From: Food Quality & Safety magazine, February/March 2014

Fingerprinting Food: ­Augmenting Existing Near Infrared Technology to Fight Dairy Adulteration

by Sharon Palmer

Testing: Dairy

Due to the nature of economically motivated adulteration (EMA) and mislabeling, it is difficult to predict the exact nature of potential threats, so many in the food industry are looking to detection techniques that help detect “unknown-unknowns.” The analytical testing strategy identified to provide this type of detection is known as “food fingerprinting.” Unlike conventional approaches, which rely on detection of a known number of analytes as an indicator of authenticity, food fingerprinting measures a large number of variables and applies mathematics to generate a fingerprint specific to authentic samples of the commodity or ingredient of adulteration concern. A wide number of analytical techniques have been identified as useful in this approach, including nuclear magnetic resonance, molecular spectroscopy, stable isotope analysis, and mass spectrometry.

For a new approach to be successful and adopted widely, several characteristics are desirable. Namely, it should provide a rapid answer and be deployable in a manner that allows a large number of samples to be screened. Of course, it is highly desirable that it incurs minimal additional testing expense.

Fingerprinting of high-risk food types such as milk powder is ­valuable and NIR spectroscopy clearly has a role to play given its ubiquity in raw materials testing.


Near infrared spectroscopy (NIR) is an ideal choice as it is extensively used today in the food industry, and as a result, capital investments in new detection instruments are minimized. In addition, NIR does not demand laboratory-type sample preparation protocols, lab-based environmental conditions or specific gases, and it generally provides an answer in less than a minute. This enables NIR to be deployed in manufacturing facilities and operated by non-laboratory trained personal, resulting in cost-effective, fast screening for adulteration issues.

Example: Fingerprinting of Milk Powder

Milk powder is one of the most widely traded food commodities, with over 2.5 million metric tons exported annually, and is used in a huge array of food products, from infant formula to baked goods and confectionary. NIR is already widely applied to measure concentrations of key quality parameters such as protein, moisture, lactose, ash, and fat. Protein is a key quality parameter in milk linked to its value, and standard methods for protein analysis rely on a simple nitrogen assay with the protein concentration inferred from the nitrogen content. Addition of chemicals rich in nitrogen can artificially increase the apparent protein and the price demanded. Whilst regulators have responded and enforced tight regulations around some high nitrogen containing chemicals such as melamine, the “chemical space” is vast, and there are many more high-nitrogen compounds that could potentially be used in the same way. To stay ahead of criminals, it’s important to look beyond currently known adulterants and consider other possibilities.

Testing: Dairy
Residual spectra for a contaminated sample. (Red trace: PCA residual, showing evidence of un-modelled components. Green trace: Adulterant Screen residual, showing a much improved fit.)

NIR’s capability can be easily extended to screen samples of these potential unknown threats. NIR spectra contain information about the whole sample—including any adulterants present. There is no physical separation process at work, so the spectra must be processed with appropriate chemometric and mathematical tools to separate the contributions of the milk powder matrix and any adulterants.

A principal components analysis (PCA)-based method such as Soft Independent Modelling Class Analogy or SIMCA, in which a “fingerprint” is built for the unadulterated milk powder, and the degree of fit of the sample spectrum to this model is used to determine whether the result is a pass or a fail, can be used. While this approach is truly non-targeted and potentially sensitive to any adulterant, there is no indication of why a failing sample has failed (no identification of the adulterant) and, because the method makes no use of the adulterant spectrum, the sensitivity cannot be expected to be as high as a quantitative method.

Recent algorithm advances designed specifically to address the problem of screening for potentially numerous adulterants in a complex matrix combines the generality and simplicity of “fingerprinting” with some of the sensitivity benefits of a targeted approach. These algorithms require some information about the potential adulterants but are just a single spectrum of the pure sample. They can be readily shared between sites and even generated by the instrument manufacturers. The PerkinElmer DairyGuard Milk Powder Analyzer is an example of a complete system configured with a unique Adulterant Screen algorithm for the analysis of milk powders.


Adulteration of food and food ingredients for economic gain is an old practice and, sadly, one that is unlikely to be eliminated in the near future. This problem needs to be tackled with all the analytical techniques at our disposal. Fingerprinting of high-risk food types such as milk powder is valuable and NIR spectroscopy clearly has a role to play given its ubiquity in raw materials testing. Food companies can find that extending their existing testing equipment is a fast and cost-effective way to enhancing their portfolio to help detect food fraud. 

Palmer is the food director for PerkinElmer. Reach her at


  1. MacMahon, Shaun; Begley, Timothy H.; Diachenko, Gregory W.; Stromgren, Selen A. A liquid chromatography–tandem mass ­spectrometry method for the detection of economically motivated adulteration in ­protein-containing foods. Journal of Chromatography A, 1220 (2012), 101-107.
  2. United States Department of Agriculture Foreign Agricultural Service. Dairy: World Markets and Trade. 25 July 2011.
  3. U.S. FDA. Press release: “FDA Issues Interim Safety and Risk Assessment of Melamine and Melamine-related Compounds in Food.” October 3, 2008. (accessed November 2013).
  4. United States Pharmacopoeia. Food Fraud Database. (accessed November 2013).
  5. Sherri Turnipseed, Christine Casey, Cristina Nochetto, David N. Heller. Determination of Melamine and Cyanuric Acid Residues in Infant Formula using LC-MS/MS. US FDA Laboratory Information Bulletin 4421, October 2008.



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