Non-aqueous phase hydride generation and determination of trace bismuth by atomic fluorescence spectrometry

Preparation of Paper-Based Fluorescent Sensors and Their Application for the Detection of Cu2+ in Water

Excessive copper (Cu2+) causes adverse effects on human health and the ecological environment. Traditional methods for detecting Cu2+ have drawbacks such as high detection costs, complex operating conditions, and being time consuming. Therefore, there is an urgent need to develop simple detection methods to better meet specific health and environment quality needs. In this work, a paper-based fluorescence sensor was prepared (herein referred to as the as-prepared method) by immersing filter paper in aqueous polyethyleneimine (PEI) solution, and its potential use in Cu2+ detection was investigated. The results showed that the as-prepared paper samples, with fluorescence properties obtained by aggregation-induced luminescence of PEI, have selective recognition of Cu2+ based on the internal filtration effect, and the lowest detection limit is 0.03 μM. In addition, the relative error of this method is in the range of 1.80~2.23%, which is relatively comparable to the national standard method (0.63~630 μM), demonstrating high accuracy. Therefore, paper-based sensors with a simple preparation method have potential applications in the detection of Cu2+ in water.

Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging

Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with R_p² of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with R_p² of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with R_p² of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.