Optical path length and wavelength selection using Vis/NIR spectroscopy for olive oil's free acidity determination
Summary Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy. Results illustrated that the use of higher path length during spectra acquisition resulted in more accurate PLS models, especially when us...
Saved in:
| Published in | International journal of food science & technology Vol. 50; no. 6; pp. 1461 - 1467 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2015
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Summary
Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy. Results illustrated that the use of higher path length during spectra acquisition resulted in more accurate PLS models, especially when using solely the NIR region. The PLS model obtained with the NIR spectrum using the 10‐mm cuvette was subjected to optimisation by Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm (SPA). Both methods drastically reduced the number of spectral variables and markedly improved the performance of the PLS model, especially the SPA‐PLS model, which achieved a SEP (0.051) quite close to SEL (0.048). Interestingly, only twelve of the eighty spectral variables selected by SPA were among the 314 variables provided by MCUVE. All in all, NIRS incorporated to MCUVE‐PLS or SPA‐PLS may be applied as an alternative method for the rapid determination of olive oil's free acidity.
Scheme for selecting optical path length and wavelengths in the olive oil's free acidity determination by VisNIR spectroscopy. |
|---|---|
| AbstractList | Summary Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy. Results illustrated that the use of higher path length during spectra acquisition resulted in more accurate PLS models, especially when using solely the NIR region. The PLS model obtained with the NIR spectrum using the 10-mm cuvette was subjected to optimisation by Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm (SPA). Both methods drastically reduced the number of spectral variables and markedly improved the performance of the PLS model, especially the SPA-PLS model, which achieved a SEP (0.051) quite close to SEL (0.048). Interestingly, only twelve of the eighty spectral variables selected by SPA were among the 314 variables provided by MCUVE. All in all, NIRS incorporated to MCUVE-PLS or SPA-PLS may be applied as an alternative method for the rapid determination of olive oil's free acidity. Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy. Results illustrated that the use of higher path length during spectra acquisition resulted in more accurate PLS models, especially when using solely the NIR region. The PLS model obtained with the NIR spectrum using the 10‐mm cuvette was subjected to optimisation by Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm (SPA). Both methods drastically reduced the number of spectral variables and markedly improved the performance of the PLS model, especially the SPA‐PLS model, which achieved a SEP (0.051) quite close to SEL (0.048). Interestingly, only twelve of the eighty spectral variables selected by SPA were among the 314 variables provided by MCUVE. All in all, NIRS incorporated to MCUVE‐PLS or SPA‐PLS may be applied as an alternative method for the rapid determination of olive oil's free acidity. Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/ NIR spectroscopy. Results illustrated that the use of higher path length during spectra acquisition resulted in more accurate PLS models, especially when using solely the NIR region. The PLS model obtained with the NIR spectrum using the 10‐mm cuvette was subjected to optimisation by Monte Carlo uninformative variable elimination ( MCUVE ) and successive projections algorithm ( SPA ). Both methods drastically reduced the number of spectral variables and markedly improved the performance of the PLS model, especially the SPA ‐ PLS model, which achieved a SEP (0.051) quite close to SEL (0.048). Interestingly, only twelve of the eighty spectral variables selected by SPA were among the 314 variables provided by MCUVE . All in all, NIRS incorporated to MCUVE ‐ PLS or SPA ‐ PLS may be applied as an alternative method for the rapid determination of olive oil's free acidity. Summary Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy. Results illustrated that the use of higher path length during spectra acquisition resulted in more accurate PLS models, especially when using solely the NIR region. The PLS model obtained with the NIR spectrum using the 10‐mm cuvette was subjected to optimisation by Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm (SPA). Both methods drastically reduced the number of spectral variables and markedly improved the performance of the PLS model, especially the SPA‐PLS model, which achieved a SEP (0.051) quite close to SEL (0.048). Interestingly, only twelve of the eighty spectral variables selected by SPA were among the 314 variables provided by MCUVE. All in all, NIRS incorporated to MCUVE‐PLS or SPA‐PLS may be applied as an alternative method for the rapid determination of olive oil's free acidity. Scheme for selecting optical path length and wavelengths in the olive oil's free acidity determination by VisNIR spectroscopy. |
| Author | García Martín, Juan Francisco |
| Author_xml | – sequence: 1 givenname: Juan Francisco surname: Garcia Martin fullname: Garcia Martin, Juan Francisco |
| BookMark | eNp9kF9PFDEUxRuDiQv44ido4oPGZKD_O_MoRGCBAAHFx6bbabHrbDu2XXC_PV0WfSDG-3Jvm985ufdsg60QgwXgHUZ7uNa-n7u8h4ns0CswwVTwhgiCt8AEdRw1nBH6BmznPEcIESrZBKTLsXijBzjq8gMONtzVpkMPH_S9fX7mOpjiY4DL7MMdvPV5_2J6DfNYv1PMJo4r6GKCcfD3FkY_fMjQJWuhNr73ZQV7W2xa-KDXLrvgtdNDtm-f-w74dvTl6-FJc355PD38fN4YzhhqtGRIzjilvbCWETZjupWUzmhHTS-QbLnuTWU07mfcUEdRZ7RzmLCOc-0M3QEfN75jir-WNhe18NnYYdDBxmVWWGJEiGwFr-j7F-g8LlOo2yksWiREV7FKfdpQph6dk3VqTH6h00phpNbxq3X86in-CqMXsPHl6f6StB_-LcEbyYMf7Oo_5mp6enTzR9NsND4X-_uvRqefSkgqufp-cazag4Oz2yt5pU7oI_8rqiA |
| CitedBy_id | crossref_primary_10_1016_j_biosystemseng_2019_04_013 crossref_primary_10_1016_j_scitotenv_2024_176979 crossref_primary_10_1016_j_foodcont_2022_109266 crossref_primary_10_1016_j_foodchem_2017_02_149 crossref_primary_10_1016_j_jfoodeng_2023_111653 crossref_primary_10_1002_cem_2812 crossref_primary_10_1016_j_foodcont_2022_108897 crossref_primary_10_1016_j_talanta_2018_02_097 crossref_primary_10_3390_bios11080261 crossref_primary_10_1080_05704928_2019_1584567 crossref_primary_10_1080_10408398_2020_1779649 crossref_primary_10_1016_j_infrared_2019_05_005 crossref_primary_10_3390_foods13203241 crossref_primary_10_1016_j_fuel_2018_11_142 crossref_primary_10_1080_10408398_2016_1225666 crossref_primary_10_1111_jfpe_12496 crossref_primary_10_1007_s12161_017_0946_7 crossref_primary_10_3390_foods13233894 crossref_primary_10_1109_ACCESS_2021_3116980 crossref_primary_10_3390_pr9020316 crossref_primary_10_1016_j_saa_2019_03_113 crossref_primary_10_1002_jsfa_8746 crossref_primary_10_3390_pr8070823 crossref_primary_10_1002_jsfa_7733 crossref_primary_10_1111_1750_3841_14024 crossref_primary_10_17660_ActaHortic_2017_1152_40 crossref_primary_10_1016_j_lwt_2020_109398 crossref_primary_10_3390_foods11081092 crossref_primary_10_3390_s22082831 crossref_primary_10_1016_j_compag_2024_109101 crossref_primary_10_1016_j_foodcont_2021_108536 crossref_primary_10_3390_foods12020300 crossref_primary_10_1142_S1793545820500066 crossref_primary_10_1016_j_infrared_2016_11_006 crossref_primary_10_3390_s18040975 crossref_primary_10_1016_j_jfoodeng_2017_01_015 crossref_primary_10_1039_C7RA00634A crossref_primary_10_1016_j_foodcont_2021_108152 crossref_primary_10_1039_D3AY00436H crossref_primary_10_3390_pr7050304 |
