Marine Debris in the Beilun Estuary Mangrove Forest: Monitoring, Assessment and Implications.

A modified approach for marine debris investigation in mangrove forests is developed, including some practical programs, viz., sampling location, time, area, materials, size and sources data processing. The marine debris method was practiced in the Beilun Estuary mangrove forest region in Fangchenggang in 2019, viz., the debris items were classified, counted, weighed and recorded, and the marine debris pollution was assessed to understand the impact of human activities. The results show that the mass density is 21.123 (2.355~51.760) g/m2, and more than 90% came from the land-based and human activities. More than 60% of the total debris weights are plastics, followed by fabrics (17.91%) and Styrofoam (10.07%); the big-size and oversize debris account for 76.41% and 13.33%, respectively. The quantity density is 0.163 (0.013~0.420) item/m2, and ~95% came from land-based human activities. More than 75% of the total debris items were plastics, followed by Styrofoam (14.36%), fabrics (4.10%) and glass (3.59%); the big-size, medium-size and oversize debris are 76.41%, 13.33% and 10.26%, respectively. The results suggest that mangrove forests are barriers for the medium-/big-size marine debris, acting as traps for marine debris. Our study provides recommendations and practical guidance for establishing programs to monitor and assess the distribution and abundance of marine debris. The results show that mangrove areas in the Beilun Estuary are filled with some plastic debris (plastics plus Styrofoam) and that the density and type at Zhushan and Rongshutou near the China-Vietnam border are more than those at Shijiao and Jiaodong. The results of this study are also expected to not only provide baseline data for the future assessment of Beilun Estuary mangroves but also to help China and Vietnam strengthen marine land-based pollution control and promote coastal wetland and mangrove conservation, marine species conservation and sustainable use.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy.

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (Rp) of 0.9260 and RMSEP of 87.9132 µg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

[Near-infrared spectra combining with CARS and SPA algorithms to screen the variables and samples for quantitatively determining the soluble solids content in strawberry].

In using spectroscopy to quantitatively or qualitatively analyze the quality of fruit, how to obtain a simple and effective correction model is very critical for the application and maintenance of the developed model. Strawberry as the research object, this research mainly focused on selecting the key variables and characteristic samples for quantitatively determining the soluble solids content. Competitive adaptive reweighted sampling (CARS) algorithm was firstly proposed to select the spectra variables. Then, Samples of correction set were selected by successive projections algorithm (SPA), and 98 characteristic samples were obtained. Next, based on the selected variables and characteristic samples, the second variable selection was performed by using SPA method. 25 key variables were obtained. In order to verify the performance of the proposed CARS algorithm, variable selection algorithms including Monte Carlo-uninformative variable elimination (MC-UVE) and SPA were used as the comparison algorithms. Results showed that CARS algorithm could eliminate uninformative variables and remove the collinearity information at the same time. Similarly, in order to assess the performance of the proposed SPA algorithm for selecting the characteristic samples, SPA algorithm was compared with classical Kennard-Stone algorithm Results showed that SPA algorithm could be used for selection of the characteristic samples in the calibration set. Finally, PLS and MLR model for quantitatively predicting the SSC (soluble solids content) in the strawberry were proposed based on the variables/samples subset (25/98), respectively. Results show that models built by using the 0.59% and 65.33% information of original variables and samples could obtain better performance than using the ones obtained by using all information of the original variables and samples. MLR model was the best with R(pre)2 = 0.9097, RMSEP=0.3484 and RPD = 3.3278.