olive oil Archives - NIR-For-Food

Edible Oils Adulterant Analysis

Richard Jackson — Mon, 19 Dec 2022 20:09:56 +0000

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

The post Edible Oils Adulterant Analysis appeared first on NIR-For-Food.

Vegetable & Other Oils Analysis

Richard Jackson — Fri, 16 Dec 2022 20:26:45 +0000

Introduction

The global edible oils market is estimated at a value around $100 billion and is expected to grow substantially in coming years. Vegetable oils are a large part of the market and there are many reasons why this is the case. Health conscious consumers and growing awareness about food contents, especially in today’s social media environment, have pushed demand away from unhealthy food contents like trans fats, partially hydrogenated oils, and cholesterol and more towards healthy oils like olive and canola. Analysis of raw materials used for oil manufacturing and processing is important. Fatty acid content is the most essential parameter in differentiating between healthy and unhealthy edible oil products. Oxidation is a major issue after edible oil production. Oils rich in polyunsaturated fatty acids are susceptible to the formation of peroxides and hydroperoxides after exposure to oxygen, heat, and light. Monitoring the potential for oxidation during production and after product formation is important for quality control. Product authenticity is another parameter that requires monitoring but is often difficult to determine in practice. Two examples of this are determining if an oil is transgenic or non-transgenic and if an expensive edible oil is adulterated with a cheaper product. Health conscious consumers have also created a demand for new products, such as combinations of butterfat and vegetable oil as an alternative to butter that contains less saturated fat. This process is done using a catalyzed reaction and real-time reactor monitoring would be useful for optimizing the reaction. The demand for such products has created a need for fast, cheap, real-time monitoring of parameters in vegetable oils at all stages of the production and storage process that can replace expensive, laborious, and time-consuming wet chemistry methods. One such method that has been examined is NIR spectroscopy.

Analytes

Total Oil
Oleic Acid
Linoleic Acid
Linolenic Acid
Palmitic Acid
Stearic Acid
Essential Oil Content (EOC)
Saturated Fatty Acids (SFA)
Monounsaturated Fatty Acids (MUFA)
Polyunsaturated Fatty Acids (PUFA)
Trans Fatty Acids (Trans FA)
Natural α -Tocopherol
Transgenic and Non-Transgenic Classification
Lard Adulteration of Palm Oil
Peroxide Value (PV)
Conversion Degree/Peak Ratio
Solid Fat Content (SFC)

Summary of Published Papers, Articles, and Reference Materials

Measurement of chemical parameters of vegetable and other edible oils for quality control purposes has been studied using NIR spectroscopy. The results of most studies have been promising. Peanut oil is an important parameter in determining the use of peanuts after harvesting and two breeds of peanuts were scanned through the shell for the purpose of determining total oil as well as individual fatty acids. Results showed that the total oil content could be predicted with accurate enough results for quality control analysis, while the individual fatty acid models could work for initial screening purposes. Determining parameters in the raw material used to create and process vegetable oil is critical. One study compared using a handheld spectrometer and FT-NIR spectrometer for determining essential oil content (EOC) in oregano. Results using the FT-NIR were accurate enough for real-time monitoring while the handheld results were not as accurate. More calibration work should improve the results of using a handheld spectrometer for this purpose. Fatty acid content is an important parameter in any edible oil and one study examined measuring SFA, MUFA, PUFA, and Trans FA in various off-the-shelf edible oils. There was some difficulty in measuring Trans FA but this is likely because a “Zero Declared Trans FA” oil can in reality have up to 2% Trans FA. α-Tocopherol is an active form of Vitamin E and an important natural antioxidant of lipids that is found in vegetable oils. This compound has been successfully measured using NIR spectroscopy. Generic engineering has become an important part of food manufacturing and the feasibility of separating transgenic and non-transgenic soybean oils was studied and found to be good enough for screening purposes. Adulteration is an issue with many types of oil, especially palm oil, which is one of the edible oils most in demand as well as having the highest projected growth according to market forecasts. Lard adulteration in palm oil was studied and the calibration model proved the feasibility of detecting and quantifying the lard adulterant. Oxidation of vegetable oils is a major problem during storage and one parameter for determining oxidation is peroxide value (PV). Studies were conducted measuring PV in olive, sunflower, rapeseed, corn, and soybean oils, all showing excellent results. The demand from health conscious consumers has led to new products in the market, such as mixtures of butterfat and edible oil, which are catalyzed in a reactor using different enzymes. The feasibility of monitoring such a reaction between butterfat and oil blends has been examined. Triglyceride profile analysis and solid fat content (SFC) were modeled using reference data and NIR spectra, with the Conversion Degree/Peak Ratio showing good correlation and the SFC only showing discernible change at a certain temperature. Nevertheless, the SFC model could be used but the Conversion Degree/Peak Ratio model would be the model of choice for real-time analysis of the reaction. Overall, the papers discussed below all show promise for implementing NIR spectroscopy throughout the production and storage process for vegetable and other edible oils.

