Determining nutritional value and detecting fraud

Ensuring purity in edible oils

Edible oils with high concentrations of monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids have rich nutritional value. These fatty acids have positive benefits for human health and development, such as reducing the risk of cardiovascular disease and diabetes, attenuating the risk of Alzheimer’s disease and assisting in cancer prevention and treatment.[2]

The fats of plants and fish are generally unsaturated. In contrast, most animal fats and many processed foods are high in saturated fat content. The effect of saturated fatty acids (SFA) on heart disease has been extensively studied,[3] and many health authorities advise that saturated fat is a risk factor for developing cardiovascular diseases.[4] Several studies have also observed that SFA can induce endoplasmic reticulum stress and result in liver damage.

Discovering the thuths

So, it is important for consumers to know what they are buying – in particular the correct nutritional value. Ensuring that they do, is usually the responsibility of the governmental authorities charged with ensuring food safety and accurate food labelling. And because government regulators are frequently faced with deliberate fraud in the case of edible oils – in which higher-value oils are diluted with lower-value oils – proper testing is often the only way to find out the truth.

Gas chromatography (GC) is a well-established technique for identifying and quantifying fatty acids in edible oils, especially in combination with a flame ionization detector (GC-FID). However, GC-FID has certain limitations. First, the results obtained pertain to a limited group of fatty acids, excluding the vast majority of compounds contained in the samples. Second, GC-FID provides no information on the identity of the obtained peaks, so it is obligatory for the operator to inject reference standards for confirmation.

Researchers decided to compare this commonly used method with a new method using gas chromatography and a mass spectrometric detector (GC-MS). To assist them, they turned to Shimadzu, who produce all of the high-quality scientific instrumentation necessary for this test. Five different types of edible oils –olive oil, pomace oil, corn oil, cannabis oil and soybean oil – were then studied and analyzed using both GC-FID and GC-MS techniques.

Preparing to test the tests

Based on the number of double bonds, fatty acids are classified as: saturated fatty acids (SFA), monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), which are further sub-categorized into their specific fatty acid components.[1]

Gas chromatography requires fatty acids to be derivatized in order to become sufficiently volatile to be eluted at lower temperatures without thermal decomposition. This usually involves the substitution of a functional group containing hydrogen to form esters, thioesters or amides. Methyl esters are commonly studied derivatives which are produced by methylation. According to the method used, the ester bonds are hydrolyzed to release free fatty acids, which are transmethylated to form fatty acid methyl esters (FAMEs). The resulting FAME profile determined by GC is referred to as the fatty acid composition.

For the purposes of this comparative test, FAMEs were prepared by alkaline methylation. Note: All the chemicals used during sample preparation were of analytical reagent grade (> 98 %): methanol, heptane and potassium hydroxide. Next, aliquots of 1–2 g of sample were filtered in a beaker. If the oil had no sediment or suspension visible to the naked eye, no filtration was required. 0.5 g of oil were then weighed in a 10-ml test tube. 5 ml of heptane and 1 ml of methanolic KOH 2M solution were added to the sample with a pipette. The test tube was shaken vigorously for 30 seconds and then allowed to rest for 15 minutes. (Centrifuging the sample would also have been an option.) Two layers are generated in the test tube, and 1 μl from the upper organic phase (heptane) was then injected into the GC and GC-MS instruments.

The results

The (%) area of FAMEs was calculated separately for each oil using both the GC-FID and GC-MS techniques. Ratios of monounsaturated/polyunsaturated acids were also determined. Oleic acid is the main monounsaturated acid for edible oils, while linoleic and linolenic acids are the basic polyunsaturated acids. Tables 3 and 4 show the overall results from the area percentage of fatty acids in five different edible oils.

SFA – MUFA – PUFA

The percentages of SFA, MUFA and PUFA were compared in the five edible oils. Peaks of palmitic and stearic acids were chosen for SFA, a peak of oleic acid for MUFA and the sum area of linoleic and linolenic acids for PUFA.

The percentage of MUFA in olive oil and pomace oil showed the same values in both the GC-FID and the GC-MS techniques. The abundance of oleic acid in these samples (> 65 %) eliminated the deviations in SFA and PUFA levels. This was different from the rest of the samples as the percentages showed significant deviations. SFA (palmitic and stearic acid) always showed higher values using GC-MS.

The meaning of the results

In this study, five samples were analyzed with both GC-FID and GC-MS techniques. The quantitative results of both techniques were compared, and the evidence obtained showed conclusively why GC-FID remains the benchmark method in the quantitative analysis of fatty acids, despite the development of newer instrumentation.

Nonetheless, the GC-MS technique was shown to clearly add value to the analysis of edible oils. In particular by combining the robustness of the GC-2030 for targeted analysis with the high sensitivity of the GCMS-QP2020NX to obtain structural information for a large number of compounds in each sample, researchers were able to show how much more information could be obtained.

To summarize: While GC-FID clearly answers the question of “how much” for known peaks, GC-MS sensitively answers the question of “what else exists” for unknown peaks, thus enabling a much more thorough screening of the samples.