Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: A review

Abstract

Because honey is a natural product synthetized by bees from secretions of various flowers and plants, its composition and properties are determined by those unique floral origins. Aside from honey’s sweet and distinctive flavor, it can provide various human health benefits, which makes its market value favorable to those of other sweeteners. Consequently, honey has been a very common and easy target of adulteration for economic profit, making authenticity of honey a longtime concern for researchers and producers around the world. The oldest and most popular of these methods is melissopalynology, the analysis of pollen contained in honey, which can determine its floral origin. However, because this method is time consuming, requires a specialist, and is unable to detect fraudulent pollen contamination, several other methods for the determination of the floral and geographical origin of honey have been proposed in the scientific literature. Multivariate data analysis and machine learning are emerging areas that offer performance and cost advantages for extracting valuable information from raw data sets. They are capable of performing both exploratory and predictive analyses, which can help to uncover trends and hidden patterns within data. Most of the studies reviewed base their methods on atomic spectra and physicochemical properties as descriptive variables for honey. Sensorial data obtained from electronic tongue and nose, and color histograms of honey images also show high discriminative power for ascertaining honey origin. Principal component analysis (PCA), discriminant analysis (DA), and cluster analysis are the preferred techniques for performing exploratory and predictive analyses for the purpose of identifying origin. PCA and DA continue to be preferred due to their ease of application and interpretation, while machine learning algorithms are more complex to model. Nevertheless, both machine learning algorithms and PCA-DA models achieved excellent results in discriminating honey origin. Finally, a commonly observed tendency is the use of hybrid methods that combine multivariate data analysis and machine learning techniques, as each technique has its own strengths and weaknesses regarding uncovering and classifying patterns in honey data.