Screening of the Honey Aroma as a Potential Essence for the Aromachology

The aim of the study was to determine the aroma profiles of four kinds of Slovak honey (sunflower, honeydew, acacia, and linden) by a qualitative and quantitative screening of their volatile compounds and by gas chromatography for the potential use in the aromachology and the business sphere. The results showed that several unique volatiles were identified in one kind of honey, while they were not identified in the remaining ones. The acacia honey had the unique volatile linalool oxide (1.13–3.9%); linden honey had the unique volatiles nerol oxide (0.6–1.6%), ethyl esters (0.41–8.78%), lilac aldehyde D (6.6%), and acetophenone (0.37%). The honeydew honey had the unique volatiles santene (0.28%) and cyclofenchene (0.59–1.39%), whereas 2-bornene (0.43–0.81%) was typical for sunflower honey. While linden honey was characterized by fruity ethyl esters, honeydew honey had more monoterpenoid compounds. In the principal component analysis model, the four kinds of honey could not be differentiated by aroma volatiles. However, it was possible to classify the linden and sunflower honey using the LDA. In conclusion, the current study provided experimental evidence that the marker compounds from different kinds of honey might be promising candidates for production of inhaling aromas.

Research on athlete skipping surface electromyography and energy consumption based on principal component analysis of wavelet packet

EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.