Artificial neural network and shortwave near-infrared light in pineapple internal quality classification

The determination of the internal quality of pineapples is important for grading the fruit quality. The common procedure to determine the internal quality of the fruit is using a digital refractometer that are destructive, expensive, and time-consuming. In this research, a non-destructive appr...

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Main Author:	Jam, Mohamad Nur Hakim
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA Mathematics QA71-90 Instruments and machines QA76.75-76.765 Computer software
Online Access:	http://eprints.uthm.edu.my/7487/1/24p%20MOHAMAD%20NUR%20HAKIM%20JAM.pdf http://eprints.uthm.edu.my/7487/2/MOHAMAD%20NUR%20HAKIM%20JAM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7487/3/MOHAMAD%20NUR%20HAKIM%20JAM%20WATERMARK.pdf
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spelling	my-uthm-ep.74872022-08-15T02:59:57Z Artificial neural network and shortwave near-infrared light in pineapple internal quality classification 2018-03 Jam, Mohamad Nur Hakim QA Mathematics QA71-90 Instruments and machines QA76.75-76.765 Computer software The determination of the internal quality of pineapples is important for grading the fruit quality. The common procedure to determine the internal quality of the fruit is using a digital refractometer that are destructive, expensive, and time-consuming. In this research, a non-destructive approach was designed to classify the internal quality of pineapples using Artificial Neural Network (ANN). The device applied diffuse reflectance mode using five Near-Infrared (NIR) light with different wavelengths that were in the range of 780 nm and 940 nm. A photodiode sensor (OPT101) was used to collect the reflected light from pineapples non-destructively. Firstly, the reflected NIR light from the top, middle, and bottom sections of a pineapple were non-destructively acquired using a designed NIR device. After that, the Soluble Solids Content (SSC) of the pineapple was conventionally acquired using a digital reflectometer. Next, the relationship between the NIR light and the SSC of pineapples was established using ANN. The potential outliers were identified and excluded using box-plot and leave-one-out cross-validation. The effects of the random seed and the hidden neurons of ANN were investigated to optimize the classification accuracy. K-fold cross-validation was used to validate the performance of the classification accuracy. The results indicate that the top, middle, and bottom of pineapples can be categorized using the proposed NIR approach with the classification accuracy of 76%, 85%, and 77.78% for training, testing, and K-fold cross-validation analysis, respectively. Next, when the proposed method was tested using new pineapples, findings indicate that the predictive model is capable of achieving 77.62% accuracy without any outlier detection. Thus, the combination of ANN and shortwave NIR light is able to rapidly, and non-destructively classify the internal quality of pineapples with satisfactory accuracy 2018-03 Thesis http://eprints.uthm.edu.my/7487/ http://eprints.uthm.edu.my/7487/1/24p%20MOHAMAD%20NUR%20HAKIM%20JAM.pdf text en public http://eprints.uthm.edu.my/7487/2/MOHAMAD%20NUR%20HAKIM%20JAM%20COPYRIGHT%20DECLARATION.pdf text en public http://eprints.uthm.edu.my/7487/3/MOHAMAD%20NUR%20HAKIM%20JAM%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA Mathematics QA71-90 Instruments and machines QA76.75-76.765 Computer software
spellingShingle	QA Mathematics QA71-90 Instruments and machines QA76.75-76.765 Computer software Jam, Mohamad Nur Hakim Artificial neural network and shortwave near-infrared light in pineapple internal quality classification
description	The determination of the internal quality of pineapples is important for grading the fruit quality. The common procedure to determine the internal quality of the fruit is using a digital refractometer that are destructive, expensive, and time-consuming. In this research, a non-destructive approach was designed to classify the internal quality of pineapples using Artificial Neural Network (ANN). The device applied diffuse reflectance mode using five Near-Infrared (NIR) light with different wavelengths that were in the range of 780 nm and 940 nm. A photodiode sensor (OPT101) was used to collect the reflected light from pineapples non-destructively. Firstly, the reflected NIR light from the top, middle, and bottom sections of a pineapple were non-destructively acquired using a designed NIR device. After that, the Soluble Solids Content (SSC) of the pineapple was conventionally acquired using a digital reflectometer. Next, the relationship between the NIR light and the SSC of pineapples was established using ANN. The potential outliers were identified and excluded using box-plot and leave-one-out cross-validation. The effects of the random seed and the hidden neurons of ANN were investigated to optimize the classification accuracy. K-fold cross-validation was used to validate the performance of the classification accuracy. The results indicate that the top, middle, and bottom of pineapples can be categorized using the proposed NIR approach with the classification accuracy of 76%, 85%, and 77.78% for training, testing, and K-fold cross-validation analysis, respectively. Next, when the proposed method was tested using new pineapples, findings indicate that the predictive model is capable of achieving 77.62% accuracy without any outlier detection. Thus, the combination of ANN and shortwave NIR light is able to rapidly, and non-destructively classify the internal quality of pineapples with satisfactory accuracy
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Jam, Mohamad Nur Hakim
author_facet	Jam, Mohamad Nur Hakim
author_sort	Jam, Mohamad Nur Hakim
title	Artificial neural network and shortwave near-infrared light in pineapple internal quality classification
title_short	Artificial neural network and shortwave near-infrared light in pineapple internal quality classification
title_full	Artificial neural network and shortwave near-infrared light in pineapple internal quality classification
title_fullStr	Artificial neural network and shortwave near-infrared light in pineapple internal quality classification
title_full_unstemmed	Artificial neural network and shortwave near-infrared light in pineapple internal quality classification
title_sort	artificial neural network and shortwave near-infrared light in pineapple internal quality classification
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2018
url	http://eprints.uthm.edu.my/7487/1/24p%20MOHAMAD%20NUR%20HAKIM%20JAM.pdf http://eprints.uthm.edu.my/7487/2/MOHAMAD%20NUR%20HAKIM%20JAM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7487/3/MOHAMAD%20NUR%20HAKIM%20JAM%20WATERMARK.pdf
_version_	1747831161944539136

Artificial neural network and shortwave near-infrared light in pineapple internal quality classification

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