Effect of ultrasonic treatment during fermentation on the quality of fortified sweet wine.

The present study aimed to investigate the potential of ultrasonic treatment during fermentation for enhancing the quality of fortified wines with varying time and power settings. Chemical analysis and sensory evaluation were conducted to assess the impact of ultrasonic treatment on wine quality. Results showed that ultrasonic treatment could increase total anthocyanin and total phenol content, reduce anthocyanin degradation rate, and improve color stability. Moreover, ethyl carbamate content was lower in the ultrasonic group after aging compared to non-ultrasonic group. A combination of 200 W for 20 min resulted in higher sensory scores and more coordinated taste, while a combination of 400 W for 40 min produced higher levels of volatile compounds (21860.12 µg/L) leading to a richer and more elegant aroma. Therefore, ultrasound can be used as a potential technology to improve the quality of wine.

Effect of indoor dehydration on the chemical composition of Marselan grapes in Heyang of China.

Marselan is a red wine grape variety with great brewing prospects. The aim of this study was to investigate the effect of postharvest indoor dehydration on the quality of Marselan grapes. For two consecutive years, the harvested grapes were dehydrated naturally indoors (24-28°C). Fresh grapes were used as a control, and dehydrated samples were taken every 7 days during the period of dehydration until ending at day 28. Dehydration treatment increased degrees Brix, reducing sugars, glycerol, and malic acid. On day 7, there was an increase in protocatechuic acid, p-coumaric acid, and total tannin of 26.00%-27.73%, 11.43%-52.52%, and 39.74%-70.45%, respectively. With increasing dehydration time, total phenols, total flavonoids and total flavanols in the skins were decreased by 17.05%-38.13%, 24.32%-57.38%, and 17.05%-59.48%, respectively, with an increase in pH, citric acid, and ascorbic acid contents of grape juice by 7.66%-21.43%, 100%-137.50%, and 61.29%-258.82%, respectively. On day 21, the esters were increased by 1.10-1.75 factors. Partial least square-discriminant analysis result of volatile compounds showed that ethyl acetate, 1-propanol, 1-propanol, 2-methyl-, 1-hexanol, and 1-butanol, 3-methyl- were the predominant characteristic flavor compounds during dehydration of Marselan grapes. The effect of indoor dehydration on Marselan grape quality offered application value for China's later dehydration wine production.

Effect of exogenous organic acids on chemical compositions and sensory attributes of fortified sweet wines from dehydrated grapes.

In this study, acidity was regulated with the addition of exogenous tartaric acid and citric acid before bottling. The effect of exogenous organic acids on chemical compositions and sensory attributes of fortified sweet wines from dehydrated grapes were investigated. The results indicated that exogenous organic acids promoted the conversion of monomeric anthocyanins to copigmented anthocyanins in wines. Specifically, the combination of malvidin-3-O-glucoside and flavanols (catechin and epicatechin) was facilitated to form copigmented anthocyanins. Sensory analysis suggested that exogenous organic acids improved the balance of sugar and acidity and benefited the harmony in wines on the taste. Wines with a residual sugar and titratable acidity ratio of about 11:1 exhibited the more harmonious taste. In addition, it was also observed changes in the aroma profile related to volatile compounds, namely, more intense fruity aroma in wines with the addition of organic acids.

A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background.

It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m2 m-2) (root mean square error (RMSE): 0.23 to 0.89 m2 m-2), including for sparse canopy (LAI less than 0.3 m2 m-2) grown on different soil types (RMSE: 0.02 to 0.25 m2 m-2). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.

Dose-response relationship between pre-pregnancy body mass index and gestational diabetes mellitus / 预防医学

