Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil

Autores
Cerretani, Lorenzo; Maggio, Ruben Mariano; Barnaba, Carlo; Gallina Toschi, Tullia; Chiavaro, Emma
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A chemometric approach based on partial least (PLS) square methodology was applied to unfolded differential scanning calorimetry data obtained by 63 samples of different vegetable oils (58 extra virgin olive oils, one olive and one pomace olive oil, three seed oils) to evaluate fatty acid composition (palmitic, stearic, oleic and linoleic acids, saturated (SFA), mono (MUFA) and polysaturated (PUFA) percentages, oleic/linoleic and unsaturated/saturated ratios). All calibration models exhibited satisfactory figures of merit. Palmitic and oleic acids, as well as SFA showed very good correlation coefficients and low root mean square error values in both calibration and validation sets. Satisfactory results were also obtained for MUFA, PUFA, stearic and linoleic acids, O/L ratio in terms of percentage recoveries and relative standard deviations. No systematic and bias errors were detected in the prediction of validation samples. This novel approach could provide statistically similar results to those given by traditional official procedures, with the advantages of a very rapid and environmentally friendly methodology.
Fil: Cerretani, Lorenzo. Universidad de Bologna; Italia
Fil: Maggio, Ruben Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
Fil: Barnaba, Carlo. Università di Parma; Italia
Fil: Gallina Toschi, Tullia. Universidad de Bologna; Italia
Fil: Chiavaro, Emma. Università di Parma; Italia
Materia
DIFFERENTIAL SCANNING CALORIMETRY
FATTY ACID
OLIVE OIL
PARTIAL LEAST SQUARE REGRESSION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/127059
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oilCerretani, LorenzoMaggio, Ruben MarianoBarnaba, CarloGallina Toschi, TulliaChiavaro, EmmaDIFFERENTIAL SCANNING CALORIMETRYFATTY ACIDOLIVE OILPARTIAL LEAST SQUARE REGRESSIONhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A chemometric approach based on partial least (PLS) square methodology was applied to unfolded differential scanning calorimetry data obtained by 63 samples of different vegetable oils (58 extra virgin olive oils, one olive and one pomace olive oil, three seed oils) to evaluate fatty acid composition (palmitic, stearic, oleic and linoleic acids, saturated (SFA), mono (MUFA) and polysaturated (PUFA) percentages, oleic/linoleic and unsaturated/saturated ratios). All calibration models exhibited satisfactory figures of merit. Palmitic and oleic acids, as well as SFA showed very good correlation coefficients and low root mean square error values in both calibration and validation sets. Satisfactory results were also obtained for MUFA, PUFA, stearic and linoleic acids, O/L ratio in terms of percentage recoveries and relative standard deviations. No systematic and bias errors were detected in the prediction of validation samples. This novel approach could provide statistically similar results to those given by traditional official procedures, with the advantages of a very rapid and environmentally friendly methodology.Fil: Cerretani, Lorenzo. Universidad de Bologna; ItaliaFil: Maggio, Ruben Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; ArgentinaFil: Barnaba, Carlo. Università di Parma; ItaliaFil: Gallina Toschi, Tullia. Universidad de Bologna; ItaliaFil: Chiavaro, Emma. Università di Parma; ItaliaElsevier2011-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/127059Cerretani, Lorenzo; Maggio, Ruben Mariano; Barnaba, Carlo; Gallina Toschi, Tullia; Chiavaro, Emma; Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil; Elsevier; Food Chemistry; 127; 4; 8-2011; 1899-19040308-8146CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0308814611002998info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodchem.2011.02.041info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:39:58Zoai:ri.conicet.gov.ar:11336/127059instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:39:58.804CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
title Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
spellingShingle Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
Cerretani, Lorenzo
DIFFERENTIAL SCANNING CALORIMETRY
FATTY ACID
OLIVE OIL
PARTIAL LEAST SQUARE REGRESSION
title_short Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
