A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging
•FHSI was applied to the detection of lead content in oilseed rape leaves.•WT-SDAE model was proposed for deep feature extraction.•The WT-SDAE-SVR model achieved high-precision prediction of Pb concentration. The purpose of this study was to develop a deep learning method involving wavelet transform...
Gespeichert in:
| Veröffentlicht in: | Food chemistry Jg. 409; S. 135251 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
England
Elsevier Ltd
30.05.2023
|
| Schlagworte: | |
| ISSN: | 0308-8146, 1873-7072, 1873-7072 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •FHSI was applied to the detection of lead content in oilseed rape leaves.•WT-SDAE model was proposed for deep feature extraction.•The WT-SDAE-SVR model achieved high-precision prediction of Pb concentration.
The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals. |
|---|---|
| AbstractList | The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals. The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters R , RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals. •FHSI was applied to the detection of lead content in oilseed rape leaves.•WT-SDAE model was proposed for deep feature extraction.•The WT-SDAE-SVR model achieved high-precision prediction of Pb concentration. The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals. |
| ArticleNumber | 135251 |
| Author | Cao, Yan Sun, Jun Yao, Kunshan Zhou, Xin Zhao, Chunjiang Xu, Min |
| Author_xml | – sequence: 1 givenname: Xin surname: Zhou fullname: Zhou, Xin email: 862218958@qq.com organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 2 givenname: Chunjiang surname: Zhao fullname: Zhao, Chunjiang organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 3 givenname: Jun surname: Sun fullname: Sun, Jun email: sun2000jun@sina.com organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 4 givenname: Yan surname: Cao fullname: Cao, Yan organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 5 givenname: Kunshan surname: Yao fullname: Yao, Kunshan organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China – sequence: 6 givenname: Min surname: Xu fullname: Xu, Min organization: School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36586261$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkU1LHTEUhkOx1Kv2L0iW3cw1HzNJBrqoSKsFoZu6DrnJGW8uM8k0yQj-ezNc7cKNq0DO8x4Oz3uGTkIMgNAlJVtKqLg6bIcYnd3DtGWEsS3lHevoJ7ShSvJGEslO0IZwohpFW3GKznI-EEIYoeoLOuWiU4IJukHpGjuAGY9gUvDhEU9Q9tHhISY8J3DelvW3jh22MRQIBfuAox8zgMPJzLAOnyDjJa_kMC4xQbYQLOD98wwpz2BLMiP2k3msyAX6PJga__r6nqOHXz__3tw1939uf99c3zeWC1WatuWUdUYYJ1RL5aAk7QXr-t2OO2el6oGCbSkQ5wbCXaeko1aBIz0w4C3l5-jbce-c4r8FctGTr3eNowkQl6yZ7Pq-k62QFb18RZfdBE7Pqd6anvWbpwqII2BTzDnB8B-hRK-F6IN-K0SvhehjITX4_V3Q-mKKryqT8ePH8R_HOFRRTx6Sztavap1P1ap20X-04gWOHKxj |
| CitedBy_id | crossref_primary_10_3390_agriculture14091549 crossref_primary_10_1016_j_foodcont_2024_110531 crossref_primary_10_1016_j_saa_2024_124015 crossref_primary_10_3390_foods14020286 crossref_primary_10_1016_j_fct_2025_115401 crossref_primary_10_3390_machines13090818 crossref_primary_10_1002_jbio_202500164 crossref_primary_10_3390_foods14132350 crossref_primary_10_3390_s25185786 crossref_primary_10_1016_j_jfca_2025_107625 crossref_primary_10_3390_foods14173026 crossref_primary_10_1097_st9_0000000000000079 crossref_primary_10_3390_horticulturae11070840 crossref_primary_10_1007_s10044_025_01543_5 crossref_primary_10_1016_j_microc_2025_112913 crossref_primary_10_3390_foods14172977 crossref_primary_10_3390_f16030477 crossref_primary_10_1016_j_engappai_2025_111276 crossref_primary_10_1021_acssensors_5c00254 crossref_primary_10_3389_fpls_2023_1219476 crossref_primary_10_3390_foods14091601 