Computational simulation and preparation of fluorescent magnetic molecularly imprinted silica nanospheres for ciprofloxacin or norfloxacin sensing.

A magnetic molecularly imprinted fluorescent sensor for the sensitive and convenient determination of ciprofloxacin or norfloxacin in human urine was synthesized and characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, ultraviolet/visible spectroscopy, and fluorescence spectroscopy. Both cadmium telluride quantum dots and ferroferric oxide nanoparticles are introduced into the polymer for the rapid separation and detection of the target molecules. The synthesized molecularly imprinted polymers were applied to detect ciprofloxacin or its structural analog norfloxacin in human urine with the detection limit 130 ng/mL. A computational study was developed to evaluate the template-monomer geometry and interaction energy in the polymerization mixture to determine the reaction molar ratio of the template and monomer molecules.

[Transfer calibration for alcohol determination using temperature-induced shortwave near infrared spectra].

The authors studied the temperature influence on short-wave near-infrared spectra of ethanol aquatic solution and utilized four methods to establish the transfer partial least squares (PLS) calibration model: direct transfer calibration, global calibration, orthogonal signal correction (OSC) and generalized least squares weighting (GLSW). The PLS models were built at four temperatures: 15, 25, 35 and 40 degrees C. The results showed that direct calibration provided high prediction bias: significantly high positive prediction bias for a temperature lower than calibration temperature and negative bias for higher temperatures. By using the global correction, OSC and GLSW, the systematic errors could be reduced. However, the global correction needed more calibration samples and built a more complex model. The OSC and GLSW methods provided better predictions using fewer latent variables. By using the GLSW method, prediction bias less than 0.1% and RMSEP less than 0.9% were obtained. The absolute prediction error of GLSW method was less than 1.5%. Additionally, the GLSW provided smaller prediction error at every researched temperature using fewer latent variables than OSC. Thus, GLSW was superior to OSC and could establish more robust transfer calibration model.