Loading…
Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging
Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, amm...
Saved in:
Published in: | Journal of analytical methods in chemistry 2019-01, Vol.2019 (2019), p.1-12 |
---|---|
Main Author: | |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c604t-5b2825f370a378c89a41982c0a1ab35760ce9b568b0c7d0e9aea775248d252473 |
---|---|
cites | cdi_FETCH-LOGICAL-c604t-5b2825f370a378c89a41982c0a1ab35760ce9b568b0c7d0e9aea775248d252473 |
container_end_page | 12 |
container_issue | 2019 |
container_start_page | 1 |
container_title | Journal of analytical methods in chemistry |
container_volume | 2019 |
creator | Doi, Ryoichi |
description | Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, ammonium ion, and phosphate ion using a pseudocolor imaging method. Each PackTest product underwent reactions with standard solutions. The generated color was scanner-read. From the color image, ten grayscale images representing the intensity values of red, green, blue, cyan, magenta, yellow, key black, and L∗, and the values of a∗ and b∗ were generated. Using the grayscale images representing the red, green, and blue intensity values, 73 other grayscale images were generated. The grayscale intensity values were used to prepare datasets for the ten and 83 (=10 + 73) images. For both datasets, chemical oxygen demand quantification was successful, resulting in values of normalized mean absolute error of less than 0.4% and coefficients of determination that were greater than 0.9996. However, the quantification of ammonium and phosphate ions commonly provided false positive results for the standard solution that contained no ammonium ion/phosphate ion. For ammonium ion, multiple regression markedly improved the accuracy using the pseudocolor method. Phosphate ion quantification was also improved by avoiding the use of an estimated value for the reference solution that contained no phosphate ion. Real details of the measurements and the perspectives were discussed. |
doi_str_mv | 10.1155/2019/1685382 |
format | article |
fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_f7edfbcdfae3493893381bcd56fdd9fb</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A618362064</galeid><doaj_id>oai_doaj_org_article_f7edfbcdfae3493893381bcd56fdd9fb</doaj_id><sourcerecordid>A618362064</sourcerecordid><originalsourceid>FETCH-LOGICAL-c604t-5b2825f370a378c89a41982c0a1ab35760ce9b568b0c7d0e9aea775248d252473</originalsourceid><addsrcrecordid>eNqNkl1rFDEUhgdRrNTeeS0BbwTdNh-TTOZGKK0fhRYrtNfhTHKym7qb1GTGWv-B_9qsW2srXphAPk6e84a8OU3zjNFdxqTc45T1e0xpKTR_0DzhtKczrTvx8Hat5FazU8oFrU1Jyqh83GwJRnvRMv2k-XEC38IqfA9xTsYFkn1rpwz2miRPDlIcQ5zSVMinCeraBwtjSJGcIJQpYyHnZZ14GIrNOCI5Bfv5DMtITnNykx0LuQrjghwiXpJjhBzXNERHTgtOLtm0TJkcrWBe40-bRx6WBXdu5u3m_N3bs4MPs-OP748O9o9nVtF2nMmBay696CiITlvdQ8t6zS0FBoOQnaIW-0EqPVDbOYo9IHSd5K12vI6d2G6ONrouwYW5zGEF-dokCOZXIOW5gTwGu0TjO3R-sM4DirYXuhdCs7qXyjvX-6FqvdloXU7DCp3FOGZY3hO9fxLDwszTV6NaXb-grwIvbwRy-jJV58yqeonLJUSsvhvOBadcKy0q-uIv9CJNOVarKsW4ajt6l5pDfUCIPtV77VrU7CumheJUtZXa_QdVu8NVsCmiDzV-L-H1JsHmVEpGf_tGRs26Es26Es1NJVb8-V1fbuHfdVeBVxtgEaKDq_CfclgZ9PCHZqqnQoqfGHPv9Q</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2212647083</pqid></control><display><type>article</type><title>Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging</title><source>EBSCOhost Business Source Ultimate</source><source>Open Access: PubMed Central</source><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><source>Open Access: Wiley-Blackwell Open Access Journals</source><source>Full-Text Journals in Chemistry (Open access)</source><creator>Doi, Ryoichi</creator><contributor>Uslu, Bengi ; Bengi Uslu</contributor><creatorcontrib>Doi, Ryoichi ; Uslu, Bengi ; Bengi Uslu</creatorcontrib><description>Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, ammonium ion, and phosphate ion using a pseudocolor imaging method. Each PackTest product underwent reactions with standard solutions. The generated color was scanner-read. From the color image, ten grayscale images representing the intensity values of red, green, blue, cyan, magenta, yellow, key black, and L∗, and the values of a∗ and b∗ were generated. Using the grayscale images representing the red, green, and blue intensity values, 73 other grayscale images were generated. The grayscale intensity values were used to prepare datasets for the ten and 83 (=10 + 73) images. For both datasets, chemical oxygen demand quantification was successful, resulting in values of normalized mean absolute error of less than 0.4% and coefficients of determination that were greater than 0.9996. However, the quantification of ammonium and phosphate ions commonly provided false positive results for the standard solution that contained no ammonium ion/phosphate ion. For ammonium ion, multiple regression markedly improved the accuracy using the pseudocolor method. Phosphate ion quantification was also improved by avoiding the use of an estimated value for the reference solution that contained no phosphate ion. Real details of the measurements and the perspectives were discussed.