State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils
Abstract
:1. Introduction
2. Spectroscopic and Spectrometric Techniques
2.1. Vibrational Spectroscopy
2.1.1. Fourier Transform Infrared Spectroscopy
2.1.2. Fourier Transform Near-Infrared Spectroscopy
2.1.3. Raman Spectroscopy
2.2. Mass Spectrometry: Stable Isotope-Ratio Mass Spectrometry (IRMS)
2.3. Site-Specific Nuclear Isotopic Fractionation by Nuclear Magnetic Resonance (SNIF-NMR) Spectroscopy
2.4. Fluorescence and Ultraviolet-Visible (UV-Vis) Spectroscopy
3. Chromatographic Separation Techniques
3.1. Gas Chromatography (GC)
3.2. High-Performance Liquid Chromatography (HPLC)
4. Other Methodology and Analytical Approaches
4.1. DNA-Based Techniques
4.2. Protein-Based Biomolecular Techniques
4.3. Metabolomics and Chemometrics
4.4. Hyperspectral Imaging and Chemometrics
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Technique | Adulterant | Detection (%) | Quantification (%) | Conditions | Ref. |
---|---|---|---|---|---|
NIR | Soybean Oil | - | 1.76 | 12,000–4000 cm−1 | [16] |
NIR | Olive Pomace Oil | - | 3.27 | 8000–2000 cm−1 | [17] |
NIR | Corn, sunflower, soya, walnut and hazelnut oil | 2 | 0.57, 1.32, 0.96, 0.56, 0.57 | 12,000–4000 cm−1 | [18] |
NIR | Sunflower Oil | 1 | - | 2498–1100 nm | [19] |
NIR | Adulterants | 1 | 5280 cm−1 | [20] | |
FT-IR | Low-cost edible oils | 5 | - | 4000–500 cm−1 | [21] |
FT-IR | Olive Pomace Oil | - | 3.28 | 4000–400 cm−1 | [17] |
FT-IR | Peanut Oil | 1 | - | 3050–600 cm−1 | [22] |
FT-IR | Peanut Oil | 5 | - | 4000–400 cm−1 | [23] |
FT-IR | Hazelnut Oil | 25 | - | 3100–800 cm−1 | [24] |
FT-IR | Palm, corn, canola and sunflower oil | - | 1 | 1500–1000 cm−1 | [25] |
FT-IR | Soybean and tea seed oil | 1 | 4000–650 cm−1 | [26] | |
MIR | Old olive oil | 1–50 | 4000–600 cm−1 | [27] | |
MIR | Soybean Oil | 4.89 | 4000–350 cm−1 | [16] | |
MIR | Corn-sunflower mixture, cottonseed, and rapeseed | 5 | 4000–650 cm−1 | [28] | |
Raman | Soybean Oil | 1.57 | 3500–50 cm−1 | [16] | |
Raman | Olive Pomace Oil | 1.72 | 3700–400 cm−1 | [17] | |
Raman | - | 5 | 2400–250 cm−1 | [29] | |
Raman | Sunflower oil | 1 | 3100–560 cm−1 | [30] | |
Raman | Soybean oil | 1 | 1800–1000 cm−1 | [31] | |
Raman | Waste cooking oil | 2.5 | 3500–100 cm−1 | [32] | |
UV-VIS | Refined oil | <10 | 0–650 nm | [33] | |
UV-VIS | Frying oils | 1 | 0–650 nm | [34] | |
UV-VIS | Olive oil | 8.4 | 60–700 nm | [35] | |
NMR | Lampante olive oil, Refined olive oil | 5 | - | [36] | |
NMR | Seed and nut oils | 10 | - | [37] |
Technique | Adulterant | Conditions | Ref. |
---|---|---|---|
GC | Soybean, corn, sunflower oil | Column Agilent CP-Sil88 (50 m × 0.25 mm, 0.20 μm). FID (HP 6890N, Agilent, 250 °C). t0 = 165 °C, 25 min; gradient of 5 °C/min tf = 195 °C.Percentage of adulteration detection: 1–3% | [101] |
HPLC | Hazelnut, olive and their mixtures | Column Spherisorb ODS2 (octadodecylsilane) (46 cm × 0.25 m, 5 µm). 25 °C, 50 min. (A): A–Act (64:36, v/v) 1 mL/min. Percentage of adulteration detection: 2% | [104] |
