Drying ginger and preserving 6-gingerol content

Ginger rhizome (Zingiber officinale Roscoe) is widely used as a spice or a folk medicine. 6-gingerol is the major bioactive component in fresh ginger and has numerous physiological effects. 6-gingerol is heat sensitive while cooking and drying will transform 6-gingerol to 6-shogaol. Therefore, 6-gingerol content is used to determine the quality of ginger after drying. A drying model called the Two layer model was tested for prediction of drying ginger and compared with a single layer model. In this study, two layer model was used to describe ginger drying process. 6-gingerol content was measured by using HPLC method. Several factors which could affect 6-gingerol content were reviewed and a 6-gingerol prediction model was established from the experimental data. The results showed that the two layer drying model gave no significant improvement to describing the ginger drying process compared with the single layer model. Drying time and relative humidity (ranging from 10% to 40%) impacted 6-gingerol content, although drying temperature (ranging from 30°C to 60°C) had less effects on 6-gingerol content. It was found that 6-gingerol content was highly variable in fresh ginger, which making conclusions on models difficult

Drying ginger and preserving 6-gingerol: Poster

Ginger rhizome (Zingiber officinale) is widely used as a spice or as a medicinal plant. The major bioactive compound in fresh ginger rhizome is 6-gingerol and it is known for having a number of physiological effects. This compound is heat-sensitive and during cooking or drying will transform into 6-shogaol. Hence, the 6- gingerol content is used to evaluate the quality of dried ginger. The content of 6-gingerol during drying was measured using HPLC. Several factors that could affect the 6-gingerol content were considered and a predictive model for changes in 6-gingerol has been developed from the experimental data. The predictive model includes a single term drying model that predicts the changes of moisture content during drying. Drying time and relative humidity (ranging from 10% to 40%) impacted 6-gingerol content whereas drying air temperature (ranging from 30ºC to 60ºC) had a lesser effect. It was also found that the 6-gingerol content in fresh rhizomes was highly variable and thus required thorough testing prior to drying to be able to make the prediction more accurate.Ginger rhizome (Zingiber officinale) is widely used as a spice or as a medicinal plant. The major bioactive compound in fresh ginger rhizome is 6-gingerol and it is known for having a number of physiological effects. This compound is heat-sensitive and during cooking or drying will transform into 6-shogaol. Hence, the 6- gingerol content is used to evaluate the quality of dried ginger. The content of 6-gingerol during drying was measured using HPLC. Several factors that could affect the 6-gingerol content were considered and a predictive model for changes in 6-gingerol has been developed from the experimental data. The predictive model includes a single term drying model that predicts the changes of moisture content during drying. Drying time and relative humidity (ranging from 10% to 40%) impacted 6-gingerol content whereas drying air temperature (ranging from 30ºC to 60ºC) had a lesser effect. It was also found that the 6-gingerol content in fresh rhizomes was highly variable and thus required thorough testing prior to drying to be able to make the prediction more accurate

Wheat Teacher: A One-Stage Anchor-Based Semi-Supervised Wheat Head Detector Utilizing Pseudo-Labeling and Consistency Regularization Methods

Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and robust models for extracting traits from raw data remains a significant challenge. Numerous fully supervised target detection algorithms have been employed to address the wheat head detection problem. However, constrained by the exorbitant cost of dataset creation, especially the manual annotation cost, fully supervised target detection algorithms struggle to unleash their full potential. Semi-supervised training methods can leverage unlabeled data to enhance model performance, addressing the issue of insufficient labeled data. This paper introduces a one-stage anchor-based semi-supervised wheat head detector, named “Wheat Teacher”, which combines two semi-supervised methods, pseudo-labeling, and consistency regularization. Furthermore, two novel dynamic threshold components, Pseudo-label Dynamic Allocator and Loss Dynamic Threshold, are designed specifically for wheat head detection scenarios to allocate pseudo-labels and filter losses. We conducted detailed experiments on the largest wheat head public dataset, GWHD2021. Compared with various types of detectors, Wheat Teacher achieved a mAP0.5 of 92.8% with only 20% labeled data. This result surpassed the test outcomes of two fully supervised object detection models trained with 100% labeled data, and the difference with the other two fully supervised models trained with 100% labeled data was within 1%. Moreover, Wheat Teacher exhibits improvements of 2.1%, 3.6%, 5.1%, 37.7%, and 25.8% in mAP0.5 under different labeled data usage ratios of 20%, 10%, 5%, 2%, and 1%, respectively, validating the effectiveness of our semi-supervised approach. These experiments demonstrate the significant potential of Wheat Teacher in wheat head detection

Optimal Effects of Combined Application of Nitrate and Ammonium Nitrogen Fertilizers with a Ratio of 3:1 on Grain Yield and Water Use Efficiency of Maize Sowed in Ridge–Furrow Plastic Film Mulching in Northwest China

