Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

The first kinetic evidence for acid catalysis in a monocyclic rearrangement of heterocycles: conversion of the Z-phenylhydrazone of 5-amino-3-benzoyl-1,2,4-oxadiazole into N,5-diphenyl-2H-1,2,3-triazol-4-ylurea

The title reaction has been studied in dioxane/water in a large (0.1-14.9) pS+ range, evidencing, together with an uncatalyzed process at intermediate (3.5-8.0) pS+ values, the occurrence of a catalyzed pathway both in the acidic (pS+ 0.1-3.5) and in the basic region (pS+ 8.0-14.9): specific-acid catalysis and general-base catalysis, respectively, have been found to take place by means of kinetic investigations at different buffer concentrations. Mechanisms for the three pathways have been advanced on the grounds of structural features. In a comparison with previous data particular attention has been paid to the acid-catalyzed pathway, herein observed for the first time in an azole-to-azole interconversion. The mechanistic hypotheses seem well supported by ab initio calculations.     

Probing the dark energy with quasar clustering

We show through Monte Carlo simulations that the Alcock-Paczyński test, as applied to quasar clustering, is a powerful tool to probe the cosmological density and equation of state parameters Omega(m0), Omega(x0), and w. By taking into account the effect of peculiar velocities upon the correlation function we obtain for the Two-Degree Field QSO Redshift Survey the predicted confidence contours for the cosmological constant (w = -1) and spatially flat (Omega(m0)+Omega(x0) = 1) cases. For w = -1, the test is especially sensitive to the difference Omega(m0)-Omega(Lambda0), thus being ideal to combine with cosmic microwave background results. For the flat case, it is competitive with future supernova and galaxy number count tests, besides being complementary to them.