Foodomics in action: finding contaminated baby food

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  • Published: Mar 10, 2016
  • Author: Ryan De Vooght-Johnson
  • Channels: Laboratory Informatics / Chemometrics & Informatics
thumbnail image: Foodomics in action: finding contaminated baby food

The melamine scandal

Adding melamine to food makes its protein content appear higher, and can allow for dilution. This is precisely what happened in China, where companies added melamine to baby milk so they could dilute it with water, allowing them to increase their profit margin.

In 2008, a major scandal broke out in China when melamine was found in baby food. Melamine, an organic chemical most often found in the form of white crystals, is rich in nitrogen (67% by mass). Because amino acids also contain lots of nitrogen, and amino acids make up protein, the protein content of food is usually checked through a test of nitrogen content.

Adding melamine to food therefore makes its protein content appear higher, and can allow for dilution. This is precisely what happened in China, where companies added melamine to baby milk so they could dilute it with water, allowing them to increase their profit margin.

This turned out to have serious implications however, as melamine can form crystals that go onto to form kidney stones. These small crystals may block the tubes in the kidney, potentially causing kidney failure. An estimated 300 000 babies were affected by the melamine-contaminated milk, 54 000 of whom were hospitalised, and six lost their lives.

As this case illustrates, it is essential to perform regular and comprehensive quality checks of food products, and especially those targeted at the vulnerable. Essential to this is a non-target screening approach, a non-biased approach to chemical analysis able to pick up both known and unexpected contaminants.

The next big ‘omics?

Beginning with genomics, scientists have developed a suite of large-scale, high-throughput technologies, now including metabolomics, proteomics and transcriptomics. In recent years foodomics has emerged, defined as ‘the comprehensive, high-throughput approach for the exploitation of food science in the light of an improvement of human nutrition’. This typically involves high-throughput screening approaches such as mass spectrometry (MS) (sometimes combined with liquid chromatography) and nuclear magnetic resonance (NMR).

While non-target screening approaches like these have the advantage of detecting a broader range of signals and potentially picking up ‘unknown unknowns’, they also generate a much larger dataset than targeted screening. To make sense of the large amount of data, statistical techniques have been developed. One of the most popular is principal component analysis (PCA), which can model variation in data and is frequently used to derive useful information from metabolomics data.

In a paper published earlier this year, scientists from Japan combined NMR, LC-MS and PCA to comprehensively analyse baby formula. The researchers tested three baby food samples using LC-MS (with two separating modes) and proton NMR. The large amount of molecular signal data, including mass-to-charge ratio and parts per million (ppm) values, was then processed by PCA.

Simultaneous monitoring of metabolites

Using NMR, the researchers were able to achieve a simple preparation method, based only on centrifugation of the formula. They evaluated contaminants using ppm values, finding extremely high levels of methamidophos, an insecticide known to have toxic properties. However, specificity and sensitivity was subpar and the technique was unable to detect melamine.

LC-MS was more sensitive and selective than NMR, and allowed the researchers to detect contamination by melamine, pesticides and even heavy metals. They were also able to identify one product as out-of-date.

The paper describes a simple yet comprehensive method for routine risk assessment of food products. It shows how MS and NMR can be used to monitor thousands of metabolites at a time to provide a molecular fingerprint for food, which can be correlated to quality when used alongside statistical techniques such as PCA.

The novel foodomics approach described here, able to highlight contamination as well as degradation, could help to re-assure consumers of the safety and quality of the products they buy, and ultimately avoid a repeat of tampering scandals.

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