Improving water use efficiency is essential for the advancement of agricultural production, particularly in arid and semiarid regions. Two-year field experiments were conducted to study the effects of ridge–furrow (RF) and flat planting (FP) plastic film mulching combined with five different nitrogen (N) fertilizers, N1 (KNO3), the nitrate (NO3−)/ammonium (NH4+) mixtures with different pure nitrogen ratios N2 (1:1), N3 (1:3), and N4 (3:1), and the control N5 (urea) on maize dry matter accumulation, soil water content, grain yield, water use efficiency (WUE), and N partial factor productivity. Our results showed that RF and N4 were more efficient than FP for increasing maize grain yield, WUE, and nitrogen partial factor productivity, and there was a significant interaction for cultivation practices × N formulation. RF and 3:1 NO3−/NH4+ significantly increased grain yield by 14.73% and 13.15%, and 20.07% and 24.14% in 2016 and 2017, respectively, compared to FP and nitrate only. RFN4 produced the highest grain yield in 2016 and 2017 due to the highest dry matter accumulation at filling and physiological maturity stage, ear rows per spike, and row grains per row. Over two growing seasons, the WUE and N partial factor productivity under RFN4 were 18.75% and 29.17% more on average than those of other treatments. Therefore, RFN4 is an effective planting system for increasing the simultaneity of grain yield and WUE for maize production in rain-fed agriculture

Effect of Different Types of Organic Manure on Oil and Fatty Acid Accumulation and Desaturase Gene Expression of Oilseed Flax in the Dry Areas of the Loess Plateau of China

In order to understand the mechanism of action of oil and fatty acid accumulation and desaturase gene expression in how oilseed flax responds to different fertilization conditions, a three-factor split-plot experiment was conducted to investigate the accumulation trends of oil and fatty acids. The results revealed that soluble sugar (SS) and sucrose (SUC) contents decreased, and the starch (ST) content increased gradually with the grain development and maturity of oilseed flax. The application of sheep manure promoted the accumulation of nonstructural carbohydrates in the grains. Soluble sugar (SS) and sucrose (SUC) contents were negatively correlated with the oil content. Compared with chemical fertilizer, organic manure decreased the total saturated fatty acid but increased the unsaturated fatty acid. Organic manure significantly upregulated the expression of various genes, and fad2a gene expression was higher with the 5.8 t ha−1 chicken manure treatment. The 25 t ha−1 sheep manure treatment was more conducive to the expression of fad3a and fad3b genes and promoted the accumulation of linolenic acid (LIN), and the LIN content increased by 0.64–3.90% compared with other treatments. Consequently, an ongoing anthropogenic sheep manure input may impact the regulation of grain oil quality and yield per unit area

Unravelling the effects of layered supports on Ru nanoparticles for enhancing N2 reduction in photocatalytic ammonia synthesis

Harnessing the vast supply of solar energy as the driving force to produce ammonia from abundant nitrogen gas and water is beneficial for both relieving energy demands and developing sustainable chemical industry. Bulk carbon nitride (B-g-C3N4), exfoliated carbon nitride (E-g-C3N4) and graphite (g-C) supported Ru-K catalysts, denoted as Ru-K/B-g-C3N4, Ru-K/E-g-C3N4 and Ru-K/g-C, respectively, with the layered materials serving both as supports and light harvesters, were designed for photocatalytic ammonia synthesis. It was discovered that, besides the light harvesting properties of the catalysts which played roles in photocatalytic reactions, the structure of the supports influenced greatly the preferential locations of Ru species, which further exerted effects on the N2 activation process and ultimately impacted the ammonia production rate. The fine Ru nanoparticles uniformly and randomly dispersed on the monolayered E-g-C3N4 did not provide outstanding activity in ammonia photosynthesis; in contrast, Ru nanoparticles at the step edges of bulk g-C3N4 exhibited lower overall barriers for N2 activation and a much enhanced photocatalytic ammonia synthesis rate due to the synergy effects between metal and support as confirmed by density functional theory (DFT) calculations. The discovery of the relationship between reactivity and support geometry in this study will be important in guiding the rational predesign of efficient photocatalysts

Remedying Defects in Carbon Nitride To Improve both Photooxidation and H₂ Generation Efficiencies

The outstanding visible light response of carbon nitride has aroused intense expectations regarding its photocatalysis, but it is impeded by the inevitable defects. Here, we report on a facile melamine-based defect-remedying strategy and resultant carbon nitride high-performance photocatalysts (R-C₃N₄). Melamine with amino groups and a triazine structure was selected as a “little patch” to passivate and remedy various defects inside carbon nitride. Such a remedying effect has been comprehensively proven by Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD) analyses, and the ninhydrin test. In addition, their effects on photocatalysis were also individually confirmed by chemical methods, including cyano reduction reactions and deamination reactions. Furthermore, melamine remediation can result in g-C₃N₄/mpg-C₃N₄ junctions, which also favors electron transfer and charge separation during the photocatalytic reaction. In order to explore its broader applications, R-C₃N₄ was used as a photocatalyst for the photooxidation reaction of 1,4-dihydro-2,6-dimethylpyridine-3,5-dicarboxylate (1,4-DHP) and simultaneous H₂ evolution. The conversion rates of 1,4-DHP and H₂ production catalyzed by R-C₃N₄ were enhanced 2 and 6.5 times, respectively. This rational design is beneficial for the conversion of 1,4-DHP during the preparation of bioactive compounds and clean hydrogen production at the same time