Spectral data mining for rapid measurement of organic matter in unsieved moist compost

Somsubhra Chakraborty, Nasim Ali, David C. Weindorf, Bin Li, Yufeng Ge, Jeremy L. Darilek

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Fifty-five compost samples were collected and scanned as received by visible and near-IR (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy. The raw reflectance and first-derivative spectra were used to predict log10-transformed organic matter (OM) using partial least squares (PLS) regression, penalized spline regression (PSR), and boosted regression trees (BRTs). Incorporating compost pH, moisture percentage, and electrical conductivity as auxiliary predictors along with reflectance, both PLS and PSR models showed comparable cross-validation r2and validation root-mean-square deviation (RMSD). The BRTreflectance model exhibited best predictability (residual prediction deviation = 1.61, crossvalidation r2= 0.65, and RMSD = 0.09 log10%). These results proved that the VisNIR BRT model, along with easy-to measure auxiliary variables, has the potential to quantify compost OM with reasonable accuracy.

Original languageEnglish
Pages (from-to)B82-B92
JournalApplied Optics
Volume52
Issue number4
DOIs
StatePublished - Feb 1 2013

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