Nazri, Muhammad Zulhelmi and Abang Zaidel, Dayang Norulfairuz and Abdullah Sani, Muhamad Shirwan and A. Karim, Hajar Aminah and Abd Rashid, Siti Nor Azlina and Windarsih, Anjar
(2026)
The FTIR-ATR spectroscopy as a biosensing strategy for thermal effects in permissibility verification of lard-adulterated palm oil.
Journal of Chemometrics, 40 (6).
pp. 1-16.
ISSN 0886-9383
E-ISSN 1099-128X
Abstract
Ensuring authenticity of edible oils is vital for consumer trust, regulatory compliance and permissibility assurance, particularly for Muslim consumers. This study introduces a spectral biosensing strategy using Fourier transform infrared spectroscopy
coupled with attenuated total reflectance (FTIR-ATR) and chemometric modelling to detect lard adulteration in palm oil (PO)
under thermal stress. PO samples spiked with 1%–50% v/v lard were heated from 25°C to 200°C for 30min, simulating industrial
and culinary conditions. FTIR-ATR spectra showed distinct shifts in carbonyl and fingerprint regions due to lard incorporation
and heat-induced lipid degradation. Discriminant analysis (DA) achieved 100% classification accuracy across the full spectrum
(4000–650 cm−1
), demonstrating strong discriminatory power. Partial least squares–discriminant analysis (PLS-DA) identified
the fingerprint region (1000–650 cm−1
) as most diagnostic, yielding robust performance with R2
Y=0.895, R2X=1.000, Q2=0.893
and 100% correct classification in both training and validation datasets. Principal component analysis (PCA) revealed clear
clustering of pure and adulterated samples, even under severe thermal conditions. Moreover, using the optimised FTIR-ATR/
PLS model, the lard adulteration in thermally treated PO could be reliably detected at levels as low as 1% v/v with LOD and LOQ
ranges of 0.01%–1.14% and 0.02%–3.34% v/v, respectively. These findings position FTIR-ATR with multivariate chemometrics as
a rapid, nondestructive and thermally resilient platform for lard detection in PO. The approach extends to broader food quality
and safety monitoring in real-world processing scenarios.
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