A Novel One‐Class Convolutional Autoencoder Combined With Excitation–Emission Matrix Fluorescence Spectroscopy for Authenticity Identification of Food
ABSTRACT In this work, a novel one‐class classification algorithm one‐class convolutional autoencoder (OC‐CAE) was proposed for the detection of abnormal samples in the excitation–emission matrix (EEM) fluorescence spectra dataset. The OC‐CAE used Boxplot to analyze the reconstruction errors and use...
Uložené v:
| Vydané v: | Journal of chemometrics Ročník 38; číslo 11 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Chichester
Wiley Subscription Services, Inc
01.11.2024
|
| Predmet: | |
| ISSN: | 0886-9383, 1099-128X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | ABSTRACT
In this work, a novel one‐class classification algorithm one‐class convolutional autoencoder (OC‐CAE) was proposed for the detection of abnormal samples in the excitation–emission matrix (EEM) fluorescence spectra dataset. The OC‐CAE used Boxplot to analyze the reconstruction errors and used the LOF algorithm to handle features extracted by the hidden layer in the convolutional autoencoder (CAE). The fused information provides the basis for more accurate pattern recognition, ensures flexibility in model training, and can obtain higher model specificity, which is important in the field of food quality control. To demonstrate the reliability and advantages of OC‐CAE, two EEM cases related to the authentication of food including the Zhenjiang aromatic vinegar (ZAV) case and the camellia oil (CAO) case were studied. The results showed that OC‐CAE identified all abnormal samples in the two cases, reflecting excellent performance in the detection of abnormal samples, and that it, coupled with EEM, would be an effective tool for the authenticity identification of food. |
|---|---|
| AbstractList | In this work, a novel one‐class classification algorithm one‐class convolutional autoencoder (OC‐CAE) was proposed for the detection of abnormal samples in the excitation–emission matrix (EEM) fluorescence spectra dataset. The OC‐CAE used Boxplot to analyze the reconstruction errors and used the LOF algorithm to handle features extracted by the hidden layer in the convolutional autoencoder (CAE). The fused information provides the basis for more accurate pattern recognition, ensures flexibility in model training, and can obtain higher model specificity, which is important in the field of food quality control. To demonstrate the reliability and advantages of OC‐CAE, two EEM cases related to the authentication of food including the Zhenjiang aromatic vinegar (ZAV) case and the camellia oil (CAO) case were studied. The results showed that OC‐CAE identified all abnormal samples in the two cases, reflecting excellent performance in the detection of abnormal samples, and that it, coupled with EEM, would be an effective tool for the authenticity identification of food. ABSTRACT In this work, a novel one‐class classification algorithm one‐class convolutional autoencoder (OC‐CAE) was proposed for the detection of abnormal samples in the excitation–emission matrix (EEM) fluorescence spectra dataset. The OC‐CAE used Boxplot to analyze the reconstruction errors and used the LOF algorithm to handle features extracted by the hidden layer in the convolutional autoencoder (CAE). The fused information provides the basis for more accurate pattern recognition, ensures flexibility in model training, and can obtain higher model specificity, which is important in the field of food quality control. To demonstrate the reliability and advantages of OC‐CAE, two EEM cases related to the authentication of food including the Zhenjiang aromatic vinegar (ZAV) case and the camellia oil (CAO) case were studied. The results showed that OC‐CAE identified all abnormal samples in the two cases, reflecting excellent performance in the detection of abnormal samples, and that it, coupled with EEM, would be an effective tool for the authenticity identification of food. |
