Literature DB >> 16454905

Evaluation of pretreatment strategies for near-infrared spectroscopy calibration development of unground and ground compound feedingstuffs.

V M Fernández-Cabanás1, A Garrido-Varo, D Pérez-Marín, P Dardenne.   

Abstract

Chemometric procedures are usually applied to near-infrared (NIR) spectra in order to obtain prediction models. These procedures include the application of different combinations of spectral mathematical pretreatments for the improvement of calibrations and the selection of the best model on the basis of validation results. In this work, we used an automatic routine to obtain calibrations for unground and ground compound feedingstuffs (N=354 samples), including 49 combinations of pretreatments (first and second derivatives, an auto scaling procedure, detrending and two versions of multiplicative scatter correction). Calibrations for crude fiber and crude protein were developed without elimination of outliers and with 2 or 9 maximum passes of elimination of outliers. Validation statistics were highly influenced by the pretreatments used, as a combined result of their ability to improve the detection of outliers and the model adjustment. The standard error of prediction (SEP) values ranged from 0.61 to 1.27 for crude protein (CP) and from 0.74 to 1.33 for crude fiber (CF). In spite of the fact that validation statistics did not show a clear distribution pattern, some combinations of pretreatments provided consistently better results.

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Year:  2006        PMID: 16454905     DOI: 10.1366/000370206775382839

Source DB:  PubMed          Journal:  Appl Spectrosc        ISSN: 0003-7028            Impact factor:   2.388


  3 in total

1.  Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis.

Authors:  Cláudia Domiciano Tormena; Gustavo Galo Marcheafave; Elis Daiane Pauli; Roy Edward Bruns; Ieda Spacino Scarminio
Journal:  Environ Sci Pollut Res Int       Date:  2019-08-21       Impact factor: 4.223

2.  Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods.

Authors:  Soo-In Sohn; Subramani Pandian; John-Lewis Zinia Zaukuu; Young-Ju Oh; Soo-Yun Park; Chae-Sun Na; Eun-Kyoung Shin; Hyeon-Jung Kang; Tae-Hun Ryu; Woo-Suk Cho; Youn-Sung Cho
Journal:  Int J Mol Sci       Date:  2021-12-25       Impact factor: 5.923

3.  Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

Authors:  Salvador Gutiérrez; Javier Tardaguila; Juan Fernández-Novales; María P Diago
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

  3 in total

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