Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm
Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in dee...
Ausführliche Beschreibung
Autor*in: |
Huang, Zongyu [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022transfer abstract |
---|
Übergeordnetes Werk: |
Enthalten in: A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report - Sato, Junichi ELSEVIER, 2014, an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology, New York, NY [u.a.] |
---|---|
Übergeordnetes Werk: |
volume:304 ; year:2022 ; pages:0 |
Links: |
---|
DOI / URN: |
10.1016/j.cis.2022.102663 |
---|
Katalog-ID: |
ELV057679851 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV057679851 | ||
003 | DE-627 | ||
005 | 20230626045544.0 | ||
007 | cr uuu---uuuuu | ||
008 | 220808s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.cis.2022.102663 |2 doi | |
028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica |
035 | |a (DE-627)ELV057679851 | ||
035 | |a (ELSEVIER)S0001-8686(22)00065-3 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 610 |q VZ |
082 | 0 | 4 | |a 370 |q VZ |
100 | 1 | |a Huang, Zongyu |e verfasserin |4 aut | |
245 | 1 | 0 | |a Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm |
264 | 1 | |c 2022transfer abstract | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
520 | |a Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. | ||
520 | |a Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. | ||
700 | 1 | |a Ni, Yang |4 oth | |
700 | 1 | |a Yu, Qun |4 oth | |
700 | 1 | |a Li, Jinwei |4 oth | |
700 | 1 | |a Fan, Liuping |4 oth | |
700 | 1 | |a Eskin, N.A. Michael |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier |a Sato, Junichi ELSEVIER |t A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report |d 2014 |d an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology |g New York, NY [u.a.] |w (DE-627)ELV018033865 |
773 | 1 | 8 | |g volume:304 |g year:2022 |g pages:0 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.cis.2022.102663 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a GBV_ILN_21 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_70 | ||
951 | |a AR | ||
952 | |d 304 |j 2022 |h 0 |
author_variant |
z h zh |
---|---|
matchkey_str |
huangzongyuniyangyuqunlijinweifanliuping:2022----:eperignodcecaisgtnvlaigikrneusopoetebdoltcasfctoadun |
hierarchy_sort_str |
2022transfer abstract |
publishDate |
2022 |
allfields |
10.1016/j.cis.2022.102663 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057679851 (ELSEVIER)S0001-8686(22)00065-3 DE-627 ger DE-627 rakwb eng 610 VZ 370 VZ Huang, Zongyu verfasserin aut Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Ni, Yang oth Yu, Qun oth Li, Jinwei oth Fan, Liuping oth Eskin, N.A. Michael oth Enthalten in Elsevier Sato, Junichi ELSEVIER A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report 2014 an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology New York, NY [u.a.] (DE-627)ELV018033865 volume:304 year:2022 pages:0 https://doi.org/10.1016/j.cis.2022.102663 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 AR 304 2022 0 |
spelling |
10.1016/j.cis.2022.102663 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057679851 (ELSEVIER)S0001-8686(22)00065-3 DE-627 ger DE-627 rakwb eng 610 VZ 370 VZ Huang, Zongyu verfasserin aut Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Ni, Yang oth Yu, Qun oth Li, Jinwei oth Fan, Liuping oth Eskin, N.A. Michael oth Enthalten in Elsevier Sato, Junichi ELSEVIER A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report 2014 an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology New York, NY [u.a.] (DE-627)ELV018033865 volume:304 year:2022 pages:0 https://doi.org/10.1016/j.cis.2022.102663 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 AR 304 2022 0 |
allfields_unstemmed |
10.1016/j.cis.2022.102663 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057679851 (ELSEVIER)S0001-8686(22)00065-3 DE-627 ger DE-627 rakwb eng 610 VZ 370 VZ Huang, Zongyu verfasserin aut Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Ni, Yang oth Yu, Qun oth Li, Jinwei oth Fan, Liuping oth Eskin, N.A. Michael oth Enthalten in Elsevier Sato, Junichi ELSEVIER A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report 2014 an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology New York, NY [u.a.] (DE-627)ELV018033865 volume:304 year:2022 pages:0 https://doi.org/10.1016/j.cis.2022.102663 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 AR 304 2022 0 |
allfieldsGer |
10.1016/j.cis.2022.102663 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057679851 (ELSEVIER)S0001-8686(22)00065-3 DE-627 ger DE-627 rakwb eng 610 VZ 370 VZ Huang, Zongyu verfasserin aut Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Ni, Yang oth Yu, Qun oth Li, Jinwei oth Fan, Liuping oth Eskin, N.A. Michael oth Enthalten in Elsevier Sato, Junichi ELSEVIER A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report 2014 an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology New York, NY [u.a.] (DE-627)ELV018033865 volume:304 year:2022 pages:0 https://doi.org/10.1016/j.cis.2022.102663 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 AR 304 2022 0 |
allfieldsSound |
10.1016/j.cis.2022.102663 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057679851 (ELSEVIER)S0001-8686(22)00065-3 DE-627 ger DE-627 rakwb eng 610 VZ 370 VZ Huang, Zongyu verfasserin aut Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. Ni, Yang oth Yu, Qun oth Li, Jinwei oth Fan, Liuping oth Eskin, N.A. Michael oth Enthalten in Elsevier Sato, Junichi ELSEVIER A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report 2014 an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology New York, NY [u.a.] (DE-627)ELV018033865 volume:304 year:2022 pages:0 https://doi.org/10.1016/j.cis.2022.102663 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 AR 304 2022 0 |
language |
English |
source |
Enthalten in A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report New York, NY [u.a.] volume:304 year:2022 pages:0 |
