Digital Prediction of the Purchase Price of Fresh Tea Leaves of Enshi Yulu Based on Near-Infrared Spectroscopy Combined with Multivariate Analysis.

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm-1-4751.74 cm-1, 4755.63 cm-1-5129.75 cm-1, 6262.70 cm-1-6633.93 cm-1, and 7386 cm-1-7756.32 cm-1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology.

Directed solutions to address differences in access to liver transplantation.

The United Network for Organ Sharing recently altered current liver allocation with the goal of decreasing Model for End-Stage Liver Disease (MELD) variance at transplant. Concerns over these and further planned revisions to policy include predicted decrease in total transplants, increased flying and logistical complexity, adverse impact on areas with poor quality health care, and minimal effect on high MELD donor service areas. To address these issues, we describe general approaches to equalize critical transplant metrics among regions and determine how they alter MELD variance at transplant and organ supply to underserved communities. We show an allocation system that increases minimum MELD for local allocation or preferentially directs organs into areas of need decreases MELD variance. Both models have minimal adverse effects on flying and total transplants, and do not disproportionately disadvantage already underserved communities. When combined together, these approaches decrease MELD variance by 28%, more than the recently adopted proposal. These models can be adapted for any measure of variance, can be combined with other proposals, and can be configured to automatically adjust to changes in disease incidence as is occurring with hepatitis C and nonalcoholic fatty liver disease.