Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC–MS fingerprints
•Fatty acids and sterols with chemometrics were used to authenticate camellia oil.•PCA can differentiate camellia oil using triterpene alcohols.•PLS-DA can help discriminate the specific adulterated oil types.•PLS can be successfully applied for the adulterated level prediction.•Less than 22 key mar...
Uloženo v:
| Vydáno v: | Food chemistry Ročník 352; s. 129422 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Elsevier Ltd
01.08.2021
|
| Témata: | |
| ISSN: | 0308-8146, 1873-7072, 1873-7072 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •Fatty acids and sterols with chemometrics were used to authenticate camellia oil.•PCA can differentiate camellia oil using triterpene alcohols.•PLS-DA can help discriminate the specific adulterated oil types.•PLS can be successfully applied for the adulterated level prediction.•Less than 22 key markers with VIP scores were useful for PLS optimization.
The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%–100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO. |
|---|---|
| AbstractList | The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%-100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO. •Fatty acids and sterols with chemometrics were used to authenticate camellia oil.•PCA can differentiate camellia oil using triterpene alcohols.•PLS-DA can help discriminate the specific adulterated oil types.•PLS can be successfully applied for the adulterated level prediction.•Less than 22 key markers with VIP scores were useful for PLS optimization. The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%–100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO. The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%-100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO.The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%-100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO. |
| ArticleNumber | 129422 |
| Author | Wang, Xingguo Wu, Gangcheng Jin, Qingzhe Shi, Ting |
| Author_xml | – sequence: 1 givenname: Ting surname: Shi fullname: Shi, Ting – sequence: 2 givenname: Gangcheng surname: Wu fullname: Wu, Gangcheng – sequence: 3 givenname: Qingzhe surname: Jin fullname: Jin, Qingzhe – sequence: 4 givenname: Xingguo surname: Wang fullname: Wang, Xingguo email: wangxg1002@gmail.com |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33714164$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkbFuFDEQhi0URC6BV4hc0tzhsXe9XokCdJCAFEQB1JbXHhOfdteH7Y10HQVvkDfkSdjL5QpoQjXFfP8_mv8_IydjHJGQC2ArYCBfbVY-RmdvcFhxxmEFvK04f0IWoBqxbFjDT8iCCaaWCip5Ss5y3jDGOAP1jJwK0UAFslqQX--woC0hjjR6as2AfR8MjaGnxk19wWTul1MO43e6vxcHLCnYTDuT0dF5500pO2pscJleramfSUzbFMaSqRkd3d7sSsyzVez3wO-fd5--_EU9J0-96TO-eJjn5Nvl-6_rD8vrz1cf12-vl7aSTVk6kCCRKWG5b5WAprFWVr4TXtYNx9YrbqCtnXJQ1RYrwK5Vygvbtb4R1opz8vLgu03xx4S56CFkO39sRoxT1rzmFa9baOE_UAa8VkrJGb14QKduQKfnnwaTdvoY8gy8PgA2xZwTem1DuY-1JBN6DUzvO9UbfexU7zvVh05nufxHfrzwqPDNQYhzprcBk8424GjRhTR3rl0Mj1n8AQ4sweM |
| CitedBy_id | crossref_primary_10_1016_j_jfca_2025_107949 crossref_primary_10_1016_j_foodchem_2023_135837 crossref_primary_10_1016_j_foodchem_2025_143181 crossref_primary_10_1016_j_foodcont_2023_109744 crossref_primary_10_1007_s00217_023_04373_z crossref_primary_10_1016_j_infrared_2025_106089 crossref_primary_10_1016_j_cjac_2025_100552 crossref_primary_10_3390_foods11213489 crossref_primary_10_1016_j_foodcont_2021_108565 crossref_primary_10_1016_j_lwt_2024_117069 crossref_primary_10_3390_foods12020374 crossref_primary_10_3390_separations11030068 crossref_primary_10_1016_j_jfca_2024_106690 crossref_primary_10_3390_foods14071235 crossref_primary_10_3390_plants10101984 crossref_primary_10_1093_fqsafe_fyad034 crossref_primary_10_1016_j_lwt_2024_116822 crossref_primary_10_1016_j_jfca_2024_106447 crossref_primary_10_3390_foods11081134 crossref_primary_10_3390_foods11223644 crossref_primary_10_1039_D4FO01409J crossref_primary_10_3390_pr10030503 crossref_primary_10_1016_j_saa_2022_122120 crossref_primary_10_1016_j_jfca_2024_106926 crossref_primary_10_1016_j_tifs_2025_104889 crossref_primary_10_1016_j_foodchem_2025_142930 crossref_primary_10_3390_molecules26164738 crossref_primary_10_1016_j_lwt_2024_115970 crossref_primary_10_1007_s00217_024_04486_z crossref_primary_10_1016_j_aca_2022_339891 crossref_primary_10_1016_j_talanta_2022_123733 crossref_primary_10_1016_j_saa_2024_125524 crossref_primary_10_1016_j_idairyj_2022_105536 crossref_primary_10_3390_foods11050680 crossref_primary_10_1016_j_foodcont_2022_109326 crossref_primary_10_1007_s11694_025_03491_4 crossref_primary_10_1080_00387010_2021_1986543 