| Author | Yan, Xiaoqin Long, Wanjun Yu, Ruqin Wu, Hailong Wang, Tong Jia, Baoshuo Huang, Kun |
| Author_xml | – sequence: 1 givenname: Xiaoqin surname: Yan fullname: Yan, Xiaoqin organization: Hunan University – sequence: 2 givenname: Baoshuo surname: Jia fullname: Jia, Baoshuo organization: Hunan University – sequence: 3 givenname: Wanjun surname: Long fullname: Long, Wanjun organization: South‐Central Minzu University – sequence: 4 givenname: Kun surname: Huang fullname: Huang, Kun organization: Hunan University – sequence: 5 givenname: Tong orcidid: 0000-0002-5062-6103 surname: Wang fullname: Wang, Tong email: wangtong@hnu.edu.cn organization: Hunan University – sequence: 6 givenname: Hailong orcidid: 0000-0003-4483-2557 surname: Wu fullname: Wu, Hailong email: hlwu@hnu.edu.cn organization: Hunan University – sequence: 7 givenname: Ruqin surname: Yu fullname: Yu, Ruqin organization: Hunan University |
| BookMark | eNp1kM1Kw0AUhQdRsK2CjzDgxk10fpJmZllCq0KrCxXdhcnkBqekmTqTqN35CII7H69P4sS6dXUPnO8eOGeI9hvbAEInlJxTQtiFhtU5TyTbQwNKpIwoE0_7aECEGEeSC36Iht4vCQkejwfoe4Jv7CvU-LaB7cdnVivvcWabV1t3rbGNqvGkay002pbggrMqTAMlfjTtM56-a9OqHtt-fE1Xxvsg8UK1zrzjWd1ZB16HV8B3a9Cts17b9QZX1vWhz9C0JgRs8HXZy8ro3yxsKzyztjxCB5WqPRz_3RF6mE3vs6tofnt5nU3mkWZJzCIm0zEZC6CUShErqkvgpSyEJqygVcwBUqZlKWPFKOepJoKVIFVBVUIlT4GP0Okud-3sSwe-zZe2c6G5zzlliaDjhKWBOttROtTwDqp87cxKuU1OSd4vn4fl8375gEY79M3UsPmXy7Pp4pf_AeGMixA |
| Cites_doi | 10.1088/1742-6596/1237/5/052007 10.1111/j.1750-3841.2011.02417.x 10.1145/342009.335388 10.1016/j.fbio.2022.101855 10.1016/j.ins.2023.119758 10.1016/j.rse.2018.09.018 10.1016/0893-6080(95)00120-4 10.1109/ACCESS.2022.3187961 10.1016/j.foodchem.2022.135050 10.1039/c3ay40582f 10.1016/j.tifs.2019.01.017 10.1109/FG.2019.8756525 10.1186/s40537-021-00514-x 10.1002/cem.3371 10.1109/TSMC.2017.2771341 10.1016/j.tifs.2021.06.010 10.1080/09537287.2023.2175736 10.3390/foods10020344 10.1016/j.chemolab.2016.10.002 10.1017/S026988891300043X 10.1016/j.foodchem.2023.136406 10.1109/ACCESS.2020.2988796 10.1016/j.aca.2017.05.013 10.1016/j.patrec.2020.03.016 10.1016/j.foodchem.2019.04.109 10.1214/aoms/1177704472 10.1002/cem.3173 10.1016/j.chemolab.2020.104064 10.1002/jsfa.8364 10.1080/00401706.1999.10485670 10.3390/sym13091705 10.1016/j.chemolab.2017.05.010 10.1007/978-3-642-21735-7_7 10.1002/cem.3252 |
| ContentType | Journal Article |
| Copyright | 2024 John Wiley & Sons Ltd. 2024 John Wiley & Sons, Ltd. |
| Copyright_xml | – notice: 2024 John Wiley & Sons Ltd. – notice: 2024 John Wiley & Sons, Ltd. |
| DBID | AAYXX CITATION 7SC 7U5 8FD JQ2 L7M L~C L~D |
| DOI | 10.1002/cem.3592 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Solid State and Superconductivity Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Solid State and Superconductivity Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Chemistry |
| EISSN | 1099-128X |
| EndPage | n/a |
| ExternalDocumentID | 10_1002_cem_3592 CEM3592 |
| Genre | article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China funderid: 22204049; 22174036 – fundername: Key Scientific Research Project of Hunan Provincial Department of Education funderid: 23A0028 – fundername: Natural Science Foundation of Hunan Province funderid: 2022JJ40042 |
