Cover Image

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.

Saved in:
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/
Tags: Add Tag
No Tags, Be the first to tag this record!
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
score 13.214268

Similar Items