• Aucun résultat trouvé

Locally-Weighted Partial Least Squares Regression for infrared spectra analysis

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Locally-Weighted Partial Least Squares Regression for infrared spectra analysis"

Copied!
3
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

HAL Id: hal-00717517

https://hal.archives-ouvertes.fr/hal-00717517

Submitted on 13 Jul 2012

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Locally-Weighted Partial Least Squares Regression for infrared spectra analysis

Aurélie Thébault, Damien Juery

To cite this version:

Aurélie Thébault, Damien Juery. Locally-Weighted Partial Least Squares Regression for infrared

spectra analysis. 1ères Rencontres R, Jul 2012, Bordeaux, France. �hal-00717517�

(2)

Locally-Weighted Partial Least Squares Regression for infrared spectra analysis A. Thébault and D. Juery

UMR MISTEA INRA Montpellier

2 place Pierre Viala, 34060 Montpellier Cedex 2 Contact author : aurelie.thebault@supagro.inra.fr

Keywords : Partial least square regression, Neighbour selection, Local calibration

Recent developments in infrared spectroscopy offer advantages in terms of speed and simplicity for routine analysis of chemical or biological data. Many chemometric tools have been developped to predict chemical and biological properties of samples from their infrared spectrum. Among them, the Partial Least Squares regression (PLS) is widely used to get information from large spectral databases. However, while PLS is efficient for predicting biological or chemical parameters from homogeneous spectral databases, it performs poorly on heteregeneous databases, leading to the use of local PLS. In local PLS, the chemical or biological parameters of an unknown sample are predicted from a subset of selected, spectrally similar, samples from a largest heterogeneous spectral database.

Several R-packages such as pls (Mevik et al., 2012), plspm (Sanchez and Trinchera, 2012), plsRglm (Bertrand et al., 2011), ChemoSpec (Hanson, 2012) and soil.spec (Terhoeven- Urselmans, 2012) already deal with spectral data analyses but none of them propose local calibration. Based on the LOCAL calibration algorithm (Shenk et al., 1997) and the Locally Weighted Regression (LWR ; Naes et al., 1990), we developed a powerful routine associating local calibration and weighting of the calibration samples for prediction of chemical or biological parameters from large and heterogeneous spectral databases.

The Locally-Weighted PLS routine allows optimization of spectral pre-processing within a large choice of available pre-treatments. The selection of the local calibration samples can be done either through the use of a metrics (Mahalanobis distance or correlation coefficient) or through Dirichlet Process (Neal, 2000). An optimization function of the number of local calibration samples is also available. The locally-weighted PLS routine achieves a trade-off between Locally Weighted Regression (Naes et al., 1990) and LOCAL algorithm (Shenk et al., 1997). Weights can be assigned to the selected calibration samples following the Locally Weighted Regression theory. Since the best number k of PLS components is unknown a priori, we adopt a model averaging strategy adapted from the LOCAL algorithm: the final estimator is based on the aggregation of many PLS estimators with an increasing number of components.

The steps of the Locally-Weighted PLS will be briefly presented. In a second time, the routine will be applied on a real dataset to present main functions and options.

References

[1] Mevik, B.H., Wehrens, R., Liland, K.H. (2012). pls – Partial least squares and principal component regression. R package version 2.3-0.

http://www.cran.r-object.org/package=pls/.

[2] Sanchez, G., Trinchera, L. (2012). pls pm– Partial least squares data analysis methods. R package version 0.2-2.

http://www.cran.r-object.org/package=plspm/.

(3)

[3] Bertrand, F., Meyer, N., Maumy-Bertrand, M. (2011). plsRglm – Partial least squares regression for generalized linear models. R package version 0.7-6.

http://www.cran.r-object.org/package=plsRglm/.

[4] Hanson, B.A. (2012). ChemoSpec – Exploratory chemometrics for spectroscopy. R package version 1.50-2.

http://www.cran.r-object.org/package=ChemoSpec/.

[5] Terhoeven-Urselmans, T. (2012). soil.spec – Spectral data exploration and regression functions. R package version 1.4.

http://www.cran.r-object.org/package=soil.spec/.

[6] Shenk, S., Berzaghi, P., Westerhaus, M.O. (1997). Investigation of a LOCAL calibration procedure for near infrared instruments. J. Near Infrared Spectrosc. 5, 223-232.

[7] Naes, T., Isaksson, T, Kowalski, B. (1990). Locally weighted regression and scatter correction for near-infrared reflectance data. Analytical Chemistry 62(7), 664-673.

[8] Neal, R.M. (2000). Markov Chain Sampling Methods for Dirichlet Process Mixture

Models. J. Computational and Graphical Statistics 9(2), 249-265.

Références

Télécharger maintenant ( PDF - 3 Page - 151.24 KB )

Documents relatifs

Brownian Motions and Scrambled Wavelets for Least-Squares Regression

We make use of two theories: (1) Gaussian Random Function Theory (that studies precise properties of objects like Brownian bridges, Strassen balls, etc.) that allows for

A Weighted Least-Squares Approach to Parameter Estimation Problems Based on Binary Measurements

This approach is based on the use of original weighted least-squares criteria: as opposed to other existing methods, it requires no dithering signal and it

A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data

The copyright holder for this preprint which was not peer-reviewed is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.. It is made

Locally Weighted Full Covariance Gaussian Density Estimation

Notice that locally weighted density estimation yields an unnormalized density estimate: in general it won’t integrate to 1, as is also the case of several classical

Supplement to "Robust linear least squares regression"

For any realization of this event for which the polynomial described in Theorem 1.4 does not have two distinct positive roots, the statement of Theorem 1.4 is void, and

Robust linear least squares regression

For the ridge estimator and the ordinary least squares estimator, and their variants, we provide new risk bounds of order d/n without logarithmic factor unlike some stan- dard

Data-driven calibration of penalties for least-squares regression

Indeed, it generalizes the results of Birg´e and Massart (2007) on the existence of minimal penalties to heteroscedastic regression with a random design, even if we have to restrict

M-Power Regularized Least Squares Regression

In this paper we proposed m-power regularized least squares regression (RLSR), a supervised regression algorithm based on a regularization raised to the power of m, where m is with