Analysis of Fatty Acids in 12 Mediterranean Fish Species: Advantages and Limitations of a New GC-FID/GC–MS Based Technique View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-07

AUTHORS

Teresina Nevigato, Maurizio Masci, Elena Orban, Gabriella Di Lena, Irene Casini, Roberto Caproni

ABSTRACT

When fatty acids in fish are analyzed, results in percentage form (profile analysis) are mostly reported. However, the much more useful results expressed as mg/100 g (absolute analysis) is the main information required. Absolute methods based on calibration curves are of good accuracy but with a high degree of complexity if applied to a great number of analytes. Procedures based on the sequence profile analysis-total FA determination-absolute analysis may be suitable for routine use, but suffer from a number of uncertainties that have never been really resolved. These uncertainties are mainly related to the profile analysis. In fact, most profile analyses reported in the literature disagree about the number and type of fatty acids monitored as well as about the total percentage to assign to their sum so leading to possible inaccuracies; in addition the instrumental response factor for all FAME (fatty acid methyl esters) is often considered as a constant, but this is not exactly true. In this work, a set of 24 fatty acids was selected and studied on 12 fish species in the Mediterranean area (variable in lipid content and month of sampling): in our results, and in these species, this set constitutes, on average, 90 ± 3 % of the total fatty acid content. Moreover the error derived from the assumption of a unique response factor was investigated. Two different detection techniques (GC-FID and GC-MS) together with two capillary columns (different in length and polarity) were used in order to acquire complementary data on the same sample. With the protocol here proposed absolute analyses on the 12 cited species are easily achievable by the total FA determination procedure. The accuracy of this approach is good in general, but in some cases (DHA for example) is lower than the accuracy of calibration-based methods. The differences were evaluated on a case by case basis. More... »

PAGES

741-753

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11745-012-3679-9

DOI

http://dx.doi.org/10.1007/s11745-012-3679-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022590190

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22644810


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fatty Acids", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fatty Acids, Omega-3", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fishes", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gas Chromatography-Mass Spectrometry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mediterranean Sea", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione", 
          "id": "https://www.grid.ac/institutes/grid.419539.3", 
          "name": [
            "National Research Institute for Food and Nutrition (INRAN), Via Ardeatina 546, 00178, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nevigato", 
        "givenName": "Teresina", 
        "id": "sg:person.01153410073.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01153410073.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione", 
          "id": "https://www.grid.ac/institutes/grid.419539.3", 
          "name": [
            "National Research Institute for Food and Nutrition (INRAN), Via Ardeatina 546, 00178, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Masci", 
        "givenName": "Maurizio", 
        "id": "sg:person.01221523273.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01221523273.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione", 
          "id": "https://www.grid.ac/institutes/grid.419539.3", 
          "name": [
            "National Research Institute for Food and Nutrition (INRAN), Via Ardeatina 546, 00178, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Orban", 
        "givenName": "Elena", 
        "id": "sg:person.015331652007.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015331652007.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione", 
          "id": "https://www.grid.ac/institutes/grid.419539.3", 
          "name": [
            "National Research Institute for Food and Nutrition (INRAN), Via Ardeatina 546, 00178, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Di Lena", 
        "givenName": "Gabriella", 
        "id": "sg:person.011220771313.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011220771313.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione", 
          "id": "https://www.grid.ac/institutes/grid.419539.3", 
          "name": [
            "National Research Institute for Food and Nutrition (INRAN), Via Ardeatina 546, 00178, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Casini", 
        "givenName": "Irene", 
        "id": "sg:person.0612345573.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0612345573.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione", 
          "id": "https://www.grid.ac/institutes/grid.419539.3", 
          "name": [
            "National Research Institute for Food and Nutrition (INRAN), Via Ardeatina 546, 00178, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Caproni", 
        "givenName": "Roberto", 
        "id": "sg:person.016536245547.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016536245547.14"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1139/o59-099", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001206449"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.foodchem.2010.10.107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003120094"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jfca.2010.03.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004338653"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1096-4959(01)00506-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007835295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2009.02.084", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008723008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.amjcard.2004.01.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021729738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0271-5317(95)02045-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024547028"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jfca.2005.12.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031023572"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.plefa.2007.10.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031636875"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.foodchem.2010.02.059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033091131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02671025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033774647", 