| GroupedDBID | .3N .GA .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 31~ 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABEML ABIJN ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACGFS ACIWK ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFNX AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB AQPKS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DR1 DR2 DRFUL DRSTM DU5 EBS EJD F00 F01 F04 F5P FEDTE G-S G.N GNP GODZA H.T H.X HF~ HGLYW HHZ HVGLF HZ~ IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LH5 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QRW R.K RIWAO RJQFR RNS ROL RWI RX1 RYL SAMSI SUPJJ UB1 W8V W99 WBFHL WBKPD WH7 WIB WIH WIK WJL WOHZO WQJ WRC WRJ WXSBR WYISQ XG1 XPP XV2 ZZTAW ~IA ~WT AAMMB AAYXX AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AIDQK AIDYY AIQQE CITATION O8X 7SC 7U5 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c2542-2976068e111984a1cde3d9b8c02b1f43ee72c9d94a21337c082de9ab1a51937e3 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001283263500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0886-9383 |
| IngestDate | Fri Jul 25 19:36:01 EDT 2025 Sat Nov 29 07:13:34 EST 2025 Wed Jan 22 17:15:12 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2542-2976068e111984a1cde3d9b8c02b1f43ee72c9d94a21337c082de9ab1a51937e3 |
| Notes | Funding This work was supported by the National Natural Science Foundation of China (nos. 22204049 and 22174036), the Key Scientific Research Project of Hunan Provincial Department of Education (no. 23A0028), and the Natural Science Foundation of Hunan Province (no. 2022JJ40042). ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-4483-2557 0000-0002-5062-6103 |
| PQID | 3125816527 |
| PQPubID | 37374 |
| PageCount | 10 |
| ParticipantIDs | proquest_journals_3125816527 crossref_primary_10_1002_cem_3592 wiley_primary_10_1002_cem_3592_CEM3592 |
| PublicationCentury | 2000 |
| PublicationDate | November 2024 2024-11-00 20241101 |
| PublicationDateYYYYMMDD | 2024-11-01 |
| PublicationDate_xml | – month: 11 year: 2024 text: November 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Chichester |
| PublicationPlace_xml | – name: Chichester |
| PublicationTitle | Journal of chemometrics |
| PublicationYear | 2024 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 2021; 8 2011 2023; 37 2023; 424 2020; 203 2011; 76 1999; 41 2020; 34 2023; 406 2014; 29 1962; 33 2013; 5 2022; 49 1977 2020; 8 2021; 13 2019; 1237 2017; 97 2021; 10 2023 2019; 85 2000 2018; 218 2020; 50 2021; 114 2019 2020; 133 2022; 10 2024; 652 2016; 159 2017; 167 2017; 982 2019; 293 1996; 9 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_28_1 e_1_2_7_29_1 Tukey J. W. (e_1_2_7_27_1) 1977 e_1_2_7_30_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_22_1 e_1_2_7_34_1 e_1_2_7_21_1 e_1_2_7_35_1 e_1_2_7_20_1 e_1_2_7_36_1 |
| References_xml | – start-page: 1 year: 2019 end-page: 8 – volume: 293 start-page: 348 year: 2019 end-page: 357 article-title: Rapid Identification and Quantification of Cheaper Vegetable Oil Adulteration in Camellia Oil by Using Excitation‐Emission Matrix Fluorescence Spectroscopy Combined With Chemometrics publication-title: Food Chemistry – start-page: 52 year: 2011 end-page: 59 – volume: 13 start-page: 1705 issue: 9 year: 2021 article-title: Plant Leaves Recognition Based on a Hierarchical One‐Class Learning Scheme With Convolutional Auto‐Encoder and Siamese Neural Network publication-title: Symmetry – volume: 76 start-page: R157 issue: 9 year: 2011 end-page: R163 article-title: Defining the Public Health Threat of Food Fraud publication-title: Journal of Food Science – volume: 85 start-page: 163 year: 2019 end-page: 176 article-title: Current Trends and Recent Advances on Food Authenticity Technologies and Chemometric Approaches publication-title: Trends in Food Science and Technology – volume: 8 start-page: 80716 year: 2020 end-page: 