title: Fourier transform infrared spectroscopy and partial least square regression for the prediction of substrate maturity indexes.
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 NH4(+)/NO3(-) 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(2)cal) close to 0.90 and correlation coefficient (R(2)) of cross validation (R(2)cv) presented acceptable values (>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(2)pred) 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.
authors.affiliation: Department of Soil Science, Federal University of Lavras, P.O. Box 3037, 37200-000 Lavras, Minas Gerais, Brazil. Electronic address: rioleste@gmail.com.
authors.forename: Fábio Satoshi
authors.initials: FS
authors.lastname: Higashikawa
authors.affiliation: Department of Soil Science, Federal University of Lavras, P.O. Box 3037, 37200-000 Lavras, Minas Gerais, Brazil.
authors.forename: Carlos Alberto
authors.initials: CA
authors.lastname: Silva
authors.affiliation: Department of Food Science, Federal University of Lavras, P.O. Box 3037, 37200-000 Lavras, Minas Gerais, Brazil.
authors.forename: Cleiton Antônio
authors.initials: CA
authors.lastname: Nunes
authors.affiliation: Department of Soil and Water Conservation and Organic Waste Management, Centro de Edafología y Biología Aplicada del Segura (CEBAS), Consejo Superior de Investigaciones Científicas (CSIC), P.O. Box 164, 30100 Murcia, Spain.
authors.forename: Miguel Angel
authors.initials: MA
authors.lastname: Sánchez-Monedero
mesh_terms: D052918:Environmental Restoration and Remediation / Q000379:methods / Q000706:statistics & numerical data*; D006812:Humic Substances; D016018:Least-Squares Analysis; D008956:Models, Chemical; D017550:Spectroscopy, Fourier Transform Infrared
chemical_list: D006812:Humic Substances
keywords: Compost; FTIR; Growing media; Multivariate calibration