At present, the technology used for the extraction and purification of Camellia oleifera saponins generally has the problems of high cost and low purity, and the quantitative detection of Camellia oleifera saponins also has the problems of low sensitivity and easy interference from impurities. To solve these problems, this paper aimed to use liquid chromatography for the quantitative detection of Camellia oleifera saponins, and to adjust and optimize the related conditions. In our study, the average recovery of Camellia oleifera saponins obtained was 100.42%. The RSD of precision test was 0.41%. The RSD of the repeatability test was 0.22%. The detection limit of the liquid chromatography was 0.06 mg/L, and the quantification limit was 0.2 mg/L. In order to improve the yield and purity, the Camellia oleifera saponins were extracted from Camellia oleifera Abel. seed meal by methanol extraction. Then, the extracted Camellia oleifera saponins were extracted with an ammonium sulfate/propanol aqueous two-phase system. We optimized the purification process of formaldehyde extraction and aqueous two-phase extraction. Under the optimal purification process, the purity of Camellia oleifera saponins extracted by methanol was 36.15%, and the yield was 25.24%. The purity of Camellia oleifera saponins obtained by aqueous two-phase extraction was 83.72%. Thus, this study can provide a reference standard for rapid and efficient detection and analysis of Camellia oleifera saponins for industrial extraction and purification.

Nanocapsule preparation technology, as an emerging technology with great development prospects, has uniqueness and superiority in various industries. In this paper, the preparation technology of nanocapsules was systematically divided into three categories: physical methods, chemical methods, and physicochemical methods. The technological innovation of different methods in recent years was reviewed, and the mechanisms of nanocapsules prepared via emulsion polymerization, interface polymerization, layer-by-layer self-assembly technology, nanoprecipitation, supercritical fluid, and nano spray drying was summarized in detail. Different from previous reviews, the renewal iteration of core–shell structural materials was highlighted, and relevant illustrations of their representative and latest research results were reviewed. With the continuous progress of nanocapsule technology, especially the continuous development of new wall materials and catalysts, new preparation technology, and new production equipment, nanocapsule technology will be used more widely in medicine, food, cosmetics, pesticides, petroleum products, and many other fields.

To explore the radial variation in wood properties of slash pine (Pinus elliottii Engelm.) during its growth process and to achieve the early prediction of these properties, our study was carried out in three slash pine harvest-age plantations in Ganzhou, Jian, and Jingdezhen, Jiangxi province of South China. Wood core samples were collected from 360 sample trees from the three plantations. SilviScan technology was utilized to acquire wood property parameters, such as tangential fiber widths (TFWs), radial fiber widths (RFWs), fiber wall thickness (FWT), fiber coarseness (FC), microfibril angle (MFA), modulus of elasticity (MOE), wood density (WD) and ring width (RD). Subsequent systematic analysis focused on the phenotypic and radial variation patterns of wood properties, aiming to establish a clear boundary between juvenile and mature wood. Based on determining the boundary between juvenile and mature wood, a regression equation was used to establish the relationship between the properties of juvenile wood and the ring ages. This relationship was then extended to the mature wood section to predict the properties of mature wood. Our results indicated significant differences in wood properties across different locations. The coefficients of variation for RD and MOE were higher than other properties, suggesting a significant potential for selective breeding. Distinct radial variation patterns in wood properties from the pith to the bark were observed. The boundary between juvenile and mature wood was reached at the age of 22. The prediction models developed for each wood property showed high accuracy, with determination coefficients exceeding 0.87. Additionally, the relative and standard errors between the measured and predicted values were kept below 10.15%, indicating robust predictability. Mature wood exhibited greater strength compared to juvenile wood. The approach of using juvenile wood properties to predict those of mature wood is validated. This method provides a feasible avenue for the early prediction of wood properties in slash pine.