| Cites_doi | 10.1007/BF02541084 10.1016/j.chemolab.2007.10.001 10.1016/j.snb.2004.11.103 10.1255/jnirs.263 10.1021/ac00144a725 10.1007/BF02660596 10.3989/gya.097308 10.1016/j.aca.2008.02.032 10.1016/j.foodres.2008.03.007 10.1016/S0169-7439(01)00119-8 10.1007/978-1-4614-7777-8_10 10.1021/jf4021575 10.1166/sl.2011.1385 10.1080/00401706.1969.10490666 |
| ContentType | Journal Article |
| Copyright | 2015 Institute of Food Science and Technology International Journal of Food Science and Technology © 2015 Institute of Food Science and Technology |
| Copyright_xml | – notice: 2015 Institute of Food Science and Technology – notice: International Journal of Food Science and Technology © 2015 Institute of Food Science and Technology |
| DBID | BSCLL AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SP 7SR 7ST 7T7 7TA 7TB 7U5 8BQ 8FD C1K F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 SOI 7S9 L.6 |
| DOI | 10.1111/ijfs.12790 |
| DatabaseName | Istex CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Environment Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database Environmental Sciences and Pollution Management ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts Environment Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Environmental Sciences and Pollution Management Aerospace Database Copper Technical Reference Library Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Environment Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Materials Research Database AGRICOLA CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics Diet & Clinical Nutrition |
| EISSN | 1365-2621 |
| EndPage | 1467 |
| ExternalDocumentID | 3683432641 10_1111_ijfs_12790 IJFS12790 ark_67375_WNG_8BBKVP7P_H |
| Genre | article |
| GroupedDBID | .3N .GA .Y3 05W 0R~ 10A 1OB 1OC 29J 3-9 31~ 33P 3EH 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5HH 5LA 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHBH AAHHS AAIKC AAMNW AANLZ AAONW AASGY AAXRX AAZKR ABCQN ABCUV ABEJV ABEML ABJNI ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACGFO ACGFS ACIWK ACKIV ACPOU ACPRK ACSCC ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN ADZOD AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFPM AFGKR AFPWT AFRAH AFZJQ AHBTC AHEFC AI. AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 C45 CAG COF CS3 D-E D-F DC6 DCZOG DPXWK DR2 DRFUL DROCM DRSTM DU5 EBS EJD ESX F00 F01 F04 FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ IHE IX1 J0M LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG OVD P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 R.K RIWAO RJQFR ROL RX1 SAMSI SUPJJ TEORI TOX UB1 V8K VH1 W8V W99 WBFHL WBKPD WH7 WIH WIK WOHZO WQJ WRC WXSBR WYISQ XG1 Y6R YFH YUY ZZTAW ~IA ~WT AANHP AAYCA ABXVV ACRPL ACYXJ ADNMO AFWVQ ALVPJ AAYXX ABGNP AGHNM AGQPQ AMNDL CITATION GROUPED_DOAJ 7QF 7QO 7QQ 7QR 7SC 7SE 7SP 7SR 7ST 7T7 7TA 7TB 7U5 8BQ 8FD AAMMB AEFGJ AGXDD AIDQK AIDYY C1K F28 FR3 H13 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 SOI 7S9 L.6 |
| ID | FETCH-LOGICAL-c5440-a7407b533d6ee424b4a8733b393cd60785adc740a1db5c3f309caff124955afc3 |
