Sugar Tech最新文献

This study investigates the impact of various nanoparticles, specifically cobalt ferrite (CoFe₂O₄), graphitic carbon nitride (g-C₃N₄), and their composite (CoFe₂O₄/g-C₃N₄), on the ethanol production capabilities of Saccharomyces cerevisiae using banana peel biomass. Notably, the control group without any nanoparticle addition yielded a modest 11.157% bioethanol. However, the introduction of 100 ppm of CoFe₂O₄ nanoparticles significantly enhanced ethanol production, achieving 52.157%. Additionally, increasing the concentration of the CoFe₂O₄/g-C₃N₄ composite from 0 mg to 100 ppm resulted in a notable improvement, reaching an ethanol yield of 35.44%. Even with 100 ppm of g-C₃N₄ alone, ethanol production increased to 23%. To further optimize the fermentation process, the study also examined the effects of visible light irradiation on ethanol production, both in the presence and absence of these nanomaterials. The results revealed that light irradiation could stimulate bioethanol production by 15.44% compared to the control. When light was combined with nanoparticles, the stimulatory effects were even more pronounced, indicating that light-activated nanoparticles represent a promising strategy for enhancing ethanol yields. The findings highlight the potential of CoFe₂O₄ nanoparticles, particularly when photoactivated, to significantly elevate ethanol yields from banana peels. Furthermore, the study identifies the optimal concentration of the CoFe₂O₄/g-C₃N₄ composite as a viable pathway for sustainable bioethanol production. This research not only advances the understanding of nanomaterial applications in fermentation processes but also contributes to the development of more efficient and eco-friendly strategies for renewable fuel generation.

This research focused on exploring the efficacy of sugarcane bagasse-derived materials in a packed bed system for remediating aquaculture wastewater, with the goal of ensuring environmentally safe discharge. The bagasse fibres (SCB) were mercerized with sodium hydroxide (NaOH-SCB) and carbonized into biochar (SCB). Characterization techniques revealed significant chemical and physical changes in the modified bagasse, including changes in elemental composition, surface area, and thermal stability. The modified materials exhibited increased surface area and reduced pore size, and SCBB being the most thermally stable. The effect of contact times (30 min, followed by 1, 2, and 24 h) was studied on the treatment of fish pond effluent with bagasse-based materials. Physico-chemical parameters measured were temperature, pH, total dissolved solids, total suspended solids, electrical conductivity, dissolved oxygen, chemical oxygen demand, biological oxygen demand, and colour. The concentrations of metals (iron, magnesium, manganese, and copper) were also analysed. The treatment with the filter materials was effective in positively regulating various physico-chemical parameters of the effluent; however, it had a negative effect on the electrical conductivity and total dissolved solids. Among the materials, SCBB was the most effective for improving physico-chemical parameters, while NaOH-SCB was more effective for reducing the concentration of metals in the effluent. The optimum contact time for the treatment was observed to be 24 h for SCB, 2 h for NaOH-SCB, and either 30 min or one hour for SCBB. This study examines the effectiveness of sugarcane bagasse-based materials as a cost-effective and green alternative for industrial wastewater treatment.

Brazil is the world's largest producer of sugarcane, and mechanized management has increased the crop's productive efficiency. However, the intensity of machine traffic causes soil compaction, limiting the increase in productivity and longevity of crops. The harvesting operation compacts the soil as heavy machines move over the ground in inadequate moist conditions. This study evaluated soil compaction and sugarcane productivity in different productive areas with manual and mechanized harvesting practices over four years of harvest. Samples of soil resistance to penetration were taken after planting, harvesting, and from harvesting rows and between rows. The data were subjected to statistical analysis and Pearson's coefficient determination for soil penetration resistance and productivity variables over time. Resistance to soil penetration was observed in agricultural areas, regardless of management practices, with machinery primarily affecting the surface soil layers (0 to 0.3 m). The mechanized system, in the first year of the harvest, increased resistance to soil penetration by 270%, compared to 75% in the manual method. Manual harvesting management presented a cone index similar to a mechanized harvesting system, with a 14% increase in resistance to soil penetration. No significant differences in compaction were found between areas with manual and mechanized management at depths above 0.3 m. Productivity decreased linearly with cultivation time for both harvesting methods (r² 0.90 and 0.97—manual and mechanized), indicating that, independent of the harvesting model, sugarcane productivity tends to stabilize after the fourth year of production.

