The Impact of Chemometrics on Food Safety
Proper application of informatics can have tremendous effect
Food Safety, or the potential lack of it, is a topic frequently on our minds; the various news media do their best to be sure of that. Whether it is because of a food-borne disease outbreak or chemical contamination, such as melamine, there seems to be no end to all of the food recalls. However, in many of these cases, the risk is frequently overblown, whether because of ratings or concerns over liability. This doesn’t mean that there isn’t a severe health risk to those exposed to the contaminated foodstuffs, but rather that in attempting to remove the contaminated materials from the market, an overly broad recall is frequently initiated. There are likely multiple reasons for this, but one key is that it is frequently impossible to trace contaminated items back to their source in a timely enough manner to allow similar uncontaminated items to remain on the market. The proper application of laboratory informatics to food safety can do a great deal to change this situation, but it is important to realize that this is not a silver bullet with one system that will resolve all food safety problems.
In this situation, you might look upon laboratory informatics as a multifaceted gem, with each facet representing a different type of informatics system. No single facet is more important than another. However, taken together, they can create a jewel of great fire that can go a long way to resolving this issue. While some try, you really can’t assign strong compartmentalization to these facets, as many of them have broad functional overlap. While we do not have room to cover these facets in depth, the following is a non-exclusive list of the various types of laboratory informatics systems you might encounter.
• laboratory information management system (LIMS)
• analysis automation/chromatography data systems (e.g. CDS)
• scientific data management system (SDMS)
• manufacturing process control
• tracking systems
• quality management system (QMS)
The purposes of these systems range from producing quality foodstuffs, to analyzing food samples and storing the analytical results, to using these results to make evaluations and predictions regarding the quality and safety of these foods. However, while each system can be free-standing, the maximum value is obtained when they are all interfaced and integrated together. As we have recently examined several of these data systems, we’ll narrow our focus and limit our examination to chemometrics.
According to the International Union of Pure and Applied Chemistry (IUPAC) Wiki,1 Chemometrics is “The science of relating measurements made on a chemical system or process to the state of the system via application of mathematical or statistical methods. The application of multivariate and empirical design and modeling tools to chemical problems.”2 It is a relatively young science, with the name first being applied by Svante Wold in 1971.3 By using a variety of mathematical and statistical techniques, such as principal component analysis (PCA), multivariate discriminant analysis, and regression/classification trees, it can identify relationships between samples and conditions. For those interested in learning more about the principles of chemometrics, there are a number of tutorials, including Johan Trygg’s “Chemometrics Made Easy” on the Chemometrics Society’s Web site.4
While all of the above is true, I suspect that most of my readers are more interested in the practical applications of these techniques, so let’s take a look at some of the problems they are being used to solve.
When discussing food safety, there are generally two broad classes of issues with which you are concerned. The first is whether the food product or ingredient has been adulterated and, if so, whether the adulterating agent is hazardous/toxic. There have been plenty of instances of someone deliberately adding toxic materials to food or pharmaceutical products to harm others, but many more instances of people substituting lower-cost materials to make a larger profit. Unfortunately, their substitute material is not always a safe choice. The second class of issues results from biological contamination of a food ingredient or product. There have been cases of deliberate insertion of an infective agent, but most instances result from accidental contamination or storage under inappropriate conditions allowing natural occurring microorganisms to proliferate. Regrettably, the origin of the agent doesn’t affect the impact on someone ingesting the contaminated product.
A good example of using chemometrics to identify adulterated products can be seen in a survey of virgin olive oils incorporating Fourier Transform-Raman (FT-Raman) spectroscopy with multivariate processing procedures.5 In this European study, the chemometric procedures were able to differentiate between the pure and adulterated samples with 100-percent accuracy, as well as provide a strong qualitative estimate of the degree of adulteration. Keep in mind that it managed this despite natural variations in the olive oils because of the soils in which the trees grew and any variations in the harvested species. Whether it’s the dollar or the euro, the sad fact is that there are plenty of people out there willing to put greed before the safety of their fellow man. Don’t believe it? You might find the book Swindled6 to be an eye opening experience to this long, if not honored, tradition.
