Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters

Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters

This paper presents an intelligent method to differentiate levels of roasting of coffee based on data collected from Microwave Non-Destructive Testing (MNDT) method. The MNDT method beams microwaves through several types of coffee (dark, medium, and light roast) and obtains the s-parameter readings...

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Published in:IEEE Symposium on Wireless Technology and Applications, ISWTA
Main Author: Salim A.; Yassin I.M.; Mahmood M.K.A.; Khan Z.I.; Ali M.S.A.M.; Mohd Shariff K.K.; Osman F.N.; Ahmad A.; Eskandari F.
Format: Conference paper
Language:English
Published: IEEE Computer Society 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142730037&doi=10.1109%2fISWTA55313.2022.9942782&partnerID=40&md5=a6143456ae82e693bb2237ce8d0e6c6e
id 2-s2.0-85142730037
spelling 2-s2.0-85142730037
Salim A.; Yassin I.M.; Mahmood M.K.A.; Khan Z.I.; Ali M.S.A.M.; Mohd Shariff K.K.; Osman F.N.; Ahmad A.; Eskandari F.
Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
2022
IEEE Symposium on Wireless Technology and Applications, ISWTA
2022-August

10.1109/ISWTA55313.2022.9942782
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142730037&doi=10.1109%2fISWTA55313.2022.9942782&partnerID=40&md5=a6143456ae82e693bb2237ce8d0e6c6e
This paper presents an intelligent method to differentiate levels of roasting of coffee based on data collected from Microwave Non-Destructive Testing (MNDT) method. The MNDT method beams microwaves through several types of coffee (dark, medium, and light roast) and obtains the s-parameter readings from them. A multi-layer perceptron neural network was then then fed to the MLP to determine the degree of different coffee roasts. The MLP could differentiate between the different roasts (with 6,400 data points per sample) with a modest number of hidden units. © 2022 IEEE.
IEEE Computer Society
23247843
English
Conference paper
author Salim A.; Yassin I.M.; Mahmood M.K.A.; Khan Z.I.; Ali M.S.A.M.; Mohd Shariff K.K.; Osman F.N.; Ahmad A.; Eskandari F.
spellingShingle Salim A.; Yassin I.M.; Mahmood M.K.A.; Khan Z.I.; Ali M.S.A.M.; Mohd Shariff K.K.; Osman F.N.; Ahmad A.; Eskandari F.
Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
author_facet Salim A.; Yassin I.M.; Mahmood M.K.A.; Khan Z.I.; Ali M.S.A.M.; Mohd Shariff K.K.; Osman F.N.; Ahmad A.; Eskandari F.
author_sort Salim A.; Yassin I.M.; Mahmood M.K.A.; Khan Z.I.; Ali M.S.A.M.; Mohd Shariff K.K.; Osman F.N.; Ahmad A.; Eskandari F.
title Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
title_short Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
title_full Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
title_fullStr Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
title_full_unstemmed Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
title_sort Classification of Coffee Roast Levels using Multi-Layer Perceptron on MNDT s-Parameters
publishDate 2022
container_title IEEE Symposium on Wireless Technology and Applications, ISWTA
container_volume 2022-August
container_issue
doi_str_mv 10.1109/ISWTA55313.2022.9942782
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142730037&doi=10.1109%2fISWTA55313.2022.9942782&partnerID=40&md5=a6143456ae82e693bb2237ce8d0e6c6e
description This paper presents an intelligent method to differentiate levels of roasting of coffee based on data collected from Microwave Non-Destructive Testing (MNDT) method. The MNDT method beams microwaves through several types of coffee (dark, medium, and light roast) and obtains the s-parameter readings from them. A multi-layer perceptron neural network was then then fed to the MLP to determine the degree of different coffee roasts. The MLP could differentiate between the different roasts (with 6,400 data points per sample) with a modest number of hidden units. © 2022 IEEE.
publisher IEEE Computer Society
issn 23247843
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1809677892058087424

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