Objective@#To explore the dose-response relationship between pre-pregnancy body mass index (BMI) and gestational diabetes mellitus (GDM), so as to provide insights into the cut-off values of pre-pregnancy BMI and optimizing GDM prevention and control strategies. @*Methods@#Pregnant women that admitted to Zhengzhou Central hospital in 2021 were recruited, and demographics, family history, pregnancy and delivery history and blood glucose levels during pregnancy were collected. The dose-response relationship between pre-pregnancy BMI and GDM was analyzed using restricted cubic spline (RCS) analysis. The predictive ability of pre-pregnancy BMI for GDM risk was evaluated using receiver operating characteristic (ROC) curve. @*Results@#A total of 2 279 participants were included in the study. The median age was 29.0 (interquartile range, 5.0) years. The median pre-pregnancy BMI was 21.1 (interquartile range, 3.8) kg/m2. There were 312 underweight women (13.69%), 825 women with low-normal weight (36.20%), 730 women with high-normal weight (32.03%), 345 overweight women (15.14%) and 67 obese women (2.94%).The prevalence of GDM was 17.20%. RCS analysis suggested a linear dose-response relationship between age, pre-pregnancy BMI and GDM (P<0.05). When pre-pregnancy BMI was higher than 21.1 kg/m2, the risk of GDM increased with pre-pregnancy BMI (P<0.05). When women aged over 29.0 years, the risk of GDM increased with age, and the dose-response relationship of GDM caused by pre-pregnancy BMI was stronger in the women aged over 29.0 years than in the women aged 29.0 years and below (P<0.05). The area under curve (AUC) was 0.654 (95%CI: 0.624-0.684). If the cut-off value of pre-pregnancy BMI was 23.0 kg/m2, the Youden index, sensitivity and specificity was 0.238, 0.472 and 0.766, respectively. If it was 24.0 kg/m2, the Youden index, sensitivity and specificity was 0.195, 0.342 and 0.853, respectively. If it was 21.1 kg/m2, the Youden index, sensitivity and specificity was 0.213, 0.676 and 0.537, respectively.@* Conclusions @# There is a linear dose-response relationship between pre-pregnancy BMI and GDM, and higher than 21.1 kg/m2 of the pre-pregnancy BMI could increase the risk of GDM.

Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning.

High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI<2 due to background effect, which can be addressed by applying background correction on original reflectance map based on vegetation-background classification. Overall, RFR models achieved accurate LAI prediction from background-corrected reflectance for Exp16 (correlation coefficient (r) of 0.95, determination coefficient (R 2) of 0.90~0.91, root mean squared error (RMSE) of 0.36~0.40 m2 m-2, relative root mean squared error (RRMSE) of 25~28%) and less accurate for Exp19 (r =0.80~0.83, R 2 = 0.63~0.69, RMSE of 0.84~0.86 m2 m-2, RRMSE of 30~31%). Additionally, RFR models correctly captured spatiotemporal variation of observed LAI as well as identified variations for different growing stages and treatments in terms of genotypes and management practices (i.e., planting density, irrigation, and fertilization) for two experiments. The developed hybrid method allows rapid, accurate, nondestructive phenotyping of the dynamics of LAI during vegetative growth to facilitate assessments of growth rate including in breeding program assessments.

Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multispectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from using wavelengths in the visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of the proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding.

Climate-associated major food crops production change under multi-scenario in China.

To inform targeted adaptation measures, comprehensive assessments of climate change impacts on agricultural systems are urgently needed. The current study analyzed the production (including phenology, yield, ET, and WUE) of major crops in the near future (2011-2040) through probabilistic assessment. The Crop-Environment Resource Synthesis (CERES)-Wheat/Maize model was driven by ensemble climate projections from five global climate models (GCMs) under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). Results showed that: (1) Compared with the base period, the probability of advanced maturity for wheat and maize was 90.36-91.18% and 62.96-64.50%, respectively. The probability of yield reduction for wheat and maize was 64.12-68.93% and 40.44-41.41%, respectively. The probability of water use efficiency (WUE) reduction for wheat and maize was 51.09-53.94% and 35.86-37.93%, respectively. (2) In the absence of adaptation measures, substantial yield loss was found in major crop-producing areas, including the northern winter wheat planting area and Huang-Huai Plain spring-summer maize zone. The spatial overlap of the vulnerable area will exacerbate food insecurity. (3) The decrease in wheat yield and WUE were both greater than that of maize. Replacing highly sensitive crops with heat-tolerant varieties and dietary diversity should be advocated to cope with future climate change. The results will contribute to adaptive decision-making in China.

Design and Test Research on Cutting Blade of Corn Harvester Based on Bionic Principle.

Existing corn harvester cutting blades have problems associated with large cutting resistance, high energy consumption, and poor cut quality. Using bionics principles, a bionic blade was designed by extracting the cutting tooth profile curve of the B. horsfieldi palate. Using a double-blade cutting device testing system, a single stalk cutting performance contrast test for corn stalks obtained at harvest time was carried out. Results show that bionic blades have superior performance, demonstrated by strong cutting ability and good cut quality. Using statistical analysis of two groups of cutting test data, the average cutting force and cutting energy of bionic blades and ordinary blades were obtained as 480.24 N and 551.31 N and 3.91 J and 4.38 J, respectively. Average maximum cutting force and cutting energy consumption for the bionic blade were reduced by 12.89% and 10.73%, respectively. Variance analysis showed that both blade types had a significant effect on maximum cutting energy and cutting energy required to cut a corn stalk. This demonstrates that bionic blades have better cutting force and energy consumption reduction performance than ordinary blades.