Preprint Article Version 1 This version not peer reviewed

Moisture Content Measurement of Broadleaf Litters using Near-Infrared Spectroscopy Technique

Version 1 : Received: 20 September 2017 / Approved: 21 September 2017 / Online: 21 September 2017 (04:36:21 CEST)

How to cite: Kim, G.; Hong, S.; Lee, A.; Lee, Y.; Im, S. Moisture Content Measurement of Broadleaf Litters using Near-Infrared Spectroscopy Technique. Preprints 2017, 2017090099 (doi: 10.20944/preprints201709.0099.v1). Kim, G.; Hong, S.; Lee, A.; Lee, Y.; Im, S. Moisture Content Measurement of Broadleaf Litters using Near-Infrared Spectroscopy Technique. Preprints 2017, 2017090099 (doi: 10.20944/preprints201709.0099.v1).

Abstract

Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. Maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture.

Subject Areas

near-infrared spectroscopy; multivariate analysis; partial least-squares regression; floor litter; optimal wavelength selection

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