Grasas y Aceites, Vol 64, No 1 (2013)

Control of quality and silo storage of sunflower seeds using near infrared technology


https://doi.org/10.3989/gya.096312

I. González-Martín
Departamento de Química Analítica, Nutrición y Bromatología, Facultad de Ciencias Químicas. Universidad de Salamanca, Spain

V. Villaescusa-García
Departamento de Química Analítica, Nutrición y Bromatología, Facultad de Ciencias Químicas. Universidad de Salamanca - Compañía Española Comercializadora de Oleaginosas, S.A., Spain

F. López-González
Compañía Española Comercializadora de Oleaginosas, S.A., Spain

C. Oiz-Jiménez
Compañía Española Comercializadora de Oleaginosas, S.A., Spain

I. A. Lobos-Ortega
Departamento de Química Analítica, Nutrición y Bromatología, Facultad de Ciencias Químicas. Universidad de Salamanca, Spain

B. Gordillo
Lab. Color y Calidad de Alimentos, Universidad de Sevilla, Spain

J. M. Hernández-Hierro
Lab. Color y Calidad de Alimentos, Universidad de Sevilla, Spain

Abstract


This work assesses the application of near infrared spectroscopy technology for the quality control of sunflower seeds direct from farmers and from a storage silo. The results show that the analytical method employing near infrared spectroscopy can be used as a rapid and non-destructive tool for the determination of moisture, fat and high/low oleic acid contents in samples of sunflower seeds. The ranges obtained were comparable to those reported for classic chemical methods, and were between 4.6-21.4% for moisture; 38.4-49.6% for fat, and 60.0-93.1% for oleic acid expressed as percentage of total fatty acids. A stepwise discriminant analysis was performed to determine the most useful wavelengths for classifying sunflower seeds in terms of their (high/low) oleic acid composition. The discriminant model allows the classification of sunflower seeds with high or low oleic acid contents, with a prediction rate of 90.5% for internal validation and of 89.4% for cross-validation.

Keywords


Near infrared spectroscopy; Oleic acid; Quality control; Sunflower seeds

Full Text:


PDF

References


AOAC (1990). Official Methods of Analysis, 15th ed. Association Official Analytical Chemists, Arlington, VA.

Cantamutto M, Poverene, M. 2007. Genetically modified sunflower release: Opportunities and risks. Field Crop Res. 101, 133-144. http://dx.doi.org/10.1016/j.fcr.2006.11.007

Cantarelli MA, Funes IG, Marchevsky EJ, Camiña JM. 2009. Determination of oleic acid in sunflower seeds by infrared spectroscopy and multivariate calibration method. Talanta. 80, 489-492. http://dx.doi.org/10.1016/j.talanta.2009.07.004 PMid:19836509

Christensen CM. 1982. Storage of Cereal Grains and their Products. AACC, St. Paul, Minnesota, USA.

Codex alimentarius. 1999. Norma del CODEX para aceites vegetales especificados (Codex STAN 210- 1999). Revisión 2011.

Fassio A, Cozzolino D. 2004. Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy. Ind. Crop Prod. 20, 321-329. http://dx.doi.org/10.1016/j.indcrop.2003.11.004

Flagela Z, Rotunno T, Tarantino E, Di Caterina R, De Caro A. 2002. Eur. J. Agr. 17, 221-230. http://dx.doi.org/10.1016/S1161-0301(02)00012-6

Frankel EN.1998. In Lipid oxidation, The Oily Press Ltd. Scotland Dundee, U.K.

Hajimahmoodi MY, Vander Heyden Y, Sadeghi NB, Jannat B, Oveisi MR, Shahbazian S. 2005. Gas-chromatographic fatty-acid fingerprints and partial least squares modeling as a basis for the simultaneous determination of edible oil mixtures. Talanta 66, 1108-1116. http://dx.doi.org/10.1016/j.talanta.2005.01.011 PMid:18970097

ISO. International Organization for Standardization (1978). International Standard ISO 5509.

Jurado-Expósito M, López-Granados F, Atenciano S, García-Torres L, González-Andújar JL. (2003). Discrimination of weed seedlings, wheat (Triticum aestivum) stubble and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS). Crop Prot. 22,1177-1180. http://dx.doi.org/10.1016/S0261-2194(03)00159-5

Kent NL. 1975. Technology of Cereals Pergamon Press UK, USA.

Le Dréau Y, Dupuy N, Artaud J, Ollivier D, Kister J. 2009. Infrared study of aging of edible oils by oxidative spectroscopic index and MCR-ALS chemometric method. Talanta, 77, 1748-1756. http://dx.doi.org/10.1016/j.talanta.2008.10.012 PMid:19159793

López-Feria S, Cárdenas S, García-Mesa JA, Valcárcel M. 2008. Classification of extra virgin olive oils according to the protected designation of origin, olive variety and geographical origin. Talanta, 75, 937-943. http://dx.doi.org/10.1016/j.talanta.2007.12.033 PMid:18585166

Maggio RR, Kaufman TS, Carlo MD, Cerretani L, Bendini A, Cichelli A, Compagnone D. 2009. Monitoring of fatty acid composition in virgin olive oil by Fourier transformed infrared spectroscopy coupled with partial least squares. Food Chem. 114, 1549-1554. http://dx.doi.org/10.1016/j.foodchem.2008.11.029

Massart DL, Vandeginste BGM, Buydens LMC, De Jong SPJ, Lewi PJ, Smeyers-Verbeke S. 1998. Handbook of Chemometrics and Qualimetrics: Part B, Elsevier, The Netherlands.

Moschne CR, Biskupek-Korell B. 2006. Estimating the content of free fatty acids in high-oleic sunflower seeds by near-infrared spectroscopy. Eur. J. Lipid Sci. Tech. 108, 606-613. http://dx.doi.org/10.1002/ejlt.200600032

Mosser B. 2008. Energy Fuel 22, 4301. http://dx.doi.org/10.1021/ef800588x

Pereyra-Irujo GA, Izquierdo NG, Covi M, Nolasco SM, Quiroz F, Luis AN, Aguirrezábal LAN. 2009. Variability in sunflower oil quality for biodiesel production: A simulation study. Biomass Bioenerg. 3, 459-468. http://dx.doi.org/10.1016/j.biombioe.2008.07.007

Pomeranz Y. 1978. Advances in Cereal Science and Technology, vol II. AACC, St. Paul, Minn. USA.

Roberts CA, Ren C, Beuselinck PR, Benedict HR, Bilyeu K. 2006. Fatty acid profiling of soybean cotyledons by near-infrared spectroscopy. Appl. Spectrosc. 60, 1328- 1333. http://dx.doi.org/10.1366/000370206778998932 PMid:17132452

Salunkhe DK, Chavan JK, Adsule RN, Kadam SS. 1991. World Oilseeds: Chemistry, Technology and utilization. Publish Van Nostrand Reinhold New York, USA. PMid:2004796

Weinstock BA, Janni J, Hagen L, Wrigth S. 2006. Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using nearinfrared reflectance hyperspectral imaging and multivariate analysis. Appl. Spectrosc. 60, 9-16. http://dx.doi.org/10.1366/000370206775382631 PMid:16454902




Copyright (c) 2013 Consejo Superior de Investigaciones Científicas (CSIC)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Contact us grasasyaceites@ig.csic.es

Technical support soporte.tecnico.revistas@csic.es