Cargando…
A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy
Airborne crop diseases cause great losses to agricultural production and can affect people’s physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment m...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654373/ https://www.ncbi.nlm.nih.gov/pubmed/36360075 http://dx.doi.org/10.3390/foods11213462 |
_version_ | 1784828914947850240 |
---|---|
author | Zhang, Xiaodong Bian, Fei Wang, Yafei Hu, Lian Yang, Ning Mao, Hanping |
author_facet | Zhang, Xiaodong Bian, Fei Wang, Yafei Hu, Lian Yang, Ning Mao, Hanping |
author_sort | Zhang, Xiaodong |
collection | PubMed |
description | Airborne crop diseases cause great losses to agricultural production and can affect people’s physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment microfluidic chip with arcuate pretreatment channel was designed for the separation and enrichment of crop disease spores, which was combined with micro Raman for Raman fingerprinting of disease conidia and quasi identification. The chip was mainly composed of arc preprocessing and two separated enriched structures, and the designed chip was numerically simulated using COMSOL multiphysics5.5, with the best enrichment effect at W2/W1 = 1.6 and W4/W3 = 1.1. The spectra were preprocessed with standard normal variables (SNVs) to improve the signal-to-noise ratio, which was baseline corrected using an iterative polynomial fitting method to further improve spectral features. Raman spectra were dimensionally reduced using principal component analysis (PCA) and stability competitive adaptive weighting (SCARS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) were employed to identify fungal spore species, and the best discrimination effect was achieved using the SCARS-SVM model with 94.31% discrimination accuracy. Thus, the microfluidic-chip- and micro-Raman-based methods for spore capture and identification of crop diseases have the potential to be precise, convenient, and low-cost methods for fungal spore detection. |
format | Online Article Text |
id | pubmed-9654373 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96543732022-11-15 A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy Zhang, Xiaodong Bian, Fei Wang, Yafei Hu, Lian Yang, Ning Mao, Hanping Foods Article Airborne crop diseases cause great losses to agricultural production and can affect people’s physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment microfluidic chip with arcuate pretreatment channel was designed for the separation and enrichment of crop disease spores, which was combined with micro Raman for Raman fingerprinting of disease conidia and quasi identification. The chip was mainly composed of arc preprocessing and two separated enriched structures, and the designed chip was numerically simulated using COMSOL multiphysics5.5, with the best enrichment effect at W2/W1 = 1.6 and W4/W3 = 1.1. The spectra were preprocessed with standard normal variables (SNVs) to improve the signal-to-noise ratio, which was baseline corrected using an iterative polynomial fitting method to further improve spectral features. Raman spectra were dimensionally reduced using principal component analysis (PCA) and stability competitive adaptive weighting (SCARS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) were employed to identify fungal spore species, and the best discrimination effect was achieved using the SCARS-SVM model with 94.31% discrimination accuracy. Thus, the microfluidic-chip- and micro-Raman-based methods for spore capture and identification of crop diseases have the potential to be precise, convenient, and low-cost methods for fungal spore detection. MDPI 2022-11-01 /pmc/articles/PMC9654373/ /pubmed/36360075 http://dx.doi.org/10.3390/foods11213462 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Xiaodong Bian, Fei Wang, Yafei Hu, Lian Yang, Ning Mao, Hanping A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy |
title | A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy |
title_full | A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy |
title_fullStr | A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy |
title_full_unstemmed | A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy |
title_short | A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy |
title_sort | method for capture and detection of crop airborne disease spores based on microfluidic chips and micro raman spectroscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654373/ https://www.ncbi.nlm.nih.gov/pubmed/36360075 http://dx.doi.org/10.3390/foods11213462 |
work_keys_str_mv | AT zhangxiaodong amethodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT bianfei amethodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT wangyafei amethodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT hulian amethodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT yangning amethodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT maohanping amethodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT zhangxiaodong methodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT bianfei methodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT wangyafei methodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT hulian methodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT yangning methodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy AT maohanping methodforcaptureanddetectionofcropairbornediseasesporesbasedonmicrofluidicchipsandmicroramanspectroscopy |