Flavor characteristics of Shanlan rice wines with different fermentation time were analyzed. Results suggested that 3-methyl-1-butanol, phenylethyl alcohol, ethyl acetate, and diethyl succinate were the characteristic volatiles of Shanlan rice wine by using headspace solid-phase microextraction-gas chromatography-mass spectrometry-olfactometry. The most varieties (38) of volatiles appeared at a 3-year-fermentation time, contributing a unique and harmonious aroma to the Shanlan rice wine fermented for 3 years, but only 19 types were observed at 45-days and 1-year fermentation times. A similar trend was intuitively visible in the headspace-gas chromatography-ion mobility spectrometry analysis. The Shanlan rice wine fermented for 3 years has a similar taste profile to that fermented for 45 days, but with distinguishing contents of free amino acids (1352.8 mg/L and 2261.5 mg/L, respectively) and organic acids (9.58 g/L and 49.88 g/L, respectively). The Shanlan rice wine fermented for 1 year has a strong taste with more intensity of most taste attributes. 

Flavor characteristics of Shanlan rice wines fermented for different time based on HS-SPME-GC-MS-O, HS-GC-IMS, and electronic sensory analyses.

Today, with the rapid development of economic level, people’s esthetic requirements are also rising, they have a deeper emotional understanding of art, and the voice of their traditional art and culture is becoming higher. The study expects to explore the performance of advanced affective computing in the recognition and analysis of emotional features of Chinese paintings at the 13th National Exhibition of Fines Arts. Aiming at the problem of “semantic gap” in the emotion recognition task of images such as traditional Chinese painting, the study selects the AlexNet algorithm based on convolutional neural network (CNN), and further improves the AlexNet algorithm. Meanwhile, the study adds chi square test to solve the problems of data redundancy and noise in various modes such as Chinese painting. Moreover, the study designs a multimodal emotion recognition model of Chinese painting based on improved AlexNet neural network and chi square test. Finally, the performance of the model is verified by simulation with Chinese painting in the 13th National Exhibition of Fines Arts as the data source. The proposed algorithm is compared with Long Short-Term Memory (LSTM), CNN, Recurrent Neural Network (RNN), AlexNet, and Deep Neural Network (DNN) algorithms from the training set and test set, respectively, The emotion recognition accuracy of the proposed algorithm reaches 92.23 and 97.11% in the training set and test set, respectively, the training time is stable at about 54.97 s, and the test time is stable at about 23.74 s. In addition, the analysis of the acceleration efficiency of each algorithm shows that the improved AlexNet algorithm is suitable for processing a large amount of brain image data, and the acceleration ratio is also higher than other algorithms. And the efficiency in the test set scenario is slightly better than that in the training set scenario. On the premise of ensuring the error, the multimodal emotion recognition model of Chinese painting can achieve high accuracy and obvious acceleration effect. More importantly, the emotion recognition and analysis effect of traditional Chinese painting is the best, which can provide an experimental basis for the digital understanding and management of emotion of quintessence.

/pdf/emotion-recognition-of-chinese-paintings-at-the-thirteenth-295280tc7h.pdf

Emotion Recognition of Chinese Paintings at the Thirteenth National Exhibition of Fines Arts in China Based on Advanced Affective Computing.

Correlation between changes in flavor compounds and microbial community ecological succession in the liquid fermentation of rice wine

Fermented red pepper (FRP) sauce serves as a representative condiment, and the traditional fermentation of FRP requires high salt content, which is detrimental to human health, thus developing low-salt FRP is a trend. However, the effects of low-salt fermentation on quality and flavor substances in FRP sauce are poorly understood. The aim of this study is to reveal the dynamic changes of microbial community and flavor compounds in low-salt FRP sauce, analyze the interactions between microorganisms and key flavor compounds, and characterize the metabolic pathways of the main flavor components. The results showed that Levilactobacillus, Companilactobacillus and Candida were the core dominant genera in low-salt FRP sauce based on the analysis of the data of amplicon sequence variants. Further, the amplicon data were subjected to spearman correlation analysis found that Levilactobacillus was negatively correlated with other genera, while Companilactobacillus was proved to be remarkably positively correlated with other microorganisms at the strain level. Additionally, 18 volatile compounds and 40 non-volatile compounds were identified as the key flavor substances by HPLC, HS-SPME-GC-MS and UHPLC-QE-MS. Finally, combined metabolomics and metagenomics data revealed that the metabolism of various amino acids was the main pathway for flavor formation, and Companilactobacillus and Candida were significantly positively correlated with flavor formation, which were therefore identified as the core genera for flavor production. These findings deepened our understanding of the effects of low-salt fermentation on microbial communities and flavor metabolites in FRP sauce, which will accelerate the development of reduced-salt fermented foods in the future. 

Dynamic analysis of microbial community, flavor components of low-salt fermented red pepper sauce and exploring the key flavours formation

The music industry in the context of digitization

https://doi.org/10.1016/j.fochx.2023.100620

Dynamic analysis of volatile metabolites and microbial community and their correlations during the fermentation process of traditional Huangjiu (Chinese rice wine) produced around Winter Solstice

In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.

/pdf/music-classification-and-detection-of-location-factors-of-18a32sdobe.pdf

Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

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Dynamic analysis of volatile metabolites and microbial community and their correlations during the fermentation process of traditional Huangjiu (Chinese rice wine) produced around Winter Solstice

Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment