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Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation

Near infrared spectroscopic (NIRS) data from different harvested seasons may consist of different feature spaces even though the samples have the same label values. This is because the spectral response could be affected by the changes in environmental parameters, internal quality, and the reprod...

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Main Authors: Suarin, Nur Aisyah Syafinaz, Kim, Seng Chia
Format: Book Section
Language:English
Published: Springer 2022
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/7593/1/C3879_ae4cee62c76825b1343f870777ce3a0e.pdf
http://eprints.uthm.edu.my/7593/
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spelling my.uthm.eprints.75932022-08-29T07:35:33Z http://eprints.uthm.edu.my/7593/ Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation Suarin, Nur Aisyah Syafinaz Kim, Seng Chia T Technology (General) Near infrared spectroscopic (NIRS) data from different harvested seasons may consist of different feature spaces even though the samples have the same label values. This is because the spectral response could be affected by the changes in environmental parameters, internal quality, and the reproducibility of NIRS instruments. Thus, this study aims to investigate the ability of Joint Distribution Adaptation (JDA) transfer learning algorithm in addressing the assumption of traditional machine learning i.e. both training and testing data must come from the same feature spaces and data distribution. First, NIRS data acquired from two different harvested seasons were used as the source domain and the target domain, respectively. Next, JDA was implemented to produce an adaptation matrix using the source domain and transfer datasets. This adaptation matrix would be used to transform the source and target domain datasets. After that, a calibration model was developed by means of Partial Least Squares (PLS) using the transformed training dataset; and validated using the trans�formed independent testing dataset. The proposed JDA-PLS was compared to the PLS without transfer learning as the baseline learning. Findings show that the proposed JDA-PLS with 10 LVs achieved the lowest RMSEP of 1.134% and the highest RP 2 of 0.826. Springer 2022 Book Section PeerReviewed text en http://eprints.uthm.edu.my/7593/1/C3879_ae4cee62c76825b1343f870777ce3a0e.pdf Suarin, Nur Aisyah Syafinaz and Kim, Seng Chia (2022) Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation. In: Control, Instrumentation and Mechatronics: Theory and Practice. Springer, pp. 707-716.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Suarin, Nur Aisyah Syafinaz
Kim, Seng Chia
Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation
description Near infrared spectroscopic (NIRS) data from different harvested seasons may consist of different feature spaces even though the samples have the same label values. This is because the spectral response could be affected by the changes in environmental parameters, internal quality, and the reproducibility of NIRS instruments. Thus, this study aims to investigate the ability of Joint Distribution Adaptation (JDA) transfer learning algorithm in addressing the assumption of traditional machine learning i.e. both training and testing data must come from the same feature spaces and data distribution. First, NIRS data acquired from two different harvested seasons were used as the source domain and the target domain, respectively. Next, JDA was implemented to produce an adaptation matrix using the source domain and transfer datasets. This adaptation matrix would be used to transform the source and target domain datasets. After that, a calibration model was developed by means of Partial Least Squares (PLS) using the transformed training dataset; and validated using the trans�formed independent testing dataset. The proposed JDA-PLS was compared to the PLS without transfer learning as the baseline learning. Findings show that the proposed JDA-PLS with 10 LVs achieved the lowest RMSEP of 1.134% and the highest RP 2 of 0.826.
format Book Section
author Suarin, Nur Aisyah Syafinaz
Kim, Seng Chia
author_facet Suarin, Nur Aisyah Syafinaz
Kim, Seng Chia
author_sort Suarin, Nur Aisyah Syafinaz
title Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation
title_short Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation
title_full Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation
title_fullStr Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation
title_full_unstemmed Transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation
title_sort transferring near infrared spectroscopic calibration model across different harvested seasons using joint distribution adaptation
publisher Springer
publishDate 2022
url http://eprints.uthm.edu.my/7593/1/C3879_ae4cee62c76825b1343f870777ce3a0e.pdf
http://eprints.uthm.edu.my/7593/
_version_ 1743109104960798720
score 13.211869

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