| IEDL.DBID | DR2 |
| ISSN | 0950-5423 |
| IngestDate | Fri Jul 11 18:22:56 EDT 2025 Wed Aug 13 07:03:20 EDT 2025 Thu Apr 24 23:11:45 EDT 2025 Tue Jul 01 02:40:43 EDT 2025 Wed Jan 22 16:52:16 EST 2025 Wed Oct 30 09:52:13 EDT 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c5440-a7407b533d6ee424b4a8733b393cd60785adc740a1db5c3f309caff124955afc3 |
| Notes | Figure S1. Selected wavelengths (squares) by the SPA method. Figure S2. Variation of root mean square error of cross-validation (dashed line) and root mean square error of prediction (continuous line) with the number of selected variables by Monte Carlo uninformative variable elimination for the free aidity of olive oil. Table S1. Criteria for the evaluation of the results. Table S2. Calibration and prediction results of free acidity by partial least squares models with 0.5-mm path-length cuvette. Table S3. Calibration and prediction results of Free acidity by partial least squares models with 2-mm path-length cuvette. Table S4. Calibration and prediction results of free acidity by partial least squares models with 5-mm path-length cuvette. Table S5. Calibration and prediction results of free acidity by partial least squares models with 10-mm path-length cuvette. istex:4C8013DD6B029B1AB2B4745380577472BBB42DE0 ark:/67375/WNG-8BBKVP7P-H ArticleID:IJFS12790 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | http://hdl.handle.net/10261/116607 |
| PQID | 1680669786 |
| PQPubID | 2026381 |
| PageCount | 7 |
| ParticipantIDs | proquest_miscellaneous_1710227865 proquest_journals_1680669786 crossref_primary_10_1111_ijfs_12790 crossref_citationtrail_10_1111_ijfs_12790 wiley_primary_10_1111_ijfs_12790_IJFS12790 istex_primary_ark_67375_WNG_8BBKVP7P_H |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | June 2015 |
| PublicationDateYYYYMMDD | 2015-06-01 |
| PublicationDate_xml | – month: 06 year: 2015 text: June 2015 |
| PublicationDecade | 2010 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | International journal of food science & technology |
| PublicationTitleAlternate | Int J Food Sci Technol |
| PublicationYear | 2015 |
| Publisher | Blackwell Publishing Ltd Wiley Subscription Services, Inc |
| Publisher_xml | – name: Blackwell Publishing Ltd – name: Wiley Subscription Services, Inc |
| References | Li, H.D., Xu, Q.S. & Liang, Y.Z. (2014). libPLS: an integrated library for partial least squares regression and discriminant analysis. PeerJ PrePrints, 2, e190v1. Beebe, K.R. & Kowalsky, B.R. (1987). An introduction to multivariate calibration and analysis. Analytical Chemistry, 59(17), 1007-1017. Cayuela, J.A., García, J.M. & Caliani, N. (2009). NIR prediction of fruit moisture, free acidity and oil content in intact olives. Grasas y Aceites, 60, 194-202. Wesley, I.J., Barnes, R.J. & McGill, A. (1995). Measurement of adulteration of olive oils by near infrared spectroscopy. Journal of the American Oil Chemists' Society, 3, 289-298. Bertran, E., Blanco, M., Coello, J., Iturriaga, H., Maspoch, S. & Monteliu, I. (2000). Near infrared spectrometry and pattern recognition as screening methods for the authentication of virgin olive oils of very close geographical origins. Journal of Near Infrared Spectroscopy, 8, 45-52. Cayuela Sánchez, J.A., Moreda, W. & García, J.M. (2013). Rapid determination of olive oil oxidative stability and its major quality parameters using Vis/NIR transmittance spectroscopy. Journal of Agricultural and Food Chemistry, 61, 8056-8062. Moyano, M.J., Meléndez, A.J., Alba, J. & Heredia, F.J. (2008). A comprehensive study on the colour of virgin olive oils and its relationship with their chlorophylls and carotenoids indexes (I): CIEXYZ non-uniform colour space. Food Research International, 41, 505-512. Cai, W., Li, Y. & Shao, X. (2008). A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems, 90, 188-194. Han, Q.