Screening for adulterants with Infrared Analytical Technology is gaining momentum as a rapid and convenient way to spot risky samples as a supplement to existing controls, but what is possible and where are we right now? Lars Nørgaard, Head of Chemometric Development at FOSS gives a timely update in this Q & A session.
by Nirperformance staff
The food supply can be prey to all manner of adulterants, accidental or otherwise and another melamine crisis is likely to hit sooner or later.
Except that next time it might not be melamine. It could be something else, as yet, unknown. That’s why the terms targeted and untargeted adulterants are used in connection with adulteration screening. The targeted screening goes after the known substances like melamine, sugar or lard, while untargeted, goes after anything that shows up as abnormal compared to a normal sample.
Herein lies the great strength of infrared-based methods. Based on the infrared spectral signature of a normal sample, a milk sample for example, an infrared analyser can be programmed to recognise what is normal and thereby abnormal. Plug in the data for known adulterants and they can be programmed to recognise those too.
This programming work is done through chemometrics where mathematical models are created based on chemical data extracted from various samples. Accordingly, chemometrics is becoming increasingly important in the fight against adulteration of the food and feed supply chain. Head of chemometric development at FOSS, Lars Nørgaard gives an update on recent developments and what we can expect from Infrared screening methods in the future.
Can infrared (IR) spectroscopy be used for untargeted detection of contaminants in food and feed chains? Will we be able to detect the next melamine crisis?
|In the complex food supply chain there are three main areas to be concerned with: adulteration of incoming raw material, process deviations and mishaps, and deviations from end-product quality. Mid-IR and NIR targeting different sample matrices can provide an analytical platform which is capable of detecting the yet unknown adulterants or contaminants in a high sample-throughput industrial setting. Mid-IR-based global untargeted models for raw milk analysis are examples of already commercially available global applications. So yes, we have the methods to screen for the next melamine crisis, but the untargeted mid-IR or NIR approach should always be considered a screening tool which raises an alert. Further investigations are required to determine the nature of the abnormality.||The samples and the spectrum:NIRThe near infrared (NIR) portion of the infrared spectrum is particularly good at yielding data from solid samples such as feed, grains and powders.Mid-IR The Mid Infrared spectrum is a natural choice for liquid and semi-solid samples such as milk and cream, also referred to as Fourier Transform Infrared after the method used to extract data from this type of sample.|
Is infrared spectroscopy the right method for official controls? What is the place of such technique in an official laboratory?
Infrared spectroscopy can be used for screening of adulterants or contaminants in feed and food products – also in the official control laboratory. Due to the non-invasive aspect and very short analysis time, it is a powerful screening technique with an excellent cost-benefit level compared to other techniques. However, it is important to recognise that a positive result provided by any screening method always has to be verified by primary reference methods – and such methods are often readily available in the official control laboratory. The Food and Drug Administration has introduced a regulatory framework for Process Analytical Technology implementation boosting infrared spectroscopy for control in the pharmaceutical industry, and the same routines are now seen in the food segment.
What about handling heterogeneous samples?
For liquid samples, where mid-IR spectroscopy based methods are a natural choice, the analytical sampling is of course much easier to perform than for powder or solid samples. But advances in technology are also helping to handle these heterogeneous samples. In a new solution for wheat and barley whole grain grading we utilise the power of vision based analysis, both in the visual and NIR spectral range, to overcome the sampling problem by analysing every single seed in the sample. In this solution it is possible to classify 10,000 single seeds for 10-15 defects in around three minutes – a truly impressive system for analytical sampling of heterogeneous samples.
Software for handling and presenting data is a critical part of infrared screening technology. What are the key criteria of good spectroscopy software?
Good spectroscopy software is essential for realising the solution’s full potential to create value to the users. The software must be dedicated with respect to application development and offer seamless integration with databases, and instrument and networking software. The software should provide options to identify, to qualify and to quantify – IQ2 – the sample in question in a logical way with efficient cutting-edge chemometric algorithms. Looking at each of these functions in turn, an identification model can determine if we have skimmed milk powder or whole milk powder in the process pipe or if the sample is deviating from these groups. The qualification model zooms into the actual product – often through a tighter model. And finally, quantification of composition can be performed confidently.
What will be the next revolution in NIR spectroscopy?
FOSS believes that the next revolution in NIR spectroscopy, among other things, will address the difficulties in analysing heterogonous samples. With, for example, hyperspectral NIR cameras it will be possible to predict the chemical composition for a large number of small subsamples without sacrificing speed and in addition decrease the limit of detection for adulterants and contaminants. Also the next generation of process analysers will make it possible to provide much more confidence within feed and food manufacturing.
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