{"id":"https://openalex.org/W4379352308","doi":"https://doi.org/10.1145/3583788.3583801","title":"Machine Learning on Spectral Data from Miniature Devices for Food Quality Analysis - A Case Study","display_name":"Machine Learning on Spectral Data from Miniature Devices for Food Quality Analysis - A Case Study","publication_year":2023,"publication_date":"2023-01-05","ids":{"openalex":"https://openalex.org/W4379352308","doi":"https://doi.org/10.1145/3583788.3583801"},"language":"en","primary_location":{"id":"doi:10.1145/3583788.3583801","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583788.3583801","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583788.3583801","source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583788.3583801","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059241587","display_name":"Fayas Asharindavida","orcid":"https://orcid.org/0000-0001-7931-8836"},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Fayas Asharindavida","raw_affiliation_strings":["School of Computing, Ulster University, UK"],"raw_orcid":"https://orcid.org/0000-0001-7931-8836","affiliations":[{"raw_affiliation_string":"School of Computing, Ulster University, UK","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030458751","display_name":"Omar Nibouche","orcid":"https://orcid.org/0000-0003-2149-2975"},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Omar Nibouche","raw_affiliation_strings":["School of Computing, Ulster University, UK"],"raw_orcid":"https://orcid.org/0000-0003-2149-2975","affiliations":[{"raw_affiliation_string":"School of Computing, Ulster University, UK","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076322327","display_name":"James Uhomoibhi","orcid":null},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"James Uhomoibhi","raw_affiliation_strings":["School of Engineering, Ulster University, UK"],"raw_orcid":"https://orcid.org/0000-0002-0229-7245","affiliations":[{"raw_affiliation_string":"School of Engineering, Ulster University, UK","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100361707","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0001-8859-5405"},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jun Liu","raw_affiliation_strings":["School of Computing, Ulster University, UK"],"raw_orcid":"https://orcid.org/0000-0001-8859-5405","affiliations":[{"raw_affiliation_string":"School of Computing, Ulster University, UK","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100460917","display_name":"Hui Wang","orcid":"https://orcid.org/0000-0003-2633-6015"},"institutions":[{"id":"https://openalex.org/I126231945","display_name":"Queen's University Belfast","ror":"https://ror.org/00hswnk62","country_code":"GB","type":"education","lineage":["https://openalex.org/I126231945"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hui Wang","raw_affiliation_strings":["School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, UK"],"raw_orcid":"https://orcid.org/0000-0003-2633-6015","affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, UK","institution_ids":["https://openalex.org/I126231945"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5059241587"],"corresponding_institution_ids":["https://openalex.org/I138801177"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0631745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"89","last_page":"95"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9998999834060669,"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":0.9998999834060669,"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/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/software-portability","display_name":"Software portability","score":0.7351951599121094},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6976000070571899},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5712361931800842},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5390517115592957},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.5286712646484375},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.5004220008850098},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.4527944326400757},{"id":"https://openalex.org/keywords/spectrometer","display_name":"Spectrometer","score":0.4451625347137451},{"id":"https://openalex.org/keywords/data-processing","display_name":"Data processing","score":0.4300987422466278},{"id":"https://openalex.org/keywords/food-quality","display_name":"Food quality","score":0.4263136684894562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41686689853668213},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3757561445236206},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3478139638900757},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17514696717262268},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13969692587852478}],"concepts":[{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.7351951599121094},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6976000070571899},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5712361931800842},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5390517115592957},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.5286712646484375},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.5004220008850098},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.4527944326400757},{"id":"https://openalex.org/C33390570","wikidata":"https://www.wikidata.org/wiki/Q188463","display_name":"Spectrometer","level":2,"score":0.4451625347137451},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.4300987422466278},{"id":"https://openalex.org/C51592225","wikidata":"https://www.wikidata.org/wiki/Q2498665","display_name":"Food quality","level":2,"score":0.4263136684894562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41686689853668213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3757561445236206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3478139638900757},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17514696717262268},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13969692587852478},{"id":"https://openalex.org/C31903555","wikidata":"https://www.wikidata.org/wiki/Q1637030","display_name":"Food science","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3583788.3583801","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583788.3583801","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583788.3583801","source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/7fcf9503-acf2-4a9e-a5c5-7138907d481e","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/7fcf9503-acf2-4a9e-a5c5-7138907d481e","pdf_url":"https://pure.ulster.ac.uk/ws/files/121221123/3583788.3583801.pdf","source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Asharindavida, F, Nibouche, O, Uhomoibhi, J, Liu, J & Wang, H 2023, Machine Learning on Spectral Data from Miniature Devices for Food Quality Analysis - A Case Study. in ICMLSC '23: Proceedings of the 2023 7th International Conference on Machine Learning and Soft Computing. 