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Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics

Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral...

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Autores principales: Haghbin, Najmeh, Bakhshipour, Adel, Zareiforoush, Hemad, Mousanejad, Sedigheh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236597/
https://www.ncbi.nlm.nih.gov/pubmed/37268945
http://dx.doi.org/10.1186/s13007-023-01032-y
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author Haghbin, Najmeh
Bakhshipour, Adel
Zareiforoush, Hemad
Mousanejad, Sedigheh
author_facet Haghbin, Najmeh
Bakhshipour, Adel
Zareiforoush, Hemad
Mousanejad, Sedigheh
author_sort Haghbin, Najmeh
collection PubMed
description Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1(st) derivative, and Savitzky–Golay 2(nd) derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits’ firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky–Golay 1(st) derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R(2)) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R(2) values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.
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spelling pubmed-102365972023-06-03 Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics Haghbin, Najmeh Bakhshipour, Adel Zareiforoush, Hemad Mousanejad, Sedigheh Plant Methods Research Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1(st) derivative, and Savitzky–Golay 2(nd) derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits’ firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky–Golay 1(st) derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R(2)) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R(2) values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage. BioMed Central 2023-06-02 /pmc/articles/PMC10236597/ /pubmed/37268945 http://dx.doi.org/10.1186/s13007-023-01032-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Haghbin, Najmeh
Bakhshipour, Adel
Zareiforoush, Hemad
Mousanejad, Sedigheh
Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics
title Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics
title_full Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics
title_fullStr Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics
title_full_unstemmed Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics
title_short Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics
title_sort non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236597/
https://www.ncbi.nlm.nih.gov/pubmed/37268945
http://dx.doi.org/10.1186/s13007-023-01032-y
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