Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks

This paper reports a novel application of a type of neural network committee, called AdaBoost, to the estimation of grape anthocyanin concentration using hyperspectral data. The inputs from the neural networks were the principal components of the grapes’ spectra. Hyperspectral data were collected in the reflectance mode for 46 individual whole grapes of the Cabernet Sauvignon variety, using a hyperspectral camera that operates with wavelengths ranging from 400 to 1000 nm at an approximate 0.6 nm resolution. The hyperspectral camera was positioned a few tens of centimetres away from the grapes. The grapes were harvested on five dates between August 28th and September 23rd in 2009 and presented average sugar content values between 14.6 and 20.2 Brix. They were kept frozen until January 2010, when they were thawed and the hyperspectral data collected at ambient temperature. The anthocyanin concentration values obtained by our calibrations exhibited a squared correlation coefficient value of 0.65 compared to the values measured using conventional laboratory techniques. This correlation value is better than the value reported in another recent scientific work which estimated anthocyanin values in individual whole grapes of Cabernet Sauvignon.