Another example is identifying spoilage in milk.7 In this study by Nicoletta Nicolaou and Rowston Goodacre, they used FT-IR spectroscopy to develop a ‘metabolic fingerprint.’ Multivariate statistical methods were then used to quantify this value. Observed results showed that, despite very little sample preparation, this process could accurately quantify the bacteria loading. This compared well to traditional methods, which provide accurate results, but with much longer turnaround times.
Of course, the incidents that most frequently make the news are those that involve organisms with a more lethal reputation. Names like Botulism,8 Salmonella,9 and Listeria Monocytogenes10 are much more attention-getting. As you can see by the references, chemometrics is indeed used to assist in the identification and quantization of these biological agents as well.
The above examples barely scratch the surface in terms of illustrating the impact and potential of chemometrics on food safety. For those interested in learning more about the topic and the diverse approaches being taken, I recommend checking out several of the review articles currently available.11,12,13 Some of them are a bit dated, but they should still give you a good idea of the diversity of the work currently going on to keep our food supply safe and secure.
1. Hibbert, D.B., Minkkinen, P., Faber, N.M. & Wise, B.M. IUPAC project: A glossary of concepts and terms in chemometrics. Anal Chim Acta 642, 3-5 (2009).
2. Chemometrics – IUPAC Wiki. www.iupacterms.eigenvector.com/index.php?title=Chemometrics
3. Vandeginste, B. How it Began? The story from Svante Wold. From Chemometrics to Genealogy (2009). blogger.xs4all.nl/bgv/articles/438700.aspx
4. Chemometrics.se [Homepage of Chemometrics] – Tutorial. www.chemometrics.se/index.php?option=com_content&task=blogcategory&id=14&Itemid=27
5. Baeten, V., Meurens, M., Morales, M.T. & Aparicio, R. Detection of Virgin Olive Oil Adulteration by Fourier Transform Raman Spectroscopy. J. Agric. Food Chem. 44, 2225-2230 (1996).
6. Wilson, B. Swindled: The Dark History of Food Fraud, from Poisoned Candy to Counterfeit Coffee. (Princeton University Press: 2008). press.princeton.edu/titles/8723.html 7. Nicolaou, N. & Goodacre, R. Rapid and quantitative detection of the microbial spoilage in milk using Fourier transform infrared spectroscopy and chemometrics. Analyst 133, 1424-1431 (2008).
8. Rowe, B., Schmidt, J.J., Smith, L.A. & Ahmed, S.A. Rapid product analysis and increased sensitivity for quantitative determinations of botulinum neurotoxin proteolytic activity. Anal. Biochem. 396, 188-193 (2009).
9. Männig, A., Baldauf, N., Rodriguez-Romo, L., Yousef, A. & Rodríguez-Saona, L. Differentiation of Salmonella enterica serovars and strains in cultures and food using infrared spectroscopic and microspectroscopic techniques combined with soft independent modeling of class analogy pattern recognition analysis. J Food Prot. 71, 2249-2256 (2008).
10. Ochoa, M.L. & Harrington, P.B. Chemometric Studies for the Characterization and Differentiation of Microorganisms Using in Situ Derivatization and Thermal Desorption Ion Mobility Spectrometry. Anal. Chem. 77, 854-863 (2005).
11. Reid, L.M., O’Donnell, C.P. & Downey, G. Recent technological advances for the determination of food authenticity. Trends Food Sci. Technol. 17, 344-353 (2006).
12. Harz, M., Rösch, P. & Popp, J. Vibrational spectroscopy – A powerful tool for the rapid identification of microbial cells at the single-cell level. Cytometry Part A 75A, 104-113 (2009).
13. Sohn, M., Himmelsbach, D., Barton II, F. & Cray, P. Rapid detection of bacterial pathogens using flourescence spectroscopy and chemometrics. United States Japan Natural Resources Food & Agriculture Panel, 37th Annual Meeting (2008). www.ars.usda.gov/research/publications/publications.htm?seq_no_115=227898
John Joyce is the LIMS manager for Virginia’s State Division of Consolidated Laboratory Services. He may be contacted at editor@ScientificComputing.com.