Authenticity of Honey: Characterization, Bioactivities and Sensorial Properties Series II.

Honey is a food product globally appreciated by consumers due to its extremely reduced processing requirements and its nutritional properties [...].

Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions.

Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic fungicides, moving away from a sustainable production system. In support of integrated pest management practices, machine learning algorithms were proposed as tools to forecast aerobiological risk level (ARL) of Phytophthora infestans (>10 sporangia/m3) as inoculum to new infections. For this, meteorological and aerobiological data were monitored during five potato crop seasons in Galicia (northwest Spain). Mild temperatures (T) and high relative humidity (RH) were predominant during the foliar development (FD), coinciding with higher presence of sporangia in this phenological stage. The infection pressure (IP), wind, escape or leaf wetness (LW) of the same day also were significantly correlated with sporangia according to Spearman's correlation test. ML algorithms such as random forest (RF) and C5.0 decision tree (C5.0) were successfully used to predict daily sporangia levels, with an accuracy of the models of 87% and 85%, respectively. Currently, existing late blight forecasting systems assume a constant presence of critical inoculum. Therefore, ML algorithms offer the possibility of predicting critical levels of Phytophthora infestans concentration. The inclusion of this type of information in forecasting systems would increase the exactitude in the estimation of the sporangia of this potato pathogen.

Authenticity of Honey: Characterization, Bioactivities and Sensorial Properties.

Honey is a natural product well known for its beneficial properties, which depend on its composition [...].

Assessment of the In Vivo and In Vitro Release of Chemical Compounds from Vespa velutina.

Vespa velutina has been rapidly expanding throughout Galicia since 2012. It is causing human health risks and well-known losses in the beekeeping sector. Control methods are scarce, unspecific, and ineffective. Semiochemicals are insect-derived chemicals that play a role in communication and they could be used an integrated pest management tool alternative to conventional pesticides. A previous determination of the organic chemical profile should be the first step in the study of these semiochemicals. HS-SPME in living individuals and the sting apparatus extraction followed by GC-MS spectrometry were combined to extract a possible profile of these compounds in 43 hornets from Galicia. The identified compounds were hydrocarbons, ketones, terpenes, and fatty acid, and fatty acid esters. Nonanal aldehyde appeared in important concentrations in living individuals. While pentadecane, 8-hexyl- and ethyl oleate were mainly extracted from the venom apparatus. Ketones 2-nonanone, 2-undecanone and 7-nonen-2-one, 4,8-dimethyl- were identified by both procedures, as was 1,7-Nonadiene, 4,8-dimethyl-. Some compounds were detected for the first time in V. velutina such as naphthalene, 1,6-dimethyl-4-(1-methylethyl). The chemical profile by caste was also characterized.

Description of the volatile fraction of Erica honey from the northwest of the Iberian Peninsula.

Heather honey is highly appreciated by consumers for its sensorial profile, which varies depending on the flora used by the honeybees. Volatile compounds contribute to these qualities. Characterisation of the volatile profile related to the botanical origin is of great interest for the standardization of unifloral honey. For this reason, 33 heather honey samples from northwest of the Iberian Peninsula were analysed by headspace solid-phase microextraction (HS-SPME) to identify the key volatile compounds in this type of honey. The aim of this research was to provide a descriptive analysis of these compounds, and to find whether there is any relationship with the main Erica species. A total of 58 volatile organic compounds were found, with hotrienol, phenylacetaldehyde, and cis-linalool being the most abundant. A principal component analysis and Spearman's rank correlation showed the homogeneity of the volatile profile in the samples, and their close relationship with the main pollen types.

Botanical Origin, Pollen Profile, and Physicochemical Properties of Algerian Honey from Different Bioclimatic Areas.

The palynological and physicochemical analysis of 62 honey samples produced in different biogeographical areas of Algeria was conducted. Results showed high variety in the botanical origin of samples and their physicochemical profile. Twenty-six samples were polyfloral honey, 30 were unifloral honey from different botanical sources such as Eucalyptus, Citrus, Apiaceae, Punica, Erica, Rosmarinus, Eriobotrya, or Hedysarum, and 6 were characterized as honeydew honey. Pollen analysis allowed the identification of 104 pollen types belonging to 51 botanical families, whereas the physicochemical profile showed important variations between samples. Multivariate techniques were used to compare the characteristics of samples from different biogeographical areas, showing significant differences between humid-area samples, located in the northeast of the country, and samples taken in semiarid, subhumid, and arid zones. Principal-component analysis (PCA) extracted nine components explaining 72% of data variance, being 30%, the sum of Component 1 and Component 2. The plot of both components showed samples grouped upon botanical and geographical origin. The results of this paper highlighted the great variability in honey production of Algeria, evidencing the importance of honey characterization to guarantee authenticity and to valorize local production.

