{"id":"https://openalex.org/W4288723136","doi":"https://doi.org/10.3390/pr10081484","title":"Identification of Four Chicken Breeds by Hyperspectral Imaging Combined with Chemometrics","display_name":"Identification of Four Chicken Breeds by Hyperspectral Imaging Combined with Chemometrics","publication_year":2022,"publication_date":"2022-07-28","ids":{"openalex":"https://openalex.org/W4288723136","doi":"https://doi.org/10.3390/pr10081484"},"language":"en","primary_location":{"id":"doi:10.3390/pr10081484","doi":"https://doi.org/10.3390/pr10081484","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr10081484","pdf_url":"https://www.mdpi.com/2227-9717/10/8/1484/pdf?version=1659002375","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2227-9717/10/8/1484/pdf?version=1659002375","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066772307","display_name":"Tiande Cheng","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405280512","display_name":"Qingyuan Polytechnic","ror":"https://ror.org/031dnbd86","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280512"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiande Cheng","raw_affiliation_strings":["School of Food and Drug, Qingyuan Polytechnic, Qingyuan 511510, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Food and Drug, Qingyuan Polytechnic, Qingyuan 511510, China","institution_ids":["https://openalex.org/I4405280512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100432734","display_name":"Peng Li","orcid":"https://orcid.org/0000-0002-7820-4412","observed_orcids":["https://orcid.org/0000-0002-7820-4412"]},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Li","raw_affiliation_strings":["College of Engineering, South China Agricultural University, Guangzhou 510642, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering, South China Agricultural University, Guangzhou 510642, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110495324","display_name":"Junchao Ma","orcid":"https://orcid.org/0009-0008-6575-0802","observed_orcids":["https://orcid.org/0009-0008-6575-0802"]},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junchao Ma","raw_affiliation_strings":["College of Engineering, South China Agricultural University, Guangzhou 510642, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering, South China Agricultural University, Guangzhou 510642, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071932347","display_name":"Xingguo Tian","orcid":"https://orcid.org/0000-0003-0225-9676","observed_orcids":["https://orcid.org/0000-0003-0225-9676"]},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingguo Tian","raw_affiliation_strings":["College of Food Science, South China Agricultural University, Guangzhou 510642, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Food Science, South China Agricultural University, Guangzhou 510642, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009612623","display_name":"Nan Zhong","orcid":"https://orcid.org/0000-0001-5371-9957","observed_orcids":["https://orcid.org/0000-0001-5371-9957"]},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Nan Zhong","raw_affiliation_strings":["College of Engineering, South China Agricultural University, Guangzhou 510642, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering, South China Agricultural University, Guangzhou 510642, China","institution_ids":["https://openalex.org/I101479585"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009612623"],"corresponding_institution_ids":["https://openalex.org/I101479585"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2094},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2094},"fwci":0.705,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.55911915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"10","issue":"8","first_page":"1484","last_page":"1484"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10333","display_name":"Meat and Animal Product Quality","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1103","display_name":"Animal Science and Zoology"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"hyperspectral imaging","score":0.996999979019165},{"id":"https://openalex.org/keywords/chemometrics","display_name":"chemometrics","score":0.7120000123977661},{"id":"https://openalex.org/keywords/spectral-preprocessing","display_name":"spectral preprocessing","score":0.8550000190734863},{"id":"https://openalex.org/keywords/successive-projections-algorithm","display_name":"successive projections algorithm","score":0.7089999914169312},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.6330000162124634},{"id":"https://openalex.org/keywords/competitive-adaptive-reweighted-sampling","display_name":"competitive adaptive reweighted sampling","score":0.9459999799728394},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9129999876022339},{"id":"https://openalex.org/keywords/partial-least-squares-discriminant-analysis","display_name":"PLS-DA","score":0.8669999837875366},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.859000027179718},{"id":"https://openalex.org/keywords/mean-normalization","display_name":"mean normalization","score":0.5149999856948853},{"id":"https://openalex.org/keywords/wavelength-selection","display_name":"wavelength