Introduction

Edible oils present an appealing target for adulteration because of their value, especially in the case of extra virgin olive oil (EVOO). Standards are strict for EVOO and market price is much more expensive than that of lower-grade olive oil. In 2015, seven of Italy’s largest olive oil producers were under investigation for selling virgin olive oil as the higher quality EVOO. One study tested twenty shelf brands of EVOO and nine of them did not meet standards. Adulteration methods can include adding a lower grade olive oil or a cheaper type of oil. Standards include proper representation of provenance of origin and misrepresentation constitutes adulteration as well. Visual inspection is usually insufficient for determining edible oil adulteration and more advanced methods are often expensive, time-consuming, and unsuitable for implementing large-scale quality control testing. NIR spectroscopy has been examined for determining the presence of adulterants in edible oils and the results of some studies are presented below.

Analytes

Olive Oil

Products:

• Extra Virgin Olive Oil

Adulterants (Lower-Grade Oils):

• Soybean
• Sunflower
• Corn
• Canola
• Hazelnut
• High Oleic Acid Safflower
• Peanut
• Palm Olein
• Refined Olive Oil

Adulteration by Misrepresentation of Provenance of Origin

Soybean and Palm Oil

Products:

• Soybean Oil
• Palm Oil

Adulterants:

• Transgenic vs. Non-Transgenic
• Lard

Scientific References and Statistics

Olive Oil

Novel, Rapid Identification, and Quantification of Adulterants in Extra Virgin Olive Oil Using Near-Infrared Spectroscopy and Chemometrics – Azizian, Mossoba, Fardin-Kia, et al., Lipids 2015 50:705-718 Three studies were conducted from 2015 to 2017 at the Food and Drug Administration, Center of Food Safety and Applied Nutrition to assess determining the authenticity of extra virgin olive oil (EVOO) using FT-NIR spectroscopy. Detecting adulteration of EVOO is an ongoing and always evolving concern for regulatory agencies. There are numerous established standards all over the world that grade olive oil by specifying chemical composition and quality parameters. Standards are continually upgraded and they also specify methods of analysis for assessing quality. Many of these standard methods are expensive, labor-intensive, time-consuming, and often require more than one method to achieve the desired results. Vibrational spectroscopy offers a fast and non-destructive alternative to these methods and this study evaluated using FT-NIR spectroscopy to rapidly authenticate EVOO, identify the nature of an adulterant in commercial products, and determine the concentration of the adulterant. A newly created FT-NIR index based on the relative intensities of two unique carbonyl overtones in EVOO spectra was proposed as a potential screening tool for the authenticity of EVOO. The absorption spectrum of EVOO shows two broad bands near 5280 cm^-1 (attributed to volatiles) and 5180 cm^-1 (attributed to non-volatiles). The following samples were acquired for the study: reference EVOO and refined olive oil from Sigma-Aldrich, several authentic EVOO samples from California, commercially available EVOO from Italy, various refined vegetable oils from local grocery stores, and two palm olein samples from Indonesia and Thailand. NIR spectra were collected using different FT-NIR spectrometers from the same vendor using a diffuse reflectance probe with a liquid attachment and a 2 mm pathlength. All spectra were collected at 8 cm^-1 resolution and six replicate spectra were collected for each sample and averaged into one spectrum. Before calibration work, tests were conducted to assess the effects of external changes on the aforementioned absorption bands. The same EVOO sample was measured for over an hour at room temperature and it was noted that the absorption band at 5280 cm^-1 decreased while the other band was unchanged. To confirm this change was because of temperature, an EVOO sample was heated at 50°C for ten minutes and spectra were acquired over the ten minute span. Next, two tests were run to see if the decrease in absorption was due to the loss in volatile compounds. A vacuum at 260 mBar was applied to an EVOO sample for 50 minutes and rescanned, showing a significant decrease in the 5280 cm^-1 band. Likewise, bubbling nitrogen gas was passed through an EVOO sample for fifty minutes and after rescanning, a similar result was observed. A decrease in the 5280 cm^-1 band was also observed after scanning spiked samples of EVOO with fully refined olive oil, corn oil, vegetable oil, and palm olein. The band at 5180 cm^-1 showed little or no change with heat, vacuuming, bubbling with nitrogen gas, or the addition of other oils. The last test added 10 µL of water to an EVOO sample to ensure that changes in the two bands were not occurring because of changes in moisture. After scanning the sample, no spectral change was observed in the two bands of interest after the water added. After the external tests were conducted, chemometric software was used to choose wavenumber ranges that included the characteristic features of the two bands of interest. In order to eliminate the need to measure and integrate the area of the two absorption bands, a Partial Least Squares (PLS) calibration model was created. For all samples, the normalized ratio of the two integrated band areas was calculated. The sample with the highest value (about 1.7 to 1) was arbitrarily assigned a value of 100 for FT-NIR index. California EVOO samples ranged from 100 to 92 on the scale. The certified EVOO sample from Sigma Aldrich was 90 while the refined olive oil was 40. Commercial EVOO from Italy ranged from 86 to 97. To expand the scale and more accurately estimate the lower end, a synthetic triolein was scanned and a very low FT-NIR index score of 5 was determined. The second portion of this study used prepared gravimetric mixtures of authentic EVOO spiked with nine common adulterants. The adulterants used are: soybean oil, sunflower oil, corn oil, canola oil, hazelnut oil, high oleic acid safflower oil, peanut oil, palm olein, and refined olive oil. The first eight listed here were mixed in concentrations from 3% to 30% by weight adulterant and in the case of refined olive oil, up to 60% total weight was used. Initial analysis indicated that one universal PLS model for all adulterants was not feasible. However, the fatty acid profiles for all adulterants was examined and it was determined that spitting the adulterants into four groups based on fatty acid profiles might show better results. This was the case after creating four PLS models from the four groups. Shown below are the results for the FT-NIR index and four adulterant PLS models.

