear Múltipla (RLM) que sejam robustos com respeito a diferenças na resposta instrumental de dois espectrômetros (primário e secundário). Para isso, um pequeno conjunto de amostras de transferência, com espectros adquiridos no instrumento secundário, é empregado para guiar o procedimento de seleção de variáveis. A eficiência da técnica proposta é demonstrada em um estudo de caso envolvendo a determinação por FT-IR da massa específica e duas temperaturas de destilação (T10%, T90%) em amostras de gasolina e a determinação por NIR de umidade em amostras de milho. Em termos do erro quadrático médio de predição no espectrômetro secundário, os modelos RLM gerados pela abordagem APS-subagging forneceram resultados melhores que os obtidos por Mínimos Quadrados Parciais empregando Padronização Direta por Partes. Em particular, o uso de subagging resultou em uma redução mais sistemática do erro de predição com a inclusão progressiva de amostras de transferência. This paper proposes a new technique for calibration transfer, which combines the Successive Projections Algorithm (SPA) for robust variable selection with the subsampling and model aggregation technique known as subagging. The proposed technique is aimed at building Multiple Linear Regression (MLR) models that are robust with respect to differences in the instrumental response of two spectrometers (primary and secondary). For this purpose, a small set of transfer samples with spectra acquired at the secondary instrument is employed to guide the variable selection procedure. The efficiency of the proposed technique is demonstrated in a case study concerning the FT-IR determination of specific mass and two distillation temperatures (T10%, T90%) for gasoline samples and the NIR determination of moisture in corn samples. In terms of the root-mean-square error of prediction at the secondary spectrometer, the MLR models obtained according to the SPA-subagging approach provided better results in comparison with Partial Least Squares employing Piecewise Direct Standardization. In particular, the use of subagging resulted in a more systematic reduction in the prediction error with the progressive inclusion of transfer samples.Keywords: multivariate calibration, calibration transfer, variable selection, successive projections algorithm, subagging IntroductionIn a wide sense, the term Calibration Transfer concerns a body of methods aimed at compensating for changes in experimental conditions that would compromise the prediction accuracy and reliability of a multivariate model. Such changes may refer to physical/ chemical characteristics of the sample (such as viscosity, granularity, surface texture, and presence of interferent species), environmental conditions (te...

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Transfer of multivariate calibration models: a review

Cited by 461 publications

References 80 publications

Off-line and on-line measurements of drug-loaded hot-melt extruded films using Raman spectroscopy

Off-line and on-line measurements of drug-loaded hot-melt extruded films using Raman spectroscopy

Chemometric strategies for enhancing the chromatographic methodologies with second-order data analysis of compounds when peaks are overlapped

Multivariate calibration transfer employing variable selection and subagging

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