| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1242595 | Talanta | 2016 | 6 Pages |
•IR spectroscopy was applied to predict total phenolic compounds (TPC) in compost samples.•Classification of compost according to their origin was made by principal component analysis (PCA).•Direct analysis without sample pretreatment or analyte extraction.•Fast, non-cost and environmentally friendly green alternative to spectrophotometric based methods.
Middle and near infrared (MIR and NIR) were applied to determine the total phenolic compounds (TPC) content in compost samples based on models built by using partial least squares (PLS) regression. The multiplicative scatter correction, standard normal variate and first derivative were employed as spectra pretreatment, and the number of latent variable were optimized by leave-one-out cross-validation. The performance of PLS-ATR-MIR and PLS-DR-NIR models was evaluated according to root mean square error of cross validation and prediction (RMSECV and RMSEP), the coefficient of determination for prediction (Rpred2) and residual predictive deviation (RPD) being obtained for this latter values of 5.83 and 8.26 for MIR and NIR, respectively.
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