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Updated: Jan 8, 2026

HPLC Coupled with Chemical Fingerprinting for Multi-Pattern Recognition for Identifying the Authenticity of Clematidis Armandii Caulis

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Published on: November 11, 2022

Wheat flour tortilla authenticity verification using targeted elemental profiling-based multifunction classification

Uriel Arellano¹, Ilse María Hernández-Romero¹, Lucy T González¹

¹Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, N.L., Mexico.

|December 17, 2025

Summary

This summary is machine-generated.

This study used elemental analysis to authenticate wheat flour tortillas, identifying geographic origin and manufacturing methods. Decision tree models achieved 100% accuracy, offering a reliable framework for tortilla authentication.

Keywords:

Elemental composition Food authentication Geographical origin Manufacturing process Multifunction classification Wheat flour tortillas

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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Area of Science:

Food Science
Analytical Chemistry
Chemometrics

Background:

Wheat flour tortillas are a Mexican staple with limited authentication methods.
Reliable methods are needed for quality control and consumer confidence.

Purpose of the Study:

To develop and evaluate data-driven methods for authenticating wheat flour tortillas.
To determine geographic origin and manufacturing processes using elemental profiling.

Main Methods:

Analysis of 20-element profiles in 145 tortilla samples.
Data preprocessing including autoscaling and mean centering.
Application of principal component analysis, linear discriminant analysis, k-nearest neighbors, and decision tree models.

Main Results:

Geographic origin significantly influenced elemental composition more than the manufacturing process.
Decision tree models demonstrated 100% sensitivity, specificity, and accuracy for multifunction classification.
K-nearest neighbors and linear discriminant analysis showed high accuracies (88-100% and 83-92%, respectively).

Conclusions:

A robust, data-driven framework for tortilla authentication was established.
The findings support enhanced quality control, regulatory oversight, and consumer trust in tortilla products.
Elemental profiling combined with chemometric models offers a powerful tool for food authentication.