Prediction of milk protein content based on improved sparrow search algorithm and optimized back propagation neural network
The quality of milk is largely determined by the protein content. The feasibility of predicting the protein content of milk by hyperspectral image has attracted more attentions from researchers for minor detection cost and high efficiency. In this paper, a prediction modeling method based on improve...
Uloženo v:
| Vydáno v: | Spectroscopy letters Ročník 55; číslo 4; s. 229 - 239 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Abingdon
Taylor & Francis
21.04.2022
Taylor & Francis Ltd |
| Témata: | |
| ISSN: | 0038-7010, 1532-2289 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The quality of milk is largely determined by the protein content. The feasibility of predicting the protein content of milk by hyperspectral image has attracted more attentions from researchers for minor detection cost and high efficiency. In this paper, a prediction modeling method based on improved sparrow search algorithm (SSA) and optimized back propagation (BP) neural network is proposed, in which sine chaotic map is introduced to initialize the population position to improve the optimization performance of SSA. In the experiment, hyperspectral images of each kind of milk were collected by visible/near infrared hyperspectral imaging system to acquire hyperspectral data, then the spectral data were pretreated by Savitzky-Golay smoothing, and the competitive adaptive reweighted sampling combined with successive projections algorithm to select 13 characteristic bands. Subsequently, the spectral data corresponding to the characteristic bands are used as the input of back propagation neural network, optimized by the improved sparrow search algorithm for the initial weight and threshold of BP neural network, to establish three prediction models(BP model, the BP model based on SSA optimization and the BP model based on improved SSA optimization).Experimental results demonstrate that the BP model based on improved SSA optimization has better fitting ability and higher prediction accuracy for milk protein content. This research provides algorithm support and theoretical basis for the rapid nondestructive detection of milk protein content based on BP neural network. |
|---|---|
| AbstractList | The quality of milk is largely determined by the protein content. The feasibility of predicting the protein content of milk by hyperspectral image has attracted more attentions from researchers for minor detection cost and high efficiency. In this paper, a prediction modeling method based on improved sparrow search algorithm (SSA) and optimized back propagation (BP) neural network is proposed, in which sine chaotic map is introduced to initialize the population position to improve the optimization performance of SSA. In the experiment, hyperspectral images of each kind of milk were collected by visible/near infrared hyperspectral imaging system to acquire hyperspectral data, then the spectral data were pretreated by Savitzky–Golay smoothing, and the competitive adaptive reweighted sampling combined with successive projections algorithm to select 13 characteristic bands. Subsequently, the spectral data corresponding to the characteristic bands are used as the input of back propagation neural network, optimized by the improved sparrow search algorithm for the initial weight and threshold of BP neural network, to establish three prediction models(BP model, the BP model based on SSA optimization and the BP model based on improved SSA optimization).Experimental results demonstrate that the BP model based on improved SSA optimization has better fitting ability and higher prediction accuracy for milk protein content. This research provides algorithm support and theoretical basis for the rapid nondestructive detection of milk protein content based on BP neural network. |