Scientific References and Statistics

Determination of In-Shell Peanut Oil and Fatty Acid Composition Using Near-Infrared Reflectance Spectroscopy – Sundaram, Kandala, Holser, et al., J Am Oil Chem Soc (2010) 87:1103-1114

NIR spectroscopy was used to analyze two different varieties of in-shell peanuts for total oil and fatty acid concentration. 50 kg of Virginia and Valencia peanut pods obtained over two consecutive harvests were procured for the study. For each group, sample sets were divided out for calibration and validation. Within each set, thirty spectra were collected from 400 nm to 2500 nm at 0.5 nm intervals. The thirty spectra were then averaged into one spectrum for one data point per set. Reference tests were performed for total oil and fatty acid concentration.

Virginia:

Total Oil	R² = 0.9772
Oleic Acid	R² = 0.9838
Linoleic Acid	R² = 0.977
Linolenic Acid	R² = 0.981
Palmitic Acid	R² = 0.9838
Stearic Acid	R² = 0.9507

Valencia:

Total Oil	R² = 0.9601
Oleic Acid	R² = 0.5734
Linoleic Acid	R² = 0.8289
Linolenic Acid	R² = 0.8475
Palmitic Acid	R² = 0.9532
Stearic Acid	R² = 0.7681

Correlation coefficients were all above 0.95 for the Virginia data set but lower for the Valencia set. Predictions on the validation set for both groups indicated that the total oil content could be predicted with results good enough for quality control and analysis. In the case of the individual fatty acids, the results were not as accurate but good enough to work for initial screening purposes. Oleic acid showed especially low correlation for the Valencia data set. While it is hard to pinpoint the exact reason for this, model data showed numerous outlier samples that were not fit into the model with good correlation. It should be noted that oleic acid has been correlated using NIR spectra in other studies in different types of oils (including the Virginia set in this study), so the reason clearly is due to something either in the samples themselves or in the reference method. More calibration work will be required to validate the fatty acid measurements for use in a real-time setting.

https://link.springer.com/article/10.1007%2Fs11746-010-1589-7

Prediction of Essential Oil Content of Oregano by Hand-held and Fourier Transform NIR Spectroscopy – Camps, Gerard, Quennox, et al., J Sci Food Agric 2014; 94: 1397 – 1402

Several species of oregano over two separate harvests encompassing a wide range of essential oil content (EOC) were procured for the study. Samples were scanned using both a MEMS-based handheld spectrometer and an FT-NIR spectrometer. The MEMS instrument scanned through a glass vial from 1000 nm to 1800 nm using a resolution of 8 nm and thirty scans per average. Multiple spectra were collected per sample and the vial was slightly rotated between each spectrum collection. The FT-NIR instrument scanned from 1000 nm to 2500 nm using a resolution of 12 cm^-1. Spectra were collected in reflectance mode and the samples were rotated in a glass dish. Six spectra were collected per sample. After all the data was collected, the spectra for both instruments were split into calibration and validation sets.

