Beeswax is widely used in apiculture and can accumulate neonicotinoid residues due to the intensive use of systemic pesticides in agriculture. These contaminants pose potential risks to honeybee health and may indirectly affect the quality and safety of hive products such as honey, pollen, and royal jelly. This study evaluates several decontamination methods for neonicotinoid removal from contaminated beeswax, including modern techniques (microwaves, ultrasonic baths, and magnetic stirring with heating) and conventional approaches based on heat, agitation, and water—either pure or acidified. Among these, the traditional method that uses an aqueous oxalic acid solution proved highly effective, removing over 99% of neonicotinoid residues after two treatment cycles, even at wax quantities up to 200 g. The treatment also improved the colour and physical properties of the wax and was well tolerated by bees, according to a qualitative acceptance test. The simplicity, low cost, and absence of hazardous by-products make this method suitable for scale-up and adoption in real apicultural practices. These findings support the development of accessible and sustainable strategies for the decontamination of wax matrices that may otherwise act as long-term reservoirs of pesticide residues in the food chain.

Honeybee population decline poses a serious threat to global biodiversity and agricultural productivity, underscoring the need for continuous and non-invasive hive monitoring solutions. In particular, early detection of queen absence is critical for maintaining colony viability. This study investigates the effectiveness of machine learning and deep learning models for acoustic-based queen-presence detection using short-duration hive audio recordings. Audio data collected from multiple sources were processed to extract spectrogram, Mel-spectrogram, and Mel-frequency cepstral coefficient features, which were evaluated using classical ML classifiers and convolutional neural networks. Experimental results indicate that MFCC-based representations consistently outperform spectrogram-based features across segment lengths, achieving higher accuracy and greater stability. The best performance was obtained with Mel features using convolutional neural networks for short segments and gradient-boosted models for longer windows. These findings demonstrate that brief acoustic segments are sufficient for reliable classification, supporting real-time monitoring under noisy field conditions. The proposed approach offers a scalable and low-cost framework for precision beekeeping and contributes to sustainable beekeeping through early, automated anomaly detection. The proposed framework supports real-time, low-cost deployment scenarios, enabling scalable precision apiculture solutions.

Honeybee population decline poses a serious threat to global biodiversity and agricultural productivity, underscoring the need for continuous and non-invasive hive monitoring solutions. In particular, early detection of queen absence is critical for maintaining colony viability. This study investigates the effectiveness of machine learning and deep learning models for acoustic-based queen-presence detection using short-duration hive audio recordings. Audio data collected from multiple sources were processed to extract spectrogram, Mel-spectrogram, and Mel-frequency cepstral coefficient features, which were evaluated using classical ML classifiers and convolutional neural networks. Experimental results indicate that MFCC-based representations consistently outperform spectrogram-based features across segment lengths, achieving higher accuracy and greater stability. The best performance was obtained with Mel features using convolutional neural networks for short segments and gradient-boosted models for longer windows. These findings demonstrate that brief acoustic segments are sufficient for reliable classification, supporting real-time monitoring under realistic urban recording conditions with moderate environmental noise. The proposed approach offers a scalable and low-cost framework for precision beekeeping and contributes to sustainable beekeeping through early, automated anomaly detection. The proposed framework supports real-time, low-cost deployment scenarios, enabling scalable precision apiculture solutions.

Beeswax is a beekeeping product used in comb foundation manufacturing and in cosmetic, pharmaceutical, and food applications. Data on their physicochemical and rheological behavior remain scarce. We compared capping wax (CW), old comb wax (OW), and foundation wax (FW) from Apis mellifera. FTIR spectra were similar and melting points did not differ significantly (65–66 °C). Color was the most sensitive solid-state property, differing among all samples. At 80 °C, density (821–822 kg·m⁻³) and surface tension (39–41 mN·m⁻¹) were similar. Under concentric-cylinder geometry, flow curves followed the Power Law model, with n = 1.121–1.153, indicating slight shear-thickening behavior; all individual replicate fits returned n > 1, with no statistical differences among wax types. Viscosity remained constant for 30 min at 400 s⁻¹, indicating no thixotropy or rheopexy. A stirrer-type relative geometry returned higher apparent indices (1.734–1.899) and viscosities (45–50 mPa·s); being non-viscometric, these are process descriptors rather than intrinsic constants. Oscillatory rheology near the solid–liquid transition was more discriminating: unlike CW and OW, FW showed no elastic-to-viscous crossover in any replicate within the analyzed strain range. The distinct response of FW may reflect differences in microstructural organization rather than chemical composition, a hypothesis that remains to be confirmed and is potentially relevant to stamping, rolling, and molding.