Preprocessing Methods

Zeaiter, Magida

doi:10.1016/b978-044452701-1.00074-0

Cited by 43 publications

(38 citation statements)

References 78 publications

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“…If a large number of samples are available in the secondary condition, the standardization set can be selected from these samples using the Kennard Stone algorithm (a commonly used algorithm where the goal is to select samples spanning the respective space) [36]. In this case, the standardization set should be representative of the new condition.…”

Section: Standardization Samplesmentioning

confidence: 99%

Impact of standardization sample design on Tikhonov regularization variants for spectroscopic calibration maintenance and transfer

Kunz

Ottaway

Kalivas

et al. 2010

Journal of Chemometrics

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Multivariate spectroscopic calibration models are only valid to predict samples within the span of the calibration sample space measured relative to the current instrument environment (the primary conditions). Predicting samples in secondary conditions with new variances, same sample variances as the calibration space but a new instrument environment, or both, requires some form of continual model maintenance and/or transfer. Previous work has shown that a Tikhonov regularization (TR) approach is capable of accomplishing both tasks by updating the primary model based on only a few samples (transfer or standardization set) measured under the secondary conditions. A distinction of the TR design for calibration maintenance and transfer is a defined weighting scheme for the small set of standardization samples augmented to the full set of primary calibration samples. Critical to successful calibration maintenance or transfer is the standardization sample set composition, i.e. standardization samples should properly represent that are less secondary conditions. This paper reports on using TR-based methods to investigate this issue and a consensus modeling approach is briefly evaluated.

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Section: Standardization Samplesmentioning

confidence: 99%

Impact of standardization sample design on Tikhonov regularization variants for spectroscopic calibration maintenance and transfer

Kunz

Ottaway

Kalivas

et al. 2010

Journal of Chemometrics

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“…Previous research has been done to compare pre-processing methods for scatter correction in spectra: multiplicative scatter correction (MSC), inverse MSC, extended MSC (EMSC), de-trending, standard normal variate (SNV), normalization and spectral derivatives (Fearn et al, 2009;Laxalde et al, 2011;Rinnan et al, 2009;Zeaiter et al, 2005;Zeaiter and Rutledge, 2009). Geometric preprocessing methods are widely carried out to correct spectral data from drift in baseline, non-linearity, curvilinearity, as well as additive and multiplicative effects.…”

Section: Introductionmentioning

confidence: 99%

Monitoring spinach shelf-life with hyperspectral image through packaging films

Lara¹,

Lleó²,

Diezma³

et al. 2013

Journal of Food Engineering

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“…To assist in developing a model insensitive to the interferent space, pre‐processing methods are commonly used to remove the interferent information (Zeaiter et al ; Zeaiter and Rutledge, ). The methods are varied and well reviewed including examples and lists of pros and cons (Zeaiter and Rutledge, ).…”

Section: Introductionmentioning

confidence: 99%

Interrelationships between generalized Tikhonov regularization, generalized net analyte signal, and generalized least squares for desensitizing a multivariate calibration to interferences

Andries

Kalivas

2013

Journal of Chemometrics

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Orthogonal pre‐processing (orthogonal projection) of spectral data is a common approach to generate analyte‐specific information for use in multivariate calibration. The goal of this pre‐processing is to remove from each spectrum the respective sample interferent contributions (spectral interferences from overlap, scatter, noise, etc.). Two approaches to accomplish orthogonal pre‐processing are net analyte signal (NAS) and generalized least squares (GLS). Developed in this paper is the mathematical relationship between NAS and GLS. It is also realized that orthogonal NAS pre‐processing can remove too much analyte signal and that the degree of interferent correction can be regulated. Similar to GLS, the degree of correction is accomplished by using a regularization (tuning) parameter to form generalized NAS (GNAS). Also developed in this paper is an alternative to GNAS and GLS based on generalized Tikhonov regularization (GTR). The mathematical relationships between GTR, GNAS, and GLS are derived. A result is the ability to express the model vector as the sum of two contributions: the orthogonal NAS contribution and a non‐NAS contribution from the interferent components. Thus, rather than the usual situation of sequentially pre‐processing data by either GNAS or GLS followed by model building with the pre‐processed data, the methods of GTR, GNAS, and GLS are expressed as direct computations of model vectors allowing concurrent pre‐processing and model building to occur. Simultaneous pre‐processing and model forming are shown to be natural to the GTR process. Two near‐infrared spectroscopic data sets are studied to compare the theoretical relationships between GTR, GNAS, and GLS. One data set covers basic calibration, and the other data set is for calibration maintenance. Filter factor representation is key to developing the interprocess relationships. Copyright © 2013 John Wiley & Sons, Ltd.

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Preprocessing Methods

Cited by 43 publications

References 78 publications

Impact of standardization sample design on Tikhonov regularization variants for spectroscopic calibration maintenance and transfer

Impact of standardization sample design on Tikhonov regularization variants for spectroscopic calibration maintenance and transfer

Monitoring spinach shelf-life with hyperspectral image through packaging films

Interrelationships between generalized Tikhonov regularization, generalized net analyte signal, and generalized least squares for desensitizing a multivariate calibration to interferences

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