Handheld:

EOC

R² = 0.58

FT-NIR:

EOC

R² = 0.91

Correlation was much better for the FT-NIR model than the handheld model. The FT-NIR instrument gave validated results proving an accurate model for prediction and can be used in a practical real-time setting. The handheld instrument did not show accurate enough results for real-time use, especially when considering that a bias correction was necessary to achieve decent prediction results. One likely reason for the poorer correlation when using the handheld instrument is the limited spectral range. C-H combination bands in oil are prevalent in the wavelength range from 2300 nm to 2500 nm. The FT-NIR model contained this range while the handheld model did not. However, the handheld approach is promising and more calibration work should improve the calibration model.

Rapid FT-NIR Analysis of Edible Oils for Total SFA, MUFA, PUFA, and Trans FA with Comparison to GC – Mossoba, Azizian, Tyburczy, et al., J Am Oil Chem Soc (2013) 90:757-770

Thirty commercial brands of oils and fats were procured for the study. Samples included olive, canola, vegetable, corn, walnut, grapeseed, peanut, flax, coconut, sunflower, and safflower oils as well as one shortening. Two different FT-NIR spectrometers were used, both fit with an adjustable pathlength transflectance probe. Five spectra were collected per sample and averaged into one spectrum using 8cm^-1 resolution. Pre-developed PLS calibration models were used for the SFA, MUFA, PUFA, and Trans FA predictions and the results were compared to a reference GC method.

SFA (FT-NIR vs. GC	R² = 0.9993
MUFA (FT-NIR vs. GC)	R² = 0.9957
PUFA (FT-NIR vs. GC)	R² = 0.9974
Trans FA (FT-NIR vs. GC)	Poor Correlation for quantifying declared zero Trans FA samples

The FT-NIR spectroscopic results were successful for SFA, MUFA, and PUFA and showed agreement with the GC results for these three parameters. There was a marked difference between values declared on the product labels and the two reference methods. Possible reasons for this are new high-oleic oil varieties and oxidation of the products during storage. In the case of Trans FA, the actual Trans FA for a declared value of zero is defined as having a concentration of less than 2% Trans FA as a percentage of total fat. While the results obtained by both reference methods did successfully measure the samples to meet the zero criteria, actual predicted values were different when the Trans FA was less than two percent. The inconsistency likely occurs due to differences in the spectroscopic and chromatographic techniques as well as from the addition of minor components that affect the NIR spectra. Results from this study are very promising when considering that GC is very laborious and expensive to implement in a process setting. While more calibration work would be required to implement measuring edible oil FA on-line using NIR spectroscopy, the benefits from a financial standpoint would be substantial.

https://link.springer.com/article/10.1007%2Fs11746-013-2234-z

NIR Spectroscopy and Partial Least-Squares Regression for Determination of Natural α- Tocopherol in Vegetable Oil – Szlyk, Szydlowska-Czerniak, Kowalczyk-Marzec, Journal of Agricultural and Food Chemistry, Vol. 53, No. 18, 2005

α -Tocopherol is the most active form of Vitamin E and an important natural antioxidant of lipids that are present in vegetable oils. Multiple types of commercially available edible oil were used for this study, including sunflower, soybean, corn, rapeseed, a mix of rapeseed, soybean, & corn, grapeseed, extra virgin olive oil, and a mixture of virgin and refined olive oil. Samples of α -Tocopherol were extracted from each oil with ethanol ranging from 0.54 to 53.54 mg/ml. Extracted samples were scanned from 10,000 cm^-1 to 4000 cm^-1 with fifty scans per average and three averaged spectra used per reading. Resolution was 8 cm^-1. A calibration model was created using the spectra and HPLC references values. Selective wavelength analysis and mathematical algorithms were applied to the calibration model.