FT-NIR Index

R² = 0.995

RMSEP= 1.7

Group 1 Adulterants

Soybean, Sunflower, Corn, Canola (High Linoleic Acid)

R² = 0.999

RMSEP= 0.9% w/w

Group 2 Adulterants

Hazelnut, High Oleic Acid Safflower Oil, Peanut Oil (High Oleic Acid)

R² = 0.995

RMSEP= 2.2% w/w

Group 3 Adulterant

Palm Olein

R² = 0.999

RMSEP= 1.0% w/w

Group 4 Adulterant

Refined Olive Oil

R² = 0.976

RMSEP= 3.7% w/w

All model results were excellent and proved the feasibility of using NIR spectroscopy and calibration models as a tool for determining the presence of and quantifying an adulterant in EVOO. The FT-NIR index model showed the ability to predict the index irrespective of sample type or place or origin. In the case of the adulterant models, it was necessary to divide the samples into four groups but once that was done, the prediction ability worked for all four groups. These models could be used for real-time screening of olive oil samples as the amount of adulterant that would be added in a practical setting to make an economic difference is far larger than the prediction error of the models. In the case of refined olive oil, the correlation was lower and prediction error was higher but the concentration of refined olive oil added as an adulterant is likely to be higher than other types of oil. Overall, this study demonstrated the potential of NIR spectroscopy as a tool for analysis of EVOO adulterant and could replace the current expensive, time-consuming, and labor-intensive methods used for this analysis. https://link.springer.com/article/10.1007/s11745-015-4038-4 Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils – Azizian, Mossoba, Fardin-Kia, et al., Lipids (2016) 51: 1309-1321 The second study conducted at the Food and Drug Administration, Center of Food Safety and Applied Nutrition examined different extra virgin olive oil (EVOO) varieties procured in different countries. While the Partial Least Squares (PLS) models created in the previous study showed good results, it must be noted that all the EVOO samples provided belonged to a single variety. In this study, the initial set of models was expanded to include different varieties of EVOO and subsequently, different fatty acid profiles. Fifty new samples were obtained from California as well as sixteen from Italy, Spain, Greece, Portugal, Croatia, and France. Adulterants were the same as the previous study: refined vegetable oils purchased locally, palm olein from both Thailand and Indonesia, and reference grade refined olive oil from Sigma-Aldrich. NIR spectra were collected of all samples using the same parameters as the first study: FT-NIR spectrometers, diffuse reflectance probe with a liquid attachment and 2mm pathlength, 8 cm^-1 resolution, and six replicate spectra collected per sample and averaged into one spectrum. The averaged spectra were used with previously created PLS models to determine FT-NIR index, fatty acid composition, and the type and amount of potential adulterants. Based on the results and varieties, sixteen different varieties or blends were chosen for further calibration work, seven from California and nine from Europe. The following single variety or blends of multiple varieties from Europe were used: Arbequina, Cerasuola, Cobrancosa, Cordovil, Frantoio, Hojiblanca, Koroneiki, Leccino, Mandural, Moraiolo, Nocella del Belice, Nostrane, Ogliarola, and Picual. The EVOO varieties or blends from California were Arbequina, Arbosana, and Koroneiki. For each one of these, the same procedure was followed as the previous study for spiking samples with the nine adulterants, except that spiking was done for all vegetable oils and palm olein up to 65% and refined olive oil was used in a concentration all the way to 100% pure refined olive oil. After spectra were collected, chemometric software was used to establish a library for identifying a sample to one of the four adulterant groups. Thresholds were determined to assign a sample to one of the four sets of PLS calibrations for adulterant determination. The four distinct sets of PLS models for the high lineolic, high oleic, palm olein, and refined olive oil adulterants showed similar results to the first study, expanding the scope of the blends and varieties that can be tested for adulterants using NIR spectroscopy. The important conclusion from this study is that the scope of blends and varieties can be successfully expanded based on the results obtained in the first study and the potential exists to create PLS regression models for quality assessment and adulterant identification and quantification for all varieties and blends of olive oil. https://link.springer.com/article/10.1007/s11745-016-4195-0 First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA – Mossoba, Azizian, Fardin-Kia, et al., Lipids (2017) 52:443-455 The third study conducted at the Food and Drug Administration, Center of Food Safety and Applied Nutrition used the previously developed methodologies to test commercial samples of extra virgin olive oil (EVOO) to predict their authenticity, potential mixture with vegetable oil, palm olein, or refined olive oil, and quality level. Eighty-eight commercial samples labeled as EVOO were procured for the study. Three essential requirements were