Search Results (5)

Industrial residues are sources of functional biopolymers with interesting properties for textile applications. This study aims to evaluate the impact of enzymatic pre-treatment on oil yield and phenolic compounds’ content in an aqueous extraction process, as well as the functional properties incorporated into textiles. This research investigated the influence of residue granulometry, biomass percentage, and the application of enzymatic pre-treatment with different enzymes (cellulase, pectinase, xylanase) individually or in combination. Chestnut hedgehog (CH), tobacco plant stems (TPSs), vine shoot trimmings (VSTs), and beer spent grain (BSG) were explored. For textile functionalization, the extracted oils were incorporated into a bio-based formulation and applied on cotton fabric through pad-dry-cure. For CH, the pre-treatment with cellulase and xylanase achieved an oil yield of 149 and 148 mg oil/mL extract, respectively. With the combination of both enzymes, the richest oil in phenolic compounds was extracted: 1967.73 ± 16.86 mg GAE/g biomass. CH and TPS oils presented an antioxidant activity above 60%, and the functionalized textiles also showed the highest antioxidant potential and a UPF of 30. The textiles presented water repellence and washing fastness. This study demonstrates a sustainable oil extraction method and its potential application in the development of functional textiles. Full article

Climate change and water scarcity bring significant challenges to agricultural systems in the Mediterranean region. Novel methods are required to rapidly monitor the water stress of the crop to avoid qualitative losses of agricultural products. This study aimed to predict the stem water potential of cotton (Gossypium hirsutum L., 1763) using Sentinel-2 satellite imagery and machine learning techniques to enhance monitoring and management of cotton’s water status. The research was conducted in Rutigliano, Southern Italy, during the 2023 cotton growing season. Different machine learning algorithms, including random forest, support vector regression, and extreme gradient boosting, were evaluated using Sentinel-2 spectral bands as predictors. The models’ performance was assessed using R² and root mean square error (RMSE). Feature importance was analyzed using permutation importance and SHAP methods. The random forest model using Sentinel-2 bands’ reflectance as predictors showed the highest performance, with an R² of 0.75 (±0.07) and an RMSE of 0.11 (±0.02). XGBoost (R²: 0.73 ± 0.09, RMSE: 0.12 ± 0.02) and AdaBoost (R²: 0.67 ± 0.08, RMSE: 0.13 ± 0.02) followed in performance. Visible (blue and red) and red edge bands were identified as the most influential predictors. The trained RF model was used to model the seasonal trend of cotton’s stem water potential, detecting periods of acute and moderate water stress. This approach demonstrates the prospective for high-frequency, non-invasive monitoring of cotton’s water status, which could support smart irrigation strategies and improve water use efficiency in Mediterranean cotton production. Full article

Magnetized water has been a promising approach to improve crop productivity but the conditions for its effectiveness remain contradictory and inconclusive. The objective of this research was to understand the influences of different magnetized water with varying quality on seed absorption, germination, and early growth of cotton. To this end, a series of experiments involving the seed soaking process, germination test, and pot experiment were carried out to study the effects of different qualities (fresh and brackish water) of magnetized water on seed water absorption, germination, seedling growth, photosynthetic characteristics, and biomass of cotton in 2018. The results showed that the maximum relative water absorption of magnetized fresh and magnetized brackish water relatively increased by 16.76% and 19.75%, respectively, and the magnetic effect time of brackish water was longer than fresh water. The relative promotion effect of magnetized brackish water on cotton seed germination and growth potential was greater than magnetized fresh water. The cotton seeds germination rate under magnetized fresh and magnetized brackish water irrigation relatively increased by 13.14% and 41.86%, respectively, and the relative promoting effect of magnetized brackish water on the vitality indexes and the morphological indexes of cotton seedlings was greater than magnetized fresh water. Unlike non-magnetized water, the net photosynthetic rate (P_n), transpiration rate (T_r), and instantaneous water use efficiency (iWUE) of cotton irrigated with magnetized water increased significantly, while the stomatal limit value (L_s) decreased. The influences of photosynthesis and water use efficiency of cotton under magnetized brackish water were greater than magnetized fresh water. Magnetized fresh water had no significant effect on biomass proportional distribution of cotton but magnetized brackish water irrigation markedly improved the root-to-stem ratio of cotton within a 35.72% range. Therefore, the magnetization of brackish water does improve the growth characteristics of cotton seedlings, and the biological effect of magnetized brackish water is more significant than that of fresh water. It is suggested that magnetized brackish water can be used to irrigate cotton seedlings when freshwater resources are insufficient. Full article

Over the last several decades, Cannabis sativa L. has become one of the most fashionable plants. To use the hemp potential for the development of a sustainable textile bio-product sector, it is necessary to learn about the effect of the processes creating hemp’s value chain on fibre properties. This review presents a multi-perspective approach to industrial hemp as a resource delivering textile fibres. This article extensively explores the current development of hemp fibre processes including methods of fibre extraction and processing and comprehensive fibre characteristics to indicate the challenges and opportunities regarding Cannabis sativa L. Presented statistics prove the increasing interest worldwide in hemp raw material and hemp-based bio-products. This article discusses the most relevant findings in terms of the effect of the retting processes on the composition of chemical fibres resulting in specific fibre properties. Methods of fibre extraction include dew retting, water retting, osmotic degumming, enzymatic retting, steam explosion and mechanical decortication to decompose pectin, lignin and hemicellulose to remove them from the stem with varying efficiency. This determines further processes and proves the diversity of ways to produce yarn by employing different spinning systems such as linen spinning, cotton and wool spinning technology with or without the use of the decortication process. The aim of this study is to provide knowledge for better understanding of the textile aspects of hemp fibres and their relationship to applied technological processes. Full article

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R² = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R² = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred. Full article