crossref_primary_10_1007_s10921_024_01049_w crossref_primary_10_3390_agronomy15081926 crossref_primary_10_1016_j_ijbiomac_2025_147398 crossref_primary_10_3390_en18164408 crossref_primary_10_3389_fpls_2023_1271320 crossref_primary_10_1109_ACCESS_2025_3591279 crossref_primary_10_1016_j_foodchem_2025_143391 crossref_primary_10_1016_j_talanta_2024_127347 crossref_primary_10_1016_j_energy_2025_138228 crossref_primary_10_1016_j_saa_2025_126387 crossref_primary_10_1016_j_trac_2024_117944 crossref_primary_10_1007_s11760_025_04274_6 crossref_primary_10_1016_j_talanta_2024_127473 crossref_primary_10_1016_j_microc_2024_110997 crossref_primary_10_1016_j_tifs_2024_104821 crossref_primary_10_1016_j_trac_2024_117980 crossref_primary_10_1021_acssensors_5c01499 crossref_primary_10_34133_plantphenomics_0124 crossref_primary_10_1007_s11947_025_03822_9 crossref_primary_10_3389_fpubh_2025_1608129 crossref_primary_10_1016_j_foodres_2024_115330 crossref_primary_10_1021_acs_inorgchem_5c00714 crossref_primary_10_1002_smll_202412271 crossref_primary_10_3390_agriculture15161775 crossref_primary_10_1016_j_microc_2024_110317 crossref_primary_10_1080_15376494_2025_2518475 crossref_primary_10_3390_app15158438 crossref_primary_10_3390_computers14080309 crossref_primary_10_1016_j_foodchem_2025_143799 crossref_primary_10_3390_rs16122190 crossref_primary_10_1007_s10489_025_06879_3 crossref_primary_10_3390_fi17070308 crossref_primary_10_3390_su16146064 crossref_primary_10_1002_jsfa_14177 crossref_primary_10_1016_j_saa_2024_123991 crossref_primary_10_3390_foods14071241 crossref_primary_10_1021_acs_jafc_5c06078 crossref_primary_10_3390_foods14111929 crossref_primary_10_1016_j_scitotenv_2024_175076 crossref_primary_10_1007_s11694_024_02745_x crossref_primary_10_3390_foods14173114 crossref_primary_10_1016_j_microc_2025_112974 crossref_primary_10_3390_foods12102089 crossref_primary_10_3390_foods14142488 crossref_primary_10_1016_j_cej_2025_162796 crossref_primary_10_1038_s41598_025_16923_4 |
| Cites_doi | 10.1016/j.compag.2021.106557 10.1016/j.compeleceng.2022.107809 10.1111/jfpe.13293 10.1016/j.biosystemseng.2021.09.010 10.1021/cr050019q 10.1016/j.resconrec.2022.106261 10.1016/j.apnum.2020.05.019 10.1016/j.snb.2017.12.102 10.1016/j.saa.2019.117357 10.1016/j.foodchem.2021.131666 10.1016/j.saa.2021.120460 10.1016/j.scitotenv.2021.146567 10.1364/AO.448454 10.1016/j.compag.2022.106802 10.1016/j.infrared.2021.103936 10.1016/j.foodchem.2019.05.019 10.1080/01431161.2019.1685721 10.1016/j.foodchem.2012.05.114 10.1016/j.saa.2021.120591 10.1016/j.foodchem.2020.126503 10.1016/j.scitotenv.2020.138123 10.1016/j.ces.2022.117556 10.1111/1750-3841.14706 10.1016/j.jclepro.2017.12.214 10.1111/jfpe.13793 10.1016/j.envpol.2021.117829 10.1016/j.foodchem.2014.09.040 10.1111/jfpe.13897 10.1016/j.jfoodeng.2021.110840 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Ltd Copyright © 2022 Elsevier Ltd. All rights reserved. |
| Copyright_xml | – notice: 2022 Elsevier Ltd – notice: Copyright © 2022 Elsevier Ltd. All rights reserved. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1016/j.foodchem.2022.135251 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics Chemistry Diet & Clinical Nutrition |
| EISSN | 1873-7072 |
