What is the most effective analytical method for quantification and identification of microplastics in contaminated soils?

The increasing concern over microplastics (MPs) contamination in agricultural soils due to excessive plastic use is a worldwide concern. The objective of this study was to determine which analytical technique is most effective for the analysis of MPs in agricultural soils. Near-infrared spectroscopy (NIR), scanning electron microscopy (SEM), multispectral analysis, and X-ray diffraction were used to analyze sections of clay soil containing varying percentages of virgin white MPs from 0 to 100%. X-ray analysis only detected MPs at high concentrations (20%). However, NIR at 2.300 nm and multispectral analysis at 395 nm demonstrated greater accuracy and sensitivity in distinguishing between all MPs levels. SEM revealed that MPs have an amorphous structure that is distinct from crystalline soil, potentially influencing their interactions with other soil constituents. These findings highlight the value of NIR and multispectral analysis in accurately identifying and measuring MPs in soil. Efficient management plans rely on increased awareness of MPs' environmental impact.

Interactive machine learning for soybean seed and seedling quality classification.

New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on interactive and traditional machine learning methods to classify soybean seeds and seedlings according to their appearance and physiological potential. In addition, we correlated the appearance of seeds to their physiological performance. Images of soybean seeds and seedlings were used to develop models using low-cost approaches and free-access software. The models developed showed high performance, with overall accuracy reaching 0.94 for seeds and seedling classification. The high precision of the models that were developed based on interactive and traditional machine learning demonstrated that the method can easily be used to classify soybean seeds according to their appearance, as well as to classify soybean seedling vigor quickly and non-subjectively. The appearance of soybean seeds is strongly correlated with their physiological performance.

Sugar metabolism and developmental stages of rubber tree (Hevea brasiliensis L.) seeds.

Changes in the concentration of sugars and sucrose metabolism enzymes can characterize the developmental stages of a seed. In recalcitrant species such as Hevea brasiliensis L., little is known about these changes. We aimed to evaluate the three main stages of development of rubber tree seeds - histodifferentiation, cell elongation and accumulation of reserves. The activities of acid and neutral invertases (E.C. 3.2.1.26) and sucrose synthase (EC 2.4.1.13), and the concentrations of reducing sugars (RS), total soluble sugars (TSS) and sucrose (Suc) were determined concomitantly with the histochemical and anatomical evaluation of seed structure. Histodifferentiation in rubber tree seeds occurs up to 75 days after anthesis (DAA). The concentration of RS is high and of Suc is low during seed histodifferentiation, which occurs along with a visible increase in the number of cell divisions. After that period, there is an increase in the concentration of Suc (mg g-1 ) and in the number and size of starch granules, and a decrease in the concentration of RS (mg g-1 ). At that point, cell elongation occurs. At 135 DAA, there is an inversion in the concentration of these two sugars and an increase in reserve accumulation. Thus, in seeds of the evaluated clone, the period up to 75 DAA is characterized as the histodifferentiation stage, while from that time up to 120 DAA the cell elongation stage takes place. The final stage of seed maturation and reserve accumulation begins at 135 DAA, and the seed, including the embryo, is completely formed at 175 DAA.