RESUMO
A method for the simultaneous determination of 21 triazole fungicides in animal-origin foods was established by using UPLC-MS/MS. The dilution solvent, extraction solvent, and QuEChERS purification adsorbent composition, were optimized. The response value of the target compound was the highest and the chromatographic peak shape was optimal under the following conditions: water-acetonitrile as the mobile phase, acetonitrile to extract the target compound, C18 (100 mg) as the adsorbent, and water-acetonitrile as the diluent. Our method was validated under electrospray ionization (ESI) + conditions with six animal-origin foods. The 21 triazole fungicides showed good linear relationships (0.1-20 µgâL-1, R2 > 0.99). The limits of detection and quantitation ranged from 0.1 to 0.3 µgâkg-1 and 0.3 to 0.9 µgâkg-1, respectively. The average recoveries ranged from 72.0% to 114.8% with RSDs < 9.9%. Therefore, our method was suitable for the determination of pesticide residues in commercially available animal-origin samples.
RESUMO
The rapid development of Internet technology has promoted the vigorous development of the multimedia. As one of the most classic instruments, the violin has been fully developed in its creation, education, and performance. In the face of more and more violin performances, the effective organization and retrieval of these musical works is an urgent problem to be solved, while it is common to classify and organize music based on the emotional properties of the performance. Deep learning is a model based on feature hierarchy and unsupervised feature learning, which has strong learning ability and adaptability. Based on the recurrent neural network (RNN) method, long short-term memory (LSTM) is one of the classic models of deep learning that can effectively learn the characteristics of time series data and achieve effective predictions. Therefore, based on the classical Hevner emotion classification model, this paper proposes an emotion recognition method for dynamic violin performances based on LSTM, which selects acoustic features and classifies the audio acoustic signals contained in the violin performances. In order to verify the effectiveness of this method, this paper carries out data labeled, feature selection, and model test on the actual violin music data by turn. The results show that the proposed method can greatly reduce the training time and improve the prediction accuracy, which reached 83%, higher than the existing methods. Meanwhile, the accuracy and iteration times of violin playing music of different emotional categories are also counted. Moreover, the method is robust to the genre, timbre, and noise changes, and the emotion recognition effect is superior.
Assuntos
Análise de Dados , Música , Emoções , Redes Neurais de Computação , TecnologiaRESUMO
A modified quick, easy, cheap, effective, rugged, and safe method coupled with ultra-high performance liquid chromatography-tandem mass spectrometry was established for the simultaneous detection of five Alternaria toxins (tenuazonic acid, alternariol, alternariol monomethyl ether, altenuene, and tentoxin) in wolfberry. The sample pretreatment conditions including the dilution solvent, the extraction solvent and the QuEChERS purification parameters were optimized. Detection of the five Alternaria toxins was performed in MRM mode under ESI + conditions. The results showed that the five Alternaria toxins exhibitedgood linearity (1-200 µg/L, with correlation coefficients > 0.999). The limits of detection were 0.07-0.24 µg/kg, and the limits of quantification were 0.32-0.77 µg/kg. The trueness was between 73.8% and 111.5%, and the precision was lower than 10%. The established method was effectively used for the determination of five Alternaria toxins in 155 wolfberry samples from Northwest China.