          "https://doi.org/10.1007/bf02671025"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.foodchem.2005.09.069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034089833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02671370", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035882221", 
          "https://doi.org/10.1007/bf02671370"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.nutres.2011.01.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036777558"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.amjcard.2005.12.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040955643"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mvr.2010.11.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041427215"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urolonc.2005.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042731518"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urolonc.2005.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042731518"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.foodchem.2010.09.103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042829225"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.chroma.2003.10.032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046132053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11745-006-1399-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050696701", 
          "https://doi.org/10.1007/s11745-006-1399-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.chroma.2005.07.104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052162097"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cca.2010.01.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052816155"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jf051468u", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055904118"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jf051468u", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055904118"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-07", 
    "datePublishedReg": "2012-07-01", 
    "description": "When fatty acids in fish are analyzed, results in percentage form (profile analysis) are mostly reported. However, the much more useful results expressed as mg/100\u00a0g (absolute analysis) is the main information required. Absolute methods based on calibration curves are of good accuracy but with a high degree of complexity if applied to a great number of analytes. Procedures based on the sequence profile analysis-total FA determination-absolute analysis may be suitable for routine use, but suffer from a number of uncertainties that have never been really resolved. These uncertainties are mainly related to the profile analysis. In fact, most profile analyses reported in the literature disagree about the number and type of fatty acids monitored as well as about the total percentage to assign to their sum so leading to possible inaccuracies; in addition the instrumental response factor for all FAME (fatty acid methyl esters) is often considered as a constant, but this is not exactly true. In this work, a set of 24 fatty acids was selected and studied on 12 fish species in the Mediterranean area (variable in lipid content and month of sampling): in our results, and in these species, this set constitutes, on average, 90\u00a0\u00b1\u00a03\u00a0% of the total fatty acid content. Moreover the error derived from the assumption of a unique response factor was investigated. Two different detection techniques (GC-FID and GC-MS) together with two capillary columns (different in length and polarity) were used in order to acquire complementary data on the same sample. With the protocol here proposed absolute analyses on the 12 cited species are easily achievable by the total FA determination procedure. The accuracy of this approach is good in general, but in some cases (DHA for example) is lower than the accuracy of calibration-based methods. The differences were evaluated on a case by case basis.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11745-012-3679-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1006395", 
        "issn": [
          "0024-4201", 
          "1558-9307"
        ], 
        "name": "Lipids", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "47"
      }
    ], 
    "name": "Analysis of Fatty Acids in 12 Mediterranean Fish Species: Advantages and Limitations of a New GC-FID/GC\u2013MS Based Technique", 
    "pagination": "741-753", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6b9fa375c3b31468944cadb330e79c414661155c93989ad9c605f9365fa40dd8"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "22644810"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0060450"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11745-012-3679-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1022590190"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11745-012-3679-9", 
      "https://app.dimensions.ai/details/publication/pub.1022590190"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T17:34", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000521.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11745-012-3679-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11745-012-3679-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11745-012-3679-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11745-012-3679-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11745-012-3679-9'