Calibration Model for α	Tocopherol in extracted samples	R² = 0.9931
Validation samples	NIR vs. HPLC method	R² = 0.9515

Correlation for the calibration model was high and samples of all eight types of edible oils were chosen for validation samples. After α -Tocopherol extraction, the samples were scanned and the correlation between the NIR and HPLC was above 0.95. The results prove that NIR can be used as a quality control tool for monitoring the oxidative stability of edible oils.

https://www.ncbi.nlm.nih.gov/pubmed/16131099

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 of the spectra and classification methods were performed to determine the best method for separating the two types of samples. 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 showed, 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 regression models for quantifying lard in palm oil was created from both sets of spectra.

Transmission:

Classification Accuracy	0.95
Lard	R² = 0.9998

Transflectance:

Classification Accuracy	0.93
Lard	R² = 0.9996

https://www.sciencedirect.com/science/article/pii/S1386142516305455

Near and Mid Infrared Spectroscopy and Multivariate Data Analysis in Studies of Oxidation of Edible Oils – Wojcicki, Khmelinskii, Sikorski, Sikorska, Food Chemistry 187 (2015) 416-423

Both FT-NIR and FT-MIR with an ATR crystal were used to study oxidation of retail samples of olive, sunflower, and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored for fifteen days using peroxide values and the samples were scanned in transmittance mode with both spectrometers. NIR absorption spectra were collected from 12,500 cm^-1 to 4000 cm^-1 with thirty-two scans per average at 16 cm^-1 resolution. MIR spectra were collected from 4000 cm^-1 to 650 cm^-1 with sixteen scans per average at 4 cm^-1 resolution. Three spectra were recorded for each sample using both instruments and peroxide values were recorded using the standard iodometric method.

NIR:

Peroxide Value (PV)

R² = 0.970

MIR:

Peroxide Value (PV)

R² = 0.710

NIR & MIR:

Peroxide Value (PV)

R² = 0.954

Calibration models were created using the individual sets of NIR and MIR spectra as well as combining them both. Correlation demonstrated the ability to evaluate the oxidative stability of oils, particularly using the NIR method. One likely reason for the lower correlation using MIR is the smaller pathlength and less penetration into the sample. It is known that implementing NIR in a process setting is simpler and easier to do than using MIR, especially when it comes to sample preparation. The recommended next step is continued calibration work using different oils and the NIR spectrometer.

https://www.sciencedirect.com/science/article/pii/S0308814615005920

Monitoring PV in Corn and Soybean Oils by NIR Spectroscopy – Yildiz, Wehling, Cuppett, JAOCS, Vol. 79, no. 11 (2002)

Samples of corn and soybean oils were scanned with an NIR spectrometer for the purpose of measuring oxidation levels by peroxide value. Absorbance spectra were collected from 400 nm to 2500 nm at 2 nm intervals. PV oxidation levels were recorded as reference values using a standard method. All chemical analyses were collected in duplicate and the mean was used for the final value.

Corn Oil:

Peroxide Value (PV)

R² = 0.987

Soybean Oil:

Peroxide Value (PV)

R² = 0.996

Corn and Soybean Oils

Peroxide Value (PV)

R² = 0.993

Individual calibration models were created for both types of oil and for both types combined into one model. Correlation was excellent for all three models and the results show that the combined model is robust enough to incorporate both types of oil without introducing error. While the correlation coefficient is slightly lower for the combined model, prediction results using a validation set showed a statistically negligible difference between the individual and combined models.

https://link.springer.com/article/10.1007/s11746-002-0608-1

Monitoring Lipase-Catalyzed Butterfat Interesterfication with Rapeseed Oil by Fourier Transform Near-Infrared Spectroscopy – Zhang, Mu, Xu, Anal Bioanal Chem (2006) 386:1889-1897

