Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice

Abstract Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that emp...

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Autores principales:	Nguyen Phuoc Long, Dong Kyu Lim, Changyeun Mo, Giyoung Kim, Sung Won Kwon
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6ebd131638914f9a8e397e92b9004c61
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spelling	oai:doaj.org-article:6ebd131638914f9a8e397e92b9004c612021-12-02T12:31:53ZDevelopment and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice10.1038/s41598-017-08892-02045-2322https://doaj.org/article/6ebd131638914f9a8e397e92b9004c612017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08892-0https://doaj.org/toc/2045-2322Abstract Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample. Unsupervised data exploration showed a noticeable overlap of white rice between two countries. In addition, lysophosphatidylcholines (lysoPCs) were prominent in white rice from Korea while lysophosphatidylethanolamines (lysoPEs) were enriched in white rice from China. A deep learning prediction model was built using 2014 white rice and validated using two different batches of 2015 white rice. The model accurately discriminated white rice from two countries. Among 10 selected predictors, lysoPC(18:2), lysoPC(14:0), and lysoPE(16:0) were the three most important features. Random forest and gradient boosting machine models also worked well in this circumstance. In conclusion, this study provides an architecture for high-throughput classification of white rice from different geographical origins.Nguyen Phuoc LongDong Kyu LimChangyeun MoGiyoung KimSung Won KwonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nguyen Phuoc Long Dong Kyu Lim Changyeun Mo Giyoung Kim Sung Won Kwon Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice
description	Abstract Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample. Unsupervised data exploration showed a noticeable overlap of white rice between two countries. In addition, lysophosphatidylcholines (lysoPCs) were prominent in white rice from Korea while lysophosphatidylethanolamines (lysoPEs) were enriched in white rice from China. A deep learning prediction model was built using 2014 white rice and validated using two different batches of 2015 white rice. The model accurately discriminated white rice from two countries. Among 10 selected predictors, lysoPC(18:2), lysoPC(14:0), and lysoPE(16:0) were the three most important features. Random forest and gradient boosting machine models also worked well in this circumstance. In conclusion, this study provides an architecture for high-throughput classification of white rice from different geographical origins.
format	article
author	Nguyen Phuoc Long Dong Kyu Lim Changyeun Mo Giyoung Kim Sung Won Kwon
author_facet	Nguyen Phuoc Long Dong Kyu Lim Changyeun Mo Giyoung Kim Sung Won Kwon
author_sort	Nguyen Phuoc Long
title	Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice
title_short	Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice
title_full	Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice
title_fullStr	Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice
title_full_unstemmed	Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice
title_sort	development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/6ebd131638914f9a8e397e92b9004c61
work_keys_str_mv	AT nguyenphuoclong developmentandassessmentofalysophospholipidbaseddeeplearningmodeltodiscriminategeographicaloriginsofwhiterice AT dongkyulim developmentandassessmentofalysophospholipidbaseddeeplearningmodeltodiscriminategeographicaloriginsofwhiterice AT changyeunmo developmentandassessmentofalysophospholipidbaseddeeplearningmodeltodiscriminategeographicaloriginsofwhiterice AT giyoungkim developmentandassessmentofalysophospholipidbaseddeeplearningmodeltodiscriminategeographicaloriginsofwhiterice AT sungwonkwon developmentandassessmentofalysophospholipidbaseddeeplearningmodeltodiscriminategeographicaloriginsofwhiterice
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Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice

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