ABSTRACT
Fast determination of heavy metals is necessary and important to ensure the safety of crops. The potential of near-infrared spectroscopy coupled with chemometric technology for quantitative analysis of cadmium in rice was investigated. A total of 825 rice samples were collected and scanned by NIRS. The Kennard-Stone method was applied to divide the samples into calibration and validation sets. Before modeling, the spectrum was preprocessed using first derivation to reduce the baseline shift. Different chemometric tools such as interval partial least squares, moving window partial least squares, synergy interval partial least squares, and backward interval partial least squares were proposed to extract and optimize spectral interval from full-spectrum data. The performance of the calibration models generated on the basis of different regression algorithms was compared and evaluated. Results showed that the PLS models based on four chemometric algorithms outperformed the full-spectrum PLS model. Among the tools, biPLS performed better with the optimal subinterval selection. The root-mean-square error of prediction and correlation coefficient (R) of the biPLS model were 0.2133 and 0.9020, respectively. In addition, the low root-mean-square error of cross-validation was obtained in biPLS, which was 0.1756. NIRS technology combined with biPLS could be considered as an effective and convenient tool for primary screening and measuring of cadmium content in rice. In comparison with classical methodologies, this new technology was beneficial because of its eco-friendliness, fast analysis, and virtually no sample preparation required.
Subject(s)
Oryza , Spectroscopy, Near-Infrared , Algorithms , Cadmium , Calibration , Least-Squares AnalysisABSTRACT
In this work, a facile green hydrothermal method was developed for the synthesis of carbon dots with fluorescent property using pear juice as raw materials. The synthesized carbon dots were characterized by UV-vis, fluorescence and transmission electron microscopic techniques. The synthesized conditions were optimized and the obtained carbon dots exhibited an average size at 10 nm with bright emission centered at 455 nm (blue color) under UV light with the excitation wavelength at 360 nm. The as-prepared carbon dots exhibited quenching effect in the presence of Cu2+ ion and a method for Cu2+ ion detection in water was developed with acceptable selectivity. The synthesized fluorescent carbon dots showed advantages like easy preparation, low cost and environmental friendly. It could be useful in chemical and biochemical detection.
ABSTRACT
In this work, a rapid, environment friendly and sensitive method was established for the extraction and analysis of five organophosphorus pesticides (OPPs) (chlorpyrifos, chlorpyrifos-methyl, isocarbophos, malathion and phorate) in milk samples by means of gas chromatography-flame photometric detection. The pesticides were first extracted with acetonitrile from milk samples by using the modified "quick, easy, cheap, effective, rugged, and safe" (QuEChERS) method. No other clean-up was required after extraction. Then the above-mentioned acetonitrile extract was concentrated by using the dispersive liquid-liquid microextraction combined with solidification of floating organic droplets technique. Several factors that could influence the extraction efficiency, such as type of extraction solvent, disperser solvent, volume of extraction and disperser solvent, salt effect, sample pH, and extraction time, were investigated and optimized. As a result, 15 µL of 1-dodecanol were used as the extractant because of its lower toxicity, 300 µL methanol was chosen as dispersant and the extraction time was set to 1 min. Under the optimized conditions, good linearity was exhibited from 0.01 to 1.0 mg/L with the correlation coefficients higher than 0.9968. The limits of detection of the five OPPs were ranged in 0.1-0.3 µg/L, and the limits of quantification were at the range of 0.3-1.0 µg/L. Moreover, the recoveries of the target analytes from milk samples at spiking levels of 0.01, 0.05 and 0.1 mg/L were between 80.5 and 106.5% with the relative standard deviations varied from 3.6 to 6.3%. This method has been successfully applied to detect OPPs in real milk samples.
