Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis

Food Chem. 2014 Jun 15:153:15-9. doi: 10.1016/j.foodchem.2013.12.032. Epub 2013 Dec 12.

Abstract

In this study, near-infrared spectroscopy (NIR) coupled to chemometrics is used to develop a fast, simple, non-destructive and robust method for discriminating sweet and bitter almonds (Prunus amygdalus) by the in situ measurement of the kernel surface without any sample pre-treatment. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to discriminate both types of almonds, obtaining high levels of sensitivity and specificity for both classes, with more than 95% of the samples correctly classified and discriminated. Moreover, the almonds were also analysed by Raman spectroscopy, the reference technique for this type of analysis, to validate and confirm the results obtained by NIR.

Keywords: Bitter almonds; Classification; NIR; PLS-DA; Prunus amygdalus; Raman; Sweet almonds.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Discriminant Analysis
  • Least-Squares Analysis
  • Principal Component Analysis / methods*
  • Prunus / chemistry*
  • Seeds / chemistry*
  • Spectroscopy, Near-Infrared / methods*
  • Taste