Crop water production functions, which describe the relationship between yield and water use, are of great importance in determining the economic value of irrigation, identifying different irrigation strategies and determining optimum irrigation levels. The main objective of this study was to evaluate the performance of Stewart, Jensen, Minhas, Blank and Rao functions in predicting root yield in sugar beet. Field studies were carried out in 2019 and 2020 in Bursa Yenişehir Vocational School Production Area. Water stress sensitivity indices of crop water production functions were determined using ET and yield values in the first year. Root yield values simulated with crop water production functions were compared with root yield values measured in the field in 2020. Sensitivity indices of sugar beet to water at four different growth stages were determined using five different crop water production models. Considering the sensitivity indices of sugar beet to water in four different growth periods, it was determined that the yield formation period (Y) was the most sensitive to water. Yield formation (Y) period was followed by vegetative (V) and establishment (E) periods. The least water-sensitive period of sugar beet was the ripening (R) period. Jensen and Minhas models were recommended when the sensitivity indices to water stress calculated for four different growth stages of sugar beet were compared.

The application of phase change material (PCM) energy storage technology in the smart field has attracted a lot of attention. One of the important directions for the further development of energy storage technology is the development of shape-stabilized organic composite phase change materials. In this study, we developed a green stable composite phase change material (SCPCM) using carbonized sugarcane leaves (CSL) as a substrate. Sugarcane leaves is a thermolysis at 450 °C under three preparation conditions: aerobic unactivated (YONA), anaerobic unactivated (NONA), and anaerobic activated (NOYA), and subsequently combined with paraffin (PA) to produce paraffin/carbonized sugarcane leaves-stabilized composite phase change materials (PA/CSL SCPCM) for highly efficient thermal energy storage applications. All SCPCM had high thermal stability, good thermal properties, and good chemical compatibility of the composites, and the phase transition temperatures were controlled within the range of 24–34 °C. The support structure, specific surface area, active functional groups and high mesopore content of the activation-processed carbonized sugarcane leaves as the composite framework enhanced the capillary force of SCPCM adsorption on the CSL. The NOYACSL SCPCM had the largest latent heat storage capacity of 136.43 J/g. In contrast, the YONACSL SCPCM and NONACSL SCPCM had lower latent heat storage capacity of 108.9 J/g and 122.25 J/g. This study provides an economical and environmentally friendly method for the synthesis of shape-stabilized phase change materials based on biomass materials, which has potential applications in residual heat recovery and recycling.

Root rot poses a significant threat to sugar beet crops worldwide, making it a major concern in beet-growing regions. This study aimed to summarize the common causal organisms of sugar beet root rot and focus on identifying root rot pathogen populations, discovering germplasm resources discovery, and breeding resistance against major causal organisms. Additionally, the research progress on chemical and biological control methods for sugar beet root rot is discussed. In order to improve the resistance of sugar beet to root rot and obtain germplasm resources with high resistance to root rot, multidimensional collaboration strategy is recommended; implementation of pathogen population monitoring should be prioritized to identify predominant pathogenic fungi and elucidate their pathogenic mechanisms within cultivation zones, thereby providing scientific basis for targeted breeding of resistant cultivars; disease management systems require integration of synergistic applications between chemical and biological control agents to establish integrated management protocols; during breeding processes, molecular marker-assisted selection (MAS) technology should be incorporated to pinpoint disease resistance loci and optimize breeding efficiency; concurrent emphasis should be placed on evaluating the application potential of emerging biotechnological tools, which may overcome limitations inherent in conventional breeding methodologies.

Removal of impurities is one of the important parts of mechanized sugarcane harvesting. The main impurity (cane leaf) in the process of sugarcane machine harvesting belongs to flexible flake material. For the problem of less research on its construction simulation and lack of accurate simulation model parameters, this results in a large difference between the simulation effect and the actual operation in the equipment design, which to some extent limits the research on the design of the impurity removal device. In this paper, sugarcane material is used as the test material to carry out simulation and analysis research. After determining the intrinsic parameters of the sugarcane material through physical tests, a segmented flexible body model was established using EDEM software to calibrate the parameters of the sugarcane material. Stacking angle experiments and bending test methods were used to obtain its simulation model parameters. The coupled system model of billet–leaf—fan is constructed, and the reliability of the model is verified by the impurity rate, fan flow rate, cane leaf movement trajectory, and average speed. The experimental validation showed the simulation results and measurements at three fan speeds (1050 r/min, 1350 r/min, 1650 r/min): The average errors of the impurity rate, fan flow rate, and average cane leaf speed tests were 3.01%, 3.18%, and 4.21%, respectively; the trajectory of cane leaf movement was mainly ejected from the middle and upper middle of the fan outlet, which was the same as the test trajectory; the simulation results have high accuracy, proving that the model is relatively reliable. The model can theoretically solve the contradiction between the large size of sugarcane and impurities and the requirement that the grid size is not smaller than the particle size in the CFD-DEM coupling. It provides a reference for the coupling simulation of harvester debris removal device and the research of operation mechanism in the future.