-J., Wu, H.-L., Cai, C.-B., Xu, L. & Yu, R.-Q. (2008). An ensemble of Monte Carlo uninformative variable elimination for wavelength selection. Analytica Chimica Acta, 612, 121-125. Kennard, R.W. & Stone, L.A. (1969). Computer aided design of experiments. Technometrics, 11, 137-148. Hao, Y., Sun, X., Zhang, H. & Liu, Y. (2011). Application of effective wavelength selection methods to determine total acidity of navel orange. Sensor Letters, 9, 1229-1234. Jiménez Marquez, A., Molina Díaz, A. & Pascual Reguera, M.I. (2005). Using optical NIR sensor for on-line virgin olive oils characterization. Sensors and Actuators. B, Chemical, 107, 64-68. Araújo, M.C.U., Saldanha, T.C.B., Galvão, R.K.H., Yoneyama, T., Chame, H.C. & Visani, V. (2001). The Successive Projections Algorithm for variable selection in spectroscopic multicomponent. Chemometrics and Intelligent Laboratory Systems, 57, 65-73. Sato, T., Kawano, S. & Iwamoto, M. (1991). Near-infrared spectral patterns of fatty acid analysis from fats and oils. Journal of the American Oil Chemists' Society, 68, 827-883. García, A., Ramos, N. & Ballesteros, E. (2005). Estudio comparativo de distintas técnicas analíticas (espectroscopía de NIR y RMN y extracción mediante Soxhlet) para la determinación del contenido graso y de humedad en aceitunas y orujo de Jaén. Grasas y Aceites, 56, 220-227. 2014; 2 2001 1991; 68 2009; 60 2013; 61 2000; 8 2005; 107 1997 1969; 11 1996 2008; 612 2008; 41 1991 2013 2001; 57 1995; 3 2008; 90 2005; 56 1987; 59 2011; 9 e_1_2_5_15_1 e_1_2_5_14_1 Shenk J.S. (e_1_2_5_19_1) 1996 e_1_2_5_17_1 e_1_2_5_9_1 García A. (e_1_2_5_10_1) 2005; 56 Li H.D. (e_1_2_5_16_1) 2014; 2 e_1_2_5_8_1 e_1_2_5_11_1 e_1_2_5_7_1 e_1_2_5_6_1 e_1_2_5_13_1 e_1_2_5_21_1 e_1_2_5_5_1 e_1_2_5_12_1 e_1_2_5_4_1 e_1_2_5_2_1 Beebe K.R. (e_1_2_5_3_1) 1987; 59 e_1_2_5_18_1 Shenk J.S. (e_1_2_5_20_1) 2001 |
| References_xml | – reference: Moyano, M.J., Meléndez, A.J., Alba, J. & Heredia, F.J. (2008). A comprehensive study on the colour of virgin olive oils and its relationship with their chlorophylls and carotenoids indexes (I): CIEXYZ non-uniform colour space. Food Research International, 41, 505-512. – reference: Cayuela Sánchez, J.A., Moreda, W. & García, J.M. (2013). Rapid determination of olive oil oxidative stability and its major quality parameters using Vis/NIR transmittance spectroscopy. Journal of Agricultural and Food Chemistry, 61, 8056-8062. – reference: Wesley, I.J., Barnes, R.J. & McGill, A. (1995). Measurement of adulteration of olive oils by near infrared spectroscopy. Journal of the American Oil Chemists' Society, 3, 289-298. – reference: Li, H.D., Xu, Q.S. & Liang, Y.Z. (2014). libPLS: an integrated library for partial least squares regression and discriminant analysis. PeerJ PrePrints, 2, e190v1. – reference: Cai, W., Li, Y. & Shao, X. (2008). A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems, 90, 188-194. – reference: Araújo, M.C.U., Saldanha, T.C.B., Galvão, R.K.H., Yoneyama, T., Chame, H.C. & Visani, V. (2001). The Successive Projections Algorithm for variable selection in spectroscopic multicomponent. Chemometrics and Intelligent Laboratory Systems, 57, 65-73. – reference: Jiménez Marquez, A., Molina Díaz, A. & Pascual Reguera, M.I. (2005). Using optical NIR sensor for on-line virgin olive oils characterization. Sensors