Probing Interactions between AuNPs/AgNPs and Giant Unilamellar Vesicles (GUVs) Using Hyperspectral Dark-field Microscopy.

Noble metallic nanoparticles (NPs) such as gold and silver nanoparticles (AuNPs and AgNPs) have been shown to exhibit anti-tumor effect in anti-angiogenesis, photothermal and radio therapeutics. On the other hand, cell membranes are critical locales for specific targeting of cancerous cells. Therefore, NP-membrane interactions need be studied at molecular level to help better understand the underlying physicochemical mechanisms for future applications in cancer nanotechnology.

[Study on Outliers Influence in NIR Quantitative Analysis Model].

As a secondary analysis method, reproducibility and reliability of near-infrared spectroscopy (NIRS) quantitative analysis are quite dependent on modelling process. In this paper, it is focused on outlier analysis for protein quantitative model of wheat based on NIRS. The purpose is to discuss the outlier effect in modelling process of complex sample set.

[VMTBB-Based Spectral Radiometric Calibration of NIR Fiber Coupled Spectrometer].

The medium temperature black body (MTBB) is conventional high precision equipment used as spectral radiometric scale in infrared spectral region. However, in near-infrared (NIR) spectral region, there are few papers about spectral radiometric calibration by using MTBB, that is because NIR spectral region is the borderland of its effective spectral region. The main research of this paper is spectral radiometric calibration method by using MTBB in NIR spectral region.

[Research on variable selection of wheat near-infrared spectroscopy based on latent projective graph].

To simplify the model and improve the precision of prediction model, latent projective graph (LPG) was used for variable selection. The original spectrum was processed by continuous wavelet transform (CWT), LPG was obtained by principal component analysis (PCA), and based on the assumption that collinear wavelengths might have the same contribution to the modeling, a few latent spectral variables were selected for establishing prediction model. The root mean square error of prediction (RMSEP) model was 0.