This machine learning algorithm can recognize metallic nanoparticles in food

Nanoparticles in food consist of both organic and inorganic components. The inorganic components include silver, titanium dioxide, iron oxide and zinc oxide. Some of these nanoparticles are harmful for the human body but they are so minute that they cannot be seen with the naked eye. 

 

Researchers at Texas A&M University have used a machine learning algorithm to evaluate the properties of nanoparticles. The machine learning algorithm could indicate how much nanoparticles are absorbed in the root and shoot of plants. Nanoparticles are usually accumulated in the soil.

 

The researchers chose two different machine learning algorithms: artificial neural network and gene expression programming. The algorithms were trained on the database of past research on different metallic nanoparticles. The database consisted of information related to shape, size and other characteristics of nanoparticles. 

 

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After training the machine learning algorithm could predict whether a particular nanoparticle could be accumulated in the plant species or not. The algorithm also depicted other data related to nanoparticles like their nature. 

 

Machine learning can make predictions for most of the food crops and terrestrial plants. The model will later be extended to aquatic plants to reveal the data about nanoparticles present in them. 

 

As reported by Science Daily "Samuel" Ma, associate professor in the Zachry Department of Civil and Environmental Engineering said, "It is quite understandable that people are concerned about the presence of nanoparticles in their fruits, vegetables and grains."But instead of not using nanotechnology altogether, we would like farmers to reap the many benefits provided by this technology but avoid the potential food safety concerns."he added.