| Author | Chen, Chen Hu, Pengwei Xue, Heru Liu, Jiangping Pan, Xin |
| Author_xml | – sequence: 1 givenname: Jiangping surname: Liu fullname: Liu, Jiangping organization: College of Computer and Information Engineering of the Inner Mongolia Agricultural University – sequence: 2 givenname: Pengwei surname: Hu fullname: Hu, Pengwei organization: College of Computer and Information Engineering of the Inner Mongolia Agricultural University – sequence: 3 givenname: Heru surname: Xue fullname: Xue, Heru organization: College of Computer and Information Engineering of the Inner Mongolia Agricultural University – sequence: 4 givenname: Xin surname: Pan fullname: Pan, Xin organization: College of Computer and Information Engineering of the Inner Mongolia Agricultural University – sequence: 5 givenname: Chen surname: Chen fullname: Chen, Chen organization: College of Computer and Information Engineering of the Inner Mongolia Agricultural University |
| BookMark | eNqFkEtLxDAUhYMoOI7-BCHguppH0wduFPEFgi50HdI2mYm2Sb3JOKh_3syMblzoJjeXnHNu7reHtp13GqFDSo4pqcgJIbwqSeoYYSwdggpRbKEJFZxljFX1NpqsNNlKtIv2QngmhIqyKifo8wF0Z9tovcPe4MH2L3gEH7V1uPUuahdxo4LucBLYIT29pXsYFYBf4qAVtHOs-pkHG-cDVi4Jx2gH-5FkjWrXaaOaqfUEpxeg-lTi0sPLPtoxqg_64LtO0dPV5ePFTXZ3f317cX6XtZxXMeuapiAs5ywXpBGGUq2FKQqSli1KU2vDay0q3uV1LuqONmWX60qIsqEq7xpu-BQdbXLTV14XOkT57Bfg0kjJiqIqCK8TqikSG1ULPgTQRo5gBwXvkhK54ix_OMsVZ_nNOflOf_laG9frRlC2_9d9tnFbZzwMKnHpOxnVe-_BgHKtDZL_HfEFUROaHg |
| CitedBy_id | crossref_primary_10_1111_1750_3841_17420 crossref_primary_10_1016_j_est_2023_110187 crossref_primary_10_1016_j_jfca_2025_108234 crossref_primary_10_3390_rs15071732 crossref_primary_10_1002_fsn3_4556 crossref_primary_10_1111_1750_3841_17621 crossref_primary_10_1080_00387010_2024_2331616 crossref_primary_10_1038_s41598_022_27037_6 crossref_primary_10_1155_2022_6098797 crossref_primary_10_3390_catal14040217 crossref_primary_10_3390_foods11152278 |
| Cites_doi | 10.1080/21642583.2019.1708830 10.1016/j.amc.2006.07.025 10.1016/j.compag.2014.01.016 10.1016/j.talanta.2013.03.041 10.1016/j.asoc.2017.07.059 10.1016/j.aca.2009.06.046 |
| ContentType | Journal Article |
| Copyright | 2022 Taylor & Francis Group, LLC 2022 2022 Taylor & Francis Group, LLC |
| Copyright_xml | – notice: 2022 Taylor & Francis Group, LLC 2022 – notice: 2022 Taylor & Francis Group, LLC |
| DBID | AAYXX CITATION 7SR 7U5 8BQ 8FD JG9 L7M |
| DOI | 10.1080/00387010.2022.2051556 |
| DatabaseName | CrossRef Engineered Materials Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database Materials Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace METADEX |
| DatabaseTitleList | Materials Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Chemistry Physics |
| EISSN | 1532-2289 |
| EndPage | 239 |
| ExternalDocumentID | 10_1080_00387010_2022_2051556 2051556 |
| Genre | Research Article |
| GroupedDBID | -~X .7F .QJ 0BK 0R~ 123 30N 4.4 53G 5VS AAENE AAGDL AAHIA AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABDBF ABFIM ABHAV ABJNI ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACGEJ ACGFS ACIWK ACTIO ACUHS ADCVX ADGTB ADMLS ADXPE AEISY AENEX AEOZL AEPSL AEYOC AFKVX AFRVT AGDLA AGMYJ AHDZW AIJEM AIYEW AJWEG AKBVH AKOOK ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AVBZW AWYRJ BLEHA CCCUG CE4 CS3 DGEBU DKSSO DU5 EAP EBS EMK EPL EST ESX E~A E~B GTTXZ H13 HF~ HZ~ H~P IPNFZ J.P KYCEM LJTGL M4Z NA5 NW0 O9- P2P RIG RNANH ROSJB RTWRZ S-T SNACF TASJS TBQAZ TCY TFL TFT TFW TN5 TTHFI TUROJ TUS TWF TWZ UT5 UU3 ZGOLN ~02 ~S~ AAYXX CITATION 7SR 7U5 8BQ 8FD JG9 L7M |
| ID | FETCH-LOGICAL-c338t-dbb602432450b5f11ee5f66002267f9ef39e583d49459d1b7d4e8557b1a4db3f3 |
