Unveiling Almond Identity: Prospective Insights into Geographical and Varietal Origins through Metabolic Fingerprint Authentication

Monday, April 29, 2024

5:00 PM – 6:00 PM EDT

Co-Author(s)

SN

Soriana B. Nicotra

Ms.; student
University of Barcelona, INSA-UB, Spain
CC

Cristina Condeminas

Ms.; student
University of Barcelona, Spain
Berta Torres-Cobos (she/her/hers)

Ms.; PhD student
University of Barcelona, INSA-UB, Catalonia, Spain
AR

Agustí Romero

Dr.; Researcher
Institute of Agrifood Research and Technology (IRTA), Spain
MR

Mercè Rovira

Dr.; Researcher
Institute of Agrifood Research and Technology (IRTA), Spain
LL

Leontina Lipan

Dr.; Researcher
University Miguel Hernández, IRTA, Spain
FG

Francesc Guardiola

Dr.; Professor
University of Barcelona, INSA-UB
Santa Coloma de Gramenet (Barcelona), Catalonia, Spain
Alba Tres

Dr.; Researcher
University of Barcelona, INSA-UB
Santa Coloma de Gramenet (Barcelona), Catalonia, Spain

Presenting Author(s)

SV

Stefania Vichi

Dr.; Professor
University of Barcelona, INSA-UB
Santa Coloma DE Gramenet, Catalonia, Spain

The market price dependence of almonds (Prunus dulcis (Mill.) D.A. Webb) on cultivar and origin exposes them to potential fraud. Reliable verification tools are crucial to ensure authenticity and protect consumers.

The unsaponifiable lipid fraction, rich in stable metabolites shaped by genetics and environment, emerges as a promising candidate for authenticating almond varieties and origins. Untargeted profiling, particularly through fingerprinting methods, outperforms traditional approaches by integrating comprehensive analytical information, significantly enhancing authentication efficiency.

This prospective study aimed to assess the unsaponifiable fraction fingerprint, obtained via gas chromatography-mass spectrometry and pattern recognition analysis, for authenticating both geographical and varietal origins of almonds.

Chromatographic fingerprints from traceable almonds, harvested over two seasons, were used to develop two partial least square-discriminant analysis (PLS-DA) models. One model classified 'Nonpareil' almonds by country of origin, while the other distinguished Spanish almonds based on cultivars.

Each set was randomly split into training (80%) and validation (20%) sets, with external validation performed three times. Satisfactory correct classifications in external validation suggest the potential of unsaponifiable fraction fingerprinting for almond geographical authentication. Further testing on a larger sample set will refine and strengthen classification models. This research signifies a step toward establishing robust tools to safeguard the integrity of the almond market and consumer trust.