Laser-induced plasma spectroscopy (LIBS) combined with machine learning algorithms could be an alternative screening technique to identify and discriminate GM corn grains from conventional varieties. The methodology, developed by researchers from Embrapa, four universities from three regions of the country and an Italian institute, has proven capable of making the distinction in an accurate, fast and accessible way.<\/p>\n
Currently, the detection and quantification of genetically modified foods and feeds is performed by the standard Polymerase Chain Reaction (PCR)-based test, which detects and quantifies the presence of specific DNA proteins in the sample. Despite its good accuracy and sensitivity, this method is time-consuming and expensive.<\/p>\n
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<\/a><\/p>\nPhoto: Matheus Falanga<\/p>\n<\/div>\n
In recent decades, the production and consumption of genetically modified agricultural products worldwide has increased significantly, due to population growth and the growing demand for food. At the same time, the demand for rapid and inexpensive methods to identify and discriminate between genetically modified products (whose DNA has been altered through genetic engineering techniques) and non-genetically modified products in the food control and marketing sectors has grown.<\/p>\n
Validated techniques<\/strong><\/h3>\nThe study was part of Matheus Cicero Ribeiro's doctorate, supervised by Professor Bruno Marangoni in the Postgraduate Program in Materials Science at the Federal University of Mato Grosso do Sul (UFMS). At Embrapa Instrumentation (SP), the experiments were supervised by the coordinator of the National Agrophotonics Laboratory (Lanaf), researcher D\u00e9bora Milori.<\/p>\n
\u201cThe main novelty of our research is the combination of the use of the LIBS technique, with multivariate analyses and machine learning, in which the elementary information of the samples was evaluated and used to build decision criteria that differentiate transgenic samples from non-transgenic samples\u201d, states Ribeiro.<\/p>\n
According to him, the work showed that the combination of techniques can differentiate different varieties of genetically modified and conventional corn, if they have similar elemental composition. \u201cThey have essential advantages, such as low cost, fast response, reasonable sensitivity and easy application,\u201d he adds.<\/p>\n
The main challenge of the research involved identifying the constituents, elements such as carbon (C), nitrogen (N), magnesium (Mg), potassium (K), hydrogen (H), iron (Fe) and sodium (Na). Among these, carbon had the greatest influence in differentiating the transgenic class from the non-transgenic class of corn.<\/p>\n
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<\/a><\/p>\nPhotos: Victor Otsuka<\/p>\n<\/div>\n
\u201cSince the samples had very similar elemental composition, that is, they had the same elements, identifying specific markers for each class was a painstaking process that took a lot of analysis time. Therefore, it was necessary to combine multivariate analysis with machine learning, at which point the computer was able to identify markers that were capable of differentiating the samples in the classification process,\u201d explained Ribeiro.<\/p>\n
Another distinguishing feature of the study was that it evaluated a significant number of samples, 160 in total, consisting of a significant total of samples from different transgenic and non-transgenic varieties of corn, totaling 160. The research involved six species of corn, four transgenic and two conventional. Ribeiro says that this is the first time that an external validation protocol has been tested for the classification of transgenic corn using LIBS. \u201cThe external validation corroborated the robustness of the model,\u201d he reports.<\/p>\n
Direct impacts<\/strong><\/h3>\n\u201cThis method offers an effective solution for monitoring and traceability in the agricultural sector, meeting regulatory and food safety requirements and ensuring compliance with national and international policies,\u201d reinforces Professor Bruno Marangoni, highlighting that the methodology identifies the origin of the sample in an agile and accessible way.<\/p>\n
The technique can be used by food analysis laboratories, quality control centers, agri-food industries and regulatory bodies. But agricultural and biotechnology companies can also use the technology to monitor and certify the origin of their products. According to him, national and international health surveillance authorities can also apply it to inspect food products, ensuring consumer protection and compliance with current regulations.<\/p>\n
\u201cWith rapid testing of the origin of products, it would be possible to increase the number of items analyzed, resulting in greater safety and transparency in the market. This technology would also increase trust in the food supply chain, allowing consumers to make informed choices about what they buy and consume,\u201d says the professor.<\/p>\n
Next steps: large-scale testing<\/strong><\/h3>\n
<\/a><\/p>\nPhotos: Victor Otsuka<\/p>\n<\/div>\n
\u201cSince the samples had very similar elemental composition, that is, they had the same elements, identifying specific markers for each class was a painstaking process that took a lot of analysis time. Therefore, it was necessary to combine multivariate analysis with machine learning, at which point the computer was able to identify markers that were capable of differentiating the samples in the classification process,\u201d explained Ribeiro.<\/p>\n
Another distinguishing feature of the study was that it evaluated a significant number of samples, 160 in total, consisting of a significant total of samples from different transgenic and non-transgenic varieties of corn, totaling 160. The research involved six species of corn, four transgenic and two conventional. Ribeiro says that this is the first time that an external validation protocol has been tested for the classification of transgenic corn using LIBS. \u201cThe external validation corroborated the robustness of the model,\u201d he reports.<\/p>\n