80727 article-title: Unsupervised K‐Means Clustering Algorithm publication-title: IEEE Access – volume: 10 start-page: 70645 year: 2022 end-page: 70661 article-title: The Deep Radial Basis Function Data Descriptor (D‐RBFDD) Network: A One‐Class Neural Network for Anomaly Detection publication-title: IEEE Access – volume: 424 year: 2023 article-title: Classification of Chinese Traditional Cereal Vinegars and Antioxidant Property Predication by Fluorescence Spectroscopy publication-title: Food Chemistry – volume: 50 start-page: 386 issue: 2 year: 2020 end-page: 396 article-title: One‐Class Convex Hull‐Based Algorithm for Classification in Distributed Environments publication-title: IEEE Transactions on Systems, man, and Cybernetics: Systems – volume: 133 start-page: 280 year: 2020 end-page: 286 article-title: Sample Imbalance Disease Classification Model Based on Association Rule Feature Selection publication-title: Pattern Recognition Letters – volume: 159 start-page: 89 year: 2016 end-page: 96 article-title: Rigorous and Compliant Approaches to One‐Class Classification publication-title: Chemometrics and Intelligent Laboratory Systems – volume: 97 start-page: 3877 issue: 12 year: 2017 end-page: 3896 article-title: Modern Analytical Methods for the Detection of Food Fraud and Adulteration by Food Category publication-title: Journal of the Science of Food and Agriculture – year: 1977 – volume: 29 start-page: 345 issue: 3 year: 2014 end-page: 374 article-title: One‐Class Classification: Taxonomy of Study and Review of Techniques publication-title: Knowledge Engineering Review – start-page: 1 year: 2023 end-page: 9 article-title: Machine Learning Based Fault Detection Approach to Enhance Quality Control in Smart Manufacturing publication-title: Production Planning and Control – volume: 34 issue: 12 year: 2020 article-title: Traceability of Soybeans Produced in Argentina Based on Their Trace Element Profiles publication-title: Journal of Chemometrics – volume: 1237 issue: 5 year: 2019 article-title: Research on Abnormal Detection of One‐Class Support Vector Machine Based on Ensemble Cooperative Semi‐Supervised Learning publication-title: Journal of Physics Conference Series – volume: 8 start-page: 122 issue: 1 year: 2021 article-title: A Literature Review on One‐Class Classification and Its Potential Applications in Big Data publication-title: Journal of Big Data – volume: 9 start-page: 463 issue: 3 year: 1996 end-page: 474 article-title: Network Constraints and Multi‐Objective Optimization for One‐Class Classification publication-title: Neural Networks – volume: 41 start-page: 212 issue: 3 year: 1999 end-page: 223 article-title: A Fast Algorithm for the Minimum Covariance Determinant Estimator publication-title: Technometrics – volume: 34 issue: 7 year: 2020 article-title: Incorporating Brand Variability Into Classification of Edible Oils by Raman Spectroscopy publication-title: Journal of Chemometrics – volume: 49 year: 2022 article-title: Bioactive Substances and Therapeutic Potential of Camellia Oil: An Overview publication-title: Food Bioscience – volume: 203 year: 2020 article-title: PLS‐DA–A MATLAB GUI Tool for Hard and Soft Approaches to Partial Least Squares Discriminant Analysis publication-title: Chemometrics and Intelligent Laboratory Systems – start-page: 93 year: 2000 end-page: 104 – volume: 114 start-page: 424 year: 2021 end-page: 442 article-title: Food Frauds: Global Incidents and Misleading Situations publication-title: Trends in Food Science and Technology – volume: 652 year: 2024 article-title: Parametrized Linear Regression for Boxplot‐Multivalued Data Applied to the Brazilian Electric Sector publication-title: Information