• Detection of ground coffee adu...

*Result*: Detection of ground coffee adulteration using FTIR coupled with pattern recognition techniques.

Title:
Detection of ground coffee adulteration using FTIR coupled with pattern recognition techniques.
Authors:
Khodabakhshian R; Department of Biosystems Engineering, Ferdowsi University of Mashhad, 9177948978 Mashhad, Iran., Tarandakzad Y; Department of Biosystems Engineering, Ferdowsi University of Mashhad, 9177948978 Mashhad, Iran., Khojastehpour M; Department of Biosystems Engineering, Ferdowsi University of Mashhad, 9177948978 Mashhad, Iran.
Source:
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2026 Mar 05; Vol. 348 (Pt 1), pp. 127077. Date of Electronic Publication: 2025 Oct 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: England NLM ID: 9602533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3557 (Electronic) Linking ISSN: 13861425 NLM ISO Abbreviation: Spectrochim Acta A Mol Biomol Spectrosc Subsets: MEDLINE
Imprint Name(s):
Publication: : Amsterdam : Elsevier
Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994-
MeSH Terms:
Coffee*/chemistry , Food Contamination*/analysis , Pattern Recognition, Automated*/methods , Coffea*/chemistry, Spectroscopy, Fourier Transform Infrared/methods ; Hordeum/chemistry ; Cicer/chemistry ; Principal Component Analysis ; Cluster Analysis ; Decision Trees
Contributed Indexing:
Keywords: Chemometrics; Coffee adulteration detection; FTIR spectroscopy; Food quality control; Pattern recognition
Substance Nomenclature:
0 (Coffee)
Entry Date(s):
Date Created: 20251105 Date Completed: 20251212 Latest Revision: 20251212
Update Code:
20260130
DOI:
10.1016/j.saa.2025.127077
PMID:
41192381
Database:
MEDLINE

*Further Information*

*Ground coffee is highly susceptible to economically motivated adulteration, necessitating robust analytical methods for authenticity verification. This study investigates the efficacy of Fourier Transform Infrared (FTIR) spectroscopy combined with pattern recognition techniques for detecting adulteration in ground coffee with barley, chickpea, and date pit powders. Spectral fingerprints of pure and adulterated samples were acquired, preprocessed and analyzed via supervised (Random Forest, K-Nearest Neighbors, Decision Tree) and unsupervised (Principal Component Analysis, Hierarchical Cluster Analysis) learning models. Results demonstrated that spectral preprocessing significantly enhanced classification performance, with Random Forest achieving the highest accuracy (94 % training, 83 % testing) among supervised models. Hierarchical Cluster Analysis (HCA) outperformed all methods, achieving 96.8 % accuracy with perfect classification for pure coffee, date pit, and chickpea samples. This study uniquely provides a comprehensive comparative evaluation of supervised and unsupervised models, highlighting the superior potential of HCA for rapid and label-free screening of coffee adulteration.
(Copyright © 2024. Published by Elsevier B.V.)*

*Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*