Botanical honey recognition and quantitative mixture detection based on Raman spectroscopy and machine learning

The development of new approaches for honey recognition, based on spectroscopic techniques, presents a huge market potential especially because of the fast development of portable equipment. As an emerging approach, the association between Raman spectroscopy and Artificial Intelligence (i.e. Machine Learning algorithms) for food and beverages recognition starts to prove its efficiency, becoming an important candidate for the development of a practical application. Through this study, new recognition models for the rapid and efficient botanical differentiation of investigated honey varieties were developed, allowing the correct prediction of each type in a percentage better than 81%. The performances of the constructed models were expressed in terms of precision, sensitivity, and specificity. Moreover, through this approach, the detection of honey mixtures was possible to be made and an estimative percentage of the mixture components was obtained. Thus, the applicative potential of this new approach for honey recognition as well as a qualitative and quantitative estimation of the honey mixture was demonstrated.

Automated summary

The development of new approaches for honey recognition, based on spectroscopic techniques, presents a huge market potential especially because of the fast development of portable equipment. The association between Raman spectroscopy and Artificial Intelligence (i.e. Machine Learning algorithms) for food and beverages recognition starts to prove its efficiency, becoming an important candidate for the development of a practical application.