An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil

Abstract

Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.

Bibliometric Analysis of Food Fraud

“…PCA was used to compare olive oils to sesame, sunflower, and flax oils. These results suggest the portable prototype may identify food fraud in liquid samples [15].…”

Screening Method for the Visual Discrimination of Olive Oil from Other Vegetable Oils by a Multispecies DNA Sensor

“…The optical microfiber-based sensing device measures oil absorbance changes in the visual spectrum . A sensing device for near-infrared spectroscopy (NIR) of olive oil was also reported . Smartphone-based sensors use computer vision and pattern recognition of the reflected light from illuminated oil samples through video recording. , Smartphone-based fluorometric sensors monitor the decrease of olive oil fluorescence upon adulteration. , Again, sophisticated and difficult chemometric tools are required for data mining.…”

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods

“…However, most studies in this field typically involve the use of expensive near-infrared (NIR) spectrometers to acquire the data [ 5 , 11 , 16 ]. Moreover, the majority of studies have been performed by either using the entire spectrum bands or solely relying on the feature wavelength bands [ 17 , 18 ]. Therefore, these have led to issues such as high detection costs and a lack of ability to merge the rapidity and robustness of model authentication.…”