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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2018-01-01
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Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2018/51/matecconf_iceast2018_03021.pdf |
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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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