and Actuators. B, Chemical, 107, 64-68. – reference: Sato, T., Kawano, S. & Iwamoto, M. (1991). Near-infrared spectral patterns of fatty acid analysis from fats and oils. Journal of the American Oil Chemists' Society, 68, 827-883. – reference: García, A., Ramos, N. & Ballesteros, E. (2005). Estudio comparativo de distintas técnicas analíticas (espectroscopía de NIR y RMN y extracción mediante Soxhlet) para la determinación del contenido graso y de humedad en aceitunas y orujo de Jaén. Grasas y Aceites, 56, 220-227. – reference: Han, Q.-J., Wu, H.-L., Cai, C.-B., Xu, L. & Yu, R.-Q. (2008). An ensemble of Monte Carlo uninformative variable elimination for wavelength selection. Analytica Chimica Acta, 612, 121-125. – reference: Beebe, K.R. & Kowalsky, B.R. (1987). An introduction to multivariate calibration and analysis. Analytical Chemistry, 59(17), 1007-1017. – reference: Bertran, E., Blanco, M., Coello, J., Iturriaga, H., Maspoch, S. & Monteliu, I. (2000). Near infrared spectrometry and pattern recognition as screening methods for the authentication of virgin olive oils of very close geographical origins. Journal of Near Infrared Spectroscopy, 8, 45-52. – reference: Kennard, R.W. & Stone, L.A. (1969). Computer aided design of experiments. Technometrics, 11, 137-148. – reference: Cayuela, J.A., García, J.M. & Caliani, N. (2009). NIR prediction of fruit moisture, free acidity and oil content in intact olives. Grasas y Aceites, 60, 194-202. – reference: Hao, Y., Sun, X., Zhang, H. & Liu, Y. (2011). Application of effective wavelength selection methods to determine total acidity of navel orange. Sensor Letters, 9, 1229-1234. – volume: 107 start-page: 64 year: 2005 end-page: 68 article-title: Using optical NIR sensor for on‐line virgin olive oils characterization publication-title: Sensors and Actuators. B, Chemical – volume: 61 start-page: 8056 year: 2013 end-page: 8062 article-title: Rapid determination of olive oil oxidative stability and its major quality parameters using Vis/NIR transmittance spectroscopy publication-title: Journal of Agricultural and Food Chemistry – volume: 60 start-page: 194 year: 2009 end-page: 202 article-title: NIR prediction of fruit moisture, free acidity and oil content in intact olives publication-title: Grasas y Aceites – start-page: 419 year: 2001 end-page: 474 – volume: 8 start-page: 45 year: 2000 end-page: 52 article-title: Near infrared spectrometry and pattern recognition as screening methods for the authentication of virgin olive oils of very close geographical origins publication-title: Journal of Near Infrared Spectroscopy – start-page: 335 year: 2013 end-page: 393 – volume: 612 start-page: 121 year: 2008 end-page: 125 article-title: An ensemble of Monte Carlo uninformative variable elimination for wavelength selection publication-title: Analytica Chimica Acta – volume: 59 start-page: 1007 issue: 17 year: 1987 end-page: 1017 article-title: An introduction to multivariate calibration and analysis publication-title: Analytical Chemistry – volume: 3 start-page: 289 year: 1995 end-page: 298 article-title: Measurement of adulteration of olive oils by near infrared spectroscopy publication-title: Journal of the American Oil Chemists’ Society – volume: 57 start-page: 65 year: 2001 end-page: 73 article-title: The Successive Projections Algorithm for variable selection in spectroscopic multicomponent publication-title: Chemometrics and Intelligent Laboratory Systems – year: 1997 – volume: 9 start-page: 1229 year: 2011 end-page: 1234 article-title: Application of effective wavelength selection methods