Two⁃dimensional correlation infrared spectroscopy and hyperspectral imaging to detect the changes of polysaccharide during the drying process of Polygonatum sibiricum

ObjectiveTo study the changes of polysaccharide in the drying process of "one steam and one preparation" of Polygonatum sibiricum.MethodsThe slices were steamed by atmospheric pressure water isolation steaming, and then dried by heat pump at 60 ℃. Near infrared spectroscopy (NIR), hyperspectral imaging (HSI) and middle and far infrared spectroscopy were used to analyze the characteristic spectra of the drying process of Polygonatum sibiricum, and the changes of polysaccharide during the drying process were studied by combining two-dimensional correlation infrared spectroscopy (2D-IR) analysis method.ResultsThe content of polysaccharide was 3.39% after steam heating for 11 h, and 6.67% after drying for 11 h. The content of polysaccharide showed a gradual increase during the drying process. Through two-dimensional correlation analysis, it was showed that the characteristic functional groups of polysaccharides were constantly changing. And at 1 016 cm-1, the sequence of functional group changes was C—H stretching of —CH2 → stretching and deformation of O—H group in water → stretching vibration of N—H group → combination of O—H stretching and C—O stretching → combination of C—H stretching and C—C stretching → glucopyranoside. HSI technology combined with chemometrics, PLSR was used to establish the spectral prediction model of polysaccharide, and the Rp2 of the model was 0.903.ConclusionInfrared spectroscopy, HSI technology combined with 2D-IR technology can well monitor the changes of polysaccharide in the drying process of Polygonatum sibiricum

Detection of Pork Freshness Using NIR Hyperspectral Imaging Based on Genetic Algorithm and Deep Neural Network

To evaluate the effectiveness of a deep learning which is based intelligent assisted hyperspectral imaging system on the detection of pork freshness indicators, volatile basic nitrogen (TVB-N), total viable count (TVC), and 900~2500 nm near-infrared spectral data were collected from pork which were refrigerated at 4 ℃ for 12 days. Based on Python's TensorFlow and Keras platform, hyperspectral data was processed and a quantitative detection model of deep neural network was also established. And the characteristic spectral bands related to pork freshness were selected by genetic algorithm (GA). The results showed that the performance of the spectral model could be improved significantly by selecting the band of genetic algorithm. When the number of spectral bands reached 35 and 50, the prediction accuracy of GA+ANN model was higher than that of full-band linear regression model. The predictive performance of TVC was better than that of TVB-N, and the best Rp2 and RMSEP of TVC were 0.877 and 0.575, respectively. The best Rp2 and RMSEP for TVB-N were 0.826 and 1.01, respectively. In addition, it was also found that the NIR band selected by genetic algorithm had a high coincidence with the molecular vibration absorption bands of meat, such as O-H, N-H, C=O and so on. This study provides a new method which can be used for processing the near-infrared and hyperspectral data, and also provides a technical reference for rapid nondestructive testing of pork and other meat freshness

Assessing the Carbon Emission Driven by the Consumption of Carbohydrate-Rich Foods: The Case of China

peer-reviewedBackground: Carbohydrate-rich (CR) foods are essential parts of the Chinese diet. However, CR foods are often given less attention than animal-based foods. The objectives of this study were to analyze the carbon emissions caused by CR foods and to generate sustainable diets with low climate impact and adequate nutrients. Methods: Twelve common CR food consumption records from 4857 individuals were analyzed using K-means clustering algorithms. Furthermore, linear programming was used to generate optimized diets. Results: Total carbon emissions by CR foods was 683.38g CO2eq per day per capita, accounting for an annual total of 341.9Mt CO2eq. All individuals were ultimately divided into eight clusters, and none of the popular clusters were low carbon or nutrient sufficient. Optimized diets could reduce about 40% of carbon emissions compared to the average current diet. However, significant structural differences exist between the current diet and optimized diets. Conclusions: To reduce carbon emissions from the food chain, CR foods should be a research focus. Current Chinese diets need a big change to achieve positive environmental and health goals. The reduction of rice and wheat-based foods and an increase of bean foods were the focus of structural dietary change in CR food consumption.Natural Science Foundation of Guangdong Provinc

Diagnosis and management of urinary bladder paragangliomas:A Sino-American-European retrospective observational study

OBJECTIVE: Paragangliomas of the urinary bladder (UBPGLs) are rare neuroendocrine tumours and pose a diagnostic and surgical challenge. It remains unclear what factors contribute to a timely presurgical diagnosis. The purpose of this study is to identify factors contributing to missing the diagnosis of UBPGLs before surgery.DESIGN, PATIENTS AND MEASUREMENTS: A total of 73 patients from 11 centres in China, and 51 patients from 6 centres in Europe and 1 center in the United States were included. Clinical, surgical and genetic data were collected and compared in patients diagnosed before versus after surgery. Logistic regression analysis was used to identify clinical factors associated with initiation of presurgical biochemical testing.RESULTS: Among all patients, only 47.6% were diagnosed before surgery. These patients were younger (34.0 vs. 54.0 years, p &lt; .001), had larger tumours (2.9 vs. 1.8 cm, p &lt; .001), and more had a SDHB pathogenic variant (54.7% vs. 11.9%, p &lt; .001) than those diagnosed after surgery. Patients with presurgical diagnosis presented with more micturition spells (39.7% vs. 15.9%, p = .003), hypertension (50.0% vs. 31.7%, p = .041) and catecholamine-related symptoms (37.9% vs. 17.5%, p = .012). Multivariable logistic analysis revealed that presence of younger age (&lt;35 years, odds ratio [OR] = 6.47, p = .013), micturition spells (OR = 6.79, p = .007), hypertension (OR = 3.98, p = .011), and sweating (OR = 41.72, p = .013) increased the probability of initiating presurgical biochemical testing.CONCLUSIONS: Most patients with UBPGL are diagnosed after surgery. Young age, hypertension, micturition spells and sweating are clues in assisting to initiate early biochemical testing and thus may establish a timely presurgical diagnosis.</p

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

JUNO Sensitivity to Invisible Decay Modes of Neutrons

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $n \rightarrow { inv}$ and $nn \rightarrow { inv}$. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$.Comment: 28 pages, 7 figures, 4 table