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Chewing on the Fat of Food Analysis and Beyond

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To ensure safety and consistency of products within the food industry, continual analysis through the production process is essential. Factors such as water content, trace elements and sugar must all be verified. The fat content within products is also an important measurable parameter which can affect not only the appearance and taste of a product, but also whether it meets the parameters claimed on the packaging. Natural products such as meat for example may vary widely from animal to animal.

As part of a food analysis lab, being able to determine the fat content within ingredients and products is therefore an essential capability. Typically, this has involved laborious sample preparation and time-consuming wet-lab chemistry techniques. In what can be a fast-paced environment, this can be a barrier to improving sample turnaround.

We spoke to Ian Olmsted, Product Manager, Process Control Division for CEM Corporation, about ORACLE, an innovative solution that enables analysts to measure fat in food and much more without the fuss of traditional techniques.

Karen Steward (KS): What prompted the development of ORACLE? Was there a particular unmet need that had been identified? 

Ian Olmsted (IO):
There was an unmet need in the market for a universal analyzer that was not matrix-specific. Until the ORACLE, all rapid fat analysis methods required calibration and method development. These technologies include previous generations of time domain NMR (TD-NMR) as well as NIR and FT-IR spectroscopy. With other technologies, new food products have to be extensively characterized by traditional wet-chemistry methods to build a calibration before any rapid analysis can be performed. With the ORACLE, a user can place any sample in the instrument, with no prior knowledge, and get an accurate fat analysis in less than a minute.

KS: How does ORACLE improve on existing testing methods? 

Existing methods can be broken down into two categories: reference chemistry or rapid alternatives. Reference chemistry methods involve hazardous solvents and complicated procedures to extract fat from the sample so it can be gravimetrically measured. Rapid alternatives correlate reference chemistry results to an alternative signal. Because rapid alternatives such as TD-NMR, NIR and FT-IR must be calibrated to reference chemistry, the inherent error in the reference methods are transferred to the rapid method. The ORACLE has been shown in independent studies to be more precise and accurate than reference chemistry, all without the need for calibration.

KS: Are there any food products that are particularly challenging and why? 

The main challenge in most food types is in getting a homogenous, representative sample. Optical techniques only measure the surface or a small cross section of the sample. The ORACLE, which uses proprietary NMR technology, probes the entire sample for more accurate and repeatable results.

KS: From a user’s point of view is specialist training required to use ORACLE and analyze the results? Is transitioning from existing techniques a simple process for a laboratory? 

The ease of use is one of my favorite features about the ORACLE. All a user needs to do is press "start" and drop the sample in the instrument. I can teach anybody how to use the ORACLE in just a few minutes.

KS: What impact might the capabilities that ORACLE offers have on the food industry? 

The ORACLE, which is the first rapid, primary method of its kind, has the potential to replace reference chemistry methods completely. That would save countless hours of labor, quantities of hazardous solvents waste, and money through more accurate results.

KS: Do you see potential applications for the technology outside of food and beverage analysis? 

The ORACLE is already being used outside of the food industry. The most exciting non-food applications are in medical research where fat levels in human samples such as serum are needed. Anywhere fat molecules need to be quantified, we already have the capability to provide value.

Ian Olmsted was speaking to Dr Karen Steward, Science Writer for Technology Networks.