sourceStr |
Enthalten in A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report New York, NY [u.a.] volume:304 year:2022 pages:0 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
dewey-raw |
610 |
isfreeaccess_bool |
false |
container_title |
A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report |
authorswithroles_txt_mv |
Huang, Zongyu @@aut@@ Ni, Yang @@oth@@ Yu, Qun @@oth@@ Li, Jinwei @@oth@@ Fan, Liuping @@oth@@ Eskin, N.A. Michael @@oth@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
ELV018033865 |
dewey-sort |
3610 |
id |
ELV057679851 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV057679851</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626045544.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.cis.2022.102663</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV057679851</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0001-8686(22)00065-3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">370</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Zongyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ni, Yang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Qun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jinwei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fan, Liuping</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Eskin, N.A. Michael</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Sato, Junichi ELSEVIER</subfield><subfield code="t">A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report</subfield><subfield code="d">2014</subfield><subfield code="d">an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV018033865</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:304</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.cis.2022.102663</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">304</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
author |
Huang, Zongyu |
spellingShingle |
Huang, Zongyu ddc 610 ddc 370 Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm |
authorStr |
Huang, Zongyu |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV018033865 |
format |
electronic Article |
dewey-ones |
610 - Medicine & health 370 - Education |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
610 VZ 370 VZ Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm |
topic |
ddc 610 ddc 370 |
topic_unstemmed |
ddc 610 ddc 370 |
topic_browse |
ddc 610 ddc 370 |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
y n yn q y qy j l jl l f lf n m e nm nme |
hierarchy_parent_title |
A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report |
hierarchy_parent_id |
ELV018033865 |
dewey-tens |
610 - Medicine & health 370 - Education |
hierarchy_top_title |
A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV018033865 |
title |
Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm |
ctrlnum |
(DE-627)ELV057679851 (ELSEVIER)S0001-8686(22)00065-3 |
title_full |
Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm |
author_sort |
Huang, Zongyu |
journal |
A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report |
journalStr |
A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
600 - Technology 300 - Social sciences |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
zzz |
container_start_page |
0 |
author_browse |
Huang, Zongyu |
container_volume |
304 |
class |
610 VZ 370 VZ |
format_se |
Elektronische Aufsätze |
author-letter |
Huang, Zongyu |
doi_str_mv |
10.1016/j.cis.2022.102663 |
dewey-full |
610 370 |
title_sort |
deep learning in food science: an insight in evaluating pickering emulsion properties by droplets classification and quantification via object detection algorithm |
title_auth |
Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm |
abstract |
Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. |
abstractGer |
Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. |
abstract_unstemmed |
Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_70 |
title_short |
Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm |
url |
https://doi.org/10.1016/j.cis.2022.102663 |
remote_bool |
true |
author2 |
Ni, Yang Yu, Qun Li, Jinwei Fan, Liuping Eskin, N.A. Michael |
author2Str |
Ni, Yang Yu, Qun Li, Jinwei Fan, Liuping Eskin, N.A. Michael |
ppnlink |
ELV018033865 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth oth |
doi_str |
10.1016/j.cis.2022.102663 |
up_date |
2024-07-06T16:51:03.090Z |
_version_ |
1803849210885308416 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV057679851</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626045544.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.cis.2022.102663</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV057679851</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0001-8686(22)00065-3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">370</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Zongyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ni, Yang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Qun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jinwei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fan, Liuping</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Eskin, N.A. Michael</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Sato, Junichi ELSEVIER</subfield><subfield code="t">A prospective multicenter study of immediate function of 1-piece implants: A 3-year follow-up report</subfield><subfield code="d">2014</subfield><subfield code="d">an international journal devoted to experimental and theoretical developments in interfacial and colloidal phenomena and their implications in biology, chemistry, physics and technology</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV018033865</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:304</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.cis.2022.102663</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">304</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
score |
7.3994665 |