Cargando…

Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device

Grading is a decisive step in the successful distribution of mangoes to customers according to their preferences for the maturity index. A non-destructive method using near-infrared spectroscopy has historically been used to predict the maturity of fruit. This research classifies the maturity indexe...

Descripción completa

Detalles Bibliográficos
Autores principales: Khumaidi, Ali, Purwanto, Yohanes Aris, Sukoco, Heru, Wijaya, Sony Hartono
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9781779/
https://www.ncbi.nlm.nih.gov/pubmed/36560072
http://dx.doi.org/10.3390/s22249704
_version_ 1784857157849579520
author Khumaidi, Ali
Purwanto, Yohanes Aris
Sukoco, Heru
Wijaya, Sony Hartono
author_facet Khumaidi, Ali
Purwanto, Yohanes Aris
Sukoco, Heru
Wijaya, Sony Hartono
author_sort Khumaidi, Ali
collection PubMed
description Grading is a decisive step in the successful distribution of mangoes to customers according to their preferences for the maturity index. A non-destructive method using near-infrared spectroscopy has historically been used to predict the maturity of fruit. This research classifies the maturity indexes in five classes using a new approach involving classification modeling and the application of fuzzy logic and indirect classification by measuring four parameters: total acidity, soluble solids content, firmness, and starch. These four quantitative parameters provide guidelines for maturity indexes and consumer preferences. The development of portable devices uses a neo spectra micro development kit with specifications for the spectrum of 1350–2500 nm. In terms of computer technology, this study uses a Raspberry Pi and Python programming. To improve the accuracy performance, preprocessing is carried out using 12 spectral transformation operators. Next, these operators are collected and combined to achieve optimal performance. The performance of the classification model with direct and indirect approaches is then compared. Ultimately, classification of the direct approach with preprocessing using linear discriminant analysis offered an accuracy of 91.43%, and classification of the indirect approach using partial least squares with fuzzy logic had an accuracy of 95.7%.
format Online
Article
Text
id pubmed-9781779
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97817792022-12-24 Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device Khumaidi, Ali Purwanto, Yohanes Aris Sukoco, Heru Wijaya, Sony Hartono Sensors (Basel) Article Grading is a decisive step in the successful distribution of mangoes to customers according to their preferences for the maturity index. A non-destructive method using near-infrared spectroscopy has historically been used to predict the maturity of fruit. This research classifies the maturity indexes in five classes using a new approach involving classification modeling and the application of fuzzy logic and indirect classification by measuring four parameters: total acidity, soluble solids content, firmness, and starch. These four quantitative parameters provide guidelines for maturity indexes and consumer preferences. The development of portable devices uses a neo spectra micro development kit with specifications for the spectrum of 1350–2500 nm. In terms of computer technology, this study uses a Raspberry Pi and Python programming. To improve the accuracy performance, preprocessing is carried out using 12 spectral transformation operators. Next, these operators are collected and combined to achieve optimal performance. The performance of the classification model with direct and indirect approaches is then compared. Ultimately, classification of the direct approach with preprocessing using linear discriminant analysis offered an accuracy of 91.43%, and classification of the indirect approach using partial least squares with fuzzy logic had an accuracy of 95.7%. MDPI 2022-12-11 /pmc/articles/PMC9781779/ /pubmed/36560072 http://dx.doi.org/10.3390/s22249704 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Khumaidi, Ali
Purwanto, Yohanes Aris
Sukoco, Heru
Wijaya, Sony Hartono
Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device
title Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device
title_full Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device
title_fullStr Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device
title_full_unstemmed Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device
title_short Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device
title_sort using fuzzy logic to increase accuracy in mango maturity index classification: approach for developing a portable near-infrared spectroscopy device
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9781779/
https://www.ncbi.nlm.nih.gov/pubmed/36560072
http://dx.doi.org/10.3390/s22249704
work_keys_str_mv AT khumaidiali usingfuzzylogictoincreaseaccuracyinmangomaturityindexclassificationapproachfordevelopingaportablenearinfraredspectroscopydevice
AT purwantoyohanesaris usingfuzzylogictoincreaseaccuracyinmangomaturityindexclassificationapproachfordevelopingaportablenearinfraredspectroscopydevice
AT sukocoheru usingfuzzylogictoincreaseaccuracyinmangomaturityindexclassificationapproachfordevelopingaportablenearinfraredspectroscopydevice
AT wijayasonyhartono usingfuzzylogictoincreaseaccuracyinmangomaturityindexclassificationapproachfordevelopingaportablenearinfraredspectroscopydevice