A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration

Food safety, interpreting spectroscopic data, and predicting physical, chemical, functional, and sensory properties of various food products are the fundamental concerns in the field of food science.

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Bibliographic Details
Main Author:	SIDDIQUI, MUHAMMAD AADIL
Format:	Thesis
Language:	English
Published:	2023
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://utpedia.utp.edu.my/id/eprint/24884/1/MuhammadAadilSiddiqui_18003606.pdf http://utpedia.utp.edu.my/id/eprint/24884/
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Institution:	Universiti Teknologi Petronas
Language:	English

id	oai:utpedia.utp.edu.my:24884
record_format	eprints
spelling	oai:utpedia.utp.edu.my:248842024-07-22T03:20:45Z http://utpedia.utp.edu.my/id/eprint/24884/ A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration SIDDIQUI, MUHAMMAD AADIL TK Electrical engineering. Electronics Nuclear engineering Food safety, interpreting spectroscopic data, and predicting physical, chemical, functional, and sensory properties of various food products are the fundamental concerns in the field of food science. 2023-09 Thesis NonPeerReviewed text en http://utpedia.utp.edu.my/id/eprint/24884/1/MuhammadAadilSiddiqui_18003606.pdf SIDDIQUI, MUHAMMAD AADIL (2023) A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration. Doctoral thesis, Universiti Teknologi PETRONAS.
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Electronic and Digitized Intellectual Asset
url_provider	http://utpedia.utp.edu.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering SIDDIQUI, MUHAMMAD AADIL A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration
description	Food safety, interpreting spectroscopic data, and predicting physical, chemical, functional, and sensory properties of various food products are the fundamental concerns in the field of food science.
format	Thesis
author	SIDDIQUI, MUHAMMAD AADIL
author_facet	SIDDIQUI, MUHAMMAD AADIL
author_sort	SIDDIQUI, MUHAMMAD AADIL
title	A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration
title_short	A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration
title_full	A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration
title_fullStr	A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration
title_full_unstemmed	A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration
title_sort	machine learning based multiclass classification model using ftir spectroscopy for evaluating the lard adulteration
publishDate	2023
url	http://utpedia.utp.edu.my/id/eprint/24884/1/MuhammadAadilSiddiqui_18003606.pdf http://utpedia.utp.edu.my/id/eprint/24884/
_version_	1805891077751701504

A Machine Learning Based Multiclass Classification Model Using Ftir Spectroscopy For Evaluating The Lard Adulteration

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