defined using the PLS calibration models created in previous studies. The first requirement is that the FT-NIR index created in the first study had to exceed 75. This threshold was determined from previous analysis of authenticated EVOO products. The second requirement is that concentrations of the five major fatty acids fall within the official International Olive Council (IOC) standards. Calibration models were created for this purpose in a study not documented here. The third requirement is the determination of adulteration by the presence of high linoleic acid, high oleic acid, palm olein, refined olive oil, or a combination of them. This analysis used the four PLS models from both studies. NIR spectra were collected under the same conditions as the previous studies and the spectra were used with the models to analyze EVOO. In order to be classified as compliant with EVOO standards, all three criteria had to be met from the NIR predictions. Of the eighty-eight commercial products, only thirty-three of them met all three criteria for EVOO authenticity, making 33.5% of the samples authentic and 62.5% unauthentic. Of the fifty-five unauthentic products, fourteen were potentially mixed with high linoleic acid, six with high oleic acid, three with palm olein, fourteen with refined olive oil, and eighteen with a combination of these four. Some interesting conclusions can be developed from this analysis. If these assessments were based strictly on the established ranges by the IOC for fatty acid composition, less than 10% of the samples would have been classified as unauthentic. The reminder of the unauthentic samples all failed either the FT-NIR index test, an adulterant was detected, or both. The results here are quite similar to a study conducted at the University of California Davis. Commercial EVOO was analyzed but on the basis of sensory analysis. Only 27% of the products were found to be authentic, a number very close to the 33.5% in this study. Official methods were used to determine the fatty acid content in the sensory study and just as occurred here, the percentage of authentic samples was shown as much higher than what was determined from the sensory analysis. These studies have not only shown the potential to analyze EVOO for authenticity at multiple levels of criteria of quality and purity, they can do so in a fast, non-invasive manner that can be implemented in a real-time setting to determine if EVOO meets IOC standards, the newly established FT-NIR index standard, and being pure of adulterants. https://link.springer.com/article/10.1007%2Fs11745-017-4250-5 Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analysis – Karunathilaka, Fardin Kia, Srigley, Chung, Mossoba, Journal of Food Science, Vol. 81, Nr. 10, 2016 Samples of extra virgin olive oil (both reference and retail products), edible oil adulterants, and blends of extra virgin olive oil spiked with 10% to 20% adulterants were scanned using an FT-NIR spectrometer. The following ten adulterants were used: sunflower, soybean, canola, high oleic safflower, peanut, corn, palm olein, and three varieties of hazelnut oil. No sample preparation was required for scanning and samples were scanned in transmission mode. 16 scans were collected per spectrum from 12500 cm^-1 to 4000 cm^-1 using 8 cm^-1 resolution. Two multivariate classification methods were applied to determine the feasibility of classifying authentic olive oil from the NIR spectra. Both Comformity Index (CI) and SIMCA classification methods were applied to the data and better results were shown using the SIMCA method. The SIMCA classification was applied to validation sets for each group and showed a perfect predictive capability to classify the control reference extra virgin olive oil, spiked extra virgin olive oils at 10% and 20% adulterant, pure adulterant oils, and blends of extra virgin olive oil and refined vegetable oil that are marketed in that fashion. However, the commercial products labeled as extra virgin olive oil were predicted at a value of less than 50% accuracy. The likely reason for this is because the off-the-shelf products included oxidation, different kinds of adulterants present than those used in the study, or overall quality degradation. In order to make a model robust enough for accurate predictions of off-the-shelf products, more samples will need to be scanned, analyzed, and added to the calibration models. https://pubs.acs.org/doi/abs/10.1021/jf4000538 Confirmation of Declared Provenance of European Extra Virgin Olive Oil Samples by NIR Spectroscopy – Woodcock, Downey, O’Donnell, Journal of Agricultural and Food Chemistry, 2008, 56, 11520-11525 Over nine hundred extra virgin olive oil samples were collected over three consecutive harvests for the purposes of the study. The purpose of the study was to determine if NIR spectroscopy could determine if a sample came from the Ligurian region of Italy or somewhere else. Approximately twenty percent of the samples were of Ligurian origin. The other samples came from different regions of Italy and other European countries. Samples were scanned using a transflectance probe from 400 nm to 2498 nm. Three spectra were collected for each sample and averaged into one spectrum.