Sciences – volume: 167 start-page: 23 year: 2017 end-page: 28 article-title: DD‐SIMCA–A MATLAB GUI Tool for Data Driven SIMCA Approach publication-title: Chemometrics and Intelligent Laboratory Systems – volume: 37 issue: 3 year: 2023 article-title: Automatic Food and Beverage Authentication and Adulteration Detection by Classification Hybrid Fusion publication-title: Journal of Chemometrics – volume: 10 start-page: 344 issue: 2 year: 2021 article-title: Health Promoting Properties of Cereal Vinegars publication-title: Food – volume: 5 start-page: 3790 issue: 16 year: 2013 end-page: 3798 article-title: Classification Tools in Chemistry. Part 1: Linear Models. PLS‐DA publication-title: Analytical Methods – volume: 406 year: 2023 article-title: Establishment and Evaluation of Multiple Adulteration Detection of Camellia oil by Mixture Design publication-title: Food Chemistry – volume: 982 start-page: 9 year: 2017 end-page: 19 article-title: Class‐Modelling in Food Analytical Chemistry: Development, Sampling, Optimisation and Validation Issues – A Tutorial publication-title: Analytica Chimica Acta – volume: 33 start-page: 1065 issue: 3 year: 1962 end-page: 1076 article-title: On Estimation of a Probability Density Function and Mode publication-title: Annals of Mathematical Statistics – volume: 218 start-page: 119 year: 2018 end-page: 131 article-title: Invasive Tree Species Detection in the Eastern Arc Mountains Biodiversity Hotspot Using One Class Classification publication-title: Remote Sensing of Environment – ident: e_1_2_7_33_1 doi: 10.1088/1742-6596/1237/5/052007 – ident: e_1_2_7_17_1 doi: 10.1111/j.1750-3841.2011.02417.x – ident: e_1_2_7_29_1 doi: 10.1145/342009.335388 – ident: e_1_2_7_24_1 doi: 10.1016/j.fbio.2022.101855 – ident: e_1_2_7_28_1 doi: 10.1016/j.ins.2023.119758 – ident: e_1_2_7_13_1 doi: 10.1016/j.rse.2018.09.018 – ident: e_1_2_7_9_1 doi: 10.1016/0893-6080(95)00120-4 – ident: e_1_2_7_14_1 doi: 10.1109/ACCESS.2022.3187961 – ident: e_1_2_7_25_1 doi: 10.1016/j.foodchem.2022.135050 – ident: e_1_2_7_36_1 doi: 10.1039/c3ay40582f – ident: e_1_2_7_19_1 doi: 10.1016/j.tifs.2019.01.017 – ident: e_1_2_7_15_1 doi: 10.1109/FG.2019.8756525 – ident: e_1_2_7_4_1 doi: 10.1186/s40537-021-00514-x – ident: e_1_2_7_21_1 doi: 10.1002/cem.3371 – volume-title: Exploratory Data Analysis year: 1977 ident: e_1_2_7_27_1 – ident: e_1_2_7_2_1 doi: 10.1109/TSMC.2017.2771341 – ident: e_1_2_7_18_1 doi: 10.1016/j.tifs.2021.06.010 – ident: e_1_2_7_34_1 doi: 10.1080/09537287.2023.2175736 – ident: e_1_2_7_22_1 doi: 10.3390/foods10020344 – ident: e_1_2_7_31_1 doi: 10.1016/j.chemolab.2016.10.002 – ident: e_1_2_7_6_1 doi: 10.1017/S026988891300043X – ident: e_1_2_7_23_1 doi: 10.1016/j.foodchem.2023.136406 – ident: e_1_2_7_10_1 doi: 10.1109/ACCESS.2020.2988796 – ident: e_1_2_7_3_1 doi: 10.1016/j.aca.2017.05.013 – ident: e_1_2_7_5_1 doi: 10.1016/j.patrec.2020.03.016 – ident: e_1_2_7_26_1 doi: 10.1016/j.foodchem.2019.04.109 – ident: e_1_2_7_8_1 doi: 10.1214/aoms/1177704472 – ident: e_1_2_7_12_1 doi: 10.1002/cem.3173 – ident: e_1_2_7_35_1 doi: 10.1016/j.chemolab.2020.104064 – ident: e_1_2_7_20_1 doi: 10.1002/jsfa.8364 – ident: e_1_2_7_7_1 doi: 10.1080/00401706.1999.10485670 – ident: e_1_2_7_16_1 doi: 10.3390/sym13091705 – ident: e_1_2_7_32_1 doi: 10.1016/j.chemolab.2017.05.010 – ident: e_1_2_7_30_1 doi: 10.1007/978-3-642-21735-7_7 – ident: e_1_2_7_11_1 doi: 10.1002/cem.3252 |
| SSID | ssj0009934 |
| Score | 2.423162 |
| Snippet | ABSTRACT
In this work, a novel one‐class classification algorithm one‐class convolutional autoencoder (OC‐CAE) was proposed for the detection of abnormal... In this work, a novel one‐class classification algorithm one‐class convolutional autoencoder (OC‐CAE) was proposed for the detection of abnormal samples in the... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Index Database Publisher |
