Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils

Chitin, a natural polymer, has been added to soil to enhance plant resistance to pathogens, yet its impact on soil’s chemical and biochemical properties remains unclear. Standard methods to measure these properties are often slow and labor-intensive. This study aimed to assess pH, total DNA, chitin...

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Main Authors: Ken Abamba Omwange, Ana Chen, Renfu Lu, Carly Daiek, Julie Celini, Yan Liu
Format: Article
Language:English
Published: PAGEPress Publications 2025-02-01
Series:Journal of Agricultural Engineering
Subjects:
Online Access:https://www.agroengineering.org/jae/article/view/1710
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author Ken Abamba Omwange
Ana Chen
Renfu Lu
Carly Daiek
Julie Celini
Yan Liu
author_facet Ken Abamba Omwange
Ana Chen
Renfu Lu
Carly Daiek
Julie Celini
Yan Liu
author_sort Ken Abamba Omwange
collection DOAJ
description Chitin, a natural polymer, has been added to soil to enhance plant resistance to pathogens, yet its impact on soil’s chemical and biochemical properties remains unclear. Standard methods to measure these properties are often slow and labor-intensive. This study aimed to assess pH, total DNA, chitinase activity, and trpB gene DNA targeting for the identification of pathogen Streptomyces scabies in chitin-treated soil using both conventional wet chemical analysis methods and near-infrared (NIR) spectroscopy for rapid measurement. Five soil groups were treated with varying chitin levels (control, 0.2% and 2% fungus chitin, 0.2% and 2% Sigma chitin) and sampled every 9 days over 122 days. NIR reflectance spectra (900-1,685 nm) from the soil samples were acquired at each sampling time. Partial least squares regression models  were developed using preprocessing methods like Kubelka-Munk, second derivative, and smoothed data. The second derivative model for 900-1,660 nm yielded high predictive accuracy with R² values of 0.915, 0.892, and 0.810 for pH, total DNA, and trpB gene DNA respectively. This study demonstrates the potential of NIR  spectroscopy coupled with partial least squares algorithms as a rapid, cost-effective method for estimating soil biochemical properties.
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institution Kabale University
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publishDate 2025-02-01
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spelling doaj-art-367728bfee52445a913c1a5f1f9d7a242025-02-04T02:35:00ZengPAGEPress PublicationsJournal of Agricultural Engineering1974-70712239-62682025-02-0156110.4081/jae.2025.1710Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soilsKen Abamba Omwange0https://orcid.org/0000-0002-8268-1940Ana Chen1Renfu Lu2Carly Daiek3https://orcid.org/0000-0001-5593-1812Julie Celini4https://orcid.org/0009-0002-3499-8430Yan Liu5https://orcid.org/0000-0002-5039-6731USDA-ARS Midwest Area Sugarbeet and Bean Research Unit, East Lansing, MISchool of Biological and Chemical Engineering, Anhui Polytechnic University, AnhuiUSDA-ARS Midwest Area Sugarbeet and Bean Research Unit, East Lansing, MIDepartment of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MIDepartment of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MIDepartment of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MI Chitin, a natural polymer, has been added to soil to enhance plant resistance to pathogens, yet its impact on soil’s chemical and biochemical properties remains unclear. Standard methods to measure these properties are often slow and labor-intensive. This study aimed to assess pH, total DNA, chitinase activity, and trpB gene DNA targeting for the identification of pathogen Streptomyces scabies in chitin-treated soil using both conventional wet chemical analysis methods and near-infrared (NIR) spectroscopy for rapid measurement. Five soil groups were treated with varying chitin levels (control, 0.2% and 2% fungus chitin, 0.2% and 2% Sigma chitin) and sampled every 9 days over 122 days. NIR reflectance spectra (900-1,685 nm) from the soil samples were acquired at each sampling time. Partial least squares regression models  were developed using preprocessing methods like Kubelka-Munk, second derivative, and smoothed data. The second derivative model for 900-1,660 nm yielded high predictive accuracy with R² values of 0.915, 0.892, and 0.810 for pH, total DNA, and trpB gene DNA respectively. This study demonstrates the potential of NIR  spectroscopy coupled with partial least squares algorithms as a rapid, cost-effective method for estimating soil biochemical properties. https://www.agroengineering.org/jae/article/view/1710Soil property modelingoptical analysispartial least squares regression
spellingShingle Ken Abamba Omwange
Ana Chen
Renfu Lu
Carly Daiek
Julie Celini
Yan Liu
Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils
Journal of Agricultural Engineering
Soil property modeling
optical analysis
partial least squares regression
title Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils
title_full Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils
title_fullStr Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils
title_full_unstemmed Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils
title_short Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils
title_sort exploring the feasibility of near infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin amended soils
topic Soil property modeling
optical analysis
partial least squares regression
url https://www.agroengineering.org/jae/article/view/1710
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