| ExternalDocumentID | 36586261 10_1016_j_foodchem_2022_135251 S0308814622032137 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JM 9JN AABNK AABVA AACTN AAEDT AAEDW AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AARLI AATLK AAXUO ABFNM ABFRF ABGRD ABGSF ABJNI ABMAC ABUDA ABYKQ ACDAQ ACGFO ACGFS ACIUM ACRLP ADBBV ADECG ADEZE ADQTV ADUVX AEBSH AEFWE AEHWI AEKER AENEX AEQOU AFKWA AFTJW AFXIZ AFZHZ AGUBO AGYEJ AHHHB AIEXJ AIKHN AITUG AJOXV AJSZI ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BKOJK BLXMC CBWCG CS3 DOVZS DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FLBIZ FNPLU FYGXN G-Q GBLVA IHE J1W K-O KOM KZ1 LW9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SAB SCC SDF SDG SDP SES SEW SPC SPCBC SSA SSK SSU SSZ T5K WH7 ~G- ~KM 29H 53G 9DU AAHBH AALCJ AAQXK AATTM AAXKI AAYJJ AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGHFR AGQPQ AGRDE AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLV HVGLF HZ~ R2- SCB VH1 WUQ Y6R ~HD CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c368t-443125a6ad68417f87196259bb3ddc789e1ec41e0ddf03d587d1c8ed09e2e3413 |
| ISICitedReferencesCount | 74 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000923190600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0308-8146 1873-7072 |
| IngestDate | Sun Sep 28 00:26:44 EDT 2025 Wed Feb 19 02:26:33 EST 2025 Sat Nov 29 07:21:14 EST 2025 Tue Nov 18 22:30:37 EST 2025 Fri Feb 23 02:38:34 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Oilseed rape Wavelet transform Heavy metal lead Stacked denoising autoencoder Nondestructive testing Fluorescence hyperspectral imaging |
| Language | English |
| License | Copyright © 2022 Elsevier Ltd. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c368t-443125a6ad68417f87196259bb3ddc789e1ec41e0ddf03d587d1c8ed09e2e3413 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 36586261 |
| PQID | 2759957467 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2759957467 pubmed_primary_36586261 crossref_primary_10_1016_j_foodchem_2022_135251 crossref_citationtrail_10_1016_j_foodchem_2022_135251 elsevier_sciencedirect_doi_10_1016_j_foodchem_2022_135251 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-05-30 |
| PublicationDateYYYYMMDD | 2023-05-30 |
| PublicationDate_xml | – month: 05 year: 2023 text: 2023-05-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Food chemistry |
| PublicationTitleAlternate | Food Chem |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Kortesniemi, Vuorinen, Sinkkonen, Yang, Rajala, Kallio (b0060) 2015; 172 Li, Zhao, Yang (b0075) 2021; 191 Birch (b0010) 2020; 727 He, Zhang, Yang, Chen, Pang, Shen (b0055) 2022; 375 Ozbekova, Kulmyrzaev (b0080) 2019; 223 Fu, Li, Xu, Xie, Yang, Xu (b0040) 2022; 61 Fridrihsone, Romagnoli, Cabulis (b0035) 2018; 177 Hattie, Ian, Felicity, Kirsti (b0050) 2022; 316 Li, Sun, Wu, Lu, Dai (b0070) 2019; 84 Sun, Wu, Hang, Lu, Wu, Chen (b0090) 2019; 42 Zhou, Zhao, Sun, Cao, Fu (b0145) 2021; 119 Zhou, Sun, Tian, Yao, Xu (b0150) 2022; 266 Lee, Kim, Lee, Cho (b0065) 2018; 259 Farroni, Buera (b0025) 2012; 135 Cao, Sun, Yao, Xu, Tang, Zhou (b0015) 2021; 44 Altunay, Elik, Gürkan (b0005) 2019; 293 Fazeli, Hojjati. (b0030) 2020; 156 Zhuang, Peng, Yang, Wang, Zhao, Chao (b0125) 2022; 316 Zhao, An, Tang (b0120) 2022; 195 Wan, Hu, Wang, Tian, Huang (b0095) 2021; 780 Sabeti, V., Sobhani, M., Hasheminejad, S. (2022). An adaptive image steganography method based on integer wavelet transform using genetic algorithm. Computers and Electrical Engineering 99, 107809. https://doi.org/10.1016/j.compeleceng.2022.107809. Wu, Li, Yu, Wang, Wang, Liu (b0105) 2022; 181 Zhou, Sun, Tian, Lu, Hang, Chen (b0130) 2020; 321 Christensen, Noergaard, Bro, Engelsen (b0020) 2006; 37 Yu, Fang, Zhangjin, Mi, Feng, He (b0110) 2021; 212 Zeid, Aboshabana, Ibrahim (b0115) 2022; 267 Zhou, Sun, Tian, Lu, Hang, Chen (b0135) 2020; 41 Zhou, Sun, Zhang, Tian, Yao, Xu (b0140) 2021; 44 Gao, Wei, Huang, Gao (b0045) 2022; 253 Wang, Tan, Zhou, Lian, Zhu (b0100) 2021; 289 Christensen (10.1016/j.foodchem.2022.135251_b0020) 2006; 37 Fridrihsone (10.1016/j.foodchem.2022.135251_b0035) 2018; 177 Zhao (10.1016/j.foodchem.2022.135251_b0120) 2022; 195 Zhou (10.1016/j.foodchem.2022.135251_b0140) 2021; 44 10.1016/j.foodchem.2022.135251_b0085 