Cargando…

Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging

Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of...

Descripción completa

Detalles Bibliográficos
Autores principales: Sahachairungrueng, Woranitta, Thompson, Anthony Keith, Terdwongworakul, Anupun, Teerachaichayut, Sontisuk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340739/
https://www.ncbi.nlm.nih.gov/pubmed/37444257
http://dx.doi.org/10.3390/foods12132519
_version_ 1785072151461625856
author Sahachairungrueng, Woranitta
Thompson, Anthony Keith
Terdwongworakul, Anupun
Teerachaichayut, Sontisuk
author_facet Sahachairungrueng, Woranitta
Thompson, Anthony Keith
Terdwongworakul, Anupun
Teerachaichayut, Sontisuk
author_sort Sahachairungrueng, Woranitta
collection PubMed
description Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares–discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.
format Online
Article
Text
id pubmed-10340739
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-103407392023-07-14 Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging Sahachairungrueng, Woranitta Thompson, Anthony Keith Terdwongworakul, Anupun Teerachaichayut, Sontisuk Foods Article Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares–discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods. MDPI 2023-06-28 /pmc/articles/PMC10340739/ /pubmed/37444257 http://dx.doi.org/10.3390/foods12132519 Text en © 2023 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
Sahachairungrueng, Woranitta
Thompson, Anthony Keith
Terdwongworakul, Anupun
Teerachaichayut, Sontisuk
Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
title Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
title_full Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
title_fullStr Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
title_full_unstemmed Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
title_short Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
title_sort non-destructive classification of organic and conventional hens’ eggs using near-infrared hyperspectral imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340739/
https://www.ncbi.nlm.nih.gov/pubmed/37444257
http://dx.doi.org/10.3390/foods12132519
work_keys_str_mv AT sahachairungruengworanitta nondestructiveclassificationoforganicandconventionalhenseggsusingnearinfraredhyperspectralimaging
AT thompsonanthonykeith nondestructiveclassificationoforganicandconventionalhenseggsusingnearinfraredhyperspectralimaging
AT terdwongworakulanupun nondestructiveclassificationoforganicandconventionalhenseggsusingnearinfraredhyperspectralimaging
AT teerachaichayutsontisuk nondestructiveclassificationoforganicandconventionalhenseggsusingnearinfraredhyperspectralimaging