crossref_primary_10_1016_j_foodchem_2021_131534 crossref_primary_10_1016_j_lwt_2023_114935 crossref_primary_10_1080_10408347_2024_2407615 crossref_primary_10_1002_aocs_12796 crossref_primary_10_1016_j_heliyon_2024_e27167 crossref_primary_10_1016_j_jfp_2024_100221 crossref_primary_10_1002_fft2_395 crossref_primary_10_1016_j_jfca_2024_106432 crossref_primary_10_1016_j_lwt_2022_114293 crossref_primary_10_3390_foods14030466 crossref_primary_10_3390_foods14091624 crossref_primary_10_1007_s00217_025_04832_9 crossref_primary_10_1016_j_foodchem_2024_142501 crossref_primary_10_1007_s11694_021_01216_x crossref_primary_10_3390_metabo13121170 crossref_primary_10_3390_foods12112273 crossref_primary_10_1016_j_foodcont_2024_111033 crossref_primary_10_1016_j_talanta_2023_125443 crossref_primary_10_1134_S1061934825700364 crossref_primary_10_1016_j_infrared_2025_106034 crossref_primary_10_3390_chemosensors11010045 crossref_primary_10_3390_molecules29040915 crossref_primary_10_1111_ijfs_15561 crossref_primary_10_1016_j_foodres_2023_112544 crossref_primary_10_1016_j_foodchem_2024_142370 crossref_primary_10_1016_j_jaap_2024_106605 crossref_primary_10_3389_fphar_2024_1325283 crossref_primary_10_1016_j_jfca_2022_105094 |
| Cites_doi | 10.1002/ejlt.200501224 10.1002/jsfa.9424 10.1002/ejlt.201500463 10.1039/C4MB00414K 10.1016/j.forc.2019.100188 10.1016/j.foodchem.2017.09.061 10.1007/s11306-008-0115-5 10.1186/1471-2164-7-142 10.1016/j.foodchem.2013.08.013 10.1016/j.jfca.2007.07.012 10.1016/j.aca.2008.06.018 10.1016/j.lwt.2019.108725 10.1142/S1793545818500062 10.1080/10942912.2015.1021929 10.1002/mnfr.201800136 10.1016/j.foodchem.2014.02.016 10.1016/j.foodchem.2018.12.016 10.1016/j.foodchem.2009.01.094 10.1002/jccs.200600078 10.3390/molecules23020241 10.1016/j.talanta.2018.07.078 10.1038/s41598-018-32223-6 10.1016/j.foodchem.2012.09.136 10.1016/j.chemolab.2016.03.028 10.1016/j.foodchem.2017.02.009 10.1021/acs.jafc.9b03001 10.1016/j.chemolab.2016.11.005 10.1016/j.foodchem.2019.04.109 10.5650/jos.ess18234 10.1016/j.lwt.2019.03.085 10.1039/c2ay25373a 10.1016/j.foodchem.2018.08.128 10.1016/j.foodchem.2017.04.110 10.1016/S0021-9673(00)00355-1 10.1007/s11746-010-1564-3 10.1016/j.aca.2016.01.025 10.1016/j.foodchem.2019.02.072 10.1007/s10973-016-5606-4 10.1016/j.foodchem.2005.04.015 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier Ltd Copyright © 2021 Elsevier Ltd. All rights reserved. |
| Copyright_xml | – notice: 2021 Elsevier Ltd – notice: Copyright © 2021 Elsevier Ltd. All rights reserved. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 7S9 L.6 |
| DOI | 10.1016/j.foodchem.2021.129422 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic AGRICOLA |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics Chemistry Diet & Clinical Nutrition |
| EISSN | 1873-7072 |
| ExternalDocumentID | 33714164 10_1016_j_foodchem_2021_129422 S0308814621004283 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JM 9JN AABNK AABVA AACTN AAEDT AAEDW AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AARLI AATLK AAXUO ABFNM ABFRF ABGRD ABGSF ABJNI ABMAC ABUDA ABYKQ ACDAQ ACGFO ACGFS ACIUM ACRLP ADBBV ADECG ADEZE ADQTV ADUVX AEBSH AEFWE AEHWI AEKER AENEX AEQOU AFKWA AFTJW AFXIZ AFZHZ AGUBO AGYEJ AHHHB AIEXJ AIKHN AITUG AJOXV AJSZI ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BKOJK BLXMC CBWCG CS3 DOVZS DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FLBIZ FNPLU FYGXN G-Q GBLVA IHE J1W K-O KOM KZ1 LW9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SAB SCC SDF SDG SDP SES SPC SPCBC SSA SSK SSU SSZ T5K WH7 ~G- ~KM 29H 53G 9DU AAHBH AALCJ AAQXK AATTM AAXKI AAYJJ AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGHFR AGQPQ AGRDE AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLV HVGLF HZ~ R2- SCB SEW VH1 WUQ Y6R ~HD CGR CUY CVF ECM EIF NPM 7X8 7S9 L.6 |
| ID | FETCH-LOGICAL-c467t-d1616e083c2f983177cc64fb3f6572e9f82a195d8d145ce41eb988f3cb9f73cc3 |
| ISICitedReferencesCount | 76 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000636068300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0308-8146 1873-7072 |
| IngestDate | Sat Sep 27 19:21:01 EDT 2025 Mon Sep 29 02:05:35 EDT 2025 Wed Feb 19 02:27:48 EST 2025 Sat Nov 29 07:25:04 EST 2025 Tue Nov 18 19:37:09 EST 2025 Fri Feb 23 02:40:01 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Adulteration Fatty acid Camellia oil Chemometrics Phytosterols |
| Language | English |
| License | Copyright © 2021 Elsevier Ltd. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c467t-d1616e083c2f983177cc64fb3f6572e9f82a195d8d145ce41eb988f3cb9f73cc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 33714164 |
