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Keywords:

  • classification;
  • marketing group;
  • nut quality;
  • Prunus dulcis;
  • varietal standard

Abstract

More than 80% of the world's almonds are grown in California with several dozen almond cultivars available commercially. To facilitate promotion and sale, almond cultivars are categorized into marketing groups based on kernel shape and appearance. Several marketing groups are recognized, with the Nonpareil Marketing Group (NMG) demanding the highest prices. Placement of cultivars into the NMG is historical and no objective standards exist for deciding whether newly developed cultivars belong in the NMG. Principal component analyses (PCA) were used to identify nut and kernel characteristics best separating the 4 NMG cultivars (Nonpareil, Jeffries, Kapareil, and Milow) from a representative of the California Marketing Group (cultivar Carmel) and the Mission Marketing Group (cultivar Padre). In addition, discriminant analyses were used to determine cultivar misclassification rates between and within the marketing groups. All 19 evaluated carpological characters differed significantly among the 6 cultivars and during 2 harvest seasons. A clear distinction of NMG cultivars from representatives of the California and Mission Marketing Groups was evident from a PCA involving the 6 cultivars. Further, NMG kernels were successfully discriminated from kernels representing the California and Mission Marketing Groups with overall kernel misclassification of only 2% using 16 of the 19 evaluated characters. Pellicle luminosity was the most discriminating character, regardless of the character set used in analyses. Results provide an objective classification of NMG almond kernels, clearly distinguishing them from kernels of cultivars representing the California and Mission Marketing Groups.

Practical Application

A method to distinguish kernels from almond cultivars representing the Nonpareil, California, and Mission Marketing Groups was described. Kernels were categorized with less than 2% misclassification using multivariate statistics based on measurements involving weight, linear dimensions, and kernel skin color.