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NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study
Commercial mushroom growth on substrate material produces a heterogeneous waste that can be used for bioenergy purposes. Hyperspectral imaging in the near-infrared (NHI) was used to experimentally study a number of spent mushroom substrate (SMS) packed samples under different conditions (wet vs. dry...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5352737/ https://www.ncbi.nlm.nih.gov/pubmed/28116491 http://dx.doi.org/10.1007/s00216-017-0192-2 |
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author | Wei, Maogui Geladi, Paul Xiong, Shaojun |
author_facet | Wei, Maogui Geladi, Paul Xiong, Shaojun |
author_sort | Wei, Maogui |
collection | PubMed |
description | Commercial mushroom growth on substrate material produces a heterogeneous waste that can be used for bioenergy purposes. Hyperspectral imaging in the near-infrared (NHI) was used to experimentally study a number of spent mushroom substrate (SMS) packed samples under different conditions (wet vs. dry, open vs. plastic covering, and round or cuboid) and to explore the possibilities of direct characterization of the fresh substrate within a plastic bag. Principal components analysis (PCA) was used to remove the background of images, explore the important studied factors, and identify SMS and mycelia (Myc) based on the pixel clusters within the score plot. Overview PCA modeling indicated high moisture content caused the most significant effects on spectra followed by the uneven distribution of Myc and the plastic cover. There were well-separated pixel clusters for SMS and Myc under different conditions: dry, wet, or wet and plastic covering. The loading peaks of the related component and the second derivative of the mean spectra of pixel clusters of SMS and Myc indicated that there are chemical differences between SMS and Myc. Partial least squares discriminant analysis (PLS-DA) models were calculated and classification of SMS and Myc was successful, whether the materials were dry or wet. Peak shifts because of high moisture content and unexpected peaks from the plastic covering were found. Although the best results were obtained for dried cylinders, it was shown that almost equally good results could be obtained for the wet material and for the wet material covered by plastic. Furthermore, PLS-DA prediction showed that a side face hyperspectral image could represent the information for the entire SMS cylinder when Myc was removed. Thus, the combination of NHI and multivariate image analysis has great potential to develop calibration models to directly predict the contents of water, carbohydrates, lignin, and protein in wet and plastic-covered SMS cylinders. |
format | Online Article Text |
id | pubmed-5352737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-53527372017-03-27 NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study Wei, Maogui Geladi, Paul Xiong, Shaojun Anal Bioanal Chem Research Paper Commercial mushroom growth on substrate material produces a heterogeneous waste that can be used for bioenergy purposes. Hyperspectral imaging in the near-infrared (NHI) was used to experimentally study a number of spent mushroom substrate (SMS) packed samples under different conditions (wet vs. dry, open vs. plastic covering, and round or cuboid) and to explore the possibilities of direct characterization of the fresh substrate within a plastic bag. Principal components analysis (PCA) was used to remove the background of images, explore the important studied factors, and identify SMS and mycelia (Myc) based on the pixel clusters within the score plot. Overview PCA modeling indicated high moisture content caused the most significant effects on spectra followed by the uneven distribution of Myc and the plastic cover. There were well-separated pixel clusters for SMS and Myc under different conditions: dry, wet, or wet and plastic covering. The loading peaks of the related component and the second derivative of the mean spectra of pixel clusters of SMS and Myc indicated that there are chemical differences between SMS and Myc. Partial least squares discriminant analysis (PLS-DA) models were calculated and classification of SMS and Myc was successful, whether the materials were dry or wet. Peak shifts because of high moisture content and unexpected peaks from the plastic covering were found. Although the best results were obtained for dried cylinders, it was shown that almost equally good results could be obtained for the wet material and for the wet material covered by plastic. Furthermore, PLS-DA prediction showed that a side face hyperspectral image could represent the information for the entire SMS cylinder when Myc was removed. Thus, the combination of NHI and multivariate image analysis has great potential to develop calibration models to directly predict the contents of water, carbohydrates, lignin, and protein in wet and plastic-covered SMS cylinders. Springer Berlin Heidelberg 2017-01-23 2017 /pmc/articles/PMC5352737/ /pubmed/28116491 http://dx.doi.org/10.1007/s00216-017-0192-2 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Paper Wei, Maogui Geladi, Paul Xiong, Shaojun NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study |
title | NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study |
title_full | NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study |
title_fullStr | NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study |
title_full_unstemmed | NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study |
title_short | NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study |
title_sort | nir hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5352737/ https://www.ncbi.nlm.nih.gov/pubmed/28116491 http://dx.doi.org/10.1007/s00216-017-0192-2 |
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