This study aimed to explore the potential of multisource and multi-temporal UAV remote sensing data for sugar yield estimation and to investigate the relationship between different remote sensing features and nitrogen accumulation at various growth stages. UAV hyperspectral images, RGB images, and light detection and ranging (LiDAR) data were collected at different growth stages, and a comprehensive set of spectral, structural, and textural features reflecting the sugar beet canopy were extracted. Three machine learning algorithms, including multiple linear regression (MLR), random forest (RF), and support vector machine (SVM), were used to construct prediction models for nitrogen accumulation and sugar yield. The results showed the following. LiDAR features and textural features that characterize the canopy structure of sugar beet are essential for reflecting nitrogen accumulation, and LiDAR features play a key role in sugar yield prediction. For nitrogen accumulation prediction, the MLR model performed best during the rapid foliage growth period (R² = 0.70, RMSE = 0.44 ). For sugar yield prediction, the MLR model, when combined with multi-temporal data, achieved the highest accuracy (R² = 0.95, RMSE = 0.16), which was 21% higher than the best single-phase prediction result (sugar accumulation stage). The collaborative use of multisource remote sensing data significantly improved accuracy compared to single data sources, with nitrogen estimation accuracy increasing by 55% and sugar yield estimation accuracy increasing by 28%. These findings indicate that multisource remote sensing data can be used to diagnose nitrogen nutrition and predict sugar yield in sugar beet.

Rhizomania disease is the most important disease of sugar beet in Iran and some other parts of the world, and plays a significant role in decreasing sugar yield. The best approach to combat the disease is to use resistant cultivars. In order to use the disease resistance genes in breeding programs, it is necessary to tag these genes with molecular markers. In order to identify the rhizomania-resistant plants within 103 sugar beet genotypes including S₁, S₂ and F₂ populations, a coupling-phase SCAR marker (named PN1) and a repulsion-phase RAPD marker (named PN3) linked to rhizomania resistance gene were used. Genomic DNA was extracted from the leaf samples taken from the single plants. In the next step, primers related to PN1 and PN3 markers were individually tested on some single plant DNAs by RAPD-PCR and Specific PCR methods. Amplified products were separated by gel electrophoresis, stained with ethidium bromide, observed using gel documentation device and scored according to the presence and absence of marker band. According to the marker data, percent dominant homozygous plants were found to be between 0 to 92%. The O-types numbers 1025, 1036, 1011, 201–20 and 201–25 were found to be the most disease resistant genotypes. Therefore, it seems PN3 marker, after converting to SCAR specific marker, along with PN1 marker can be utilized in one duplex PCR reaction to identify each of the three genotypes (Rz₁Rz₁, Rz₁rz₁ and rz₁rz₁) and screen rhizomania resistant breeding lines and populations originated from Rz₁ source.

Sugar crops such as sugarcane, sugar beet, and sweet sorghum are vital to global agriculture, serving as key sources of sweeteners, biofuels, and industrial raw materials. However, their productivity and sustainability are increasingly threatened by abiotic stresses, including drought, salinity, heat, chilling, and heavy metal toxicity. These stresses affect complex polygenic traits, necessitating precise and efficient genetic interventions for developing stress-resilient cultivars. This review aims to explore the potential of CRISPR/Cas genome editing technologies, including CRISPR/Cas9, CRISPRa, CRISPRi, prime editing, and base editing, in enhancing abiotic stress tolerance in sugar crops. These advanced genome editing tools facilitate targeted modifications, enabling gain-of-function mutations and regulatory network studies to accelerate genetic improvement. A particular focus is given to transcription factor families such as DREB, NAC, and WRKY, which regulate key genes associated with osmoprotection, stomatal regulation, and stress signaling pathways. Furthermore, strategies for generating transgene-free edited plants, including Preassembled CRISPR/Cas9 Ribonucleoproteins-Based Genome Editing, the CASE toolkit, Hi-Edit technology, and Transgene Killer CRISPR technology, are also emphasized. By integrating CRISPR-based strategies with conventional breeding, this review article aims to provide a framework for developing resilient sugar crop varieties capable of withstanding environmental challenges. Additionally, regulatory considerations for genome-edited crops are discussed to highlight the implications for commercial adoption. The insights will contribute to sustainable sugar crop production by leveraging precise genome editing approaches to enhance stress tolerance.