HPLC | Hazelnut | Kromasil 100-5C18 (3.2 mm × 250 mm; 5 μm). (A): W/AA (97:3, v/v). (B): M/Act (50:50, v/v). 30 °C, 0.490 mL/min. PDA-100, 280 nm. Isocratic (95% A–5% B, 15 min), gradient (100% B, 25 min) back to 5% B, 20 min. Percentage of adulteration detection: 5% | [105] |
HSI | Olive oil | 400–570 nm. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and x-loading weights (x-LW) | [106] |
HSI | Sesame oil | 325–1075 nm. Support Vector Machine-Multiclass Forward Feature Selection (SVM-MFFS) | [107] |
HSI | Sesame oil | 874–1734 nm. Least Squares-Support Vector Machine (LS-SVM) and the Linear Discriminant Analysis (LDA) | [108] |
HSI | Edible and waste cooking oils | 350–2500 nm. Unweighted Distance Method and Interior Square Sum Distance | [109] |
HSI | Frying oils | 400–1750 nm. PLS calibration models | [110] |
HSI | Virgin olive oils | 900–1700 nm. Genetic Algorithm (GA), Least Absolute Shrinkage and Selection Operator, and Successive Projection Algorithm (SPA) | [111] |
IRMS | Glycerol, fatty alcohols | δ13C, δ18O | [74] |
IRMS | Palmitic acid, palmitoleic acid, stearic acid, oleic acid, linoleic acid, linolenic acid | δ13C, bulk. Vegetable oils can be classified using the isotopic ratios of the bulk oil, the fatty acids, and also the composition of the fatty acids | [73] |
IRMS | Phytol, geranyl geraniol, citrostadienol, docosanol, tetracosanol, hexacosanol | δ13C. Percentage of adulteration detection: 3% | [78] |
IRMS | Methyl palmitoleate, methyl palmitate, methyl oleate | δ13C. Use of 3 FAME peaks enabled greater differentiation between samples of different geographic origin compared to using the isotopic ratios of the bulk oils | [75] |
Enzymes | Hazelnut proteins | Indirect competitive ELISA and direct immunosensor. For biosensor, LOD 0.08 μg/g olive oil, assay time 4.5 min | [112] |
Enzymes | Aflatoxin B1 | Immunostrips and indirect competitive ELISA. For strips, visual LOD 1 ng/mL, assay time 15 min | [113] |
Enzymes | OrganophosporusPesticides | Indirect and direct fluorescent competitive immunosensor. High sensitivity of the fluorescence transducer | [114] |
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González-Pereira, A.; Otero, P.; Fraga-Corral, M.; Garcia-Oliveira, P.; Carpena, M.; Prieto, M.A.; Simal-Gandara, J. State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils. Foods 2021, 10, 484. https://doi.org/10.3390/foods10030484
González-Pereira A, Otero P, Fraga-Corral M, Garcia-Oliveira P, Carpena M, Prieto MA, Simal-Gandara J. State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils. Foods. 2021; 10(3):484. https://doi.org/10.3390/foods10030484
Chicago/Turabian StyleGonzález-Pereira, Antia, Paz Otero, Maria Fraga-Corral, Paula Garcia-Oliveira, Maria Carpena, Miguel A. Prieto, and Jesus Simal-Gandara. 2021. "State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils" Foods 10, no. 3: 484. https://doi.org/10.3390/foods10030484
APA StyleGonzález-Pereira, A., Otero, P., Fraga-Corral, M., Garcia-Oliveira, P., Carpena, M., Prieto, M. A., & Simal-Gandara, J. (2021). State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils. Foods, 10(3), 484. https://doi.org/10.3390/foods10030484