A novel algorithm based on coupling of the fast wavelet transform (FWT) with MLR and PLS regression techniques for the selection of optimal regression models between matrices of signals and response variables is presented: wavelet interface to linear modelling analysis (WILMA). The algorithm decomposes each signal into the FWT domain and then, by means of proper criteria, selects the wavelet coefficients that give the best regression models, as evaluated by the leave-one-out cross-validation criterion. The predictive ability of the regression model is then checked by means of external test sets. Moreover, the signals are reconstructed back in the original domain using only the selected wavelet coefficients, to allow for chemical interpretation of the results. The algorithm was tested on different literature data sets: two near-infrared data sets from Kalivas, on which the performances of many calibration algorithms have already been tested, and a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses. Good results were obtained for all the studied data sets; in particular, for the data sets from Kalivas the WILMA models showed improved predictive capability. Copyright

Multivariate calibration of analytical signals by WILMA (wavelet interface to linear modelling analysis) / Cocchi, Marina; Seeber, Renato; Ulrici, Alessandro. - In: JOURNAL OF CHEMOMETRICS. - ISSN 0886-9383. - STAMPA. - 17 (8-9):(2003), pp. 512-527.

Multivariate calibration of analytical signals by WILMA (wavelet interface to linear modelling analysis)

COCCHI, Marina;SEEBER, Renato;ULRICI, Alessandro
2003-01-01

Abstract

A novel algorithm based on coupling of the fast wavelet transform (FWT) with MLR and PLS regression techniques for the selection of optimal regression models between matrices of signals and response variables is presented: wavelet interface to linear modelling analysis (WILMA). The algorithm decomposes each signal into the FWT domain and then, by means of proper criteria, selects the wavelet coefficients that give the best regression models, as evaluated by the leave-one-out cross-validation criterion. The predictive ability of the regression model is then checked by means of external test sets. Moreover, the signals are reconstructed back in the original domain using only the selected wavelet coefficients, to allow for chemical interpretation of the results. The algorithm was tested on different literature data sets: two near-infrared data sets from Kalivas, on which the performances of many calibration algorithms have already been tested, and a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses. Good results were obtained for all the studied data sets; in particular, for the data sets from Kalivas the WILMA models showed improved predictive capability. Copyright
17 (8-9)
512
527
Multivariate calibration of analytical signals by WILMA (wavelet interface to linear modelling analysis) / Cocchi, Marina; Seeber, Renato; Ulrici, Alessandro. - In: JOURNAL OF CHEMOMETRICS. - ISSN 0886-9383. - STAMPA. - 17 (8-9):(2003), pp. 512-527.
Cocchi, Marina; Seeber, Renato; Ulrici, Alessandro
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/304201
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 35
  • ???jsp.display-item.citation.isi??? 30
social impact