Zhou (10.1016/j.foodchem.2022.135251_b0130) 2020; 321 Wu (10.1016/j.foodchem.2022.135251_b0105) 2022; 181 Lee (10.1016/j.foodchem.2022.135251_b0065) 2018; 259 Li (10.1016/j.foodchem.2022.135251_b0070) 2019; 84 Fazeli (10.1016/j.foodchem.2022.135251_b0030) 2020; 156 Fu (10.1016/j.foodchem.2022.135251_b0040) 2022; 61 Wan (10.1016/j.foodchem.2022.135251_b0095) 2021; 780 Zhou (10.1016/j.foodchem.2022.135251_b0150) 2022; 266 Li (10.1016/j.foodchem.2022.135251_b0075) 2021; 191 Zeid (10.1016/j.foodchem.2022.135251_b0115) 2022; 267 He (10.1016/j.foodchem.2022.135251_b0055) 2022; 375 Altunay (10.1016/j.foodchem.2022.135251_b0005) 2019; 293 Ozbekova (10.1016/j.foodchem.2022.135251_b0080) 2019; 223 Farroni (10.1016/j.foodchem.2022.135251_b0025) 2012; 135 Hattie (10.1016/j.foodchem.2022.135251_b0050) 2022; 316 Zhuang (10.1016/j.foodchem.2022.135251_b0125) 2022; 316 Kortesniemi (10.1016/j.foodchem.2022.135251_b0060) 2015; 172 Birch (10.1016/j.foodchem.2022.135251_b0010) 2020; 727 Gao (10.1016/j.foodchem.2022.135251_b0045) 2022; 253 Zhou (10.1016/j.foodchem.2022.135251_b0135) 2020; 41 Cao (10.1016/j.foodchem.2022.135251_b0015) 2021; 44 Wang (10.1016/j.foodchem.2022.135251_b0100) 2021; 289 Sun (10.1016/j.foodchem.2022.135251_b0090) 2019; 42 Zhou (10.1016/j.foodchem.2022.135251_b0145) 2021; 119 Yu (10.1016/j.foodchem.2022.135251_b0110) 2021; 212 |
| References_xml | – volume: 191 year: 2021 ident: b0075 article-title: Building a new machine learning-based model to estimate county-level climatic yield variation for maize in Northeast China publication-title: Computers and Electronics in Agriculture – volume: 293 start-page: 378 year: 2019 end-page: 386 ident: b0005 article-title: Innovative and practical deep eutectic solvent based vortex assisted microextraction procedure for separation and preconcentration of low levels of arsenic and antimony from sample matrix prior to analysis by hydride generation-atomic absorption spectrometry publication-title: Food Chemistry – volume: 119 year: 2021 ident: b0145 article-title: Classification of heavy metal Cd stress in lettuce leaves based on WPCA algorithm and fluorescence hyperspectral technology publication-title: Infrared Physics & Technology – volume: 727 year: 2020 ident: b0010 article-title: An assessment of aluminum and iron in normalisation and enrichment procedures for environmental assessment of marine sediment publication-title: Science of the Total Environment – volume: 44 start-page: e13897 year: 2021 ident: b0140 article-title: Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible-near infrared hyperspectral imaging publication-title: Journal of Food Process Engineering – volume: 321 year: 2020 ident: b0130 article-title: Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce publication-title: Food Chemistry – volume: 84 start-page: 2234 year: 2019 end-page: 2241 ident: b0070 article-title: Grade identification of tieguanyin tea using fluorescence hyperspectra and different statistical algorithms publication-title: Journal of Food Science – volume: 41 start-page: 2263 year: 2020 end-page: 2276 ident: b0135 article-title: Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images publication-title: International Journal of Remote Sensing – volume: 195 year: 2022 ident: b0120 article-title: Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves publication-title: Computers and Electronics in Agriculture – reference: Sabeti, V., Sobhani, M., Hasheminejad, S. (2022). An adaptive image steganography method based on integer wavelet transform using genetic algorithm. Computers and Electrical Engineering 99, 107809. https://doi.org/10.1016/j.compeleceng.2022.107809. – volume: 156 start-page: 514 year: 2020 end-page: 