Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy

The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established...

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Main Authors: Posom Jetsada, phuphanutada Jirawat, Lapcharoensuk Ravipat
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:MATEC Web of Conferences
Online Access:https://www.matec-conferences.org/articles/matecconf/pdf/2018/51/matecconf_iceast2018_03021.pdf
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spelling doaj-e62aef89ad1742839c8490078b2f965a2021-04-02T12:20:38ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-011920302110.1051/matecconf/201819203021matecconf_iceast2018_03021Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopyPosom Jetsadaphuphanutada JirawatLapcharoensuk RavipatThe aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.https://www.matec-conferences.org/articles/matecconf/pdf/2018/51/matecconf_iceast2018_03021.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Posom Jetsada
phuphanutada Jirawat
Lapcharoensuk Ravipat
spellingShingle Posom Jetsada
phuphanutada Jirawat
Lapcharoensuk Ravipat
Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
MATEC Web of Conferences
author_facet Posom Jetsada
phuphanutada Jirawat
Lapcharoensuk Ravipat
author_sort Posom Jetsada
title Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
title_short Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
title_full Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
title_fullStr Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
title_full_unstemmed Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
title_sort gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2018-01-01
description The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.
url https://www.matec-conferences.org/articles/matecconf/pdf/2018/51/matecconf_iceast2018_03021.pdf
work_keys_str_mv AT posomjetsada grosscalorificandashcontentassessmentofrecycledsawdustfrommushroomcultivationusingnearinfraredspectroscopy
AT phuphanutadajirawat grosscalorificandashcontentassessmentofrecycledsawdustfrommushroomcultivationusingnearinfraredspectroscopy
AT lapcharoensukravipat grosscalorificandashcontentassessmentofrecycledsawdustfrommushroomcultivationusingnearinfraredspectroscopy
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