</description><identifier>ISSN: 2090-8865</identifier><identifier>EISSN: 2090-8873</identifier><identifier>DOI: 10.1155/2019/1685382</identifier><identifier>PMID: 31093418</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Backup software ; Chemical oxygen demand ; Color ; Computer software industry ; Datasets ; Deep learning ; Glucose ; Medical imaging equipment ; Organic chemistry ; Phosphates ; Product introduction ; Water quality</subject><ispartof>Journal of analytical methods in chemistry, 2019-01, Vol.2019 (2019), p.1-12</ispartof><rights>Copyright © 2019 Ryoichi Doi.</rights><rights>COPYRIGHT 2019 John Wiley & Sons, Inc.</rights><rights>Copyright © 2019 Ryoichi Doi. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2019 Ryoichi Doi. 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c604t-5b2825f370a378c89a41982c0a1ab35760ce9b568b0c7d0e9aea775248d252473</citedby><cites>FETCH-LOGICAL-c604t-5b2825f370a378c89a41982c0a1ab35760ce9b568b0c7d0e9aea775248d252473</cites><orcidid>0000-0002-3023-0136</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2212647083/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2212647083?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25752,27923,27924,37011,37012,44589,53790,53792,74897</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31093418$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Uslu, Bengi</contributor><contributor>Bengi Uslu</contributor><creatorcontrib>Doi, Ryoichi</creatorcontrib><title>Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging</title><title>Journal of analytical methods in chemistry</title><addtitle>J Anal Methods Chem</addtitle><description>Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, ammonium ion, and phosphate ion using a pseudocolor imaging method. Each PackTest product underwent reactions with standard solutions. The generated color was scanner-read. From the color image, ten grayscale images representing the intensity values of red, green, blue, cyan, magenta, yellow, key black, and L∗, and the values of a∗ and b∗ were generated. Using the grayscale images representing the red, green, and blue intensity values, 73 other grayscale images were generated. The grayscale intensity values were used to prepare datasets for the ten and 83 (=10 + 73) images. For both datasets, chemical oxygen demand quantification was successful, resulting in values of normalized mean absolute error of less than 0.4% and coefficients of determination that were greater than 0.9996. However, the quantification of ammonium and phosphate ions commonly provided false positive results for the standard solution that contained no ammonium ion/phosphate ion. For ammonium ion, multiple regression markedly improved the accuracy using the pseudocolor method. Phosphate ion quantification was also improved by avoiding the use of an estimated value for the reference solution that contained no phosphate ion. Real details of the measurements and the perspectives were discussed.</description><subject>Accuracy</subject><subject>Backup software</subject><subject>Chemical oxygen demand</subject><subject>Color</subject><subject>Computer software industry</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Glucose</subject><subject>Medical imaging equipment</subject><subject>Organic chemistry</subject><subject>Phosphates</subject><subject>Product introduction</subject><subject>Water quality</subject><issn>2090-8865</issn><issn>2090-8873</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl1rFDEUhgdRrNTeeS0BbwTdNh-TTOZGKK0fhRYrtNfhTHKym7qb1GTGWv-B_9qsW2srXphAPk6e84a8OU3zjNFdxqTc45T1e0xpKTR_0DzhtKczrTvx8Hat5FazU8oFrU1Jyqh83GwJRnvRMv2k-XEC38IqfA9xTsYFkn1rpwz2miRPDlIcQ5zSVMinCeraBwtjSJGcIJQpYyHnZZ14GIrNOCI5Bfv5DMtITnNykx0LuQrjghwiXpJjhBzXNERHTgtOLtm0TJkcrWBe40-bRx6WBXdu5u3m_N3bs4MPs-OP748O9o9nVtF2nMmBay696CiITlvdQ8t6zS0FBoOQnaIW-0EqPVDbOYo9IHSd5K12vI6d2G6ONrouwYW5zGEF-dokCOZXIOW5gTwGu0TjO3R-sM4DirYXuhdCs7qXyjvX-6FqvdloXU7DCp3FOGZY3hO9fxLDwszTV6NaXb-grwIvbwRy-jJV58yqeonLJUSsvhvOBadcKy0q-uIv9CJNOVarKsW4ajt6l5pDfUCIPtV77VrU7CumheJUtZXa_QdVu8NVsCmiDzV-L-H1JsHmVEpGf_tGRs26Es26Es1NJVb8-V1fbuHfdVeBVxtgEaKDq_CfclgZ9PCHZqqnQoqfGHPv9Q</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Doi, Ryoichi</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U5</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L7M</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-3023-0136</orcidid></search><sort><creationdate>20190101</creationdate><title>Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging</title><author>Doi, Ryoichi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c604t-5b2825f370a378c89a41982c0a1ab35760ce9b568b0c7d0e9aea775248d252473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accuracy</topic><topic>Backup