527 ident: b0030 article-title: Second derivative two-step collocation methods for ordinary differential equations publication-title: Applied Numerical Mathematics – volume: 267 year: 2022 ident: b0115 article-title: First-order derivative synchronous spectrofluorimetric determination of two antihypertensive drugs, metolazone and valsartan, in pharmaceutical and biological matrices publication-title: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy – volume: 177 start-page: 79 year: 2018 end-page: 88 ident: b0035 article-title: Life Cycle Inventory for winter and spring rapeseed production in Northern Europe publication-title: Journal of Cleaner Production – volume: 61 start-page: 2536 year: 2022 end-page: 2541 ident: b0040 article-title: Prediction of heavy metal Cd and stress on minerals in rice by analysis of LIBS spectra publication-title: Applied Optics – volume: 289 year: 2021 ident: b0100 article-title: Heavy metal fixation of lead-contaminated soil using morchella mycelium publication-title: Environmental pollution – volume: 181 year: 2022 ident: b0105 article-title: Review of soil heavy metal pollution in China: Spatial distribution, primary sources, and remediation alternatives publication-title: Resources, Conservation and Recycling – volume: 316 year: 2022 ident: b0050 article-title: Chronic tropospheric ozone exposure reduces seed yield and quality in spring and winter oilseed rape publication-title: Agricultural and Forest Meteorology – volume: 42 start-page: e13293 year: 2019 ident: b0090 article-title: Estimating cadmium content in lettuce leaves based on deep brief network and hyperspectral imaging technology publication-title: Journal of Food Process Engineering – volume: 223 year: 2019 ident: b0080 article-title: Study of moisture content and water activity of rice using fluorescence spectroscopy and multivariate analysis publication-title: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy – volume: 375 year: 2022 ident: b0055 article-title: Estimating bulk optical properties of AFB1 contaminated edible oils in 300–900 nm by combining double integrating spheres technique with laser induced fluorescence spectroscopy publication-title: Food Chemistry – volume: 44 start-page: e13793 year: 2021 ident: b0015 article-title: Nondestructive detection of lead content in oilseed rape leaves based on MRF-HHO-SVR and hyperspectral technology publication-title: Journal of Food Process Engineering – volume: 316 year: 2022 ident: b0125 article-title: Detection of frozen pork freshness by fluorescence hyperspectral image publication-title: Journal of Food Engineering – volume: 37 start-page: 1979 year: 2006 end-page: 1994 ident: b0020 article-title: Multivariate autofluorescence of intact food systems publication-title: Chemical Reviews – volume: 135 start-page: 1685 year: 2012 end-page: 1691 ident: b0025 article-title: Colour and surface fluorescence development and their relationship with maillard reaction markers as influenced by structural changes during cornflakes production publication-title: Food Chemistry – volume: 266 year: 2022 ident: b0150 article-title: Detection of heavy metal lead in lettuce leaves based on fluorescence hyperspectral technology combined with deep learning algorithm publication-title: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy – volume: 259 start-page: 532 year: 2018 end-page: 539 ident: b0065 article-title: Determination of the total volatile basic nitrogen (tvb-n) content in pork meat using hyperspectral fluorescence imaging publication-title: Sensors and Actuators B Chemical – volume: 780 year: 2021 ident: b0095 article-title: Comprehensive assessment of heavy metal risk in soil-crop systems along the Yangtze