| SubjectTerms | abnormal sample detection Algorithms Authenticity autoencoder convolutional Emission spectra Emissions Excitation spectra excitation–emission matrix fluorescence Fluorescence Food food fraud Food quality one‐class classification Pattern analysis Pattern recognition Quality control Spectrum analysis |
| Title | A Novel One‐Class Convolutional Autoencoder Combined With Excitation–Emission Matrix Fluorescence Spectroscopy for Authenticity Identification of Food |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcem.3592 https://www.proquest.com/docview/3125816527 |
| Volume | 38 |
| WOSCitedRecordID | wos001283263500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1099-128X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009934 issn: 0886-9383 databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEA6yCnrxLa4vIoi3apN0t81R1i0edBVR9FbaZIoL2shud9GbP0Hw5s_zlzjpw9WDIHjqoZ1QMo98M0m-IWRP8BQjI5OOj9breFoxJ-GaOSnXSdCKRUunBWX-qd_rBbe38qI6VWnvwpT8EF8FN-sZRby2Dh4nw8MJaaiChwPRkhh-pzmardcg08eX4fXphHJXFnvK6EZtR2IiVlPPuvywlv25GE0Q5necWiw04cJ_fnGRzFfwkh6V9rBEpiBbJrOduqvbCnk_oj0zhnt6nsHHy2vRE5N2TDauTNAKj3Jj6S01DPDNA6bOoOlNP7-j3SdVUXp_vLx1cUhbaqNnluX_iYb3IzMoyKEUUNvWPrdEmebxmSIutoPe2YNJOMAzLW8Hp1W5kJqUhsboVXIddq86J07Vn8FRmFZyhyOUcdsBYLiUgRczpUFomQTK5QlLPQHgcyW19GKOmbCvEG1okHHCYgsbfRBrpJGZDNYJFZC6KmhLlWrhKYwiMbMbfAwYgPKl2yS7taKix5KGIyoJl3mEsxzZWW6SrVqDUeWIw0gggAtYu8X9JtkvdPWrfNTpntnnxl8_3CRzHCFOeTNxizTywQi2yYwa5_3hYKcyx0_Pwuf6 |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTtwwEB6hpRK9lNIfdVtaXKnqLRDbySZWT2i7EVV3t1UFgluU2BOBBDFasiu48QhIvfXxeJKO88PCAalSTzkkM4rsmfE3Y_sbgE9SFBQZufIisl4vMJp7uTDcK4TJ4zCToSlqyvxxNJ3GR0fq5wp86e7CNPwQdwU35xl1vHYO7grSO0vWUI1n2zJUFH9XA7KisAerX38lB-Ml566qN5XJjwaeokys4571xU4n-3A1WkLM-0C1XmmS9f_6x-fwrAWYbLexiA1YwfIFrA27vm4v4c8um9oFnrIfJd5e39RdMdnQlovWCJ3wvLKO4NLgjN6cUfKMhh2eVMdsdKlbUu_b698jUumKbWzieP4vWXI6t7OaHkojc43tK0eVac-vGCFjp_TYHU0iBVesuR9ctAVDZguWWGtewUEy2h_ueW2HBk9TYik8QWDGH8RIAVPFQca1QWlUHmtf5LwIJGIktDIqyATlwpEmvGFQZTnPHHCMUL6GXmlLfANMYuHreKB0YWSgKY5k3G3xceSIOlJ-Hz52M5WeN0QcaUO5LFIa5dSNch82uylMW1e8SCVBuJgPQhH14XM9WY_Kp8PRxD3f_uuHW7C2tz8Zp-Nv0-_v4KkgwNPcU9yEXjWb43t4ohfVycXsQ2ubfwGnSevq |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5VLQIuvBELBYyEuIXGdl4Wp2q7EYjtUiEqeosSe6xWauPVNrtqb_0JSNz4ef0ljPNg4YCExCmHZKzI8_A3Y_sbgNdSWIqMXAUpWW8QGc2DShgeWGGqLC5lbGxLmT9NZ7Ps6EgdbMC74S5Mxw_xq-DmPaON197BcW7szpo1VOPZWxkrir9bUawS8sqtvc_54XTNuavaTWXyoyRQlIkN3LOh2Blk_1yN1hDzd6DarjT53f_6x3twpweYbLeziPuwgfUDuDUe-ro9hB-7bOZWeMo-1Xh99a3tisnGrl71RuiFl43zBJcGF_TmjJJnNOzrSXPMJhe6J_W-vvo-oSF9sY3te57_C5afLt2ipYfSyHxj-8ZTZbr5JSNk7Ac99keTaIBL1t0Ptn3BkDnLcufMIzjMJ1_G74O-Q0OgKbEUgSAwEyYZUsBUWVRybVAaVWU6FBW3kURMhVZGRaWgXDjVhDcMqrLipQeOKcrHsFm7Gp8Ak2hDnSVKWyMjTXGk5H6LjyNH1KkKR_Bq0FQx74g4io5yWRQ0y4Wf5RFsDyoselc8LyRBuIwnsUhH8KZV1l_li_Fk3z-f_uuHL-HmwV5eTD_MPj6D24LwTndNcRs2m8USn8MNvWpOzhcvetP8CVIr62U |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Novel+One%E2%80%90Class+Convolutional+Autoencoder+Combined+With+Excitation%E2%80%93Emission+Matrix+Fluorescence+Spectroscopy+for+Authenticity+Identification+of+Food&rft.jtitle=Journal+of+chemometrics&rft.au=Yan%2C+Xiaoqin&rft.au=Jia%2C+Baoshuo&rft.au=Long%2C+Wanjun&rft.au=Huang%2C+Kun&rft.date=2024-11-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=0886-9383&rft.eissn=1099-128X&rft.volume=38&rft.issue=11&rft_id=info:doi/10.1002%2Fcem.3592&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0886-9383&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0886-9383&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0886-9383&client=summon |