[Zurück]

B. Muik, B. Lendl, A. Molina Díaz, A. Garcia, M.J. Ayora-Cañada:
"Discrimination of olives according to fruit quality using Fourier transform Raman spectroscopy";
Journal of Agricultural and Food Chemistry, 52 (2004), S. 6055 - 6060.

Fourier transform Raman spectroscopy combined with pattern recognition has been used to discriminate olives of different qualities (sound olives, frost-damaged olives, olives collected from the ground, fermented olives, and diseased olives). Milled olives were measured in a dedicated sample cup, which was rotated during spectrum acquisition. A preliminary study of the data set structure was performed using hierarchical cluster anal. and principal component anal. Two supervised pattern recognition techniques, K-nearest neighbors and soft independent modeling of class analogy (SIMCA), were tested using a "leave-a-fourth-out" cross-validation procedure. SIMCA provided the best results, with prediction abilities of 95% for sound, 93% for frost-damaged, 96% for ground-collected, and 92% for fermented olives. Diseased olives (too few to define a class) were included in the validation and recognized as not belonging to any class. None of the damaged olive samples was wrongly predicted to the class of sound olives. With this approach a selection of sound olives for the prodn. of high-quality virgin olive oil can be achieved.

http://aleph.ub.tuwien.ac.at/F?base=tuw01&func=find-c&ccl_term=AC04969552

http://publik.tuwien.ac.at/files/pub-tch_4303.pdf