software</topic><topic>Chemical oxygen demand</topic><topic>Color</topic><topic>Computer software industry</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Glucose</topic><topic>Medical imaging equipment</topic><topic>Organic chemistry</topic><topic>Phosphates</topic><topic>Product introduction</topic><topic>Water quality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Doi, Ryoichi</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of analytical methods in chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Doi, Ryoichi</au><au>Uslu, Bengi</au><au>Bengi Uslu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging</atitle><jtitle>Journal of analytical methods in chemistry</jtitle><addtitle>J Anal Methods Chem</addtitle><date>2019-01-01</date><risdate>2019</risdate><volume>2019</volume><issue>2019</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>2090-8865</issn><eissn>2090-8873</eissn><abstract>Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, ammonium ion, and phosphate ion using a pseudocolor imaging method. Each PackTest product underwent reactions with standard solutions. The generated color was scanner-read. From the color image, ten grayscale images representing the intensity values of red, green, blue, cyan, magenta, yellow, key black, and L∗, and the values of a∗ and b∗ were generated. Using the grayscale images representing the red, green, and blue intensity values, 73 other grayscale images were generated. The grayscale intensity values were used to prepare datasets for the ten and 83 (=10 + 73) images. For both datasets, chemical oxygen demand quantification was successful, resulting in values of normalized mean absolute error of less than 0.4% and coefficients of determination that were greater than 0.9996. However, the quantification of ammonium and phosphate ions commonly provided false positive results for the standard solution that contained no ammonium ion/phosphate ion. For ammonium ion, multiple regression markedly improved the accuracy using the pseudocolor method. Phosphate ion quantification was also improved by avoiding the use of an estimated value for the reference solution that contained no phosphate ion. Real details of the measurements and the perspectives were discussed.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>31093418</pmid><doi>10.1155/2019/1685382</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-3023-0136</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2090-8865 |
ispartof | Journal of analytical methods in chemistry, 2019-01, Vol.2019 (2019), p.1-12 |
issn | 2090-8865 2090-8873 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_f7edfbcdfae3493893381bcd56fdd9fb |
source | EBSCOhost Business Source Ultimate; Open Access: PubMed Central; Publicly Available Content Database (Proquest) (PQ_SDU_P3); Open Access: Wiley-Blackwell Open Access Journals; Full-Text Journals in Chemistry (Open access) |
subjects | Accuracy Backup software Chemical oxygen demand Color Computer software industry Datasets Deep learning Glucose Medical imaging equipment Organic chemistry Phosphates Product introduction Water quality |
title | Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T11%3A02%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Maximizing%20the%20Accuracy%20of%20Continuous%20Quantification%20Measures%20Using%20Discrete%20PackTest%20Products%20with%20Deep%20Learning%20and%20Pseudocolor%20Imaging&rft.jtitle=Journal%20of%20analytical%20methods%20in%20chemistry&rft.au=Doi,%20Ryoichi&rft.date=2019-01-01&rft.volume=2019&rft.issue=2019&rft.spage=1&rft.epage=12&rft.pages=1-12&rft.issn=2090-8865&rft.eissn=2090-8873&rft_id=info:doi/10.1155/2019/1685382&rft_dat=%3Cgale_doaj_%3EA618362064%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c604t-5b2825f370a378c89a41982c0a1ab35760ce9b568b0c7d0e9aea775248d252473%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2212647083&rft_id=info:pmid/31093418&rft_galeid=A618362064&rfr_iscdi=true |