On-line system based on hyperspectral information to estimate acidity, moisture and peroxides in olive oil samples

Computers and Electronics in Agriculture

Volumn 116, Issue , 2015, Pages 1-7

  Martinez Gila D M ^a   Cano Marchal, P ^a   Gamez Garcia J ^a   Gomez Ortega J ^a  

^a UNIVERSITY OF JAÉN   (Spain)

Author keywords

Hyperspectral imaging; Machine learning; Olive oil; Process control

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHEMICAL ANALYSIS; GENETIC ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MOISTURE; MOISTURE DETERMINATION; OXIDATION; PEROXIDES; PROCESS CONTROL; QUALITY CONTROL; SOCIAL NETWORKING (ONLINE); SPECTROSCOPY;

HYPERSPECTRAL IMAGING; HYPERSPECTRAL INFORMATION; HYPERSPECTRAL SENSORS; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; OPTIMAL WAVELENGTH; ORGANOLEPTIC CHARACTERISTICS; PROCESS PARAMETERS; SUCCESSIVE PROJECTION ALGORITHMS;

OLIVE OIL;

ACIDITY; CHEMICAL ANALYSIS; ESTIMATION METHOD; MACHINE LEARNING; MOISTURE CONTENT; REAL TIME; VEGETABLE OIL;

EID: 84934944337     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.06.002     Document Type: Article

References (29)

1. Aiken L.S., West S.G., Pitts S.C. Multiple linear regression. Handbook of Psychology 2003, John Wiley and Son.
2. Andersen C.M., Bro R. Variable selection in regression - a tutorial. J. Chemometr. 2010, 24.
3. Arajo M.C.U., Saldanha T.C.B., Galvo R.K.H., Yoneyama T., Chame H.C., Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometr. Intell. Lab. Syst. 2001, 57:65-73.
4. Armenta S., Garrigues S., de la Guardia M. Determination of edible oil parameters by near infrared spectrometry. Anal. Chim. Acta 2007, 596:330-337.
5. Baiano A., Terracone C., Peri G., Romaniello R. Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Comput. Electron. Agric. 2012, 87:142-151.
6. Cano Marchal P., Gómez Ortega J., Aguilera Puerto D., Gámez García J. Current situation and future perspectives on virgin olive oil elaboration process control. Rev. Iberoam. Autom. Inf. Ind. 2011, 8:258-269.
7. CE, 2003. Regulation 1989/2003 of 6 November 2003 amending Regulation (EEC) No 2568/91 on the characteristics of olive oil-pomace oil and on the relevant methods of analysis. Technical Report. European Communities.
8. Christy A.A., Kasemsumran S., Du Y., Ozaki Y. The detection and quantification of adulterant in olive oil by near-infrared spectroscopy and chemometrics. Anal. Sci. 2004, 20:935-940.
9. Cozzolino D., Murray I., Chree A., Scaife J.R. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy. LWT-Food Sci. Technol. 2005, 38:821-828.
10. Eiben A., Smith J. Introduction to Evolutionary Computing 2003, Springer.
11. El-Abassy R., Donfack P., Materny A. Rapid determination of free fatty acid in extra virgin olive oil by raman spectroscopy and multivariate analysis. J. Am. Oil. Chem. Soc. 2009, 86:507-511.
12. Huang M., Zhu Q., Wang B., Lu R. Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification. Comput. Electron. Agric. 2012, 89:175-181.
13. ImSpector, 2003. ImSpector Imaging Spectrograph User Manual. Spectral Imaging, Ltd.
14. Inarejos García A.M., Gómez Alonso S., Fregapane G., Salvador M.D. Evaluation of minor components, sensory characteristics and quality of virgin olive oil by near infrared (NIR) spectroscopy. Food Res. Int. 2013, 50.
15. Jamshidi B., Minaei S., Mohajerani E., Ghassemian H. Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of valencia oranges. Comput. Electron. Agric. 2012, 85:64-69.
16. Jimenez A., Beltran G., Aguilera M., Uceda M. A sensor-software based on artificial neural network for the optimization of olive oil elaboration process. Sens. Actuat. B: Chem. 2008, 129:985-990.
17. Latorre Carmona P., Sotoca J.M., Pla F. Filter-type variable selection based on information measures for regression tasks. Entropy 2012, 14:323-343.
18. Mailer R.J. Rapid evaluation of olive oil quality by NIR reflectance spectroscopy. J. Am. Oil. Chem. Soc. 2004, 81.
19. Marquez A.J. Monitoring carotenoid and chlorophyll pigments in virgin olive oil by visible-near infrared transmittance spectroscopy. On-line application. J. Near Infrared Spectrosc. 2003, 11.
20. MathWorks, 2004. Genetic Algorithm and Direct Search Toolbox. The MathWorks.
21. MathWorks, 2013. Statistics Toolbox User's Guide. The MathWorks.
22. Monteiro S.T., Kosugi Y. Particle swarms for feature extraction of hyperspectral data. IECE Trans. Inf. Syst. 2007, E90D:1038-1046.
23. Ruiz Altisent M., Ruiz Garcia L., Moreda G., Lu R., Hernandez Sanchez N., Correa E., Diezma B., Nicola B., Garca Ramos J. Sensors for product characterization and quality of specialty crops. Comput. Electron. Agric. 2010, 74:176-194.
24. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36:1627-1639.
25. Sinelli N., Casale M., Di Egidio V., Oliveri P., Bassi D., Tura D., Casiraghi E. Varietal discrimination of extra virgin olive oils by near and mid infrared spectroscopy. Food Res. Int. 2010, 43:2126-2131.
26. Suphamitmongkol W., Nie G., Liu R., Kasemsumran S., Shi Y. An alternative approach for the classification of orange varieties based on near infrared spectroscopy. Comput. Electron. Agric. 2013, 91:87-93.
27. Williams P., Norris K. Near infrared technology in the agriculture and food industries. Eds. Am. Cereal Assoc. Cereal Chem. 2004, chapter 8.
28. Yao H., Lewis D., Sun P. Spectral preprocessing and calibration techniques. Hyperspectral Imaging for Food Quality Analysis and Control 2010, Academic Press, San Diego.
29. Zou H., Hastie T. Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 2005, 67:301-320.