Determination of Cadmium Concentration in Milled and Brown Rice Grains Using Graphite Furnace Atomic Absorption Spectrometry

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

Biochemical and genetic basis of cadmium biosorption by Enterobacter ludwigii LY6, isolated from industrial contaminated soil

In this study, a cadmium-tolerant bacterium, Enterobacter ludwigii LY6, was isolated from cadmium-contaminated soil in Shifang, Sichuan province, China. The cadmium chloride removal rate of the strain LY6 with a treatment of 100 mg/L cadmium chloride reached 56.0%. Scanning electron microscopy showed that exopolysaccharides (EPS) might be the main means of cadmium adsorption by the strain. X-ray powder diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS) analyses indicated that cadmium sulfide nanoparticles formed on the surface of bacteria cultured in a medium containing 100 mg/L cadmium chloride. In addition, the expression of several genes increased with the increase of the cadmium concentration in the medium, including the multiple antibiotic resistance proteins marA and marR, and the cold shock protein CspA. GO functions, such as the redox activity, respiratory chain and transport functions, and KEGG pathways involved in "bacterial chemotaxis" and "terpenoid backbone biosynthesis" were found to be closely related to bacterial cadmium tolerance and biosorption. This is the first report that E. ludwigii can reduce sulfate to form cadmium sulfide nanoparticles under high concentration cadmium exposure. The genes related to cadmium tolerance identified in this study lay a foundation for the genetic breeding of cadmium-tolerant strains.