Evaluating LDA and PLS-DA Algorithms for Food Authentication: A Chemometric Perspective

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25271121%3A_____%2F25%3AN0000107" target="_blank" >RIV/25271121:_____/25:N0000107 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1999-4893/18/12/733" target="_blank" >https://www.mdpi.com/1999-4893/18/12/733</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/a18120733" target="_blank" >10.3390/a18120733</a>

Result language

angličtina

Original language name

Evaluating LDA and PLS-DA Algorithms for Food Authentication: A Chemometric Perspective

Original language description

High-dimensional analytical datasets, such as those generated by inductively coupled plasma–mass spectrometry (ICP-MS), require robust computational frameworks for dimensionality reduction, classification, and model validation. This study presents a comparative evaluation of Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) algorithms applied to multivariate chemometric data for food origin authentication. The research employs a workflow that integrates Principal Component Analysis (PCA) for feature extraction, followed by supervised classification using LDA and PLS-DA. Model performance and stability were systematically assessed. The dataset comprised 28 apple samples from four geographical regions and was processed with normalization, scaling, and transformation prior to modeling. Each model was validated via leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, balanced accuracy, detection prevalence, p-value, and Cohen’s Kappa. The results demonstrate that, as a linear projection-based classifier, LDA provides higher robustness and interpretability in small and unbalanced datasets. In contrast, PLS-DA, which is optimized for covariance maximization, exhibits higher apparent sensitivity but lower reproducibility under similar conditions. The study also emphasizes the importance of dimensionality reduction strategies, such as PCA-based variable selection versus latent space extraction in PLS-DA, in controlling overfitting and improving model generalizability. The proposed algorithmic workflow provides a reproducible and statistically sound approach for evaluating discriminant methods in chemometric classification.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/QK1910104" target="_blank" >QK1910104: Research of metabolomic methods for laboratory authentication of apples geographicity.</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Algorithms

ISSN

1999-4893

e-ISSN

—

Volume of the periodical

18

Issue of the periodical within the volume

12

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

733

UT code for WoS article

001645957800001

EID of the result in the Scopus database

2-s2.0-105025898412