This study used FT-NIR spectroscopy to monitor the enzymatic interesterfication process for butterfat modification. Health conscious consumers have created a demand for alternative food products contained less saturated fat. Such demand has led to new products like a combination of butterfat and oil blends, made in a reactor catalyzed by different enyzmes. For this study, a blend of butterfat and rapeseed oil were placed in a flatbed reactor and catalyzed by lipase. Spectra were collected in transmission mode from 12000 cm^-1 to 4000 cm^-1 at 70°C. Reference values for conversion degree (evaluated from the triglyceride profile and obtained by the triglyceride peak ratio) and solid fat content (SFC) were collected. Triglyceride profiles were determined using reversed-phase HPLC and peaks were identified by triglyceride standards with a known equivalent carbon number (ECN). Peak ratio is defined as (ECN48/ECN46). All reference values were determined in triplicate and averaged for one value. Calibration models for these two parameters were created using the spectral data and reference values.

Peak Rati	R² = 0.935
SFC	R² = 0.939

The best correlation for both parameters was obtained used the reduced wavenumber range from 5269 cm^-1 to 4513 cm^-1. It must be noted that the SFC only changed at 5°C and the change was only slightly greater than 5%. Because of this, peak ratio is much better suited for online monitoring of this reaction. If it were required to measure SFC, reasonable precision could be expected using the calibration model created here.

https://link.springer.com/article/10.1007/s00216-006-0734-5

The post Vegetable & Other Oils Analysis appeared first on NIR-For-Food.

Fish Oil Analysis

Richard Jackson — Sat, 13 Jul 2019 16:12:31 +0000

Introduction

Fish oils are known for their benefits to human health because they contain Omega-3 fatty acids (n-3 FA), which are families of long-chain polyunsaturated fatty acids (PUFAs). Many such fatty acids have essential roles in the prevention and treatment of coronary heart disease, diabetes, autoimmune disorders, arthritis, hypertension, and inflammatory diseases. The two predominant fatty acids with these benefits are Eicosapentaenoic Acid (EPA, 20:5 n-3) and Docosahexaenoic Acid (DHA, 22:6 n-3) and these are found almost exclusively in seafood products. Dietary guidelines recommend 0.5 g to 1.0 g of these fatty acids per day and more for various treatments. Fish oil contains about twenty percent of their total fatty acids as PUFA, making them highly susceptible to both oxidation and hydrolytic degradation of lipids. These reactions can cause rancid odors and can even give rise to the formation of products like aldehydes, hydroperoxides, and epoxides that can detract from the nutritional content. Some fish oils are converted to biodiesel by alkali or lipase-catalyzed transesterification reactions and Free Fatty Acids (FFA) content is a critical parameter in these reactions. Moisture, FFA, Peroxide Value (PV), Anisidine Value (AV), EPA, and DHA are monitored using physical and chemical techniques, but these methods are slow and expensive to implement, especially in an industrial process setting. There is a pressing need to determine these parameters that cannot only test large amounts of samples but do so in a cheap and timely fashion. One such method that has been examined is NIR spectroscopy.

Analytes

Free Fatty Acids (FFA)
Moisture
Peroxide Value (PV)
Anisidine Value (AV)
Eicosapentaenoic Acid (EPA)
Docosahexaenoic Acid (DHA)
Total Omega-3 Fatty Acids (n-3 FA)

Summary of Published Papers, Articles, and Reference Materials

Measurement of chemical parameters for quality control purposes has been studied using NIR spectroscopy for fish oils. The results of most studies have been promising. One such study examined monitoring the oxidative and hydrolytic degradation of lipids in fish oil. Good correlation was shown between NIR spectral data and models for FFA and moisture. The correlation was worse for PV and AV, but this may have occurred due to an error in the reference method. Measuring these parameters with accuracy using NIR spectroscopy has been proven in studies using other types of edible oils. Another study compared using NIR, IR, and Raman spectroscopy to measure EPA, DHA, and Total n-3 FA. Another study used a variety of fish oil supplements and IR spectroscopy to measure EPA and DHA with excellent results. FTIR was examined as an alternative for replacing a conventional titration method to measure FFA in fish oils intended for biodiesel production and the results proved the feasibility of this measurement. However, implementing NIR spectroscopy as a large-scale testing method for fish 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

