Abstract

California is the number one producer of almonds worldwide (Prunus dulcis (Mill.); accounting for ~100% of the domestic production and ~80% of world production. Raw almond kernels are subject to the development of concealed damage (CD) which is characterized by a brown discoloration of the kernel interior that occurs with moderate heat treatment (e.g. blanching, drying, roasting). Little is known regarding the cause of CD and, as it is not visible until after the kernels are heat treated. To date, there are no screening methods available to detect CD in the raw almonds. To address this, we have developed a two multivariate model to help predict the development of CD in almonds before roasting. (1) The first model was developed using near infrared reflectance spectra and CIE color values (i.e. lightness, chroma, and hue) of half raw almonds, and using partial least square regression (PLSR) to predict the color value (i.e. lightness and hue) of roasted almonds classified as either CD or non-concealed damage (NCD). (2) The second model was developed using NIR of whole raw almond, and using PLS-DA to classify almonds in two groups: NCD and CD.