| PQID | 2501258886 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2524259191 proquest_miscellaneous_2501258886 pubmed_primary_33714164 crossref_citationtrail_10_1016_j_foodchem_2021_129422 crossref_primary_10_1016_j_foodchem_2021_129422 elsevier_sciencedirect_doi_10_1016_j_foodchem_2021_129422 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-08-01 2021-08-00 2021-Aug-01 20210801 |
| PublicationDateYYYYMMDD | 2021-08-01 |
| PublicationDate_xml | – month: 08 year: 2021 text: 2021-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Food chemistry |
| PublicationTitleAlternate | Food Chem |
| PublicationYear | 2021 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Aparicio, Aparicio-Ruı́z (b0015) 2000; 881 van den Berg, Hoefsloot, Westerhuis, Smilde, van der Werf (b0150) 2006; 7 Triba, Le Moyec, Amathieu, Goossens, Bouchemal, Nahon (b0145) 2015; 11 Dou, Mao, Zhang, Xie, Chen, Yu (b0040) 2018; 23 Shi, Zhu, Chen, Yan, Chen, Wu (b0135) 2018; 242 Miaw, Sena, Souza, Ruisanchez, Callao (b0100) 2018; 190 Wang, Zeng, Verardo, Contreras (b0165) 2017; 233 Ai, Bin, Zhang, Huang, Wang, Liang (b0005) 2014; 143 Galindo-Prieto, Trygg, Geladi (b0050) 2017; 160 Zeng, Endo (b0190) 2019; 68 Allen, Williams, Sigman (b0010) 2019; 16 Fang, Goh, Tay, Lau, Li (b0045) 2013; 138 Gómez-Caravaca, Maggio, Cerretani (b0060) 2016; 913 Li, Kong, Shi, Shen (b0085) 2016; 155 Su, Shih, Lin (b0140) 2014; 156 Chen, Zhu, Xie, Nie, Liu, Li (b0030) 2008; 623 Sakouhi, Absalon, Sebei, Fouquet, Boukhchina, Kallel (b0120) 2009; 116 Wang, Lee, Wang, He (b0155) 2006; 95 Zhang, Wang, Liu, Chang, Jin, Wang (b0195) 2020; 118 Gao, Liu, Jin, Wang (b0055) 2019; 279 Schwartz, Ollilainen, Piironen, Lampi (b0125) 2008; 21 Guo, Xu, Yuan, Wu, Wang (b0065) 2010; 87 Hu, Chen, Gao, Li, Du, Fu (b0075) 2019; 99 Ruiz-Aracama, Goicoechea, Guillén (b0115) 2017; 228 Shi, Zhu, Zhou, Huo, Long, Zeng (b0130) 2019; 287 Yuan, Wang, Chen, Ye, Zhou (b0185) 2015; 19 Chu, Wang, Li, Zhao, Jiang (b0035) 2018; 11 Chen, Chen, Lu (b0025) 2018; 8 Xing, Yuan, Wu, Shao, Yuan, Yan (b0180) 2019; 108 Liu, Hu, Liu, Zhang, Chen, He (b0090) 2017; 119 Xie, Ni, Su, Zhang, Zhao, Gao (b0175) 2008; 4 Hai, Wang (b0070) 2006; 108 Çelik, Asfoor, Şenol, Apak (b0020) 2019; 67 Weng, Weng, Chen (b0170) 2006; 53 Wang, Wu, Long, Hu, Cheng, Chen (b0160) 2019; 293 Ogegbo, Eyob, Parmar, Wang, Annie Bligh (b0105) 2012; 4 Pérez-Castaño, Medina-Rodríguez, Bagur-González (b0110) 2019; 274 Li, Huang, Huang, Teng, Xia, Wei (b0080) 2016; 126 Lou-Bonafonte, Martínez-Beamonte, Sanclemente, Surra, Herrera-Marcos, Sanchez-Marco (b0095) 2018; 62 Hai (10.1016/j.foodchem.2021.129422_b0070) 2006; 108 Dou (10.1016/j.foodchem.2021.129422_b0040) 2018; 23 Ogegbo (10.1016/j.foodchem.2021.129422_b0105) 2012; 4 Çelik (10.1016/j.foodchem.2021.129422_b0020) 2019; 67 van den Berg (10.1016/j.foodchem.2021.129422_b0150) 2006; 7 Wang (10.1016/j.foodchem.2021.129422_b0160) 2019; 293 Hu (10.1016/j.foodchem.2021.129422_b0075) 2019; 99 Zhang (10.1016/j.foodchem.2021.129422_b0195) 2020; 118 Aparicio (10.1016/j.foodchem.2021.129422_b0015) 2000; 881 Sakouhi (10.1016/j.foodchem.2021.129422_b0120) 2009; 116 Schwartz (10.1016/j.foodchem.2021.129422_b0125) 2008; 21 Fang (10.1016/j.foodchem.2021.129422_b0045) 2013; 138 Lou-Bonafonte (10.1016/j.foodchem.2021.129422_b0095) 2018; 62 Yuan (10.1016/j.foodchem.2021.129422_b0185) 2015; 19 Zeng (10.1016/j.foodchem.2021.129422_b0190) 2019; 68 Ruiz-Aracama (10.1016/j.foodchem.2021.129422_b0115) 2017; 228 Su (10.1016/j.foodchem.2021.129422_b0140) 2014; 156 Galindo-Prieto (10.1016/j.foodchem.2021.129422_b0050) 2017; 160 Allen (10.1016/j.foodchem.2021.129422_b0010) 2019; 16 Wang (10.1016/j.foodchem.2021.129422_b0155) 2006; 95 Li (10.1016/j.foodchem.2021.129422_b0080) 2016; 126 Li (10.1016/j.foodchem.2021.129422_b0085) 2016; 155 Shi (10.1016/j.foodchem.2021.129422_b0130) 2019; 287 Wang (10.1016/j.foodchem.2021.129422_b0165) 2017; 233 Guo (10.1016/j.foodchem.2021.129422_b0065) 2010; 87 Xie (10.1016/j.foodchem.2021.129422_b0175) 2008; 4 Xing (10.1016/j.foodchem.2021.129422_b0180) 2019; 108 Chen (10.1016/j.foodchem.2021.129422_b0030) 2008; 623 Miaw (10.1016/j.foodchem.2021.129422_b0100) 2018; 190 Shi (10.1016/j.foodchem.2021.129422_b0135) 2018; 242 Weng (10.1016/j.foodchem.2021.129422_b0170) 2006; 53 Ai (10.1016/j.foodchem.2021.129422_b0005) 2014; 143 Gao (10.1016/j.foodchem.2021.129422_b0055) 2019; 279 Triba (10.1016/j.foodchem.2021.129422_b0145) 2015; 11 Pérez-Castaño (10.1016/j.foodchem.2021.129422_b0110) 2019; 274 Chu (10.1016/j.foodchem.2021.129422_b0035) 2018; 11 Chen (10.1016/j.foodchem.2021.129422_b0025) 2018; 8 Gómez-Caravaca (10.1016/j.foodchem.2021.129422_b0060) 2016; 913 Liu (10.1016/j.foodchem.2021.129422_b0090) 2017; 119 |
| References_xml | – volume: 8 start-page: 13784 year: 2018 ident: b0025 article-title: A novel method for detection of camellia oil adulteration based on time-resolved emission fluorescence publication-title: Scientific Reports – volume: 118 start-page: 108725 year: 2020 ident: b0195 article-title: Chemical characterization of fourteen kinds of novel edible oils: A comparative study using chemometrics publication-title: LWT - Food Science and Technology – volume: 21 start-page: 152 year: 2008 end-page: 161 ident: b0125 article-title: Tocopherol, tocotrienol and plant sterol contents of vegetable oils and industrial fats publication-title: Journal of Food Composition and Analysis – volume: 108 start-page: 437 year: 2019 end-page: 445 ident: b0180 article-title: Chemometric classification and quantification of sesame oil adulterated with other vegetable oils based on fatty acids composition by gas chromatography publication-title: LWT - Food Science and Technology – volume: 7 start-page: 142 year: 2006 ident: b0150 article-title: Centering, scaling, and transformations: Improving the biological information content of metabolomics data publication-title: BMC Genomics – volume: 623 start-page: 146 year: 2008 end-page: 156 ident: b0030 article-title: Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographic fingerprints and combined chemometrics methods publication-title: Analytica Chimica Acta – volume: 913 start-page: 1 year: 2016 end-page: 21 ident: b0060 article-title: Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil publication-title: A review. – volume: 293 start-page: 348 year: 2019 end-page: 357 ident: b0160 article-title: Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics publication-title: Food Chemistry – volume: 881 start-page: 93 year: 2000 end-page: 104 ident: b0015 article-title: Authentication of vegetable oils by chromatographic techniques publication-title: Journal of Chromatography A – volume: 19 start-page: 300 year: 2015 end-page: 313 ident: b0185 article-title: Identification and Detection of AdulteratedCamellia OleiferaAbel. Oils by Near Infrared Transmittance Spectroscopy publication-title: International Journal of Food Properties – volume: 53 start-page: 597 year: 2006 end-page: 603 ident: b0170 article-title: Authentication of Camellia oleifera Abel Oil by Near Infrared Fourier Transform Raman Spectroscopy publication-title: Journal of the Chinese Chemical Society – volume: 279 start-page: 279 year: 2019 end-page: 287 ident: b0055 article-title: Comparative study of chemical compositions and antioxidant capacities of oils obtained from two species of walnut: Juglans regia and Juglans sigillata publication-title: Food Chemistry – volume: 23 start-page: 241 year: 2018 ident: b0040 article-title: Multispecies Adulteration Detection of Camellia Oil by Chemical Markers publication-title: Molecules – volume: 62 start-page: 1800136 year: 2018 ident: b0095 article-title: Current Insights Into the Biological Action of Squalene publication-title: Molecular Nutrition & Food Research – volume: 4 start-page: 2522 year: 2012 ident: b0105 article-title: Metabolomics of four TCM herbal products: Application of HPTLC analysis publication-title: Analytical Methods – volume: 242 start-page: 308 year: 2018 end-page: 315 ident: b0135 article-title: H NMR combined with chemometrics for the rapid detection of adulteration in camellia oils publication-title: Food Chemistry – volume: 87 start-page: 839 year: 2010 end-page: 848 ident: b0065 article-title: Characterization and Authentication of Significant Chinese Edible Oilseed Oils by Stable Carbon Isotope Analysis publication-title: Journal of the American Oil Chemists' Society – volume: 126 start-page: 1735 year: 2016 end-page: 1746 ident: b0080 article-title: Comparison of GC and DSC monitoring the adulteration of camellia oil with selected vegetable oils publication-title: Journal of Thermal Analysis and Calorimetry – volume: 287 start-page: 46 year: 2019 end-page: 54 ident: b0130 article-title: H NMR combined with PLS for the rapid determination of squalene and sterols in vegetable oils publication-title: Food Chemistry – volume: 119 start-page: 1500463 year: 2017 ident: b0090 article-title: Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometry fingerprints and chemometric models publication-title: European Journal of Lipid Science and Technology – volume: 95 start-page: 529 year: 2006 end-page: 536 ident: b0155 article-title: Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR publication-title: Food Chemistry – volume: 233 start-page: 302 year: 2017 end-page: 310 ident: b0165 article-title: Fatty acid and sterol composition of tea seed oils: Their comparison by the “FancyTiles” approach publication-title: Food Chemistry – volume: 16 start-page: 100188 year: 2019 ident: b0010 article-title: Application of Likelihood Ratios and Optimal Decision Thresholds in Fire Debris Analysis Based on a Partial Least Squares Discriminant Analysis (PLS-DA) Model publication-title: Forensic Chemistry – volume: 11 start-page: 1850006 year: 2018 ident: b0035 article-title: Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions publication-title: Journal of Innovative Optical Health Sciences – volume: 274 start-page: 518 year: 2019 end-page: 525 ident: b0110 article-title: Discrimination and classification of extra virgin olive oil using a chemometric approach based on TMS-4,4'-desmetylsterols GC(FID) fingerprints of edible vegetable oils publication-title: Food Chemistry – volume: 228 start-page: 301 year: 2017 end-page: 314 ident: b0115 article-title: Direct study of minor extra-virgin olive oil components without any sample modification. publication-title: Food Chemistry – volume: 116 start-page: 345 year: 2009 end-page: 350 ident: b0120 article-title: Gas chromatographic–mass spectrometric characterisation of triterpene alcohols and monomethylsterols in developing Olea europaea L. fruits publication-title: Food Chemistry – volume: 68 start-page: 649 year: 2019 end-page: 658 ident: b0190 article-title: Lipid Characteristics of Camellia Seed Oil publication-title: Journal of Oleo Science – volume: 4 start-page: 248 year: 2008 end-page: 260 ident: b0175 article-title: Application of ultra-performance LC-TOF MS metabolite profiling techniques to the analysis of medicinal Panax herbs publication-title: Metabolomics – volume: 11 start-page: 13 year: 2015 end-page: 19 ident: b0145 article-title: PLS/OPLS models in metabolomics: The impact of permutation of dataset rows on the K-fold cross-validation quality parameters publication-title: Molecular Biosystems – volume: 138 start-page: 1461 year: 2013 end-page: 1469 ident: b0045 article-title: Characterization of oils and fats by publication-title: Food Chemistry – volume: 156 start-page: 369 year: 2014 end-page: 373 ident: b0140 article-title: Chemical composition of seed oils in native Taiwanese Camellia species publication-title: Food Chemistry – volume: 67 start-page: 8279 year: 2019 end-page: 8289 ident: b0020 article-title: Screening Method for Argan Oil Adulteration with Vegetable Oils: An Online HPLC Assay with Postcolumn Detection Utilizing Chemometric Multidata Analysis publication-title: Journal of Agricultural and Food Chemistry – volume: 155 start-page: 145 year: 2016 end-page: 150 ident: b0085 article-title: A combination of chemometrics methods and GC–MS for the classification of edible vegetable oils publication-title: Chemometrics and Intelligent Laboratory Systems – volume: 143 start-page: 472 year: 2014 end-page: 478 ident: b0005 article-title: Application of random forests to select premium quality vegetable oils by their fatty acid composition publication-title: Food Chemistry – volume: 108 start-page: 116 year: 2006 end-page: 124 ident: b0070 article-title: Detection of adulteration in camellia seed oil and sesame oil using an electronic nose publication-title: European Journal of Lipid Science and Technology – volume: 99 start-page: 2285 year: 2019 end-page: 2291 ident: b0075 article-title: Fusion of near-infrared and fluorescence spectroscopy for untargeted fraud detection ofChinese tea seed oil using chemometric methods publication-title: Journal of the Science of Food and Agriculture – volume: 190 start-page: 55 year: 2018 end-page: 61 ident: b0100 article-title: Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars publication-title: Talanta – volume: 160 start-page: 110 year: 2017 end-page: 124 ident: b0050 article-title: A new approach for variable influence on projection (VIP) in O2PLS models publication-title: Chemometrics & Intelligent Laboratory Systems – volume: 108 start-page: 116 issue: 2 year: 2006 ident: 10.1016/j.foodchem.2021.129422_b0070 article-title: Detection of adulteration in camellia seed oil and sesame oil using an electronic nose publication-title: European Journal of Lipid Science and Technology doi: 10.1002/ejlt.200501224 – volume: 99 start-page: 2285 issue: 5 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0075 article-title: Fusion of near-infrared and fluorescence spectroscopy for untargeted fraud detection ofChinese tea seed oil using chemometric methods publication-title: Journal of the Science of Food and Agriculture doi: 10.1002/jsfa.9424 – volume: 119 start-page: 1500463 issue: 3 year: 2017 ident: 10.1016/j.foodchem.2021.129422_b0090 article-title: Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometry fingerprints and chemometric models publication-title: European Journal of Lipid Science and Technology doi: 10.1002/ejlt.201500463 – volume: 11 start-page: 13 issue: 1 year: 2015 ident: 10.1016/j.foodchem.2021.129422_b0145 article-title: PLS/OPLS models in metabolomics: The impact of permutation of dataset rows on the K-fold cross-validation quality parameters publication-title: Molecular Biosystems doi: 10.1039/C4MB00414K – volume: 16 start-page: 100188 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0010 article-title: Application of Likelihood Ratios and Optimal Decision Thresholds in Fire Debris Analysis Based on a Partial Least Squares Discriminant Analysis (PLS-DA) Model publication-title: Forensic Chemistry doi: 10.1016/j.forc.2019.100188 – volume: 242 start-page: 308 year: 2018 ident: 10.1016/j.foodchem.2021.129422_b0135 article-title: 1H NMR combined with chemometrics for the rapid detection of adulteration in camellia oils publication-title: Food Chemistry doi: 10.1016/j.foodchem.2017.09.061 – volume: 4 start-page: 248 issue: 3 year: 2008 ident: 10.1016/j.foodchem.2021.129422_b0175 article-title: Application of ultra-performance LC-TOF MS metabolite profiling techniques to the analysis of medicinal Panax herbs publication-title: Metabolomics doi: 10.1007/s11306-008-0115-5 – volume: 7 start-page: 142 issue: 1 year: 2006 ident: 10.1016/j.foodchem.2021.129422_b0150 article-title: Centering, scaling, and transformations: Improving the biological information content of metabolomics data publication-title: BMC Genomics doi: 10.1186/1471-2164-7-142 – volume: 143 start-page: 472 year: 2014 ident: 10.1016/j.foodchem.2021.129422_b0005 article-title: Application of random forests to select premium quality vegetable oils by their fatty acid composition publication-title: Food Chemistry doi: 10.1016/j.foodchem.2013.08.013 – volume: 21 start-page: 152 issue: 2 year: 2008 ident: 10.1016/j.foodchem.2021.129422_b0125 article-title: Tocopherol, tocotrienol and plant sterol contents of vegetable oils and industrial fats publication-title: Journal of Food Composition and Analysis doi: 10.1016/j.jfca.2007.07.012 – volume: 623 start-page: 146 issue: 2 year: 2008 ident: 10.1016/j.foodchem.2021.129422_b0030 article-title: Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographic fingerprints and combined chemometrics methods publication-title: Analytica Chimica Acta doi: 10.1016/j.aca.2008.06.018 – volume: 118 start-page: 108725 year: 2020 ident: 10.1016/j.foodchem.2021.129422_b0195 article-title: Chemical characterization of fourteen kinds of novel edible oils: A comparative study using chemometrics publication-title: LWT - Food Science and Technology doi: 10.1016/j.lwt.2019.108725 – volume: 11 start-page: 1850006 issue: 02 year: 2018 ident: 10.1016/j.foodchem.2021.129422_b0035 article-title: Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions publication-title: Journal of Innovative Optical Health Sciences doi: 10.1142/S1793545818500062 – volume: 19 start-page: 300 issue: 2 year: 2015 ident: 10.1016/j.foodchem.2021.129422_b0185 article-title: Identification and Detection of AdulteratedCamellia OleiferaAbel. Oils by Near Infrared Transmittance Spectroscopy publication-title: International Journal of Food Properties doi: 10.1080/10942912.2015.1021929 – volume: 62 start-page: 1800136 issue: 15 year: 2018 ident: 10.1016/j.foodchem.2021.129422_b0095 article-title: Current Insights Into the Biological Action of Squalene publication-title: Molecular Nutrition & Food Research doi: 10.1002/mnfr.201800136 – volume: 156 start-page: 369 issue: 3 year: 2014 ident: 10.1016/j.foodchem.2021.129422_b0140 article-title: Chemical composition of seed oils in native Taiwanese Camellia species publication-title: Food Chemistry doi: 10.1016/j.foodchem.2014.02.016 – volume: 279 start-page: 279 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0055 article-title: Comparative study of chemical compositions and antioxidant capacities of oils obtained from two species of walnut: Juglans regia and Juglans sigillata publication-title: Food Chemistry doi: 10.1016/j.foodchem.2018.12.016 – volume: 116 start-page: 345 issue: 1 year: 2009 ident: 10.1016/j.foodchem.2021.129422_b0120 article-title: Gas chromatographic–mass spectrometric characterisation of triterpene alcohols and monomethylsterols in developing Olea europaea L. fruits publication-title: Food Chemistry doi: 10.1016/j.foodchem.2009.01.094 – volume: 53 start-page: 597 issue: 3 year: 2006 ident: 10.1016/j.foodchem.2021.129422_b0170 article-title: Authentication of Camellia oleifera Abel Oil by Near Infrared Fourier Transform Raman Spectroscopy publication-title: Journal of the Chinese Chemical Society doi: 10.1002/jccs.200600078 – volume: 23 start-page: 241 issue: 2 year: 2018 ident: 10.1016/j.foodchem.2021.129422_b0040 article-title: Multispecies Adulteration Detection of Camellia Oil by Chemical Markers publication-title: Molecules doi: 10.3390/molecules23020241 – volume: 190 start-page: 55 year: 2018 ident: 10.1016/j.foodchem.2021.129422_b0100 article-title: Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars publication-title: Talanta doi: 10.1016/j.talanta.2018.07.078 – volume: 8 start-page: 13784 issue: 1 year: 2018 ident: 10.1016/j.foodchem.2021.129422_b0025 article-title: A novel method for detection of camellia oil adulteration based on time-resolved emission fluorescence publication-title: Scientific Reports doi: 10.1038/s41598-018-32223-6 – volume: 138 start-page: 1461 issue: 2-3 year: 2013 ident: 10.1016/j.foodchem.2021.129422_b0045 article-title: Characterization of oils and fats by 1H NMR and GC/MS fingerprinting: Classification, prediction and detection of adulteration publication-title: Food Chemistry doi: 10.1016/j.foodchem.2012.09.136 – volume: 155 start-page: 145 year: 2016 ident: 10.1016/j.foodchem.2021.129422_b0085 article-title: A combination of chemometrics methods and GC–MS for the classification of edible vegetable oils publication-title: Chemometrics and Intelligent Laboratory Systems doi: 10.1016/j.chemolab.2016.03.028 – volume: 228 start-page: 301 year: 2017 ident: 10.1016/j.foodchem.2021.129422_b0115 article-title: Direct study of minor extra-virgin olive oil components without any sample modification. 1H NMR multisupression experiment: A powerful tool publication-title: Food Chemistry doi: 10.1016/j.foodchem.2017.02.009 – volume: 67 start-page: 8279 issue: 29 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0020 article-title: Screening Method for Argan Oil Adulteration with Vegetable Oils: An Online HPLC Assay with Postcolumn Detection Utilizing Chemometric Multidata Analysis publication-title: Journal of Agricultural and Food Chemistry doi: 10.1021/acs.jafc.9b03001 – volume: 160 start-page: 110 year: 2017 ident: 10.1016/j.foodchem.2021.129422_b0050 article-title: A new approach for variable influence on projection (VIP) in O2PLS models publication-title: Chemometrics & Intelligent Laboratory Systems doi: 10.1016/j.chemolab.2016.11.005 – volume: 293 start-page: 348 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0160 article-title: Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics publication-title: Food Chemistry doi: 10.1016/j.foodchem.2019.04.109 – volume: 68 start-page: 649 issue: 7 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0190 article-title: Lipid Characteristics of Camellia Seed Oil publication-title: Journal of Oleo Science doi: 10.5650/jos.ess18234 – volume: 108 start-page: 437 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0180 article-title: Chemometric classification and quantification of sesame oil adulterated with other vegetable oils based on fatty acids composition by gas chromatography publication-title: LWT - Food Science and Technology doi: 10.1016/j.lwt.2019.03.085 – volume: 4 start-page: 2522 issue: 8 year: 2012 ident: 10.1016/j.foodchem.2021.129422_b0105 article-title: Metabolomics of four TCM herbal products: Application of HPTLC analysis publication-title: Analytical Methods doi: 10.1039/c2ay25373a – volume: 274 start-page: 518 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0110 article-title: Discrimination and classification of extra virgin olive oil using a chemometric approach based on TMS-4,4'-desmetylsterols GC(FID) fingerprints of edible vegetable oils publication-title: Food Chemistry doi: 10.1016/j.foodchem.2018.08.128 – volume: 233 start-page: 302 year: 2017 ident: 10.1016/j.foodchem.2021.129422_b0165 article-title: Fatty acid and sterol composition of tea seed oils: Their comparison by the “FancyTiles” approach publication-title: Food Chemistry doi: 10.1016/j.foodchem.2017.04.110 – volume: 881 start-page: 93 issue: 1-2 year: 2000 ident: 10.1016/j.foodchem.2021.129422_b0015 article-title: Authentication of vegetable oils by chromatographic techniques publication-title: Journal of Chromatography A doi: 10.1016/S0021-9673(00)00355-1 – volume: 87 start-page: 839 issue: 8 year: 2010 ident: 10.1016/j.foodchem.2021.129422_b0065 article-title: Characterization and Authentication of Significant Chinese Edible Oilseed Oils by Stable Carbon Isotope Analysis publication-title: Journal of the American Oil Chemists' Society doi: 10.1007/s11746-010-1564-3 – volume: 913 start-page: 1 year: 2016 ident: 10.1016/j.foodchem.2021.129422_b0060 article-title: Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil publication-title: A review. Analytica Chimica Acta doi: 10.1016/j.aca.2016.01.025 – volume: 287 start-page: 46 year: 2019 ident: 10.1016/j.foodchem.2021.129422_b0130 article-title: 1H NMR combined with PLS for the rapid determination of squalene and sterols in vegetable oils publication-title: Food Chemistry doi: 10.1016/j.foodchem.2019.02.072 – volume: 126 start-page: 1735 issue: 3 year: 2016 ident: 10.1016/j.foodchem.2021.129422_b0080 article-title: Comparison of GC and DSC monitoring the adulteration of camellia oil with selected vegetable oils publication-title: Journal of Thermal Analysis and Calorimetry doi: 10.1007/s10973-016-5606-4 – volume: 95 start-page: 529 issue: 3 year: 2006 ident: 10.1016/j.foodchem.2021.129422_b0155 article-title: Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR publication-title: Food Chemistry doi: 10.1016/j.foodchem.2005.04.015 |
| SSID | ssj0002018 |
| Score | 2.6194863 |
| Snippet | •Fatty acids and sterols with chemometrics were used to authenticate camellia oil.•PCA can differentiate camellia oil using triterpene alcohols.•PLS-DA can... The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO),... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 129422 |
| SubjectTerms | accuracy adulterated products Adulteration calcium oxide Camellia Camellia - chemistry Camellia oil Chemometrics corn oil detection Discriminant Analysis Fatty acid fatty acids Fatty Acids - analysis food chemistry Food Contamination - analysis Fraud - prevention & control Gas Chromatography-Mass Spectrometry Informatics Least-Squares Analysis olive oil peanut oil Phytosterols Phytosterols - analysis Plant Oils - chemistry prediction Principal Component Analysis rapeseed oil rice bran oil rice oil sesame oil soybean oil squalene sunflower oil triterpenoids vegetables |
| Title | Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC–MS fingerprints |
| URI | https://dx.doi.org/10.1016/j.foodchem.2021.129422 https://www.ncbi.nlm.nih.gov/pubmed/33714164 https://www.proquest.com/docview/2501258886 https://www.proquest.com/docview/2524259191 |
| Volume | 352 |
| WOSCitedRecordID | wos000636068300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection - Elsevier customDbUrl: eissn: 1873-7072 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002018 issn: 0308-8146 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLa6DQleEAwY5TIZCfFSpTTOxfbj1F1g0ipQh9S3KHHsrVObTG06TXvaA_9g_5BfwnEcpykwBkK8RJVzXDv6vsTnHPucg9BbUBEIZ6F0CFOh49M4cHiY-E6YhAmJEz_wiSk2QQcDNhrxT63WjY2FuZjQLGOXl_z8v0INbQC2Dp39C7jrP4UG-A2gwxVgh-sfAb8rCymsHijiqc64GXfy8cTk2pAV5AsTbnsqp_lUV9US845e0VK9e6DiAnTzWIzTeeeg31Gl6097AAuT0RmgKXRwyCyfaAF7YMI7Gq7INhXffZ09WdjqcrVfpywpDISp1k-9OixKV32cnYD0svnQ5Dr4DJJXp8v9pMrbPYLmk0XedGEQtz5AV_nVbGzN8iBTGc8FUrWDUprPM6OeQ3t05fvtmRS4P60Fxi1x1lXwgPr5unroLug3vomE_iHP9lAPqMcjbmlIemtog9CAw6dyY-fj3uiwXuBBZ2Jmc8pMsBF4_uvRbtN5brNpSt3m-BF6WBkleMeQ6TFqyWwT3e9btDZRe3csC_wOV1lkJ3hgiziAnI1tnz9BX2vy4VxhSz4M5MNN8uGSfLhJPlySD8O9kny4JB8-6OMmoTCQDzfJBwLfrm-OhitST9GX_b3j_genqvPhCFimCycFqyOUYAsIojgDhZYKEfoq8VQYUCK5YiR2eZCy1PUDIX1XJpwx5YmEK-oJ4T1D61meyecIg7WfJkyb6QmoppzHvMfTNCAg6qW9hLRRYJGIRJUEX9dimUT2tONZZBGMNIKRQbCN3tf9zk0amDt7cAt0VCmzRkmNgJ939n1jmREB0HoLL85kvphHYLCARcIYC38no90I3OVuG20ZWtVz9nSCTjf0X_zD7F6iB8uX-BVaL2YL-RrdExfFeD7bRmt0xLarV-Y7cDvrYg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detection+of+camellia+oil+adulteration+using+chemometrics+based+on+fatty+acids+GC+fingerprints+and+phytosterols+GC%E2%80%93MS+fingerprints&rft.jtitle=Food+chemistry&rft.au=Shi%2C+Ting&rft.au=Wu%2C+Gangcheng&rft.au=Jin%2C+Qingzhe&rft.au=Wang%2C+Xingguo&rft.date=2021-08-01&rft.pub=Elsevier+Ltd&rft.issn=0308-8146&rft.eissn=1873-7072&rft.volume=352&rft_id=info:doi/10.1016%2Fj.foodchem.2021.129422&rft.externalDocID=S0308814621004283 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0308-8146&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0308-8146&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0308-8146&client=summon |