Publication
Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?
| Summary: | This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations. |
|---|---|
| Main Authors: | Vasconcelos, Lia |
| Other Authors: | Dias, L.G.; Leite, Ana; Ferreira, Iasmin da Silva; Pereira, Etelvina; Bona, Evandro; Mateo, Javier; Rodrigues, Sandra; Teixeira, Alfredo |
| Subject: | Consumers Meat products Bísaro breed Food quality assessment NIR analysis Non-linear SVR models |
| Year: | 2023 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Instituto Politécnico de Bragança |
| Language: | English |
| Origin: | Biblioteca Digital do IPB |
| _version_ | 1867173124489871360 |
|---|---|
| author | Vasconcelos, Lia |
| author2 | Dias, L.G. Leite, Ana Ferreira, Iasmin da Silva Pereira, Etelvina Bona, Evandro Mateo, Javier Rodrigues, Sandra Teixeira, Alfredo |
| author2_role | author author author author author author author author |
| author_facet | Vasconcelos, Lia Dias, L.G. Leite, Ana Ferreira, Iasmin da Silva Pereira, Etelvina Bona, Evandro Mateo, Javier Rodrigues, Sandra Teixeira, Alfredo |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Vasconcelos, Lia\",\"Person.identifier.orcid\":\"0009-0003-1035-5622\"},{\"Person.name\":\"Dias, L.G.\",\"Person.identifier.orcid\":\"0000-0002-1210-4259\"},{\"Person.name\":\"Leite, Ana\",\"Person.identifier.orcid\":\"0000-0002-4480-724X\"},{\"Person.name\":\"Ferreira, Iasmin da Silva\",\"Person.identifier.orcid\":\"0000-0003-0162-7182\"},{\"Person.name\":\"Pereira, Etelvina\"},{\"Person.name\":\"Bona, Evandro\"},{\"Person.name\":\"Mateo, Javier\"},{\"Person.name\":\"Rodrigues, Sandra\",\"Person.identifier.orcid\":\"0000-0003-3301-1729\"},{\"Person.name\":\"Teixeira, Alfredo\",\"Person.identifier.orcid\":\"0000-0003-4607-4796\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Vasconcelos, Lia Dias, L.G. Leite, Ana Ferreira, Iasmin da Silva Pereira, Etelvina Bona, Evandro Mateo, Javier Rodrigues, Sandra Teixeira, Alfredo |
| datacite.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-01-11T12:15:50Z |
| datacite.date.embargoed.fl_str_mv | 2024-01-11T12:15:50Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Consumers Meat products Bísaro breed Food quality assessment NIR analysis Non-linear SVR models |
| datacite.titles.title.fl_str_mv | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Vasconcelos, Lia Dias, L.G. Leite, Ana Ferreira, Iasmin da Silva Pereira, Etelvina Bona, Evandro Mateo, Javier Rodrigues, Sandra Teixeira, Alfredo |
| dc.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-01-11T12:15:50Z |
| dc.date.embargoed.fl_str_mv | 2024-01-11T12:15:50Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/29171 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Consumers Meat products Bísaro breed Food quality assessment NIR analysis Non-linear SVR models |
| dc.title.fl_str_mv | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/6fae49ba-00c9-4834-8858-6428df815a8b/download |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 6817 - DCRRNI ID |
| id | ipb_c53d5fb634c2b321ea371bfeb536a5d0 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/29171 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/29171 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Vasconcelos, Lia Vasconcelos, Lia https://www.ciencia-id.pt/2015-35EF-B2C9 2015-35EF-B2C9 http://orcid.org/0009-0003-1035-5622 0009-0003-1035-5622 Dias, L.G. Dias, L.G. https://www.ciencia-id.pt/2F11-9092-FAAF 2F11-9092-FAAF http://orcid.org/0000-0002-1210-4259 0000-0002-1210-4259 Leite, Ana Leite, Ana https://www.ciencia-id.pt/1C15-1046-A5B0 1C15-1046-A5B0 http://orcid.org/0000-0002-4480-724X 0000-0002-4480-724X Ferreira, Iasmin da Silva Ferreira, Iasmin da Silva https://www.ciencia-id.pt/9F1F-D7E7-163A 9F1F-D7E7-163A http://orcid.org/0000-0003-0162-7182 0000-0003-0162-7182 Pereira, Etelvina Bona, Evandro Mateo, Javier Rodrigues, Sandra Rodrigues, Sandra https://www.ciencia-id.pt/651F-D964-32E1 651F-D964-32E1 http://orcid.org/0000-0003-3301-1729 