River in Nanjing, Southeast China publication-title: Science of the Total Environment – volume: 172 start-page: 63 year: 2015 end-page: 70 ident: b0060 article-title: NMR metabolomics of ripened and developing oilseed rape (brassica napus) and turnip rape (brassica rapa) publication-title: Food Chemistry – volume: 212 start-page: 46 year: 2021 end-page: 61 ident: b0110 article-title: Hyperspectral imaging technology combined with deep learning for hybrid okra seed identification publication-title: Biosystems Engineering – volume: 253 year: 2022 ident: b0045 article-title: Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder publication-title: Chemical Engineering Science – volume: 191 year: 2021 ident: 10.1016/j.foodchem.2022.135251_b0075 article-title: Building a new machine learning-based model to estimate county-level climatic yield variation for maize in Northeast China publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2021.106557 – ident: 10.1016/j.foodchem.2022.135251_b0085 doi: 10.1016/j.compeleceng.2022.107809 – volume: 42 start-page: e13293 issue: 8 year: 2019 ident: 10.1016/j.foodchem.2022.135251_b0090 article-title: Estimating cadmium content in lettuce leaves based on deep brief network and hyperspectral imaging technology publication-title: Journal of Food Process Engineering doi: 10.1111/jfpe.13293 – volume: 212 start-page: 46 year: 2021 ident: 10.1016/j.foodchem.2022.135251_b0110 article-title: Hyperspectral imaging technology combined with deep learning for hybrid okra seed identification publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2021.09.010 – volume: 37 start-page: 1979 issue: 37 year: 2006 ident: 10.1016/j.foodchem.2022.135251_b0020 article-title: Multivariate autofluorescence of intact food systems publication-title: Chemical Reviews doi: 10.1021/cr050019q – volume: 181 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0105 article-title: Review of soil heavy metal pollution in China: Spatial distribution, primary sources, and remediation alternatives publication-title: Resources, Conservation and Recycling doi: 10.1016/j.resconrec.2022.106261 – volume: 156 start-page: 514 year: 2020 ident: 10.1016/j.foodchem.2022.135251_b0030 article-title: Second derivative two-step collocation methods for ordinary differential equations publication-title: Applied Numerical Mathematics doi: 10.1016/j.apnum.2020.05.019 – volume: 259 start-page: 532 year: 2018 ident: 10.1016/j.foodchem.2022.135251_b0065 article-title: Determination of the total volatile basic nitrogen (tvb-n) content in pork meat using hyperspectral fluorescence imaging publication-title: Sensors and Actuators B Chemical doi: 10.1016/j.snb.2017.12.102 – volume: 223 year: 2019 ident: 10.1016/j.foodchem.2022.135251_b0080 article-title: Study of moisture content and water activity of rice using fluorescence spectroscopy and multivariate analysis publication-title: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy doi: 10.1016/j.saa.2019.117357 – volume: 375 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0055 article-title: Estimating bulk optical properties of AFB1 contaminated edible oils in 300–900 nm by combining double integrating spheres technique with laser induced fluorescence spectroscopy publication-title: Food Chemistry doi: 10.1016/j.foodchem.2021.131666 – volume: 266 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0150 article-title: Detection of heavy metal lead in lettuce leaves based on fluorescence hyperspectral technology combined with deep learning algorithm publication-title: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy doi: 10.1016/j.saa.2021.120460 – volume: 316 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0050 article-title: Chronic tropospheric ozone exposure reduces seed yield and quality in spring and winter oilseed rape publication-title: Agricultural and Forest Meteorology – volume: 780 year: 2021 ident: 10.1016/j.foodchem.2022.135251_b0095 article-title: Comprehensive assessment of heavy metal risk in soil-crop systems along the Yangtze River in Nanjing, Southeast China publication-title: Science of the Total Environment doi: 10.1016/j.scitotenv.2021.146567 – volume: 61 start-page: 2536 issue: 10 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0040 article-title: Prediction of heavy metal Cd and stress on minerals in rice by analysis of LIBS spectra publication-title: Applied Optics doi: 10.1364/AO.448454 – volume: 195 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0120 article-title: Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.106802 – volume: 119 year: 2021 ident: 10.1016/j.foodchem.2022.135251_b0145 article-title: Classification of heavy metal Cd stress in lettuce leaves based on WPCA algorithm and fluorescence hyperspectral technology publication-title: Infrared Physics & Technology doi: 10.1016/j.infrared.2021.103936 – volume: 293 start-page: 378 year: 2019 ident: 10.1016/j.foodchem.2022.135251_b0005 publication-title: Food Chemistry doi: 10.1016/j.foodchem.2019.05.019 – volume: 41 start-page: 2263 issue: 6 year: 2020 ident: 10.1016/j.foodchem.2022.135251_b0135 article-title: Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images publication-title: International Journal of Remote Sensing doi: 10.1080/01431161.2019.1685721 – volume: 135 start-page: 1685 issue: 3 year: 2012 ident: 10.1016/j.foodchem.2022.135251_b0025 article-title: Colour and surface fluorescence development and their relationship with maillard reaction markers as influenced by structural changes during cornflakes production publication-title: Food Chemistry doi: 10.1016/j.foodchem.2012.05.114 – volume: 267 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0115 article-title: First-order derivative synchronous spectrofluorimetric determination of two antihypertensive drugs, metolazone and valsartan, in pharmaceutical and biological matrices publication-title: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy doi: 10.1016/j.saa.2021.120591 – volume: 321 year: 2020 ident: 10.1016/j.foodchem.2022.135251_b0130 article-title: Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce publication-title: Food Chemistry doi: 10.1016/j.foodchem.2020.126503 – volume: 727 year: 2020 ident: 10.1016/j.foodchem.2022.135251_b0010 article-title: An assessment of aluminum and iron in normalisation and enrichment procedures for environmental assessment of marine sediment publication-title: Science of the Total Environment doi: 10.1016/j.scitotenv.2020.138123 – volume: 253 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0045 article-title: Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder publication-title: Chemical Engineering Science doi: 10.1016/j.ces.2022.117556 – volume: 84 start-page: 2234 issue: 2 year: 2019 ident: 10.1016/j.foodchem.2022.135251_b0070 article-title: Grade identification of tieguanyin tea using fluorescence hyperspectra and different statistical algorithms publication-title: Journal of Food Science doi: 10.1111/1750-3841.14706 – volume: 177 start-page: 79 year: 2018 ident: 10.1016/j.foodchem.2022.135251_b0035 article-title: Life Cycle Inventory for winter and spring rapeseed production in Northern Europe publication-title: Journal of Cleaner Production doi: 10.1016/j.jclepro.2017.12.214 – volume: 44 start-page: e13793 issue: 9 year: 2021 ident: 10.1016/j.foodchem.2022.135251_b0015 article-title: Nondestructive detection of lead content in oilseed rape leaves based on