Application of derivative ratio spectrophotometry for determination of β-carotene and astaxanthin from Phaffia rhodozyma extract

A derivative ratio spectrophotometric method was used for the simultaneous determination of β-carotene and astaxanthin produced from Phaffia rhodozyma. Absorbencies of a series of the standard carotenoids in the range of 441 nm to 490 nm demonstrated that their absorptive spectra accorded with Beer’s law and that the additivity when the concentrations of β-carotene and astaxanthin and their mixture were within the range of 0 to 5 μg/ml, 0 to 6 μg/ml, and 0 to 6 μg/ml, respectively. When the wavelength interval (?λ) at 2 nm was selected to calculate the first derivative ratio spectra values, the first derivative amplitudes at 461 nm and 466 nm were suitable for quantitatively determining β-carotene and astaxanthin, respectively. Effect of divisor on derivative ratio spectra could be neglected; any concentration used as divisor in range of 1.0 to 4.0 μg/ml is ideal for calculating the derivative ratio spectra values of the two carotenoids. Calibration graphs were established for β-carotene within 0?6.0 μg/ml and for astaxanthin within 0?5.0 μg/ml with their corresponding regressive equations in: y=?0.0082x?0.0002 and y=0.0146x?0.0006, respectively. R-square values in excess of 0.999 indicated the good linearity of the calibration graphs. Sample recovery rates were found satisfactory (>99%) with relative standard deviations (RSD) of less than 5%. This method was suc- cessfully applied to simultaneous determination of β-carotene and astaxanthin in the laboratory-prepared mixtures and the extract from the Phaffia rhodozyma culture.

Influence of silicate anions structure on desilication in silicate-bearing sodium aluminate solution

The structural changes of silicate anions in the desilication process with the addition of calcium hydrate alumino-carbonate were studied by measuring Raman spectra, infrared spectra and corresponding second derivative spectra. The results show that the desilication ratio in the solution prepared by the addition of sodium silicate(solution-SS) is much greater than that in the solution by the addition of green liquor(solution-GL), and low alumina concentration in the sodium aluminate solutions facilitates the desilication process. It is also shown that alumino-silicate anions in the solution-GL, and Q^3 polymeric silicate anions in solution-SS are predominant, respectively. In addition, increasing the concentration of silica favors respectively the formation of the alumino-silicate or the Q^3 silicate anions in the solution-GL or the solution-SS. Therefore, it can be inferred that the low desilication ratio in the silicate-bearing aluminate solution is mainly attributed to the existence of alumino-silicate anions.

Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement.Currently,the urban drainage pipeline network is upgrading to improve its classification and collection ability.However,there is a lack of efficient online monitoring and identification technology.UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size,reagent-free and fast detection.Because the performance parameters of probe like optic resolution,dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level,it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes.In this study,drainage network samples were online collected and tested,and four types were labeled according to sample sites and environment situation.Derivative spectrum were adopted to amplify the shape features,while convolutional neural network algorithm was established to conduct nonlinear spectrum classification.Influence of input and network structure on classification accuracy was compared.Original spectrum,first-order derivative spectrum and a combination of both were set to be three different inputs.Artificial neural network with or without convolutional layer were set be two different network structures.The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage,mixed rainwater,rainwater and industrial sewage.The recognition rate of industrial wastewater was 100%,and the recognition rate of domestic sewage and rainwater mixing system were over 90%.