Grasas y Aceites, Vol 69, No 4 (2018)

From NIR spectra to singular wavelengths for the estimation of the oil and water contents in olive fruits


https://doi.org/10.3989/gya.0457181

N. Hernández-Sánchez
Physical Properties Laboratory and Advanced Technologies in Agrifood (LPF-TAGRALIA), ETSIAAB, Universidad Politécnica de Madrid, Spain
orcid http://orcid.org/0000-0001-5710-2567

M. Gómez-del-Campo
CEIGRAM, ETSIAAB, Universidad Politécnica de Madrid, Spain
orcid http://orcid.org/0000-0003-4488-586X

Abstract


Knowledge about the oil and water contents in olive fruits is required to determine orchard management, harvest time, and the oil extraction process. The simplification of procedures and of equipment based on NIR Spectroscopy is of major interest. Estimation models for oil and water contents on a fresh matter basis were developed by partial least square regression with NIR spectral data (700 wavelengths). For raw absorbance data the r2 for the test set reached 0.9 and 0.92 for oil and water contents; and RPIQt was 4.9 and 4.3, respectively. The identification of a useful relation of the relative absorbance at 1724 nm and 1760 nm to the oil content allowed for restricting the wavelengths to three. For oil content the r2 showed 0.88 with ad RPIQt of 4.4. For water content the r2 value was 0.84 and the RPIQt was 3.1. Estimation performance with only three wavelengths was comparable to that obtained with PLSR with 700 variables.

Keywords


Food inspection; Spectral pre-treatment; Spectroscopy; Variable selection

Full Text:


HTML PDF XML

References


Aenor, Asociación Española de Normalización y Certificación. 1973. Materias Grasas. Humedad y materias volátiles. Norma UNE 55-020-73, Madrid, España.

Aparicio R, Harwood J. 2000. Manual del aceite de oliva. Ediciones Paraninfo S.A. Madrid: Mundi-Prensa.

Bellon-Maurel V, Fernandez-Ahumada E., Palagos B, Roger JM, McBratney AB. 2010. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trend. Anal. Chem. 29, 1073–1081.

Cayuela JA, Pérez-Camino MC. 2010. Prediction of quality of intact olives by near infrared spectroscopy. Eur. J. Lipid Sci. Technol. 112, 1209–1217. https://doi.org/10.1002/ejlt.201000372

Cayuela JA, García JM, Caliani N. 2009. NIR prediction of fruit moisture, free acidity and oil content in intact olives. Grasas Aceites 60, 194–202. https://doi.org/10.3989/gya.097308

Connor DJ, Centeno A, Gómez-del-Campo M. 2009. Yield determination in olive hedgerow orchards. II. Analysis of radiation and fruiting profiles. Crop Pasture Sci. 60, 443– 452. https://doi.org/10.1071/CP08253

Dytham C. 2010. Choosing and Using Statistics: A Biologist's Guide. Third Edition. Wiley-Blackwell. PMid:19924158

Fernández-Espinosa AJ. 2016. Combining PLS regression with portable NIR spectroscopy to on-line monitor quality parameters in intact olives for determining optimal harvesting time. Talanta 148, 216–228. https://doi.org/10.1016/j.talanta.2015.10.084

García Sánchez A, Ramos Martos 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 Aceites 56, 220–227.

Gómez-del-Campo M, Centeno A, Connor DJ. 2009. Yield determination in olive hedgerow orchards. I. Yield and profiles of yield components in north–south and east–west oriented hedgerows. Crop Pasture Sci. 60, 434–442. https://doi.org/10.1071/CP08252

Gomez-del-Campo M, García JM. 2013. Summer deficit-irrigation strategies in a hedgerow olive cv. Arbequina orchard: effect on oil quality. J. Agric. Food Chem. 61, 8899–8905. https://doi.org/10.1021/jf402107t PMid:23972260

Gracia A, León L. 2011. Non-destructive assessment of olive fruit ripening by portable near infrared spectroscopy. Grasas Aceites 62, 268–274. https://doi.org/10.3989/gya.089610

Guerrini L, Masella P, Angeloni G, Migliorini M, Parenti A. 2017. Changes in olive paste composition during decanter feeding and effects on oil yield: Effect of decanter feeding on olive oil yield. Eur. J. Lipid Sci. Technol. 119.