 

This table displays all metadata directly associated to this object as RDF triples.

204 TRIPLES      21 PREDICATES      59 URIs      28 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11745-012-3679-9 schema:about N0aa46bf33edb498ca07bf84c01803b95
2 N2993c7544cc44d1cb173f1ba07a8ed0a
3 N7713039ed1a042eb8ab40b0271b9b914
4 N9ee42049e3604a0c90b456499ab423b0
5 Na66fca8b7c84485fa2a61e4315fbc574
6 Nc0ed0dba598749d9a55139dee4378103
7 Nf53fd6b07d1f4cab915f622635b95f90
8 anzsrc-for:08
9 anzsrc-for:0801
10 schema:author Nd1a0b1910a66473aad466bbc72753d52
11 schema:citation sg:pub.10.1007/bf02671025
12 sg:pub.10.1007/bf02671370
13 sg:pub.10.1007/s11745-006-1399-8
14 https://doi.org/10.1016/0271-5317(95)02045-4
15 https://doi.org/10.1016/j.amjcard.2004.01.038
16 https://doi.org/10.1016/j.amjcard.2005.12.023
17 https://doi.org/10.1016/j.cca.2010.01.023
18 https://doi.org/10.1016/j.chroma.2003.10.032
19 https://doi.org/10.1016/j.chroma.2005.07.104
20 https://doi.org/10.1016/j.foodchem.2005.09.069
21 https://doi.org/10.1016/j.foodchem.2010.02.059
22 https://doi.org/10.1016/j.foodchem.2010.09.103
23 https://doi.org/10.1016/j.foodchem.2010.10.107
24 https://doi.org/10.1016/j.jacc.2009.02.084
25 https://doi.org/10.1016/j.jfca.2005.12.014
26 https://doi.org/10.1016/j.jfca.2010.03.011
27 https://doi.org/10.1016/j.mvr.2010.11.008
28 https://doi.org/10.1016/j.nutres.2011.01.002
29 https://doi.org/10.1016/j.plefa.2007.10.014
30 https://doi.org/10.1016/j.urolonc.2005.03.001
31 https://doi.org/10.1016/s1096-4959(01)00506-1
32 https://doi.org/10.1021/jf051468u
33 https://doi.org/10.1139/o59-099
34 schema:datePublished 2012-07
35 schema:datePublishedReg 2012-07-01
36 schema:description When fatty acids in fish are analyzed, results in percentage form (profile analysis) are mostly reported. However, the much more useful results expressed as mg/100 g (absolute analysis) is the main information required. Absolute methods based on calibration curves are of good accuracy but with a high degree of complexity if applied to a great number of analytes. Procedures based on the sequence profile analysis-total FA determination-absolute analysis may be suitable for routine use, but suffer from a number of uncertainties that have never been really resolved. These uncertainties are mainly related to the profile analysis. In fact, most profile analyses reported in the literature disagree about the number and type of fatty acids monitored as well as about the total percentage to assign to their sum so leading to possible inaccuracies; in addition the instrumental response factor for all FAME (fatty acid methyl esters) is often considered as a constant, but this is not exactly true. In this work, a set of 24 fatty acids was selected and studied on 12 fish species in the Mediterranean area (variable in lipid content and month of sampling): in our results, and in these species, this set constitutes, on average, 90 ± 3 % of the total fatty acid content. Moreover the error derived from the assumption of a unique response factor was investigated. Two different detection techniques (GC-FID and GC-MS) together with two capillary columns (different in length and polarity) were used in order to acquire complementary data on the same sample. With the protocol here proposed absolute analyses on the 12 cited species are easily achievable by the total FA determination procedure. The accuracy of this approach is good in general, but in some cases (DHA for example) is lower than the accuracy of calibration-based methods. The differences were evaluated on a case by case basis.
37 schema:genre research_article
38 schema:inLanguage en
39 schema:isAccessibleForFree false
40 schema:isPartOf N581d06f220ab4f3dadad2258003033a6
41 N61e899b951c94181baf3e591c20c77af
42 sg:journal.1006395
43 schema:name Analysis of Fatty Acids in 12 Mediterranean Fish Species: Advantages and Limitations of a New GC-FID/GC–MS Based Technique
44 schema:pagination 741-753
45 schema:productId N26e079fa16a341cd8cd132aba2444b06
46 N31abdf756d82424ea2c878a2d28c84fe
47 N798a8b4c3ef441778f74e0e30a73e6dd
48 Na40700f3d811443bb01607946dda8786
49 Nbacae54420e54287bdb861ab6803d8a1
50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022590190
51 https://doi.org/10.1007/s11745-012-3679-9
52 schema:sdDatePublished 2019-04-10T17:34
53 schema:sdLicense https://scigraph.springernature.com/explorer/license/
54 schema:sdPublisher N7e2d2917dd1e49338afbdeed32ad27d6
55 schema:url http://link.springer.com/10.1007%2Fs11745-012-3679-9
56 sgo:license sg:explorer/license/
57 sgo:sdDataset articles
58 rdf:type schema:ScholarlyArticle
59 N0aa46bf33edb498ca07bf84c01803b95 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
60 schema:name Mediterranean Sea
61 rdf:type schema:DefinedTerm
62 N10fb0b0dc7d343d89147d316181c0de0 rdf:first sg:person.0612345573.83
63 rdf:rest Na787ff0b328a42b382f65974c094d5fd
64 N26e079fa16a341cd8cd132aba2444b06 schema:name doi
65 schema:value 10.1007/s11745-012-3679-9
66 rdf:type schema:PropertyValue
67 N2993c7544cc44d1cb173f1ba07a8ed0a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
68 schema:name Gas Chromatography-Mass Spectrometry
69 rdf:type schema:DefinedTerm
70 N31abdf756d82424ea2c878a2d28c84fe schema:name pubmed_id
71 schema:value 22644810
72 rdf:type schema:PropertyValue
73 N399cfe4a76554e1cb3fd80ebcffe1b20 rdf:first sg:person.01221523273.14
74 rdf:rest Ne0cd9b461eb142959f77d4ace77ab96f
75 N581d06f220ab4f3dadad2258003033a6 schema:volumeNumber 47
76 rdf:type schema:PublicationVolume
77 N61e899b951c94181baf3e591c20c77af schema:issueNumber 7
78 rdf:type schema:PublicationIssue
79 N7713039ed1a042eb8ab40b0271b9b914 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
80 schema:name Animals
81 rdf:type schema:DefinedTerm
82 N798a8b4c3ef441778f74e0e30a73e6dd schema:name nlm_unique_id
83 schema:value 0060450
84 rdf:type schema:PropertyValue
85 N7e2d2917dd1e49338afbdeed32ad27d6 schema:name Springer Nature - SN SciGraph project
86 rdf:type schema:Organization
87 N9ee42049e3604a0c90b456499ab423b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Fatty Acids
89 rdf:type schema:DefinedTerm
90 Na40700f3d811443bb01607946dda8786 schema:name readcube_id
91 schema:value 6b9fa375c3b31468944cadb330e79c414661155c93989ad9c605f9365fa40dd8
92 rdf:type schema:PropertyValue
93 Na66fca8b7c84485fa2a61e4315fbc574 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