selection","score":0.4519999921321869}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7926965951919556},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7537481784820557},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7180731892585754},{"id":"https://openalex.org/C151304367","wikidata":"https://www.wikidata.org/wiki/Q910067","display_name":"Chemometrics","level":2,"score":0.6911942958831787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6482243537902832},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6328303217887878},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4511593282222748},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.4451432228088379},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.41137874126434326},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36580485105514526},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29222238063812256},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2121662199497223}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/pr10081484","doi":"https://doi.org/10.3390/pr10081484","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr10081484","pdf_url":"https://www.mdpi.com/2227-9717/10/8/1484/pdf?version=1659002375","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:11cc56dfaf8d41d5b8ba17f27e4d0e27","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/11cc56dfaf8d41d5b8ba17f27e4d0e27","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Processes, Vol 10, Iss 1484, p 1484 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/pr10081484","doi":"https://doi.org/10.3390/pr10081484","is_oa":true,"landing_page_url":"https://doi.org/10.3390/pr10081484","pdf_url":"https://www.mdpi.com/2227-9717/10/8/1484/pdf?version=1659002375","source":{"id":"https://openalex.org/S4210201879","display_name":"Processes","issn_l":"2227-9717","issn":["2227-9717"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Processes","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.958299994468689}],"study_designs":[],"awards":[{"id":"https://openalex.org/G3186667936","display_name":null,"funder_award_id":"201903010063","funder_id":"https://openalex.org/F4320335480","funder_display_name":"Guangzhou Municipal Science and Technology Project"}],"funders":[{"id":"https://openalex.org/F4320335480","display_name":"Guangzhou Municipal Science and Technology Project","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4288723136.pdf","grobid_xml":"https://content.openalex.org/works/W4288723136.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1951788057","https://openalex.org/W1965686535","https://openalex.org/W1971077749","https://openalex.org/W1999437504","https://openalex.org/W2048707409","https://openalex.org/W2054403851","https://openalex.org/W2068670606","https://openalex.org/W2069147120","https://openalex.org/W2074646062","https://openalex.org/W2086031780","https://openalex.org/W2168497616","https://openalex.org/W2278643120","https://openalex.org/W2627945719","https://openalex.org/W2776188892","https://openalex.org/W2789517714","https://openalex.org/W2790450162","https://openalex.org/W2884430770","https://openalex.org/W2933369472","https://openalex.org/W3011599857","https://openalex.org/W3080646488","https://openalex.org/W3088800960","https://openalex.org/W3093436029","https://openalex.org/W3093611367","https://openalex.org/W3097323409","https://openalex.org/W3197963284","https://openalex.org/W3207190958","https://openalex.org/W4243969031"],"related_works":["https://openalex.org/W3088800960","https://openalex.org/W1999437504","https://openalex.org/W6941726668","https://openalex.org/W4311262326","https://openalex.org/W2887596457","https://openalex.org/W2577857265","https://openalex.org/W2070290983","https://openalex.org/W3082803772","https://openalex.org/W4214835050","https://openalex.org/W4402836246"],"abstract_inverted_index":{"The":[0,24,49,71,91,118,132],"current":[1],"study":[2,151],"aims":[3],"to":[4,44],"explore":[5],"the":[6,9,17,34,46,95,124,140,158],"potential":[7],"of":[8,11,20,27,36,106,113,123,161],"combination":[10],"hyperspectral":[12,25],"imaging":[13],"and":[14,64,85,102,115,139],"chemometrics":[15],"in":[16,33],"rapid":[18],"identification":[19],"four":[21,28],"chicken":[22,29,162],"breeds.":[23,163],"data":[26],"breeds":[30],"were":[31,42,54,74],"collected":[32],"range":[35],"400\u2013900":[37],"nm.":[38],"Five":[39],"pretreatment":[40,98],"methods":[41],"used":[43],"pretreat":[45],"original":[47],"spectra.":[48],"important":[50,137],"characteristic":[51],"wavelength":[52],"variables":[53],"extracted":[55,135],"by":[56,76],"random":[57],"frog":[58],"(RF),":[59],"successive":[60],"projection":[61],"algorithm":[62],"(SPA),":[63],"competitive":[65],"adaptive":[66],"reweighted":[67],"sampling":[68],"(CARS)":[69],"algorithms.":[70],"classification":[72,104,120],"models":[73,108],"established":[75],"using":[77],"support":[78],"vector":[79],"machine":[80],"(SVM),":[81],"k-nearest":[82],"neighbor":[83],"(KNN),":[84],"partial":[86],"least":[87],"squares-discriminant":[88],"analysis":[89,160],"(PLS-DA).":[90],"results":[92],"showed":[93],"that":[94,112],"mean":[96],"normalization":[97],"method":[99,134],"was":[100,109],"preferable,":[101],"overall":[103],"accuracy":[105],"SVM-based":[107],"higher":[110],"than":[111],"KNN-based":[114],"PLS-DA-based":[116],"models.":[117],"correct":[119],"rate":[121],"(CCR)":[122],"full-spectrum":[125],"SVM":[126,141],"model":[127,142],"(Full-SVM)":[128],"could":[129],"reach":[130],"96.25%.":[131],"SPA":[133,145],"13":[136],"wavelengths,":[138],"based":[143],"on":[144],"(SPA-SVM)":[146],"achieved":[147],"90%":[148],"CCR.":[149],"This":[150],"can":[152],"provide":[153],"a":[154],"theoretical":[155],"reference":[156],"for":[157],"discriminant":[159]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2023,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2022-07-30T00:00:00"}