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|Título: ||Fourier transform infrared spectroscopy and partial least square regression for the prediction of substrate maturity indexes|
|Autor(es): ||Higashikawa, F. S.|
Silva, C. A.
Nunes, C. A.
Sánchez-Monedero, M. A.
|Assunto: ||Fourier transform infrared (FTIR) spectroscopy|
|Publicador: ||International Journal for Scientific Research|
|Data de publicação: ||22-Set-2013|
|Referência: ||HIGASHIKAWA, F. S. et al. Fourier transform infrared spectroscopy and partial least square regression for the prediction of substrate maturity indexes. Science of the Total Environment, Amsterdam, v. 470/471, p. 536-542, Feb. 2014.|
|Abstract: ||Traditional methods to evaluate the stability and maturity of organic wastes and composting matrices are
laborious, time-consuming and generate laboratory chemical wastes. This study focused on the development
of partial least square (PLS) regression models for the prediction of the stability and maturity of compost-
based substrates based on Fourier transform infrared (FTIR) spectroscopy. The following parameters, selected
as conventional maturity indexes, were modeled and used as dataset: dissolved organic carbon (DOC), C/N
and NH +
4 /NO 3 ratios, cation exchange capacity (CEC), degree of polymerization (DP), percentage of humic
acid (PHA), humification index (HI) and humification ratio (HR). Models were obtained by using data from a
wide range of compost based growing media of diverse origin and composition, including 4 commercially
available substrates and 11 substrates prepared in our facilities with varying proportions of different organic
wastes. The PLS models presented correlation coefficient of calibration (R 2cal ) close to 0.90 and correlation
coefficient (R 2 ) of cross validation (R 2cv ) presented acceptable values (N 0.6), ranging from 0.67 (HR) to 0.92
(C/N). The good performance of the method was also confirmed by the low correlation obtained from the Y-
randomization test. R 2 for test samples (R 2pred ) ranged from 0.66 (C/N) to 0.97 (HI) confirming the good
correlation between measured and PLS predicted maturity indexes. FTIR spectroscopy combined with PLS
regression represents, after modeling process, a fast and alternative method to assess substrate maturity and
stability with reduction of time, lower generation of laboratory chemical wastes residues and lower cost per
sample than conventional chemical methods. All models adjusted for maturity indexes are predictive, robust
and did not present chance correlation.|
|Aparece nas coleções: ||DCA - Artigos publicados em periódicos|
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