94 schema:name Fatty Acids, Omega-3
95 rdf:type schema:DefinedTerm
96 Na787ff0b328a42b382f65974c094d5fd rdf:first sg:person.016536245547.14
97 rdf:rest rdf:nil
98 Nbacae54420e54287bdb861ab6803d8a1 schema:name dimensions_id
99 schema:value pub.1022590190
100 rdf:type schema:PropertyValue
101 Nc0ed0dba598749d9a55139dee4378103 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
102 schema:name Reproducibility of Results
103 rdf:type schema:DefinedTerm
104 Nd1a0b1910a66473aad466bbc72753d52 rdf:first sg:person.01153410073.37
105 rdf:rest N399cfe4a76554e1cb3fd80ebcffe1b20
106 Ne0cd9b461eb142959f77d4ace77ab96f rdf:first sg:person.015331652007.45
107 rdf:rest Nfe70646f6e6c4bf59d87434913c5bcca
108 Nf53fd6b07d1f4cab915f622635b95f90 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Fishes
110 rdf:type schema:DefinedTerm
111 Nfe70646f6e6c4bf59d87434913c5bcca rdf:first sg:person.011220771313.08
112 rdf:rest N10fb0b0dc7d343d89147d316181c0de0
113 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
114 schema:name Information and Computing Sciences
115 rdf:type schema:DefinedTerm
116 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
117 schema:name Artificial Intelligence and Image Processing
118 rdf:type schema:DefinedTerm
119 sg:journal.1006395 schema:issn 0024-4201
120 1558-9307
121 schema:name Lipids
122 rdf:type schema:Periodical
123 sg:person.011220771313.08 schema:affiliation https://www.grid.ac/institutes/grid.419539.3
124 schema:familyName Di Lena
125 schema:givenName Gabriella
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011220771313.08
127 rdf:type schema:Person
128 sg:person.01153410073.37 schema:affiliation https://www.grid.ac/institutes/grid.419539.3
129 schema:familyName Nevigato
130 schema:givenName Teresina
131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01153410073.37
132 rdf:type schema:Person
133 sg:person.01221523273.14 schema:affiliation https://www.grid.ac/institutes/grid.419539.3
134 schema:familyName Masci
135 schema:givenName Maurizio
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01221523273.14
137 rdf:type schema:Person
138 sg:person.015331652007.45 schema:affiliation https://www.grid.ac/institutes/grid.419539.3
139 schema:familyName Orban
140 schema:givenName Elena
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015331652007.45
142 rdf:type schema:Person
143 sg:person.016536245547.14 schema:affiliation https://www.grid.ac/institutes/grid.419539.3
144 schema:familyName Caproni
145 schema:givenName Roberto
146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016536245547.14
147 rdf:type schema:Person
148 sg:person.0612345573.83 schema:affiliation https://www.grid.ac/institutes/grid.419539.3
149 schema:familyName Casini
150 schema:givenName Irene
151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0612345573.83
152 rdf:type schema:Person
153 sg:pub.10.1007/bf02671025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033774647
154 https://doi.org/10.1007/bf02671025
155 rdf:type schema:CreativeWork
156 sg:pub.10.1007/bf02671370 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035882221
157 https://doi.org/10.1007/bf02671370
158 rdf:type schema:CreativeWork
159 sg:pub.10.1007/s11745-006-1399-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050696701
160 https://doi.org/10.1007/s11745-006-1399-8
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/0271-5317(95)02045-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024547028
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.amjcard.2004.01.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021729738
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.amjcard.2005.12.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040955643
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.cca.2010.01.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052816155
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.chroma.2003.10.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046132053
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.chroma.2005.07.104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052162097
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.foodchem.2005.09.069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034089833
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.foodchem.2010.02.059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033091131
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1016/j.foodchem.2010.09.103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042829225
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1016/j.foodchem.2010.10.107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003120094
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1016/j.jacc.2009.02.084 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008723008
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1016/j.jfca.2005.12.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031023572
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1016/j.jfca.2010.03.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004338653
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1016/j.mvr.2010.11.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041427215
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/j.nutres.2011.01.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036777558
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1016/j.plefa.2007.10.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031636875
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1016/j.urolonc.2005.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042731518
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1016/s1096-4959(01)00506-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007835295
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1021/jf051468u schema:sameAs https://app.dimensions.ai/details/publication/pub.1055904118
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1139/o59-099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001206449
201 rdf:type schema:CreativeWork
202 https://www.grid.ac/institutes/grid.419539.3 schema:alternateName Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione
203 schema:name National Research Institute for Food and Nutrition (INRAN), Via Ardeatina 546, 00178, Rome, Italy
204 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...