MRF-HHO-SVR and hyperspectral technology publication-title: Journal of Food Process Engineering doi: 10.1111/jfpe.13793 – volume: 289 year: 2021 ident: 10.1016/j.foodchem.2022.135251_b0100 article-title: Heavy metal fixation of lead-contaminated soil using morchella mycelium publication-title: Environmental pollution doi: 10.1016/j.envpol.2021.117829 – volume: 172 start-page: 63 year: 2015 ident: 10.1016/j.foodchem.2022.135251_b0060 article-title: NMR metabolomics of ripened and developing oilseed rape (brassica napus) and turnip rape (brassica rapa) publication-title: Food Chemistry doi: 10.1016/j.foodchem.2014.09.040 – volume: 44 start-page: e13897 issue: 12 year: 2021 ident: 10.1016/j.foodchem.2022.135251_b0140 article-title: Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible-near infrared hyperspectral imaging publication-title: Journal of Food Process Engineering doi: 10.1111/jfpe.13897 – volume: 316 year: 2022 ident: 10.1016/j.foodchem.2022.135251_b0125 article-title: Detection of frozen pork freshness by fluorescence hyperspectral image publication-title: Journal of Food Engineering doi: 10.1016/j.jfoodeng.2021.110840 |
| SSID | ssj0002018 |
| Score | 2.6293015 |
| Snippet | •FHSI was applied to the detection of lead content in oilseed rape leaves.•WT-SDAE model was proposed for deep feature extraction.•The WT-SDAE-SVR model... The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 135251 |
| SubjectTerms | Algorithms Brassica napus Deep Learning Fluorescence hyperspectral imaging Heavy metal lead Hyperspectral Imaging Least-Squares Analysis Nondestructive testing Oilseed rape Plant Leaves Spectroscopy, Near-Infrared - methods Stacked denoising autoencoder Wavelet transform |
| Title | A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging |
| URI | https://dx.doi.org/10.1016/j.foodchem.2022.135251 https://www.ncbi.nlm.nih.gov/pubmed/36586261 https://www.proquest.com/docview/2759957467 |
| Volume | 409 |
| WOSCitedRecordID | wos000923190600001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-7072 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002018 issn: 0308-8146 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Jb9NAFB6lLRJcEJQtLNUgIS7IJR5vM8cotAKEIg4FmZNlz9ImCk7kxFWP_HTebHaKWkoPXCxrNs34fZ55781bEHrDpaqqSspAMVYGMVFRoNmSgDHJqlTFIjQK_e9fsumU5jn7Ohj88r4w54usrunFBVv9V1JDGRBbu87egtzdoFAA70B0eALZ4flPhB-_E1KufDqIU5cj2pgTrhp9LWMMnaFaGDt1bQsw0-kp4IwE5lM7Y-lKHYy2NXoEtWiXjQn6BFvAGYit1jvTROv4aXIcbTO4xzpKMvdZ5Hq19LLVxMxndV9UWi3tWVvPAaOn_fWUcxfpmk5syx8OyE5HQYxFoLtucb5ZI9orG92-G4_Y1s4Z6ris4ZWbutUvzA8VrEAvAKR6Qg77DpejaP9xunU2h96cbV74cQo9TmHH2UF7JEsY7It7409H-efuNAcGidqbKLuCLS_zq2d0HYNznQBjGJmTB-i-k0Dw2CLnIRrIeh_dnXiS7aPhh5nc4LfYhYxd4KnP2ADtvCP7-hFqxlgjDXukYYs0DEjDPdJ0tcAOaXhWY4c0rJGGLdKwQRreRhq-hDTskPYYfTs-Opl8DFwKj4BHKd0EMfCnJCnTUqQ0DjMF4jnTEndVRULwjDIZSh6HciSEGkUioZkIOZVixCSRmsF6gnbrZS2fIVwpSTjjJYlUGMciKytalkLyMFFKkbQaosR_94K7-PY6zcqi-Dvlh-h9129lI7zc2IN5shaOT7X8ZwGIvbHva4-DAsiqb-fKWi7bdWHgl-jsP0P01AKkm08EckJK0vD5ref6At3rf8iXaHfTtPIVusPPN7N1c4B2spweOMj_Bqtx0Oc |
| linkProvider | Elsevier |
| 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+deep+learning+method+for+predicting+lead+content+in+oilseed+rape+leaves+using+fluorescence+hyperspectral+imaging&rft.jtitle=Food+chemistry&rft.au=Zhou%2C+Xin&rft.au=Zhao%2C+Chunjiang&rft.au=Sun%2C+Jun&rft.au=Cao%2C+Yan&rft.date=2023-05-30&rft.issn=0308-8146&rft.volume=409&rft.spage=135251&rft_id=info:doi/10.1016%2Fj.foodchem.2022.135251&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_foodchem_2022_135251 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0308-8146&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0308-8146&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0308-8146&client=summon |