Effect of Oxygen Concentration in Fermentation on Black Tea Quality and Optimization of Oxygen-enriched Fermentation Process

Fermentation is a critical process of black tea quality formation and oxygen is the key factor affecting the fermentation, so it is important to analyze the effect of oxygen in fermentation on the quality and metabolites of black tea. One bud and two leaves of 'Longjing 43' tea varieties were used as materials for low oxygen fermentation (5%) , natural fermentation (21%) and oxygen-enriched fermentation (36%) treatments, and the effects of oxygen concentration on sensory quality, non-volatile and volatile metabolites of black tea were analyzed by sensory evaluation combined with gas chromatography-mass spectrometry (GC-MS) and ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS), and the parameters of oxygen-enriched fermentation of black tea were optimized by single factor combined with response surface analysis. Results showed that oxygen-enriched fermentation could significantly improve the taste and aroma quality of black tea compared with nature fermentation (P0.05) in oxygen-enriched fermentation. A total of 25 volatile compounds differed significantly in three treatments, including 12 aldehydes, 2 ketones, 3 alcohols, 3 alkenes, and 5 esters, and the content of most differing compounds increased with increasing oxygen concentration. The optimized parameters of oxygen-enriched black tea were: Oxygen concentration of 40%, oxygenation time of 1.5 h, fermentation time of 4 h. And the contents of TFs, TF, TF3G, TF3'G and TFDG of black tea were 2.86%, 0.25%, 1.71%, 0.24% and 0.68%, respectively. The results of this study would provide an important basis for guiding black tea processing and quality control

Prediction Method of the Moisture Content of Black Tea during Processing Based on the Miniaturized Near-Infrared Spectrometer

The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy

Prediction Method of the Moisture Content of Black Tea during Processing Based on the Miniaturized Near-Infrared Spectrometer

The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy

A wide star–black-hole binary system from radial-velocity measurements

All stellar mass black holes have hitherto been identified by X-rays emitted by gas that is accreting onto the black hole from a companion star. These systems are all binaries with black holes below 30 M$_{\odot}$$^{1-4}$. Theory predicts, however, that X-ray emitting systems form a minority of the total population of star-black hole binaries$^{5,6}$. When the black hole is not accreting gas, it can be found through radial velocity measurements of the motion of the companion star. Here we report radial velocity measurements of a Galactic star, LB-1, which is a B-type star, taken over two years. We find that the motion of the B-star and an accompanying H$\alpha$ emission line require the presence of a dark companion with a mass of $68^{+11}_{-13}$ M$_{\odot}$, which can only be a black hole. The long orbital period of 78.9 days shows that this is a wide binary system. The gravitational wave experiments have detected similarly massive black holes$^{7,8}$, but forming such massive ones in a high-metallicity environment would be extremely challenging to current stellar evolution theories$^{9-11}$.Comment: Published in Nature on Nov 28, 201

Overview of the DESI Legacy Imaging Surveys

The DESI Legacy Imaging Surveys (http://legacysurvey.org/) are a combination of three public projects (the Dark Energy Camera Legacy Survey, the Beijing–Arizona Sky Survey, and the Mayall z-band Legacy Survey) that will jointly image ≈14,000 deg2 of the extragalactic sky visible from the northern hemisphere in three optical bands (g, r, and z) using telescopes at the Kitt Peak National Observatory and the Cerro Tololo Inter-American Observatory. The combined survey footprint is split into two contiguous areas by the Galactic plane. The optical imaging is conducted using a unique strategy of dynamically adjusting the exposure times and pointing selection during observing that results in a survey of nearly uniform depth. In addition to calibrated images, the project is delivering a catalog, constructed by using a probabilistic inference-based approach to estimate source shapes and brightnesses. The catalog includes photometry from the grz optical bands and from four mid-infrared bands (at 3.4, 4.6, 12, and 22 μm) observed by the Wide-field Infrared Survey Explorer satellite during its full operational lifetime. The project plans two public data releases each year. All the software used to generate the catalogs is also released with the data. This paper provides an overview of the Legacy Surveys project