Artificial neural networks: A solution for increasing the accuracy of regional traceability assessments

We are presenting a feasibility study regarding the use of Artificial Neural Networks for performing more detailed (regional) traceability assessments in the case of horticultural products. The challenge is related to the significant data variability and the need of fast data analysis and processing, especially in the case of fast perishable products. A case study performed for lovage (Levisticum Officinale) indicates that ANN may provide efficient and cost-effective automated regional traceability evaluations. This method yields a remarkable correct classification rate even for a simple (three layer) architecture and a training database built with a low number of physico-chemical properties.