Combating food fraud has different meanings depending on whom you ask along the supply chain. For growers, it refers to protecting the integrity of the ingredients they introduce into the supply chain. For the regulatory community, it means helping to reinforce and establish the authenticity of the food market so consumers don’t have to worry about the safety of the food that they eat. For food retailers or manufacturers, it’s about maintaining their brands’ integrity and value with consumers and the industry. For everyone involved—from farm to fork—it’s about ensuring there is a continued supply of safe food around the globe.
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Explore This IssueAugust/September 2019
In order to battle food fraud, it is vital to provide a host of robust analytical and informatics solutions that can detect and analyze adulterants throughout the supply chain. Techniques such as infrared (IR) spectroscopy, liquid chromatography tandem mass spectrometry (LC-MS/MS), and inductively coupled plasma mass spectrometry (ICP-MS) address food quality and safety and help consumers be more confident in the integrity of the food they eat.
First Line of Defense: UV-Vis and IR Spectroscopy
There are a number of different methods and technologies used to detect adulterants in food. The chosen method will depend on the type of food fraud that is being detected.
For example, a UV-visible light (UV-vis) spectrometer is considered a useful and simple instrument that detects adulterants in extra virgin olive oil. With olive oil consumption increasing, this high-value product has become particularly susceptible to fraud.
One example of olive oil fraud is the addition of lower grade, refined olive oils to extra virgin olive oil. These lower-quality oils contain unsaturated hydrocarbons that absorb UV light in the 200 nm-300 nm spectral range. Therefore, a high absorption within this wavelength range points to a lower quality olive oil, meaning UV-vis spectroscopy can be used to differentiate between oils in a sample.
Extra virgin olive oil can also often contain significant levels of other edible oils that have a lower market price or are of a lower quality. Some examples of common adulterants include hazelnut oil, sunflower oil, soybean oil, rapeseed oil, or corn oil. UV-vis spectroscopy offers a simple method for checking whether an analysis result is above a specific limit, and therefore whether other oils have potentially been added to a sample of extra virgin olive oil.
In situations where there is uncertainty about the type of adulteration that may have taken place, IR spectroscopy is the preferred method for rapid, onsite analysis of samples in other commonly adulterated foods like honey and orange juice. As IR spectroscopy requires little sample preparation, it is also an easy-to-implement method that is useful in providing a rapid pass/fail analysis of adulteration. This, along with the fact that it does not require significant training to be operated, means IR spectroscopy can be used for testing at any point during the supply chain.
For example, herb and spice adulteration—such as replacing oregano with olive or myrtle leaves, the addition of dyes to chili powders, or adding peanut and almond material to ground cumin powder—is rapidly becoming more commonplace in the food industry and is a prime fit for IR spectroscopy. One issue with herb and spice samples, as with most food samples, is that they typically contain many sources of natural variation and are therefore difficult to analyze. Near-IR (NIR) spectroscopy can overcome this issue, enabling deeper penetration into samples in comparison to mid-IR or far-IR. NIR can therefore produce stronger spectra, making it easier to detect adulterants in these complex samples.
By combining this instrumentation with advanced analysis technology, it is possible to compare the spectra of a specific food sample with a database of known “pure” samples. These algorithms and chemometric techniques then enable users to classify complex samples, determine authenticity, and estimate the level of a certain adulterant without the need to run a further test.