title_full Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
title_fullStr Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
title_full_unstemmed Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
title_sort Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
dc.creator.none.fl_str_mv Cerretani, Lorenzo
Maggio, Ruben Mariano
Barnaba, Carlo
Gallina Toschi, Tullia
Chiavaro, Emma
author Cerretani, Lorenzo
author_facet Cerretani, Lorenzo
Maggio, Ruben Mariano
Barnaba, Carlo
Gallina Toschi, Tullia
Chiavaro, Emma
author_role author
author2 Maggio, Ruben Mariano
Barnaba, Carlo
Gallina Toschi, Tullia
Chiavaro, Emma
author2_role author
author
author
author
dc.subject.none.fl_str_mv DIFFERENTIAL SCANNING CALORIMETRY
FATTY ACID
OLIVE OIL
PARTIAL LEAST SQUARE REGRESSION
topic DIFFERENTIAL SCANNING CALORIMETRY
FATTY ACID
OLIVE OIL
PARTIAL LEAST SQUARE REGRESSION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A chemometric approach based on partial least (PLS) square methodology was applied to unfolded differential scanning calorimetry data obtained by 63 samples of different vegetable oils (58 extra virgin olive oils, one olive and one pomace olive oil, three seed oils) to evaluate fatty acid composition (palmitic, stearic, oleic and linoleic acids, saturated (SFA), mono (MUFA) and polysaturated (PUFA) percentages, oleic/linoleic and unsaturated/saturated ratios). All calibration models exhibited satisfactory figures of merit. Palmitic and oleic acids, as well as SFA showed very good correlation coefficients and low root mean square error values in both calibration and validation sets. Satisfactory results were also obtained for MUFA, PUFA, stearic and linoleic acids, O/L ratio in terms of percentage recoveries and relative standard deviations. No systematic and bias errors were detected in the prediction of validation samples. This novel approach could provide statistically similar results to those given by traditional official procedures, with the advantages of a very rapid and environmentally friendly methodology.
Fil: Cerretani, Lorenzo. Universidad de Bologna; Italia
Fil: Maggio, Ruben Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
Fil: Barnaba, Carlo. Università di Parma; Italia
Fil: Gallina Toschi, Tullia. Universidad de Bologna; Italia
Fil: Chiavaro, Emma. Università di Parma; Italia
description A chemometric approach based on partial least (PLS) square methodology was applied to unfolded differential scanning calorimetry data obtained by 63 samples of different vegetable oils (58 extra virgin olive oils, one olive and one pomace olive oil, three seed oils) to evaluate fatty acid composition (palmitic, stearic, oleic and linoleic acids, saturated (SFA), mono (MUFA) and polysaturated (PUFA) percentages, oleic/linoleic and unsaturated/saturated ratios). All calibration models exhibited satisfactory figures of merit. Palmitic and oleic acids, as well as SFA showed very good correlation coefficients and low root mean square error values in both calibration and validation sets. Satisfactory results were also obtained for MUFA, PUFA, stearic and linoleic acids, O/L ratio in terms of percentage recoveries and relative standard deviations. No systematic and bias errors were detected in the prediction of validation samples. This novel approach could provide statistically similar results to those given by traditional official procedures, with the advantages of a very rapid and environmentally friendly methodology.
publishDate 2011
dc.date.none.fl_str_mv 2011-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/127059
Cerretani, Lorenzo; Maggio, Ruben Mariano; Barnaba, Carlo; Gallina Toschi, Tullia; Chiavaro, Emma; Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil; Elsevier; Food Chemistry; 127; 4; 8-2011; 1899-1904
0308-8146
CONICET Digital
CONICET
url http://hdl.handle.net/11336/127059
identifier_str_mv Cerretani, Lorenzo; Maggio, Ruben Mariano; Barnaba, Carlo; Gallina Toschi, Tullia; Chiavaro, Emma; Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil; Elsevier; Food Chemistry; 127; 4; 8-2011; 1899-1904
0308-8146
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0308814611002998
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodchem.2011.02.041
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082888620048384
score 13.22299

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