According to Dr. Luís Abrunhosa, an assistant professor at the University of Minho, food contaminants are classified into three categories: biological, chemical, or physical. These contaminants occur in different ways, but all pose health risks at varying degrees. 

However, unlike biological contaminants, such as bacteria, viruses, and parasites, or chemical contaminants, emerging contaminants are equally harmful. Dr. Vera Homem, a University of Porto Faculty of Engineering assistant professor, said:

“One of the main concerns is the emergence of unknown risks associated with novel ingredients, such as alternative proteins derived from fungi, algae or insects. These ingredients may introduce unexpected allergens or naturally occurring toxins that have not been well studied.”

She added that processed foods may also contain process contaminants, such as acrylamide. Using treated wastewater for irrigation and biosolids as fertilizers could also pose a threat to food safety.

With the increasing number of sources of contaminants, modern food testing techniques have never been more useful. Until today, liquid and gas chromatography remain widely used chromatographic techniques for separating the components of complex food samples and detecting preservatives, veterinary drugs, pesticides, and process contaminants.

Chromatographic techniques are also combined with mass spectrometry systems, while advanced techniques such as ultra-high-performance liquid chromatography provide clearer resolutions and faster separations. 

In addition to these, infrared, near-infrared, and Raman spectroscopy techniques are also utilized for quality control and rapid screening to prevent food fraud. For detecting biological contaminants, polymerase chain reaction and enzyme-linked immunoassay are the go-to methods. 

Dr. Homem said that AI and predictive analytics have also become integral in maintaining food safety:

“Artificial intelligence can analyze complex datasets from food analysis, enabling faster and more accurate identification of contaminants. For example, machine learning algorithms can be trained to recognize patterns in mass spectrometry or chromatography data, making it easier to detect anomalies in food composition.”

However, Dr. Abrunhosa pointed out that these modern food testing techniques are still not enough:

“Key challenges include the simultaneous detection of multiple contaminants, while effectively suppressing food matrix effects to enhance the speed, sensitivity, accuracy and reproducibility in analytical determinations.”

He added that the high costs of conducting these tests and the limited access of resource-limited countries to such procedures are another set of challenges that need to be addressed.

Read the full article here to learn more about modern food testing methods.

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