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.

Keywords: Digital Light Processing (DLP); Digital Micromirror Device (DMD); Near-Infrared Spectroscopy (NIRS); Partial Least Squares (PLS); Principal Component Analysis (PCA); instrumentation; olive oil; spectroscopic.

Figure 1

Sample preparation scheme (N = 96). Three pure olive oils, 12 samples of olive oil (4 samples of extra virgin olive oil, 4 samples of virgin olive oil and 4 samples of olive oil) adulterated with flax oil, 12 samples of olive oil adulterated with sesame oil, 12 samples of olive oil adulterated with sunflower oil, and 57 samples of olive oil adulterated with two of the selected adulterant oils.

Figure 2

Transmittance measurement setup: (a) DLP NIRscan Nano EVM, (b) Graphical User Interface (GUI). Section 3.4 describes the GUI.

Figure 3

Spectroscopy architectures: (a) traditional (b) digital light processing (DLP).

Figure 4

DLP NIRscan Nano EVM with (a) reflective module and (b) transmittance module.

Figure 5

Interior view of the DLP NIRscan Nano-optical architecture.

Figure 6

Basic block diagram of the DLP NIRscan Nano hardware.

Figure 7

DLP NIRscan Nano lamp driver.

Figure 8

Transimpedance amplifier circuit.

Figure 9

DLP NIRscan Nano GUI Scan Screen.

Figure 10

SNR of the Column scan as a function of wavelength.

Figure 11

Heatmap showing the Root Mean Square (RMS) values for each number of scans to be averaged. (a) Heatmap with the RMS values using the Column scan model; (b) heatmap with the RMS values using the Hadamard scan model.

Figure 11

Heatmap showing the Root Mean Square (RMS) values for each number of scans to be averaged. (a) Heatmap with the RMS values using the Column scan model; (b) heatmap with the RMS values using the Hadamard scan model.

Figure 12

Raw spectra of all pure oils: (a) raw spectra of all mixtures, (b) seed oils and average spectrum of all adulterated samples, and (c) olive oils spectra.

Figure 12

Raw spectra of all pure oils: (a) raw spectra of all mixtures, (b) seed oils and average spectrum of all adulterated samples, and (c) olive oils spectra.

Figure 13

First-derivative Savitzky–Golay spectra plus Standard Normal Variate (SNV) of all pure oils: (a) all mixtures, (b) olive oils, and (c) seed oils and average spectrum of all adulterated samples.

Figure 13

First-derivative Savitzky–Golay spectra plus Standard Normal Variate (SNV) of all pure oils: (a) all mixtures, (b) olive oils, and (c) seed oils and average spectrum of all adulterated samples.

Figure 14

Principal component analysis of three olive oils and their corresponding mixtures: (a) extra virgin olive oil (EVOO) and mixtures of EVOO and other seed oils, (b) virgin olive oil (VOO) and mixtures of VOO and other seed oils, (c) olive oil (OO) and mixtures of OO and other seed oils, and (b,d) all the samples pure and adulterated in the sample set.