Cargando…
Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM)
The objective of this study is to use a portable visible spectral imaging system (443–726 nm) to detect poultry thawed from frozen at the pixel level using multivariate analysis methods commonly used in machine learning (decision tree, logistic regression, linear discriminant analysis [LDA], k-neare...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665413/ https://www.ncbi.nlm.nih.gov/pubmed/34894425 http://dx.doi.org/10.1016/j.psj.2021.101578 |
_version_ | 1784614004485783552 |
---|---|
author | Swanson, Anastasia Gowen, Aoife |
author_facet | Swanson, Anastasia Gowen, Aoife |
author_sort | Swanson, Anastasia |
collection | PubMed |
description | The objective of this study is to use a portable visible spectral imaging system (443–726 nm) to detect poultry thawed from frozen at the pixel level using multivariate analysis methods commonly used in machine learning (decision tree, logistic regression, linear discriminant analysis [LDA], k-nearest neighbors [KNN], support vector machines [SVM]). The selection of the most suitable method is based on the amount of data required to build an accurate model, computational speed, and the robustness of the model. The training set consists of pixel spectra from packages of chicken thighs without plastic lidding to evaluate the robustness of the models when implemented on the test set with and without plastic lidding. Data subsets were created by randomly selecting 1, 5, 10, 20, and 50% of the pixel spectra of each sample for both the training and test data sets. The subsets of pixel spectra and the full training set were used to train the machine learning algorithms to evaluate how the amount of data influences computational time. Logistic regression was found to be the best algorithm for detecting poultry thawed from frozen with and without plastic lidding film. Although logistic regression and SVM both performed with the same high accuracy and sensitivity for all training subset sizes, the computational time needed to implement SVM makes it the less suitable algorithm for detecting poultry thawed from frozen with and without plastic lidding film. |
format | Online Article Text |
id | pubmed-8665413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-86654132021-12-15 Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM) Swanson, Anastasia Gowen, Aoife Poult Sci PROCESSING AND PRODUCT The objective of this study is to use a portable visible spectral imaging system (443–726 nm) to detect poultry thawed from frozen at the pixel level using multivariate analysis methods commonly used in machine learning (decision tree, logistic regression, linear discriminant analysis [LDA], k-nearest neighbors [KNN], support vector machines [SVM]). The selection of the most suitable method is based on the amount of data required to build an accurate model, computational speed, and the robustness of the model. The training set consists of pixel spectra from packages of chicken thighs without plastic lidding to evaluate the robustness of the models when implemented on the test set with and without plastic lidding. Data subsets were created by randomly selecting 1, 5, 10, 20, and 50% of the pixel spectra of each sample for both the training and test data sets. The subsets of pixel spectra and the full training set were used to train the machine learning algorithms to evaluate how the amount of data influences computational time. Logistic regression was found to be the best algorithm for detecting poultry thawed from frozen with and without plastic lidding film. Although logistic regression and SVM both performed with the same high accuracy and sensitivity for all training subset sizes, the computational time needed to implement SVM makes it the less suitable algorithm for detecting poultry thawed from frozen with and without plastic lidding film. Elsevier 2021-11-08 /pmc/articles/PMC8665413/ /pubmed/34894425 http://dx.doi.org/10.1016/j.psj.2021.101578 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | PROCESSING AND PRODUCT Swanson, Anastasia Gowen, Aoife Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM) |
title | Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM) |
title_full | Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM) |
title_fullStr | Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM) |
title_full_unstemmed | Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM) |
title_short | Detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 NM) |
title_sort | detection of previously frozen poultry through plastic lidding film using portable visible spectral imaging (443–726 nm) |
topic | PROCESSING AND PRODUCT |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665413/ https://www.ncbi.nlm.nih.gov/pubmed/34894425 http://dx.doi.org/10.1016/j.psj.2021.101578 |
work_keys_str_mv | AT swansonanastasia detectionofpreviouslyfrozenpoultrythroughplasticliddingfilmusingportablevisiblespectralimaging443726nm AT gowenaoife detectionofpreviouslyfrozenpoultrythroughplasticliddingfilmusingportablevisiblespectralimaging443726nm |