No abstract

Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets.

The determination of total organic carbon in soils, fertilizers, sewage sludge, sediments, and humic extracts is widely performed by chemical oxidation methods with K2Cr2O7. The Yeomans-Bremner (YB) method is currently the one that stands out the most. The drawback of these methods is the large amount of concentrated H2SO4 used, which generates a large amount of hazardous waste. This work proposes using KMnO4 as an alternative to K2Cr2O7 for a lower consumption of H2SO4. The method uses the back titration of Fe2+ added to consume both the MnO2 produced and the excess KMnO4 that was not consumed in the OM oxidation. A non-trivial and yet not explored stoichiometry was applied for this purpose, providing a success not yet achieved in using permanganate to determine TOC by titration. The ideal condition for the oxidation of OC was determined by the analysis of a potassium hydrogen phthalate standard and involved the use of 0.125 mol L-1 H2SO4 and temperature of 70 °C, obtaining a significant advantage over the YB method (concentrated H2SO4 and 170 °C). The proposed method was applied to the analysis of soil samples, producing conversion factors for soil organic carbon that varied between 0.652 and 1.12.

Glycerol can be determined in several products by various analytical techniques. Titrimetric ones have stood out for their low cost, being recommended as standards. However, reliable, simple, fast, and green methods with low quantification limits are still needed. Titration of glycerol is based on its oxidation by periodate (Malaprade reaction) producing formic acid, formic aldehyde, and iodate. Iodate and periodate are iodometrically titrated, but mutual interference between these ions has produced methods with some drawbacks. Here is proposed to mask periodate with molybdate, to eliminate interference, determining the glycerol content through iodate, employing iodometric titration. Solutions containing from 10 to 1000 μg of glycerol were analyzed (error < 3.4%). The method was successfully applied for the determination of glycerol in biodiesels from different raw materials. Recoveries were from 92.9 ± 0.4 to 111 ± 3%. Semi-micro extraction was done, providing a fast procedure for determining free glycerol in biodiesel (< 10 min).