to determine total acidity of navel orange publication-title: Sensor Letters – volume: 41 start-page: 505 year: 2008 end-page: 512 article-title: A comprehensive study on the colour of virgin olive oils and its relationship with their chlorophylls and carotenoids indexes (I): CIEXYZ non‐uniform colour space publication-title: Food Research International – volume: 90 start-page: 188 year: 2008 end-page: 194 article-title: A variable selection method based on uninformative variable elimination for multivariate calibration of near‐infrared spectra publication-title: Chemometrics and Intelligent Laboratory Systems – volume: 11 start-page: 137 year: 1969 end-page: 148 article-title: Computer aided design of experiments publication-title: Technometrics – start-page: 198 year: 1996 end-page: 202 – volume: 68 start-page: 827 year: 1991 end-page: 883 article-title: Near‐infrared spectral patterns of fatty acid analysis from fats and oils publication-title: Journal of the American Oil Chemists’ Society – year: 1991 – volume: 2 start-page: e190v1 year: 2014 article-title: libPLS: an integrated library for partial least squares regression and discriminant analysis publication-title: PeerJ PrePrints – volume: 56 start-page: 220 year: 2005 end-page: 227 article-title: Estudio comparativo de distintas técnicas analíticas (espectroscopía de NIR y RMN y extracción mediante Soxhlet) para la determinación del contenido graso y de humedad en aceitunas y orujo de Jaén publication-title: Grasas y Aceites – start-page: 198 volume-title: Near‐infrared Spectroscopy: The Future Waves year: 1996 ident: e_1_2_5_19_1 – volume: 56 start-page: 220 year: 2005 ident: e_1_2_5_10_1 article-title: Estudio comparativo de distintas técnicas analíticas (espectroscopía de NIR y RMN y extracción mediante Soxhlet) para la determinación del contenido graso y de humedad en aceitunas y orujo de Jaén publication-title: Grasas y Aceites – ident: e_1_2_5_21_1 doi: 10.1007/BF02541084 – ident: e_1_2_5_5_1 doi: 10.1016/j.chemolab.2007.10.001 – ident: e_1_2_5_14_1 doi: 10.1016/j.snb.2004.11.103 – ident: e_1_2_5_4_1 doi: 10.1255/jnirs.263 – start-page: 419 volume-title: Handbook of Near‐infrared Analysis year: 2001 ident: e_1_2_5_20_1 – volume: 59 start-page: 1007 issue: 17 year: 1987 ident: e_1_2_5_3_1 article-title: An introduction to multivariate calibration and analysis publication-title: Analytical Chemistry doi: 10.1021/ac00144a725 – ident: e_1_2_5_8_1 – ident: e_1_2_5_18_1 doi: 10.1007/BF02660596 – ident: e_1_2_5_7_1 doi: 10.3989/gya.097308 – ident: e_1_2_5_12_1 doi: 10.1016/j.aca.2008.02.032 – volume: 2 start-page: e190v1 year: 2014 ident: e_1_2_5_16_1 article-title: libPLS: an integrated library for partial least squares regression and discriminant analysis publication-title: PeerJ PrePrints – ident: e_1_2_5_17_1 doi: 10.1016/j.foodres.2008.03.007 – ident: e_1_2_5_9_1 – ident: e_1_2_5_2_1 doi: 10.1016/S0169-7439(01)00119-8 – ident: e_1_2_5_11_1 doi: 10.1007/978-1-4614-7777-8_10 – ident: e_1_2_5_6_1 doi: 10.1021/jf4021575 – ident: e_1_2_5_13_1 doi: 10.1166/sl.2011.1385 – ident: e_1_2_5_15_1 doi: 10.1080/00401706.1969.10490666 |
| SSID | ssj0002374 |
| Score | 2.304511 |
| Snippet | Summary
Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy.... Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/ NIR spectroscopy. Results... Summary Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy.... Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy. Results... |
| SourceID | proquest crossref wiley istex |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1461 |