Origin Prediction Results:

Ligurian Samples	92.5%
Non-Ligurian Samples	81.5%

Different post-processing methods were applied to the spectral data and the best prediction results are shown above. Classification analysis was first performed to detect any outlier samples and investigate any grouping of samples based on provenance of origin. A Partial Least Squares Discriminant Analysis (PLS-DA) quantitative model was created which used arbitrary values of 0 and 1 for the two groups. The model will then predict a number for each sample with a cutoff of 0.5 between the two groups. Initial results were poor because nearly half the total samples were Italian samples of non-Ligurian origin and the model showed a bias towards those samples and a poor predictive capability for the other groups. A model using second derivative processing and an equal number of samples between the two groups gave the best results. Classification models can show bias when there are an uneven number of samples among the groups. These results are sufficient for screening purposes and results can be expected to improve with a larger and more balanced sample set. https://pubs.acs.org/doi/pdfplus/10.1021/jf802792d?src=recsys

Vegetable & Other Edible Oils

Rapid Characterization of Transgenic and Non-Transgenic Soybean Oils by Chemometric Methods Using NIR Spectroscopy – Luna, da Silva, Pinho, et al., Spectrochimica Acta Part A 100 (2013) 115-119 Genetic engineering of food has become advanced in recent years but in many parts of the world such food is considered undesirable, creating the need for rapid screening methods for proper labeling. Forty transgenic and forty non-transgenic soybean oil retail samples were procured for the study. Transmission spectra were collected from 1100 nm to 2500 nm at 1 nm intervals. Five spectra were collected per sample and averaged into one spectrum. Various post-processing algorithms were applied to the spectra and classification methods were performed to determine the best method for separating the two types of samples. The best results were obtained using Support Vector Machine-Discriminant Analysis (SVM-DA), a technique used for binary classification. This technique corrected predicted 100% of the non-transgenic samples and 90% of the transgenic samples in the validation set. Since there were no reference tests conducted on the samples and the classification was based strictly on what the retail label displayed, it is possible that some of the samples were misclassified to start. However, the results shown in this study are good enough for screening purposes to classify transgenic and non-transgenic soybean oils. https://www.ncbi.nlm.nih.gov/pubmed/16131099 Classification and Quantification of Palm Oil Adulteration Via Portable NIR Spectroscopy – Basri, Hussain, Bakar, et al., Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 173 (2017) 335-342 Lard can produce cheap oil and can be mixed with other vegetable oils as an adulterant. A NIR spectrometer was set up to operate in both transmission and transflectance mode for comparative purposes. Samples were prepared of pure palm oil, pure lard, and mixtures at various increments of added lard from 0.5% to 50%. Absorbance spectra of all samples were collected in both modes from 950 nm to 1650 nm and exported for chemometric analysis. Classification analysis was performed on pure vs. adulterated palm oil and Partial Least Squares (PLS) regression models for quantifying lard in palm oil was created from both sets of NIR spectra.

Transmission:

Classification Accuracy	0.95
Lard PLS	R² = 0.9998

Transflectance:

Classification Accuracy	0.93
Lard PLS	R² = 0.9996

Results for transmission were slightly better in both classifying pure palm oil and adulterated samples and in measuring the % lard adulteration in palm oil. A Cumulative Adaptive Reweight Sampling (CARS) algorithm was applied to determine wavelengths critical to the model while eliminating noisy wavelengths. Recalculation of the model after applying CARS showed improved results and this contributed to the high correlation coefficients using both scanning modes. https://www.sciencedirect.com/science/article/pii/S1386142516305455