Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) for rapid measurement of organic matter in compost

Commercial compost is the inherently variable organic product of a controlled decomposition process. In the USA, assessment of compost’s physicochemical parameters presently relies on standard laboratory analyses set forth in Test Methods for the Examination of Composting and Compost (TMECC). A rapi...

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Bibliographic Details
Published in:Waste management & research 2012-10, Vol.30 (10), p.1049-1058
Main Authors: McWhirt, Amanda L, Weindorf, David C, Chakraborty, Somsubhra, Li, Bin
Format: Article
Language:English
Subjects:
Composting
Decomposition
Laboratories
Natural products
Physical chemistry
Principal Component Analysis
Reproducibility of Results
Soil - chemistry
Spectrum analysis
Spectrum Analysis - methods
Studies
Time Factors
Yard waste
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Summary:Commercial compost is the inherently variable organic product of a controlled decomposition process. In the USA, assessment of compost’s physicochemical parameters presently relies on standard laboratory analyses set forth in Test Methods for the Examination of Composting and Compost (TMECC). A rapid, field-portable means of assessing the organic matter (OM) content of compost products would be useful to help producers ensure optimal uniformity in their compost products. Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) is a rapid, proximal-sensing technology proven effective at quantifying organic matter levels in soils. As such, VisNIR DRS was evaluated to assess its applicability to compost. Thirty-six compost samples representing a wide variety of source materials and moisture content were collected and scanned with VisNIR DRS under moist and oven-dry conditions. Partial least squares (PLS) regression and principal component regression (PCR) were used to relate the VisNIR DRS spectra with laboratory-measured OM to build compost OM prediction models. Raw reflectance, and first- and second-derivatives of the reflectance spectra were considered. In general, PLS regression outperformed PCR and the oven-dried first-derivative PLS model produced an r2 value of 0.82 along with a residual prediction deviation value of 1.72. As such, VisNIR DRS shows promise as a suitable technique for the analysis of compost OM content for dried samples.
ISSN:0734-242X
1096-3669
DOI:10.1177/0734242X12450601