0000-0003-3301-1729 Teixeira, Alfredo Teixeira, Alfredo https://www.ciencia-id.pt/2A1A-FF0C-185B 2A1A-FF0C-185B http://orcid.org/0000-0003-4607-4796 0000-0003-4607-4796 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | MDPI |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engMDPIpt_PTThis study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.application/pdfpt_PTCan near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?PersonalVasconcelos, LiaDSpacehttp://dspace.org/items/e04953ca-3cf5-47e3-b015-ce129e8de0cdDSpacehttp://dspace.org/items/e04953ca-3cf5-47e3-b015-ce129e8de0cdVasconcelosLiaCiência IDhttps://www.ciencia-id.pt2015-35EF-B2C9ORCIDhttp://orcid.org0009-0003-1035-5622Scopus Author IDhttps://www.scopus.com57224659068PersonalDias, L.G.DSpacehttp://dspace.org/items/eac8c166-4056-4ed0-8d8d-7ecb2c4481a5DSpacehttp://dspace.org/items/eac8c166-4056-4ed0-8d8d-7ecb2c4481a5DiasLuís G.Ciência IDhttps://www.ciencia-id.pt2F11-9092-FAAFORCIDhttp://orcid.org0000-0002-1210-4259Scopus Author IDhttps://www.scopus.com23569169900PersonalLeite, AnaDSpacehttp://dspace.org/items/3d42c391-03b5-4c2d-9ca7-75f8278731ccDSpacehttp://dspace.org/items/3d42c391-03b5-4c2d-9ca7-75f8278731ccLeiteAnaCiência IDhttps://www.ciencia-id.pt1C15-1046-A5B0ORCIDhttp://orcid.org0000-0002-4480-724XPersonalFerreira, Iasmin da SilvaDSpacehttp://dspace.org/items/920e15c9-3028-4a13-98eb-5cc03624eefeDSpacehttp://dspace.org/items/920e15c9-3028-4a13-98eb-5cc03624eefeFerreiraIasmin da SilvaCiência IDhttps://www.ciencia-id.pt9F1F-D7E7-163AORCIDhttp://orcid.org0000-0003-0162-7182Pereira, EtelvinaBona, EvandroMateo, JavierPersonalRodrigues, SandraDSpacehttp://dspace.org/items/684ab6a0-e3ea-4b6e-9874-d20d71e2a3f5DSpacehttp://dspace.org/items/684ab6a0-e3ea-4b6e-9874-d20d71e2a3f5RodriguesSandraCiência IDhttps://www.ciencia-id.pt651F-D964-32E1ORCIDhttp://orcid.org0000-0003-3301-1729Researcher IDhttps://www.researcherid.comC-6486-2008Scopus Author IDhttps://www.scopus.com14048942500PersonalTeixeira, AlfredoDSpacehttp://dspace.org/items/27cc89a2-6661-4d8d-a727-21109c04a74eDSpacehttp://dspace.org/items/27cc89a2-6661-4d8d-a727-21109c04a74eTeixeiraAlfredoCiência IDhttps://www.ciencia-id.pt2A1A-FF0C-185BORCIDhttp://orcid.org0000-0003-4607-4796Researcher IDhttps://www.researcherid.comG-4118-2011Scopus Author IDhttps://www.scopus.com56195849200HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptDOIIsPartOf10.3390/foods122343352024-01-11T12:15:50Z20232023-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/29171http://purl.org/coar/access_right/c_abf2open accessConsumersMeat productsBísaro breedFood quality assessmentNIR analysisNon-linear SVR models1642953 bytesFundação para a Ciência e a TecnologiaMountain Research Center6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871literaturehttp://purl.org/coar/resource_type/c_6501journal article2023http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/6fae49ba-00c9-4834-8858-6428df815a8b/downloadFoods1223115 |
| spellingShingle | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? Vasconcelos, Lia Consumers Meat products Bísaro breed Food quality assessment NIR analysis Non-linear SVR models |
| status | SINGLETON |
| subject.fl_str_mv | Consumers Meat products Bísaro breed Food quality assessment NIR analysis Non-linear SVR models |
| title | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| title_full | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| title_fullStr | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| title_full_unstemmed | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| title_short | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| title_sort | Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin? |
| topic | Consumers Meat products Bísaro breed Food quality assessment NIR analysis Non-linear SVR models |
| topic_facet | Consumers Meat products Bísaro breed Food quality assessment NIR analysis Non-linear SVR models |
| url | http://hdl.handle.net/10198/29171 |
| visible | 1 |