| IEDL.DBID | TFW |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000796408000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0038-7010 |
| IngestDate | Sun Sep 07 03:46:23 EDT 2025 Sat Nov 29 06:19:09 EST 2025 Tue Nov 18 22:32:14 EST 2025 Mon Oct 20 23:47:28 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c338t-dbb602432450b5f11ee5f66002267f9ef39e583d49459d1b7d4e8557b1a4db3f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2668603915 |
| PQPubID | 2045227 |
| PageCount | 11 |
| ParticipantIDs | proquest_journals_2668603915 crossref_primary_10_1080_00387010_2022_2051556 informaworld_taylorfrancis_310_1080_00387010_2022_2051556 crossref_citationtrail_10_1080_00387010_2022_2051556 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-04-21 |
| PublicationDateYYYYMMDD | 2022-04-21 |
| PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-21 day: 21 |
| PublicationDecade | 2020 |
| PublicationPlace | Abingdon |
| PublicationPlace_xml | – name: Abingdon |
| PublicationTitle | Spectroscopy letters |
| PublicationYear | 2022 |
| Publisher | Taylor & Francis Taylor & Francis Ltd |
| Publisher_xml | – name: Taylor & Francis – name: Taylor & Francis Ltd |
| References | Wupengyao D. (CIT0013) 2019; 39 Xiangxin K. (CIT0010) 2017; 53 CIT0012 CIT0011 Dong'e Z. (CIT0002) 2019; 39 Zizhu Z. (CIT0005) 2018; 46 Huixian S. (CIT0006) 2019; 35 Yilei L. (CIT0014) 2021; 32 Qinhong L. (CIT0020) 2012; 28 Mingbo L. (CIT0017) 2014; 43 Guohai L. (CIT0021) 2013; 29 Dan P. (CIT0001) 2020; 41 Qianqian Z. (CIT0003) 2015; 35 CIT0016 CIT0015 CIT0007 Xiang W. (CIT0018) 2019; 39 CIT0009 Mingfu Z. (CIT0004) 2014; 35 CIT0008 Dengfei J. (CIT0019) 2013; 29 |
| References_xml | – volume: 46 start-page: 45 issue: 02 year: 2018 ident: CIT0005 publication-title: China Dairy Industry – volume: 39 start-page: 921 issue: 03 year: 2019 ident: CIT0002 publication-title: Spectroscopy and Spectral Analysis – volume: 43 start-page: 1265 issue: 04 year: 2014 ident: CIT0017 publication-title: Infrared and Laser Engineering – ident: CIT0008 – volume: 41 start-page: 256 issue: 04 year: 2020 ident: CIT0001 publication-title: Food Science – volume: 29 start-page: 218 issue: 1 year: 2013 ident: CIT0021 publication-title: Journal of Agricultural Engineering – ident: CIT0011 doi: 10.1080/21642583.2019.1708830 – ident: CIT0009 doi: 10.1016/j.amc.2006.07.025 – ident: CIT0012 doi: 10.1016/j.compag.2014.01.016 – volume: 35 start-page: 3436 issue: 12 year: 2015 ident: CIT0003 publication-title: Spectroscopy and Spectral Analysis – ident: CIT0016 doi: 10.1016/j.talanta.2013.03.041 – volume: 28 start-page: 132 issue: 23 year: 2012 ident: CIT0020 publication-title: Journal of Agricultural Engineering – volume: 39 start-page: 3136 issue: 10 year: 2019 ident: CIT0018 publication-title: Spectroscopy and Spectral Analysis – volume: 29 start-page: 264 issue: 12 year: 2013 ident: CIT0019 publication-title: Journal of Agricultural Engineering – volume: 35 start-page: 44 issue: 01 year: 2014 ident: CIT0004 publication-title: Journal of Laser – volume: 32 start-page: 88 issue: 01 year: 2021 ident: CIT0014 publication-title: Optoelectronics Laser – volume: 53 start-page: 137 issue: 22 year: 2017 ident: CIT0010 publication-title: Computer Engineering and Application – volume: 39 start-page: 2800 issue: 09 year: 2019 ident: CIT0013 publication-title: Spectroscopy and Spectral Analysis – volume: 35 start-page: 196 issue: 02 year: 2019 ident: CIT0006 publication-title: Journal of Agricultural Engineering – ident: CIT0007 doi: 10.1016/j.asoc.2017.07.059 – ident: CIT0015 doi: 10.1016/j.aca.2009.06.046 |
| SSID | ssj0015787 |
| Score | 2.3382304 |
| Snippet | The quality of milk is largely determined by the protein content. The feasibility of predicting the protein content of milk by hyperspectral image has... |