Multivariate Determination of Free Fatty Acids and Moisture in Fish Oils by Partial Least-Squares Regression and Near-Infrared Spectroscopy – Cozzolino, Murray, Chree, Scaife – Science Direct LWT 38 (2005) 821-828

One hundred and sixty fish oil samples from a fishmeal factory were scanned in transflectance mode using a NIR monochromator instrument. Wavelength range was from 1100 nm to 2500 nm. The fish oil samples had different chemical compositions due to differences in species and seasonality as well as storage in different tanks. Reference values for Free Fatty Acids, Moisture, Peroxide Value, and Anisidine Value were obtained during the production of material and not at the time of scanning. There was a minimum of five days between sampling and scanning time.

Free Fatty Acids (FFA)	R²= 0.96
Moisture	R²= 0.94
Peroxide Value (PV)	R²= 0.60
Anisidine Value (AV)	R²= 0.93

Good correlation was obtained for FFA and moisture calibration models and validation predictions proved the feasibility of the models for measuring these parameters. However, the PV and AV models did not work as well for the validation set. In the case of PV, the delay in time between collecting reference data and scanning the samples contributed to the low correlation coefficients as some oxidation almost certainly occurred in the samples, creating high reference error. In the case of AV, the correlation coefficient was high, but many of the samples had a reference value of zero as secondary oxidation had not yet occurred. Some validation predictions showed a value less than zero which invalidates the model in a real-time setting. PV and AV have been accurately predicted in other types of edible oil and should be able to in fish oil as well with a more carefully constructed calibration set. The results do prove the feasibility of monitoring hydrolytic degradation of lipids in fish oil using NIR spectroscopy.
https://www.sciencedirect.com/science/article/pii/S0023643804002865

Determination of Omega-3 Fatty Acids in Fish Oil Supplements Using Vibrational Spectroscopy and Chemometric Methods – Bekhit, Grung, Mjos, Applied Spectroscopy, Volume 68, Number 10, 2014

A Fourier Transform Infrared (FT-IR), Near-Infrared (NIR), and Raman spectrometer were all used to scan sixty-one fish oil supplements to predict concentrations of Eicosapentaenoic Acid (EPA), Docosahexaenoic Acid (DHA), and Total Omega-3 Fatty Acids (n-3 FAs). GC was used as a reference method to determine concentrations of these three parameters. EPA and DHA concentrations were expressed as percentages relative to the total mass of fatty acids. Total n-3 FAs were the sum of concentrations of eight single n-3 FAs.

Scanning Parameters
FT-IR	4000 cm-¹– 650 cm^-1, 32 scans per average, 4 cm^-1 resolution, ATR Crystal background
NIR	1100 nm -2500 nm, 2 nm intervals, 32 scans per average, 4 cm^-1 resolution, adjustable transflectance probe, 1 mm pathlength
Raman	3450 cm^-1 – 0 cm^-1, 5 second exposure time, <500mW excitation laser at 785 nm

Results
FT-IR	EPA-	R²= 0.994, Range = 1820 cm^-1 – 650 cm^-1
	DHA-	R²= 0.983, Range = 1820 cm^-1– 650 cm^-1
	Total n-3 FAs-	R²= 0.985, Range = 3090 cm^-1 -2800 cm^-1 & 1790 cm^-1 –650 cm^-1
NIR	EPA-	R²= 0.979, Range = 1530 nm -1900 nm
	DHA-	R²= 0.972, Range = 1530 nm -1940 nm
	Total n-3 FAs-	R²= 0.997, Range = 1630 nm -1870 nm
Raman	EPA-	R²= 0.977, Range = 1800 cm^-1-769 cm^-1
	DHA-	R²= 0.966, Range = 1800 cm^-1-769 cm^-1
	Total n-3 FAs-	R²= 0.993, Range = 3450 cm^-1 – 0 cm^-1

