Applications

Olive Oil Analysis

The best olive oils are not easy to find and if you’re like most people, it’s quite possible you have never had a truly excellent extra virgin olive oil.

Introduction

Olive oil is an essential component for consumer diets, especially in the Mediterranean and Europe. Quality is influenced by many factors, such as plant variety, environmental conditions, harvesting, and processing.  There are strict standards applied to olive oils for international trade.  Olive oils are divided into four categories: Extra Virgin, Virgin, Lampante, and Refined with Extra Virgin being the most valuable and healthy, while Lampante is only used for refining and technical purposes and is unfit for human consumption. Extra Virgin refers to the very first cold press of the olives and has a free acidity (expressed as oleic acid) of not more than 0.8 grams per 100 grams. Virgin is the first batch of oil that is extracted after the first cold press and has a free acidity of not more than 2.0 grams per 100 grams. Refined is obtained by refining virgin olive oils that have a high acidity level. Lampante refers to oil that has a free acidity of more than 3.3 grams per 100 grams and comes from either bad fruit or careless processing. EU standards specify twenty-six physical & chemical properties as well as two sensory characteristics for these oils.  Some of these that are measured in intact olives are moisture, oil content, sugar content, and maturity index. These are also measured in olive paste during the pressing process. Physical parameters of importance in intact olives are yield point force and total deformation energy. Prominent among these standards for olive oil quality are acidity, fatty acids, and esters. Oxidation is a deterrent to olive oil quality, and peroxide value measures the primary oxidation product in oil while anisidine value measures secondary oxidation products.  Adulteration of extra virgin olive oil by adding lower quality oils is a huge problem in the industry, causing financial losses and repercussions with consumers, especially in today’s social media environment. Another form of adulteration is the misrepresentation of geographic origin. The tremendous volume of olive oil produced annually has created a need to determine quality, purity, authenticity, and geographic origin using methods that can not only test large amounts of samples but do so in a cheap and timely fashion. One such method which has been examined is NIR spectroscopy.

Analytes

  • Fatty Acids
  • Peroxide Index
  • Esters
  • Extinction Coefficients
  • Moisture & Volatile Matter
  • Insoluble Impurities
  • Linoleic, Oleic, and Palmitic Acid
  • Presence of Adulterants
  • Confirmation of Geographical Origin
  • Presence of Adulterants
  • Confirmation of Geographical Origin

Summary of Published Papers, Articles, and Reference Materials

Measurement of chemical parameters and adulteration for quality control purposes has been studied using NIR spectroscopy for olive and other types of oils. The results of most studies have been promising. One such study examined various olive oil quality measurements and showed good results for acidity, peroxide index, esters, and moisture & volatile matter, with sufficient results for classifying between high, medium, and low samples for extinction coefficients, a measure of oxidation state. Studies for adulterants have also shown good results. One study used different vegetable oils, palm olein, and refined olive oil as adulterants in extra virgin olive oil and was successfully able to identify the presence of an impure sample based on linoleic, oleic, and palmitic acid concentrations. Another used different low-quality oils as adulterants in extra virgin olive oil. Classification analysis readily identified samples with greater than 10% adulteration, and these results are good enough for screening purposes. In the case of adulteration by misrepresentation of geographical origin, Ligurian and non-Ligurian extra virgin olive oils were scanned for classification purposes, and classification analysis showed a predictive capability of greater than 90% identifying Ligurian samples and greater than 80% for identifying non-Ligurian samples. However, implementing NIR spectroscopy as a large-scale testing method for olive oil will require extensive calibration work to include different varieties and incorporation of natural sources of variability because they are natural products.

Scientific References and Statistics

Fast, Low-Cost, and Non-Destructive Physico-Chemical Analysis of Virgin Olive Oils Using Near-Infrared Reflectance Spectroscopy – Garrido-Varo, Sanchez, De La Haba, et al. – Sensors 2017, 17, 2642

Nearly five hundred olive oil samples were scanned using two different spectrometers, one spinning the samples during scanning and the other using a static cup. Both instruments collected absorbance spectra from 400 nm to 2500 nm at 2 nm intervals. Two spectra were collected per sample and averaged into one spectrum for post-processing.  Reference values were collected for the samples for acidity, peroxide index, K232 & K270 (Extinction coefficients – a measure of oxidation), alkyl and ethyl ester content, moisture & volatile matter content, and insoluble impurities in light petroleum.

Spinning Module: 
Acidity (% olecic acid)R2 = 0.99
Peroxide IndexR2 = 0.83
K232R2= 0.75
K270R2= 0.67
Alkyl EstersR2= 0.79
Ethyl EstersR2= 0.80
Moisture & Volatile MatterR2= 0.71
Insoluble ImpuritiesR2= 0.71

Initial assessment of the calibration models showed better results for the spinning mode of sample presentation so these spectra were used for the results shown above. Good correlation was shown for all parameters, especially acidity. All correlation coefficients were above 0.70 and this is high enough for screening predictive capabilities. The results show the potential to use NIR spectroscopy as a non-destructive quality control tool during production and storage in the olive oil industry.
https://core.ac.uk/display/143462613


Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils – Azizian, Mossoba, Fardin-Kia, et al., AOCS Lipids (2016) 51: 1309-1321

A range of extra virgin olive oil samples grown in different countries was obtained for the study. Extra virgin olive oil is a valuable commodity and is often adulterated with vegetable oil, palm olein, or refined olive oil. Pure samples were spiked with different varieties of adulterants at different concentrations.

Absorbance spectra of all samples were collected using 8 cm-1 resolution.  An initial investigation determined that all pure sample varieties of extra virgin olive oil could be classified into four distinct groups. Blend-specific calibration models were developed for each group to predict low concentrations of vegetable oils and/or refined olive oil high in linoleic, oleic, or palmitic acid.

Model 1:

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

Model 2:

Oleic Acid (Hazelnut, Safflower, Peanut)

Model 3:

Palm Olein

Model 4:

Refined Olive Oil

The four sets of models used an algorithm to determine an FT-NIR index, a measurement of purity based on the fatty acid concentration and prediction of the four modeled parameters. The FT-NIR for all sets of samples showed a predictive capability to determine if a sample is pure or spiked with one of the adulterants. An FT-NIR value of 100 would indicate a pure sample and samples spiked with adulterants would show a high value for one of the models and subsequently a lower FT-NIR index. The results of this study show the capability to determine the presence of adulterants in extra virgin oil using NIR spectroscopy. More varieties must be added to the calibration models to expand the predictive capabilities of the models beyond the four groups used here.

https://link.springer.com/article/10.1007/s11745-016-4195-0

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. Sixteen scans were collected per spectrum from 12500 cm-1 to 4000 cm-1 with 8 cm-1 resolution. Two multivariate classification methods were applied to determine the feasibility of classifying authentic olive oil from the spectral data.

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 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 for 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. 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

Commercial References

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