Honey: Chemical Composition, Stability and Authenticity.

This Special Issue contains innovative research papers on the characterization, chemical composition and physical properties of honey. This constitutes very useful information to avoid frauds and to guarantee the authenticity of this food product. The knowledge of the particularities of honey is increasingly demanded by beekeepers and consumers, and also by labs to typify honeys according to their botanical origin and to check their quality. Melissopalynological, sensorial and physicochemical techniques are being used to study the characteristics of honeys samples from different plant sources and geographical areas. The combination of these analytical techniques with mathematical and statistical methods or chemometrics allows researchers to identify a set of variables or individual parameters that define independent samples, providing a practical solution to classify honey according to the geographical or the botanical origin.

Differentiation of oak honeydew and chestnut honeys from the same geographical origin using chemometric methods.

Oak honeydew and chestnut honeys often share the same production area in Atlantic landscapes. Consequently these honeys have common physicochemical properties and pollen composition, making their differentiation by routine methods, a difficult task. The increase in the demands of consumers for clear honey labelling, identifying floral make-ups and the substantial health properties of both honey types, make it necessary to improve methods to differentiate the honeys. Statistical multivariate techniques were used to study the differences in the physicochemical composition and pollen spectra between chestnut honey and oak honeydew honey. Palynological analysis, moisture, pH, electrical conductivity, hydroxymethylfurfural, diastase number, colour, phenolic content, minerals and sugars were used for this purpose. The variables that had more weight in the differentiation by principal component analysis were Castanea, Cytisus type, CIELab coordinates (a* and L), RSA, Mg and trehalose; 97.6% of the honey samples were correctly classified by linear discriminant analysis.

Near infrared spectroscopy applied to the rapid prediction of the floral origin and mineral content of honeys.

Consumers demand to know the floral origins of honeys. Therefore, the use of simple and reliable techniques for differentiating among honeys by their origins is necessary. Multivariate statistical techniques and near infrared spectroscopy applied to palynological and mineral characteristics make it possible to differentiate among the types of honey collected from Northwestern Spain. Prediction models using a modified partial least squares regression for the main pollen types (Castanea, Eucalyptus, Rubus and Erica) in honeys and their mineral composition (potassium, calcium, magnesium and phosphorus) were established. Good multiple correlation coefficients (higher than 0.700) and acceptable standard errors of cross-validation were obtained. The ratio performance deviation exhibited a good prediction capacity for Rubus pollen and for Castanea pollen, whereas for minerals, for Eucalyptus pollen and for Erica pollen the ratio performance deviation was excellent. Near infrared spectroscopy was established as a rapid and effective tool to obtain equations of prediction that contribute to the honey typification.

Contribution of botanical origin and sugar composition of honeys on the crystallization phenomenon.

The present work provides information regarding the statistical relationships among the palynological characteristics, sugars (fructose, glucose, sucrose, melezitose and maltose), moisture content and sugar ratios (F+G, F/G and G/W) of 136 different honey types (including bramble, chestnut, eucalyptus, heather, acacia, lime, rape, sunflower and honeydew). Results of the statistical analyses (multiple comparison Bonferroni test, Spearman rank correlations and principal components) revealed the valuable significance of the botanical origin on the sugar ratios (F+G, F/G and G/W). Brassica napus and Helianthus annuus pollen were the variables situated near F+G and G/W ratio, while Castanea sativa, Rubus and Eucalyptus pollen were located further away, as shown in the principal component analysis. The F/G ratio of sunflower, rape and lime honeys were lower than those found for the chestnut, eucalyptus, heather, acacia and honeydew honeys (>1.4). A lower value F/G ratio and lower water content were related with a faster crystallization in the honey.

Near infrared spectroscopy for prediction of antioxidant compounds in the honey.

The selection of antioxidant variables in honey is first time considered applying the near infrared (NIR) spectroscopic technique. A total of 60 honey samples were used to develop the calibration models using the modified partial least squares (MPLS) regression method and 15 samples were used for external validation. Calibration models on honey matrix for the estimation of phenols, flavonoids, vitamin C, antioxidant capacity (DPPH), oxidation index and copper using near infrared (NIR) spectroscopy has been satisfactorily obtained. These models were optimised by cross-validation, and the best model was evaluated according to multiple correlation coefficient (RSQ), standard error of cross-validation (SECV), ratio performance deviation (RPD) and root mean standard error (RMSE) in the prediction set. The result of these statistics suggested that the equations developed could be used for rapid determination of antioxidant compounds in honey. This work shows that near infrared spectroscopy can be considered as rapid tool for the nondestructive measurement of antioxidant constitutes as phenols, flavonoids, vitamin C and copper and also the antioxidant capacity in the honey.