Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Currently, the meat industry faces several problems, one of them being consumer fraud, which has arised the need to guarantee to the consumer that the product offered for sale is what it says on the label. This leads to the main objective of this study, which was to use data obtained by Rapid Evapo...

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Bibliographic Details
Main Author:	Reyes L., Esther A.
Other Authors:	Acosta, Adela
Format:	Tesis
Language:	Inglés
Published:	Zamorano: Escuela Agrícola Panamericana 2024
Subjects:	Dimensional reduction Feature selection (FS) predictive model prime Wagyu
Online Access:	https://bdigital.zamorano.edu/handle/11036/7759

id	ZAMORANO7759
record_format	dspace
spelling	ZAMORANO77592024-01-11T10:57:01Z Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data Reyes L., Esther A. Acosta, Adela Woerner, Dale Dimensional reduction Feature selection (FS) predictive model prime Wagyu Currently, the meat industry faces several problems, one of them being consumer fraud, which has arised the need to guarantee to the consumer that the product offered for sale is what it says on the label. This leads to the main objective of this study, which was to use data obtained by Rapid Evaporative Ionization Mass Spectrometry (REIMS) analysis for the training of 12 predictive models in three different dimensional reduction methods in order to train these models for the quick and accurate identification of the bovine breed from which the meat is obtained. Steaks from the Loggisimus dorsi muscle in the USDA classification as "Prime" from the Angus breed and the Wagyu breed were used. Each method's five best predictive models were selected for analysis and discussion. The best dimensional reduction method was Feature Selection (FS), which showed accuracies ranging from 73.6 to 91.8% in the different predictive models, being the best predictive model SVM Poly, which obtained the highest percentages in the performance metrics in the three dimensional reduction methods. Thus, demonstrating the effectiveness of using REIMS data for predicting the bovine breed from which the Longissimus dorsi steaks derive. 2024-01-11T16:55:47Z 2024-01-11T16:55:47Z 2023 Thesis https://hdl.handle.net/11036/7759 eng Copyright Escuela Agrícola Panamericana, Zamorano https://creativecommons.org/licenses/by-nc-nd/3.0/es/ application/pdf Zamorano: Escuela Agrícola Panamericana
institution	Universidad Zamorano
collection	Biblioteca Digital Zamorano
language	Inglés
topic	Dimensional reduction Feature selection (FS) predictive model prime Wagyu
spellingShingle	Dimensional reduction Feature selection (FS) predictive model prime Wagyu Reyes L., Esther A. Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data
description	Currently, the meat industry faces several problems, one of them being consumer fraud, which has arised the need to guarantee to the consumer that the product offered for sale is what it says on the label. This leads to the main objective of this study, which was to use data obtained by Rapid Evaporative Ionization Mass Spectrometry (REIMS) analysis for the training of 12 predictive models in three different dimensional reduction methods in order to train these models for the quick and accurate identification of the bovine breed from which the meat is obtained. Steaks from the Loggisimus dorsi muscle in the USDA classification as "Prime" from the Angus breed and the Wagyu breed were used. Each method's five best predictive models were selected for analysis and discussion. The best dimensional reduction method was Feature Selection (FS), which showed accuracies ranging from 73.6 to 91.8% in the different predictive models, being the best predictive model SVM Poly, which obtained the highest percentages in the performance metrics in the three dimensional reduction methods. Thus, demonstrating the effectiveness of using REIMS data for predicting the bovine breed from which the Longissimus dorsi steaks derive.
author2	Acosta, Adela
author_facet	Acosta, Adela Reyes L., Esther A.
format	Tesis
author	Reyes L., Esther A.
author_sort	Reyes L., Esther A.
title	Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data
title_short	Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data
title_full	Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data
title_fullStr	Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data
title_full_unstemmed	Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data
title_sort	predicting cattle meat types through machine learning models trained on rapid evaporative ionization mass spectrometry (reims) data
publisher	Zamorano: Escuela Agrícola Panamericana
publishDate	2024
url	https://bdigital.zamorano.edu/handle/11036/7759
work_keys_str_mv	AT reyeslesthera predictingcattlemeattypesthroughmachinelearningmodelstrainedonrapidevaporativeionizationmassspectrometryreimsdata
_version_	1808118710050750464

Predicting Cattle Meat Types through Machine Learning Models Trained on Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

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