Search

The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ13C, δ15N, δ2H, δ18O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ13C, δ15N, δ2H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication. [ABSTRACT FROM AUTHOR]

Tea plant is an acidophilic plant, and soil pH has an important effect on the absorption and enrichment of elements, tea plant growth and quality. In this study, rhizosphere soils and leaves of tea plants from 30 tea plantations were collected to determine soil pH and multi-element content of soil and leaves of tea plants, to obtain and validate key elements that are enriched by pH affecting tea plants, and to analyze the effects of pH on the growth and quality of tea plants. The results showed that soil pH significantly affected the enrichment of 15 elements by tea plants, and the enrichment coefficients of 11 elements (C, Mg, Si, N, P, Mn, Sr, Cd, S, Ca and Sb) tended to increase significantly with the increase of soil pH, while the opposite was true for the other four elements (Cu, Rb, Ba and Al). TOPSIS analysis showed that soil pH had the greatest effect on tea plant enrichment of seven elements, namely N (100%), Mn (43.32%), C (39.22%), P (27.66%), Sr (15.30%), Mg (13.41%) and Ba (10.47%). Pot experiments with tea seedlings also verified that soil pH significantly affected the enrichment of tea leaves for seven key elements. Moreover, with the increase of soil pH, the growth indexes, photosynthesis indexes and quality indexes of tea seedlings showed a significant upward trend. Interaction analysis showed that the enhanced enrichment of N, Mn, C, P, Sr and Mg by tea plants was beneficial to increase the photosynthetic capacity of tea plants, promote the growth of tea plants and improve the quality of tea leaves. This study provides an important theoretical basis for the cultivation and management of tea plants. [ABSTRACT FROM AUTHOR]

The present study was conducted to clarify the differences in the multi-element, volatile organic compound, fatty acid, and metabolite fingerprints between black and white sesame seeds. A total of 53 chemical elements, 32 volatile flavor compounds, 40 fatty acids, and 283 metabolites were identified and evaluated in the two groups of sesame seeds. Univariate and multivariate statistics indicated a distinct separation between the two groups of sesame seeds. A panel of 16 chemical elements, 3 volatile compounds, 8 individual fatty acids, and 54 metabolites with p value < 0.05 and variable importance in projection score > 1 were selected as the most important discriminants for the two types of sesame seeds. Overall, these data reveal the influence of genotype on the chemical composition of sesame seeds. Our findings also demonstrate that the hybrid model of instrumental analysis and chemometrics is feasible for the discrimination of black and white sesame seeds. [ABSTRACT FROM AUTHOR]

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification. [ABSTRACT FROM AUTHOR]

In the present study, the implementation of multi-blade profiles in a Savonius rotor was evaluated in order to increase the pressure in the blade's intrados and, thus, decrease motion resistance. The geometric proportions of the secondary element were determined, which maximized the rotor's performance. For this, the response surface methodology was used through a full factorial experimental design and a face-centered central composite design, consisting of three factors, each with three levels. The response variable that was sought to be maximized was the power coefficient ( C P ), which was obtained through the numerical simulation of the geometric configurations resulting from the different treatments. All geometries were studied under the same parameters and computational fluid dynamics models through the ANSYS Fluent software. The results obtained through both experimental designs showed a difference of only 1.06% in the performance estimates using the regression model and 3.41% when simulating the optimal proportions geometries. The optimized geometry was characterized by a C P of 0.2948, which constitutes an increase of 10.8% in its performance compared to the profile without secondary elements and of 51.2% compared to the conventional semicircular profile. The numerical results were contrasted with experimental data obtained using a wind tunnel, revealing a good degree of fit. [ABSTRACT FROM AUTHOR]

In this paper, two multi-element resonant DC-DC converters are analyzed in detail. Since their resonant tanks have multiple resonant components, the converters display different resonant characteristics within different operating frequency ranges. Through appropriate design, both of the two proposed converters successfully lower the conversion losses and, meanwhile, broaden the voltage gain ranges as well: one converter is able to take full usage of the third order harmonic to deliver the active power, and thus the effective utilization rate of the resonant current is elevated; while the another minimizes the entire switching losses for power switching devices by restricting the input impedance angle of the resonant tank. Besides, the loss distribution is analyzed for the purpose of guiding the component design. In the end, two 500Wprototypes are fabricated to test the theoretical analyses. The results demonstrate that the two proposed converters can achieve wide voltage gain with the small frequency deviation, which noticeably contributes to highly efficient conversion. Their peak efficiencies are measured as 95.4% and 95.3%, respectively. [ABSTRACT FROM AUTHOR]