| SubjectTerms | Acidity algorithms Food science near-infrared spectroscopy NIR spectroscopy Olive oil optical path length rapid methods Spectroscopy Spectrum analysis wavelength selection wavelengths |
| Title | Optical path length and wavelength selection using Vis/NIR spectroscopy for olive oil's free acidity determination |
| URI | https://api.istex.fr/ark:/67375/WNG-8BBKVP7P-H/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fijfs.12790 https://www.proquest.com/docview/1680669786 https://www.proquest.com/docview/1710227865 |
| Volume | 50 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Zb9QwELaq8gAvHAVEaEFGoCKQsuzGdg6JFwos2yK2VemxLyjyWYWuktVmVxy_nhkn2bYIIcFLDmVsObbH_sYef0PIM24HInEmDrMkEiE3nIepY1Go0ky71Did-Jb-NI5Hx3xvIiZr5HV3Fqbhh1gtuKFm-PEaFVyq-pKSF19d3RtESYYGOzprISI6vOCOilhDwZyJfigANLTcpOjGc5H0ymx0DSv2-xWoeRmw-hlneIt86craOJqc95YL1dM_f6Nx_N-fuU1utlCUvmn6zh2yZssNErwr7IJu05YvdErHHV3_BrnenWKu75L5_syvglMMaUwxHAvcZGnoN4mhLPxr7WPsQFKK3vVn9KSoX413D6k_3YksmtXsBwXQTKspjLm0KqbPa-rm1lKpCwPmATWdsw7mco8cD98fvR2FbfyGUAvcMpYJWIsK8KSJreURV1ymCWOKZUybGLCJkEaDjBwYJTRzrJ9p6ZwPhy2k0-w-WS-r0j4gVKUObB9kwpGKAypJrWSaM9mXJh44ywPyomvHXLfk5hhjY5p3Rg7WcO5rOCBPV7KzhtLjj1LbvjusROT8HJ3gEpGfjj_k6c7Ox5OD5CAfBWSr6y95q_-QRZwClgMLPQ7Ik9Vn0FzcjpGlrZYgg-AuAhERkJe-c_ylOPnu3vCzf3r4L8Kb5AYgPNH4tm2R9cV8aR8Bilqox15b4Hq0P_kFP9oaVg |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bb9MwFLbQ9jBeGBsgMgYYgYZASmljO5dHBpR2lzCNbewtcnxBYVVSNa1g_HrOcZJuQwgJnpIoJ1bi-NjfsY-_j5AX3AxEZHXoJ1EgfK4592PLAj-PE2VjbVXk_vRhGo5O-d65OG9zc3AvTMMPsZxwQ89w_TU6OE5IX_Py4pute4MgSiBiX0VJbxdRHV-xRwWsIWFORN8XABtadlJM5Ll69sZ4tIpV--MG2LwOWd2YM1xvhFVrR1WIqSYXvcU876mfvxE5_vfn3CV3WjRK3zbNZ4PcMuUm8d4XZk53aEsZOqFpx9i_Sda6jcz1PTL7NHUT4RRVjSkqssBBlpp-l6hm4S5rJ7MDj1JMsP9Kz4r6TTo-pm6DJxJpVtNLCriZVhPodmlVTF7W1M6MoVIVGiIEqrt8HSzlPjkdfjh5N_JbCQdfCVw1lhEEjDlASh0awwOecxlHjOUsYUqHAE-E1Aps5EDnQjHL-omS1jpFbCGtYg_ISlmV5iGheWwh_EEyHJlzACaxkUxxJvtShwNruEdedT8yUy2_OcpsTLIuzsEazlwNe-T50nbasHr80WrHtYeliZxdYB5cJLIv6ccs3t3dPzuKjrKRR7a7BpO1XQAUEcYA5yBIDz3ybHkbnBdXZGRpqgXYIL4LwER45LVrHX95nWy8N_zszrb-xfgpWRudHB5kB-N0_xG5DYBPNKlu22RlPluYxwCq5vkT5zq_AB-gHVU |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED9NmwS8DBggMgYYgYZASmljOx8SL4xS2g1KNbaxFxQ5_kBhVVI1rfj46zk7SbchhARPSZRz5Nh39u_s8-8AnjDd45FRoZ9EAfeZYsyPDQ38LE6kiZWRkevp9-NweMz2T_npGrxsz8LU_BCrBTdrGW68tgY-U-aCkedfTdXpBVGCDvsGC7ux1en-4Tl5VEBrDuaEd32OqKEhJ7VxPOdlL01HG7Zlv1_CmhcRq5tyBtfhc1vZOtLkrLNcZB358zcex__9mxuw2WBR8qpWnpuwpost8Pq5XpBd0hCGTsm45evfgqvtMebqFsw_zNwyOLE5jYnNx4IXUSjyTdhcFu6xckl2sCix4fVfyElevRiPDok73mlpNMvZD4KomZRTHHRJmU-fVsTMtSZC5gr9A6LaaB37ldtwPHhz9HroNwkcfMntnrGI0F3MEFCqUGsWsIyJOKI0owmVKkRwwoWSKCN6KuOSGtpNpDDG5cPmwkh6B9aLstB3gWSxQefHUuGIjCEsibWgklHRFSrsGc08eNb2YyobdnObZGOatl6ObeHUtbAHj1eys5rT449Su04dViJifmaj4CKefhq_TeO9vYOTSTRJhx7stPqSNgMAfiKMEcyhix568Gj1Gk3X7seIQpdLlLHoLkAR7sFzpxx_qU462h98dHfb_yL8EK5M-oP03Wh8cA-uIdrjdZzbDqwv5kt9HxHVInvgDOcX1DscDQ |
| 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=Optical+path+length+and+wavelength+selection+using+Vis%2FNIR+spectroscopy+for+olive+oil%27s+free+acidity+determination&rft.jtitle=International+journal+of+food+science+%26+technology&rft.au=Garc%C3%ADa+Mart%C3%ADn%2C+Juan+Francisco&rft.date=2015-06-01&rft.issn=0950-5423&rft.eissn=1365-2621&rft.volume=50&rft.issue=6&rft.spage=1461&rft.epage=1467&rft_id=info:doi/10.1111%2Fijfs.12790&rft.externalDBID=10.1111%252Fijfs.12790&rft.externalDocID=IJFS12790 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0950-5423&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0950-5423&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0950-5423&client=summon |