Classification of Monofloral Honeys by Measuring a Low-Cost Electronic Nose Prototype Based on Resistive Metal Oxide Sensors

Abstract

In this article a case study of characterisation of type of honey based on floral origin is presented. It is intended to discriminate Iberian honeys from local beekeepers located in the Community of Madrid (Spain), by means of a low-cost electronic nose prototype, composed of a matrix of nonspecific resistive sensors of MQ-type metal oxides. The measurements of the honeys made with an electronic nose prototype were contrasted with physicochemical analyzes and pollen content. The experiment was carried out in two trials. A first preliminary study in which six samples of honey from different sources were used (three Blueweed, one rapeseed, one lavender and one commercial honey) and in which eight repetitions were made for each of the six samples analyzed. Due to the small sample size, conclusive results were not obtained, although the sensors did show a clear response in those that presented a higher pollen content, above 57%, however, the honey samples that reflected pollen values lower than 50% they showed no perceptible reaction on the sensors. In the second study, in which the sample size was increased to a total of 16 samples (four lavender honeys, four oak honeys, four rosemary honeys, and four chestnut honeys), a total of 10 repetitions per sample were carried out with a total of repetitions out of 160. These last data were analyzed with the principal component technique (PCA), the results of which were inconclusive. However, when applying the data analysis through the use of Support Vector Machines (SVM), it is possible to obtain a model with 87.5% accuracy in the classification. In this case, the Lavender and Chestnut honeys were the ones that achieved a precision of 90% and 100% respectively.

Low-Cost Electronic Nose for the Determination of Urinary Infections

“…Finally, we proceed to the classification of the characteristic vectors extracted and processed via PCA by using models based on support vector machines (SVMs) in order to obtain a fast data classification tool. The reason SVM was used is that the pipeline of use of the eNose shown in this work is already established and implemented in previous works that we have developed [ 25 ]. In these, the best results obtained for the eNose data that we are dealing with have been using SVM.…”

Fourier Transform Infrared Spectroscopy Use in Honey Characterization and Authentication: A systematic review

“…Honey’s botanical origin is usually assessed by melissopalynology (i.e., pollen analysis), based on the morphological aspects and the percentage of different types of pollen. , A relative frequency >45% of a specific pollen type classifies honey as monofloral . However, pollen analysis is becoming less popular due to its dependency on trained operators, plant physiology (influencing pollen production), and the loss of pollen by filtration. , Nonetheless, adulteration of monofloral honey is common due to the higher value stemming from its unique flavor and composition …”