Predicción, usando imágenes hiperespectrales, del contenido de almidón en quesos frescos adulterados con harina de maíz - Barreto Alcántara, Abel Andree (2015)
The aim of this study was to predict the cornmeal adulteration in cheese by hyperspectral imaging technique. Fresh cheese was prepared using milk with addition of cornmeal in concentrations of 0.0, 2.5, 7.5, 12.5, 17.5, 22.5 mg / ml of milk, obtaining final concentrations of starch 0,055, 2,656, 6,007, 7,946, 9,884 and 12,705 mg / g cheese; subsequently hyperspectral imaging in the range of 0 to 1200 nm, distributed in 150 bands were acquired. In order to perform the pre-processing of the images, they were developed and implemented in Matlab v. 2010ª script functions. Modeling starch content was performed by the method of partial least squares regression (PLSR), with 14 latent variables a correlation coefficient of cross validation (r2) of 0.992 was obtained. With wavelengths of 24, 272, 296, 312, 376, 392, 424, 496, 728, 840, 936, 928, 968, 984 and 1000 nm was obtained reduced model with a correlation coefficient (r2) of 0.917.