Models were created for all three instruments using different pre-processing techniques and selective wavelength ranges. The best results are shown above. Excellent correlation was shown for all parameters, demonstrating the potential to measure EPA, DHA, and Total n-3 FAs in fish oil supplement. These three vibrational spectroscopy methods have distinct advantages and disadvantages depending on the situation. While FT-IR is easy to implement in a laboratory, the small pathlength when reflecting off an ATR crystal makes it ill-suited for some measurements. Likewise, Raman is unable to detect Raman shifts in certain molecules. NIR is best suited to a process environment because no sample preparation is needed.

http://journals.sagepub.com

Application of Infrared Spectroscopy for Characterization of Dietary Omega-3 Oil Supplements – Plans, Wenstrup, Rodriguez-Saona, J Am Oil Chem Soc (2015) 92:957-966

Commercial Omega-3 dietary supplements of fish oil, cod liver oil, and flaxseed oil from different manufacturers were procured for this study. Duplicate samples for some of the supplements were also purchased at a later date, ensuring separate lots from the original supplements. All samples were scanned from 4000 cm^-1 to 700 cm^-1using 4 cm^-1resolution and an ATR crystal as a background. GC was performed to create a Fatty Acid profile for each sample. Calibration models were created for seven fatty acids using selective wavenumber ranges.

C14:0	R²= 0.99
C16:0	R²= 0.99
C16:1	R²= 0.95
C18:1	R²= 0.95
EPA (Eicosapentaenoic Acid)	R²= 0.99
DHA (Docosahexaenoic Acid)	R²= 0.99
FFA (Free Fatty Acids)	R²= 0.96

Results showed a consistent classification of four groups of samples. Based on EPA/DHA content, oil source, and factors associated with processing (FA alkyl ester or triglyceride). The samples in the grouping showed clear spectral distinction in the absorption bands for triglycerides and FA alkyl esters. Models showed excellent performance, especially for EPA and DHA which are the two primary Omega-3 fatty acids of interest in such supplements. Prediction results confirm the capability to estimate the main FAs of fish oil supplements.
https://rd.springer.com

FTIR Determination of Free Fatty Acids in Fish Oils Intended for Biodiesel Production – Aryee, Van de Voort, Simpson, Process Biochemistry 44 (2009) 401-405

Biodiesel is commonly derived from vegetable oils and animal fats by alkali or lipase-catalyzed transesterification reactions. Free Fatty Acids (FFA) is a critical parameter in the conversion of fish oil to methyl esters and the feasibility of using FTIR for determining FFA was examined. The performance of FTIR was assessed as an alternative to the conventional AOCS titration method. Spectra were collected using a transmission flow cell at a resolution of 8 cm^-1. Sixteen scans were averaged per spectrum and ratioed against an open-beam background spectrum.

The FTIR method involves simultaneously extracting FFAs, conversion to salts using a weak base, and measuring the carboxylate band (COO-) at 1573 cm^-1. The method was found to respond linearly to oleic acid addition, producing a calibration equation for FFA. Validation samples proved the FTIR method was more accurate and reproducible than the titrimetric method.
https://www.sciencedirect.com/science

Commercial References

Contact one of Galaxy Scientific’s Applications Specialists to discuss this information in further detail.

The post Fish Oil Analysis appeared first on NIR-For-Food.

Olive Oil Analysis

Richard Jackson — Sat, 13 Jul 2019 15:08:37 +0000

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 Index	R² = 0.83
K232	R²= 0.75
K270	R²= 0.67
Alkyl Esters	R²= 0.79
Ethyl Esters	R²= 0.80
Moisture & Volatile Matter	R²= 0.71
Insoluble Impurities	R²= 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^-1resolution. 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

Contact one of Galaxy Scientific’s Applications Specialists to discuss this information in further detail.

The post Olive Oil Analysis appeared first on NIR-For-Food.