{"id":"https://openalex.org/W7155416408","doi":"https://doi.org/10.48550/arxiv.2604.20206","title":"Predicting food taste with bound-driven optimization","display_name":"Predicting food taste with bound-driven optimization","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155416408","doi":"https://doi.org/10.48550/arxiv.2604.20206"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20206","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.20206","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134389519","display_name":"Pagkratis Tagkopoulos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tagkopoulos, Pagkratis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134415371","display_name":"Dimitris Sfondilis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sfondilis, Dimitris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004351739","display_name":"Ilias Tagkopoulos","orcid":"https://orcid.org/0000-0003-1104-7616"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tagkopoulos, Ilias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134391459","display_name":"Tarek Zohdi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zohdi, Tarek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11584","display_name":"Biochemical Analysis and Sensing Techniques","score":0.5270000100135803,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11584","display_name":"Biochemical Analysis and Sensing Techniques","score":0.5270000100135803,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.21289999783039093,"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"}},{"id":"https://openalex.org/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.12210000306367874,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/taste","display_name":"Taste","score":0.7024999856948853},{"id":"https://openalex.org/keywords/ingredient","display_name":"Ingredient","score":0.5206000208854675},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.48489999771118164},{"id":"https://openalex.org/keywords/flavor","display_name":"Flavor","score":0.460099995136261},{"id":"https://openalex.org/keywords/artificial-sweetener","display_name":"Artificial Sweetener","score":0.420199990272522},{"id":"https://openalex.org/keywords/food-products","display_name":"Food products","score":0.4075999855995178},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.39969998598098755},{"id":"https://openalex.org/keywords/sensory-system","display_name":"Sensory system","score":0.39309999346733093}],"concepts":[{"id":"https://openalex.org/C8868529","wikidata":"https://www.wikidata.org/wiki/Q124794","display_name":"Taste","level":2,"score":0.7024999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5404999852180481},{"id":"https://openalex.org/C2780589914","wikidata":"https://www.wikidata.org/wiki/Q10675206","display_name":"Ingredient","level":2,"score":0.5206000208854675},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.48489999771118164},{"id":"https://openalex.org/C2780719635","wikidata":"https://www.wikidata.org/wiki/Q4173974","display_name":"Flavor","level":2,"score":0.460099995136261},{"id":"https://openalex.org/C31903555","wikidata":"https://www.wikidata.org/wiki/Q1637030","display_name":"Food science","level":1,"score":0.4359999895095825},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4325999915599823},{"id":"https://openalex.org/C2911055575","wikidata":"https://www.wikidata.org/wiki/Q626292","display_name":"Artificial Sweetener","level":3,"score":0.420199990272522},{"id":"https://openalex.org/C2987718291","wikidata":"https://www.wikidata.org/wiki/Q2095","display_name":"Food products","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40299999713897705},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39969998598098755},{"id":"https://openalex.org/C94487597","wikidata":"https://www.wikidata.org/wiki/Q11101","display_name":"Sensory system","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3815000057220459},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35740000009536743},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2781404680","wikidata":"https://www.wikidata.org/wiki/Q1806341","display_name":"Novel food","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.26179999113082886}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20206","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20206","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6477789282798767,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,86],"prediction":[1],"of":[2,15,26,30,73,98],"sensory":[3],"attributes":[4],"from":[5,36,112],"ingredient-level":[6,79],"formulations":[7,207],"is":[8],"an":[9,89],"emerging":[10],"challenge":[11],"at":[12],"the":[13,23,28,103,108,143,162],"intersection":[14],"food":[16,32],"science":[17],"and":[18,49,133,136,165,176],"artificial":[19],"intelligence.":[20],"We":[21,45,118,196],"address":[22],"fundamental":[24],"question":[25],"whether":[27],"taste":[29,62,80,100,211],"a":[31,70,138,188],"can":[33],"be":[34],"predicted":[35],"its":[37],"ingredients":[38],"by":[39,170],"treating":[40],"recipes":[41,75],"as":[42],"composite":[43],"materials.":[44],"apply":[46],"Hashin--Shtrikman":[47],"(HS)":[48],"Reuss--Voigt":[50],"(RV)":[51],"bounds,":[52],"techniques":[53],"originally":[54],"developed":[55],"for":[56,172],"elastic":[57],"moduli,":[58],"to":[59,115,122,187,214],"predict":[60],"five":[61],"dimensions":[63],"(sweetness,":[64],"sourness,":[65,174],"bitterness,":[66],"umami,":[67,175],"saltiness)":[68],"on":[69,192],"curated":[71],"dataset":[72],"70":[74],"decomposed":[76],"into":[77],"209":[78],"references":[81],"with":[82,107,146],"trained-panel":[83],"ground":[84],"truth.":[85],"bounds":[87],"provided":[88],"additive":[90],"baseline":[91,145],"but":[92],"systematically":[93],"under-predict":[94],"perceived":[95],"taste:":[96],"77\\%":[97],"actual":[99],"values":[101],"exceeded":[102],"HS":[104,144],"upper":[105],"bound,":[106],"exceedance":[109],"rate":[110],"ranging":[111],"26\\%":[113],"(bitterness)":[114],"97\\%":[116],"(saltiness).":[117],"traced":[119],"this":[120],"gap":[121],"specific":[123],"processing":[124],"chemistry":[125],"(Maillard":[126],"reactions,":[127],"caramelization,":[128],"evaporative":[129],"concentration,":[130],"protein":[131],"hydrolysis,":[132],"nucleotide":[134],"synergy)":[135],"introduced":[137],"hybrid":[139,159],"model":[140,160],"that":[141,156,208],"augments":[142],"eight":[147],"chemistry-proxy":[148],"features":[149],"encoding":[150],"these":[151],"mechanisms.":[152],"Our":[153],"results":[154],"show":[155],"our":[157],"interpretable":[158,182],"eliminates":[161],"systematic":[163],"bias":[164],"reduces":[166],"mean":[167],"absolute":[168],"error":[169],"27--62\\%":[171],"sweetness,":[173],"saltiness":[177],"while":[178],"using":[179],"only":[180],"10":[181],"features,":[183],"achieving":[184],"performance":[185],"comparable":[186],"black-box":[189],"Lasso":[190],"regression":[191],"115":[193],"per-ingredient":[194],"features.":[195],"further":[197],"demonstrate":[198],"constrained":[199],"inverse":[200],"design":[201],"via":[202],"Differential":[203],"Evolution,":[204],"recovering":[205],"ingredient":[206],"match":[209],"target":[210],"profiles":[212],"subject":[213],"compositional":[215],"bounds.":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-24T00:00:00"}