Gurdeniz G & Ozen B. 2009. Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data. Food Chem. 116, 519–525. https://doi.org/10.1016/j.foodchem.2009.02.068

Herrera-Cáceres C, Pérez-Galarce F, Álvarez-Miranda E, Candia-Véjara A. 2017. Optimization of the harvest planning in the olive oil production: A case study in Chile. Comput. Electron. Agric. 141, 147–159. https://doi.org/10.1016/j.compag.2017.07.017

León-Moreno L. 2012. Usefulness of portable near infrared spectroscopy in olive breeding programs. Span. F. Agric. Res. 10, 141–148. https://doi.org/10.5424/sjar/2012101-184-11

León L, Rall, L, Garrido A. 2003. Análisis de aceituna intacta mediante espectroscopia en el infrarrojo cercano (NIRS): una herramienta de utilidad en programas de mejora de olivo. Grasas Aceites 54, 41–47. https://doi.org/10.3989/gya.2003.v54.i1.275

Morrone L, Neri L, Cantini C, Alfei B, Rotondi A. 2018. Study of the combined effects of ripeness and production area on Bosana oil's quality. Food Chem. 245, 1098–1104. https://doi.org/10.1016/j.foodchem.2017.11.061 PMid:29287327

Osborne BG, Fearn T, Hindle PT. 1993. Practical NIR spectroscopy with applications in food and beverage analysis. Second Edition. Addison-Wesley Longman Ltd: Harlow UK.

Rinnan A, Berg F, Engelsen SB. 2009a. Review of the most common pre-processing techniques for near-infrared spectra. Trends Anal. Chem. 28. https://doi.org/10.1016/j.trac.2009.07.007

Rinnan A, Nørgaard L, Frans van den Berg F, Thygesen J, Bro R. and Engelsen SB. 2009b. Data Pre-processing in Infrared Spectroscopy for Food Quality Analysis and Control, Edited by Da-Wen Sun, ISBN: 978-0-12-374136-3. PMCid:PMC2687315

Roger JM, Palagos B, Bertrand D, Fernandez-Ahumada E. 2011. CovSel: Variable selection for highly multivariate and multi-response calibration. Application to IR spectroscopy. Chemometr. Intell. Lab. Syst. 106, 216–223. https://doi.org/10.1016/j.chemolab.2010.10.003

Salguero-Chaparro L and Pe-a-Rodríguez F. 2014. On-line versus off-line NIRS analysis of intact olives. LWT - Food Sci. Technol. 56, 363–369.

Salguero-Chaparro L, Baeten V, Fernández-Pierna JA, Pe-a- Rodríguez, F. 2013. Near infrared spectroscopy (NIRS) for on-line determination of quality parameters in intact olives. Food Chem. 139, 1121–1126. https://doi.org/10.1016/j.foodchem.2013.01.002 PMid:23561217

Shenk JS, Workman JJ & Westerhaus MO. 2001. Application of NIR spectroscopy to agricultural products, in: Burns, D.A. & Ciurczak, E.W. (Eds.) 3 Handbook of near-infrared analysis. Marcel Dekker, Inc., New York, 419–474.

Sun D-W. 2009. Infrared Spectroscopy, for food quality analysis and control. (1st ed.). UK: Academic Press, (Chapter 1).

Trapani S, Migliorini M, Cecchi L, Valentina G, Roberto B, Valentina C, Giovanna F, Bruno Z. 2017. Feasibility of filter-based NIR spectroscopy for the routine measurement of olive oil fruit ripening indices. Eur. J. Lipid Sci. Technol. 119. https://doi.org/10.1002/ejlt.201600239

Trapani S, Guerrini L, Masella P, Parenti A, Canuti V, Picchi M, Caruso G, Gucci R, Zanonia B. 2017. A kinetic approach to predict the potential effect of malaxation time-temperature conditions on extra virgin olive oil extraction yield. J. Food. Eng. 195, 182–190. https://doi.org/10.1016/j.jfoodeng.2016.09.032

Zeaiter M, Roger JM, Bellon-Maurel V. 2005. Robustness of models developed by multivariate calibration. Part II: The influence of pre-processing methods. Trends Anal. Chem. 24, 437–445. https://doi.org/10.1016/j.trac.2004.11.023

Zeaiter M, Roger JM, Bellon-Maurel V, Rutledge DN. 2004. Robustness of models developed by multivariate calibration. Part I: The assessment of robustness. Trends Anal. Chem. 23, 157–170 . https://doi.org/10.1016/S0165-9936(04)00307-3




Copyright (c) 2018 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