Multivariate calibration models for ATR-FTIR spectroscopy analysis of olive oil adulterated with corn, sunflower, and palm oils

IF 3.8 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI:10.1016/j.infrared.2026.106420

Esra modares Askari , Mohammadreza Khanmohammadi Khorrami , Mahsa Mohammadi , Miguel de la Guardia

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Abstract

Rapid analysis using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been developed for the detection of extra virgin olive oil (EVOO) adulterated with corn oil, sunflower oil (SFO), and palm oil. Measurements were conducted on EVOO and samples adulterated with varying contents of oils, ranging from 1.0 to 35% by weight. ATR-FTIR spectra were analyzed using a robust principal component analysis–multivariate adaptive regression splines (rPCA-MARS) model to assess the adulterated samples. Artificial neural networks (ANN) and support vector machines regression (SVM-R) were used to predict olive oil adulteration. The performance of these models, along with linear and cubic rPCA-MARS models, was evaluated using R² and RMSE. The linear rPCA-MARS model demonstrated the highest accuracy in predicting adulteration, with an R²pred value of 0.970 and MSE (pred) of 4.950. This model outperformed ANN, SVM-R, and piecewise cubic rPCA-MARS models based on key statistical metrics.

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掺入玉米、向日葵和棕榈油的橄榄油的ATR-FTIR光谱分析的多变量校准模型

利用衰减全反射傅里叶变换红外（ATR-FTIR）光谱技术快速检测掺入玉米油、葵花籽油和棕榈油的特级初榨橄榄油（EVOO）。对EVOO和掺入不同含量油的样品进行了测量，重量从1.0到35%不等。ATR-FTIR光谱分析使用鲁棒主成分分析-多元自适应回归样条（rPCA-MARS）模型来评估掺假样品。采用人工神经网络（ANN）和支持向量机回归（SVM-R）对橄榄油掺假进行预测。使用R2和RMSE对这些模型以及线性和立方rPCA-MARS模型的性能进行评估。线性rPCA-MARS模型预测掺假的准确率最高，R2pred值为0.970,MSE （pred）为4.950。该模型在关键统计指标上优于ANN、SVM-R和分段立方rPCA-MARS模型。

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来源期刊

Infrared Physics & Technology 物理-光学

CiteScore

5.70

自引率

12.10%

发文量

400

审稿时长

67 days

期刊介绍： The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.