| SourceID | proquest crossref informaworld |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 229 |
| SubjectTerms | Adaptive sampling Algorithms Back propagation Back propagation networks back propagation neural network Hyperspectral imaging improved sparrow search algorithm Infrared imaging Neural networks Optimization Prediction models Prediction of milk protein Propagation Proteins Search algorithms spectral analysis |
| Title | Prediction of milk protein content based on improved sparrow search algorithm and optimized back propagation neural network |
| URI | https://www.tandfonline.com/doi/abs/10.1080/00387010.2022.2051556 https://www.proquest.com/docview/2668603915 |
| Volume | 55 |
| WOSCitedRecordID | wos000796408000001&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: PRVAWR databaseName: Taylor & Francis Online Journals customDbUrl: eissn: 1532-2289 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0015787 issn: 0038-7010 databaseCode: TFW dateStart: 19680101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQAsHCo4AoFOSBNahOnNeIKioGVHUo0C2KXxDRpqgJDPDnuXOSQoVQB1iiRMpZlv35zo_P3xFyIZgrVOBGjmc86XDPlU4kUMg1DTTzuNLa5iK4vw0Hg2g8joc1m7CoaZW4hjaVUIT11Ti4U1E0jDjMPwYoQxIzBCB4YJYSFN2G0I9Dc9R_WJwjIB4rYcbIQZPmDs9vpSxFpyXt0h--2gag_u4_VH2P7NSzT3pVwWWfrOm8RbZ6TdK3Ftm0jFBZHJCP4RzPcLDf6MzQaTZ5plbUIcsp8tshWFEMgYrCD5ndmoB38E8o6kirAUTTyeNsnpVPUwo1pjNwT9PsHX4TqbSlgTezyKAoqwkVyytS-iG561-PejdOnanBkbDELR0lRIDShi73u8I3jGntmwCP_NwgNLE2Xqz9yFM85n6smAgV15Hvh4KlXAmAyRFZz2e5PiY0lN0olq7UsPDhrlEC5q94IVYHbqoY89qENz2UyFrGHLNpTBL2pXZq2zjBNk7qNm6Ty4XZS6Xjscog_t79SWk3UEyV7STxVth2GqwktUsoEpgJRYHV4z_5Q9GnZBs_q02gDlkv56_6jGzItzIr5ucW_J-en_8y |
| linkProvider | Taylor & Francis |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwEB61tBVcgFIQ7_rQayrsOK8jQl1RdVn1sDxuVvxqo-5m0W7gAH-eGSehoKri0F4iS_FYlj2esT3j7wP4pLnQNhV5FPvYRDIWJso1AbmWqeOxtM4FLoKLYTYa5VdXxdO3MJRWSWdo3wJFBFtNi5suo_uUOCIgQzWjLGb0QPghmpL0NbxJ0NcSfv54cPkYSSCNbKEZ84hk-lc8f2vmmX96hl76h7UOLmiw9j86vw6r3QaUHbca8x5euXoDlk963rcNeBeSQs3iA9x_n1MYh6aOzTybVpNfLOA6VDWjFHf0V4y8oGVYoQq3E1hGE0W4jqxdQ6yc_JjNq-bnlGGX2Qwt1LS6w2q6NKE1NGhBORgha2LH6jYvfRPOB1_GJ6dRR9YQGTzlNpHVOiV0QyGTI514zp1LfEpRP5FmvnA-LlySx1YWMiks15mVLk-STPNSWo2asgVL9ax228Ayc5QXRhiHZx8pvNW4haU3sS4VpeU83gHZT5EyHZI5EWpMFP8NeBrGWNEYq26Md-Dzo9h1C-XxkkDxdP5VE-5QfEt4ouIXZPd7ZVGdVVgo3AzlaYDk3_2Hpj_C8un4bKiGX0ff9mCFflF4S_B9WGrmN-4A3prbplrMD8NKeADO2QNw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3PT9VAEJ4oonhRAQ0o6h64lrDb7bY9GvRFI3l5B1Rum-4vaHivj7xXOcA_z8y2RYkhHPDSNGlns9mdnZnd-fYbgF3DhXFKFEkaUpvIVNikMETkWinPU-m8j7UIfh7m43FxfFxOejThsodV0h46dEQR0VbT4j53YUDEUf0x1DICMaMDwgdVKVGP4QmGzoqU_Gj06yaRQArZMTMWCckMl3juauaWe7pFXvqPsY4eaPTyP_T9Fbzow0_2qdOXdXjkmw1YOxiqvm3A0wgJtctNuJosKIlDE8fmgc3q6RmLrA51wwjgjt6KkQ90DH-o49kEvqOBIlZH1q0gVk1P5ou6PZ0x7DGbo32a1Zf4m6lsbA3NWVQNRrya2LGmQ6W_hh-jL0cHX5O-VENicY_bJs4YRdyGQmb7Jguce58FRTk_ofJQ-pCWPitSJ0uZlY6b3ElfZFlueCWdQT15AyvNvPFbwHK7X5RWWI87HymCMxjA0o1Yr0TlOE-3QQ4zpG3PY07lNKaa_6E7jWOsaYx1P8bbsHcjdt4RedwnUP49_bqNJyihK3ei03tkdwZd0b1NWGoMhQoVCfnfPqDpj_Bs8nmkD7-Nv7-D5_SFcluC78BKu_jt38OqvWjr5eJDXAfXtbkCIg |
| 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=Prediction+of+milk+protein+content+based+on+improved+sparrow+search+algorithm+and+optimized+back+propagation+neural+network&rft.jtitle=Spectroscopy+letters&rft.au=Liu%2C+Jiangping&rft.au=Hu%2C+Pengwei&rft.au=Xue%2C+Heru&rft.au=Pan%2C+Xin&rft.date=2022-04-21&rft.pub=Taylor+%26+Francis&rft.issn=0038-7010&rft.eissn=1532-2289&rft.volume=55&rft.issue=4&rft.spage=229&rft.epage=239&rft_id=info:doi/10.1080%2F00387010.2022.2051556&rft.externalDocID=2051556 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0038-7010&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0038-7010&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0038-7010&client=summon |