2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC), Association for Computing Machinery, pp. 84-95. https://doi.org/10.1145/3583788.3583801","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.qub.ac.uk/portal:openaire/0c57256d-ac6b-4ce7-977f-c9b1e378152b","is_oa":true,"landing_page_url":"https://pure.qub.ac.uk/en/publications/0c57256d-ac6b-4ce7-977f-c9b1e378152b","pdf_url":"https://pureadmin.qub.ac.uk/ws/files/472945765/3583788.3583801.pdf","source":{"id":"https://openalex.org/S4306402319","display_name":"Research Portal (Queen's University Belfast)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126231945","host_organization_name":"Queen's University Belfast","host_organization_lineage":["https://openalex.org/I126231945"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Asharindavida, F, Nibouche, O, Uhomoibhi, J, Liu, J & Wang, H 2023, Machine learning on spectral data from miniature devices for food quality analysis - a case study. in ICMLSC '23: Proceedings of the 2023 7th International Conference on Machine Learning and Soft Computing. The International Conference on Machine Learning and Soft Computing (ICMLSC): Proceedings, Association for Computing Machinery, pp. 89\u201395, ICMLSC 2023: The 7th International Conference on Machine Learning and Soft Computing , Chongqing, China, 05/01/2023. https://doi.org/10.1145/3583788.3583801","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.1145/3583788.3583801","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583788.3583801","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583788.3583801","source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379352308.pdf","grobid_xml":"https://content.openalex.org/works/W4379352308.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W118598055","https://openalex.org/W1988176704","https://openalex.org/W2060221922","https://openalex.org/W2067681708","https://openalex.org/W2068295353","https://openalex.org/W2087347434","https://openalex.org/W2431355146","https://openalex.org/W2606436201","https://openalex.org/W2888611683","https://openalex.org/W2911964244","https://openalex.org/W2950397921","https://openalex.org/W2972422026","https://openalex.org/W2981803013","https://openalex.org/W2991956647","https://openalex.org/W2998700272","https://openalex.org/W3112355782","https://openalex.org/W3112610499","https://openalex.org/W3199178507","https://openalex.org/W4239944110","https://openalex.org/W4280631829","https://openalex.org/W4301971222"],"related_works":["https://openalex.org/W3202554610","https://openalex.org/W138569904","https://openalex.org/W2186437798","https://openalex.org/W176219849","https://openalex.org/W3021414116","https://openalex.org/W2380699730","https://openalex.org/W4385985500","https://openalex.org/W2344028705","https://openalex.org/W3119025276","https://openalex.org/W2791222374"],"abstract_inverted_index":{"Food":[0],"quality":[1,79,85,106,268],"analysis":[2,103,119],"can":[3,25,206,287,307],"be":[4,26,207,288,308],"carried":[5,92],"out":[6,93],"by":[7],"spectral":[8,101,125,152,239,297],"data":[9,68,102,126,154,163,236,240,282,298],"acquired":[10,136],"from":[11,137,189,241,299],"spectrometers":[12,74],"with":[13,167,191],"its":[14],"advantage":[15],"of":[16,19,76,86,150,170,185,214,234,248,258,269,295,305],"non-destructive":[17],"way":[18],"testing.":[20],"Portable":[21],"and":[22,39,64,72,80,134,145,156,176,198,222,271,284,302],"miniature":[23,71,139,242,300],"spectroscopy":[24],"a":[27,51,77,168,179],"suitable":[28],"solution":[29],"when":[30],"it":[31],"meets":[32],"the":[33,54,67,84,87,123,147,159,171,183,212,220,231,245,267,292,296],"specifications":[34],"such":[35,50,138,151],"as":[36],"portability,":[37],"cost,":[38],"short":[40],"processing":[41],"time":[42],"requirements,":[43],"to":[44,48,60,94,143,174,196,218,260,266,274,276,290],"enable":[45],"ordinary":[46],"citizens":[47],"use":[49,95],"device":[52],"in":[53,158,312],"fight":[55],"against":[56],"food":[57,105,129],"fraud.":[58],"Compared":[59],"more":[61],"expensive,":[62],"bulky,":[63],"non-portable":[65],"devices,":[66,140],"collected":[69],"using":[70,235],"portable":[73],"is":[75,120,194,224,252,263],"lower":[78],"thus":[81],"adversely":[82],"affect":[83],"analysis.":[88],"Research":[89],"have":[90],"been":[91],"machine":[96],"learning":[97],"(ML)":[98],"classifiers":[99,205,251,306],"on":[100,112,122,127],"for":[104,182,209,238],"assessment.":[107],"The":[108,227],"present":[109],"work":[110],"focuses":[111],"two":[113],"aspects:":[114],"firstly,":[115],"preliminary":[116],"exploratory":[117],"statistical":[118],"conducted":[121],"real":[124],"different":[128,215],"products":[130],"including":[131],"oils,":[132],"fruits":[133],"spices":[135],"which":[141,262],"aims":[142],"evaluate":[144,199],"illustrate":[146],"distinctive":[148],"characteristics":[149],"data,":[153,270],"distribution":[155],"difference":[157],"spectra":[160],"across":[161],"multiple":[162],"acquisitions":[164],"etc.":[165],"along":[166],"summary":[169],"key":[172,293],"challenges":[173,294],"face":[175],"explore.":[177],"Secondly,":[178],"case":[180,228],"study":[181,229],"differentiation":[184],"extra":[186],"virgin":[187],"olive":[188],"adulterated":[190],"vegetable":[192],"oil":[193],"provided":[195],"analyze":[197],"how":[200],"some":[201,278],"commonly":[202],"used":[203,208,289],"ML":[204,250],"classification,":[210],"while":[211],"impact":[213],"preprocessing":[216],"methods":[217,286],"improve":[219],"accuracy":[221],"efficiency":[223],"also":[225,309],"provided.":[226],"demonstrates":[230],"good":[232],"potential":[233],"analytics":[237],"device,":[243,301],"although":[244],"overall":[246],"performance":[247],"those":[249],"not":[253],"exceptional":[254],"(the":[255],"classification":[256],"rates":[257],"up":[259],"83.32%)":[261],"partially":[264,272],"due":[265,273],"limiting":[275],"only":[277],"classifiers.":[279],"More":[280],"elaborate":[281],"pre-processing":[283],"cleaning":[285],"address":[291],"other":[303],"types":[304],"explored":[310],"further":[311],"future":[313],"work.":[314]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}