[High precision all-reflection Fourier transform imaging spectrometer spectral calibration using homogeneous broadening of the wave number model].

All-reflection Fourier transform imaging spectrometer (ARFTIS) is a novel imaging spectrometer. The specialty is not only high spectrum resolution, but also wide band and non-chromatism. It is good for remote sensing field of wide band imaging. Single spectrum calibration, average calibration and weighted average calibration are three common calibration methods. However, they all are limited. Because they cannot meet the demand on both convenience and high precision. In the present paper, the authors propose a novel model for spectrum calibration. It can work in high precision with single spectrum calibration. At the same time, the method is steady, and the average error is less than 5% with multi-bands calibration. It provides a convenient way for the non-professional calibration situation and outer simply calibration work.

[Identification of cucumber disease using hyperspectral imaging and discriminate analysis].

Hyperspectral imaging (400-720 nm) and discriminate analysis were investigated for the detection of normal and diseased cucumber leaf samples with powdery mildew (Sphaerotheca fuliginea), angular leaf spot (Pseudomopnas syringae), downy mildew (Pseudoperonospora cubensis), and brown spot (Corynespora cassiicola). A hyperspectral imaging system was es tablished to acquire and pre-process leaf images, as well as to extract leaf spectral properties. Owing to the complexity of the original spectral data, stepwise discriminate and canonical discriminate were executed to reduce the numerous spectral information, in order to decrease the amount of calculation and improve the accuracy. By the stepwise discriminate we selected 12 optimal wavelengths from the original 55 wavelengths, and after the canonical discriminate, the 55 wavelengths were reduced to 2 canonical variables. Then the discriminate models were developed to classify the leaf samples. The result shows that the stepwise discriminate model achieved classification accuracies of 100% and 94% for the training and testing sets, respectively. For the canonical model, the classification accuracies for the training and testing sets were both 100%. These results indicated that it is feasible to identify and classify cucumber diseases using hyperspectral imaging technology and discriminate analysis. The preliminary study, which was done in a closed room with restrictions to avoid interference of the field environment, showed that there is a potential to establish an online field application in cucumber disease detection based on visible spectroscopy.

[Determination of anilines in environmental water samples by simultaneous derivatization and ultrasound assisted emulsification microextraction combined with gas chromatography-flame ionization detectors].

This research demonstrated a new method, simultaneous derivatization and ultrasound assisted emulsification microextraction combined with gas chromatography-flame ionization detector (SD-USAEME-GC-FID), for the determination of anilines in environmental water samples. In this study, several factors, such as the volume of butylchloroformate (as derivatization agent/ extraction solvent), ultrasonication time, solution pH, salt addition, and centrifuging time and speed, were optimized in order to obtain good method performance. As a result, under the optimal conditions, the method showed good linearity in the concentration range of 6-60 000 µg x L(-1) with correlation coefficients (R2) ranging from 0.999 7 to 0.999 9 for the five target anilines. The limit of detection ( LOD) , based on signal to noise ratio of 3 , ranged from 1.1-4.1 µg x L(-1). The relative standard deviations (RSD) varied from 2.4% to 5.7% (n = 6) and the enrichment factors (EF) ranged from 317 to 846. The proposed method was also successfully applied to analyze seven environmental water samples, with the relative recoveries (RR) ranging from 86.8% to 105.5%. In a conclusion, this method was convenient, highly sensitive, inexpensive and environment-friendly, and therefore, the present method can be used as a preferred method for the determination of anilines in environmental water samples.

Transformation of heavy metal speciation during sludge drying: mechanistic insights.

Speciation can fundamentally affect on the stability and toxicity of heavy metals in sludge from wastewater treatment plants. This research investigated the speciation of heavy metals in sludge from both municipal and industrial sources, and metal speciation change as a result of drying process to reduce sludge volume. The changes in sludge properties including sludge moisture content, temperature, density, and electrical conductivity were also monitored to provide insights into the mechanisms causing the change in heavy metal speciation. The results show that the drying process generally stabilized Cr, Cu, Cd, and Pb in sludge by transforming acid-soluble, reducible, and oxidizable species into structurally stable forms. Such transformation and stabilization occurred regardless of the sludge source and type, and were primarily caused by the changes in sludge properties associated with decomposition of organic matter and sulfide. The results enhanced our understanding of the geochemical behavior of heavy metals in municipal sludge, and are useful for designing a treatment system for environment-friendly disposal of sludge.