Differentiating between Monofloral Portuguese Bee Pollens Using Phenolic and Volatile Profiles and Their Impact on Bioactive Properties.

Nowadays, bee products are commended by consumers for their medicinal and dietary properties. This study aimed to differentiate between monofloral bee pollens originating from Portugal using phenolic and volatile profiles and investigate their antioxidant and cytotoxic activity. Total phenolic and flavonoid compounds were recorded between 2.9-35.8 mg GAE/g and 0.7-4.8 mg QE/g, respectively. The LC/DAD/ESI-MSn analytical results allowed us to identify and quantify a total of 72 compounds, including phenolic and phenylamide compounds, whereas GC-MS results revealed the presence of 49 different compounds, mostly ketones, aldehydes, esters, hydrocarbons, and terpenes. The highest DPPH• radical scavenging activity, EC50: 0.07 mg/mL, was recorded in the sample dominated by Castanae sp. pollen, whereas the Rubus sp. (1.59 mM Trolox/mg) and Cistaceae sp. (0.09 mg GAE/g) pollen species exhibited the highest antioxidant activity in ABTS•+ and reducing power assays, respectively. Regarding the anti-carcinogenic activity, only Carduus sp. showed remarkable cytotoxic potential against MCF-7.

Evaluation of Antioxidant and Anticancer Activity of Mono- and Polyfloral Moroccan Bee Pollen by Characterizing Phenolic and Volatile Compounds.

Bee pollen is frequently characterized as a natural source of bioactive components, such as phenolic compounds, which are responsible for its pharmaceutical potential and nutritional properties. In this study, we evaluated the bioactive compound contents of mono- and polyfloral bee pollen samples using spectroscopic and chromatographic methods and established links with their antioxidant and antitumor activity. The findings demonstrated that the botanical origin of bee pollen has a remarkable impact on its phenolic (3-17 mg GAE/g) and flavonoid (0.5-3.2 mg QE/g) contents. Liquid chromatography-mass spectrometry analysis revealed the presence of 35 phenolic and 13 phenylamide compounds in bee pollen, while gas chromatography-mass spectrometry showed its richness in volatiles, such as hydrocarbons, fatty acids, alcohols, ketones, etc. The concentration of bioactive compounds in each sample resulted in a substantial distinction in their antioxidant activity, DPPH (EC50: 0.3-0.7 mg/mL), ABTS (0.8-1.3 mM Trolox/mg), and reducing power (0.03-0.05 mg GAE/g), with the most bioactive pollens being the monofloral samples from Olea europaea and Ononis spinosa. Complementarily, some samples revealed a moderate effect on cervical carcinoma (GI50: 495 µg/mL) and breast adenocarcinoma (GI50: 734 µg/mL) cell lines. This may be associated with compounds such as quercetin-O-diglucoside and kaempferol-3-O-rhamnoside, which are present in pollens from Olea europaea and Coriandrum, respectively. Overall, the results highlighted the potentiality of bee pollen to serve health-promoting formulations in the future.

Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms.

Secondary infections of early blight during potato crop season are conditioned by aerial inoculum. However, although aerobiological studies have focused on understanding the key factors that influence the spore concentration in the air, less work has been carried out to predict when critical concentrations of conidia occur. Therefore, the goals of this study were to understand the key weather variables that affect the hourly and daily conidia dispersal of Alternaria solani and A. alternata in a potato field, and to use these weather factors in different machine learning (ML) algorithms to predict the daily conidia levels. This study showed that conidia per hour in a day is influenced by the weather conditions that characterize the hour, but not the hour of the day. Specifically, the relative humidity and solar radiation were the most relevant weather parameters influencing the conidia concentration in the air and both in a linear model explained 98% of the variation of this concentration per hour. Moreover, the dew point temperature three days before was the weather variable with the strongest effect on conidia per day. An improved prediction of Alternaria conidia level was achieved via ML algorithms when the conidia of previous days is considered in the analysis. Among the ML algorithms applied, the CART model with an accuracy of 86% were the best to predict daily conidia level.

Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device.

The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI "Patata de Galicia", Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

Effects of meteorological factors on the levels of Alternaria spores on a potato crop.

Alternaria solani Soraeur produces early blight in Solanum tuberosum L., leading to significant agricultural losses. The current study was carried out on the extensive potato crop situated in north-western of Spain during 2007, 2008 and 2009. In this area potato crops are the most important source of income. In this work we used a Hirst-type volumetric spore-trap for the aerobiological monitoring of Alternaria spores. The highest spore concentrations were recorded during the 2009 cycle (10,555 spores), and the lowest concentrations were recorded during the 2008 cycle (5,471 spores). Over the 3 years of study, the highest concentrations were registered during the last stage of the crop. The aim of the study was to observe the influence of meteorological factors on the concentration of Alternaria spores, which can lead to serious infection and early blight. Prediction of the stages during which a crop is particularly vulnerable to infection allows for adjustment of the application of fungicide and is of environmental and agricultural importance. For this reason, we tested three models (P-Days, DD and IWP) to predict the first treatment and decrease the negative effect that these spores have on potato crops. The parameter that showed the most significant correlation with spore concentrations was minimum temperature. We used ARIMA (autoregressive integrated model of running mean) time-series models to determine the forecast. We considered weather data as predictor variables and the concentration of spores on the previous day as the fixed variable.