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To further improve the sensitivity of porous silicon-assembled microcavity biosensors, the detection of porous silicon assembled microcavity by angle spectrum method is also researched. This contribution uses CdSe/ZnS quantum dot-labelled probe DNA to amplify the refractive index and detect it by the angle spectrum method. The first layer is a microcavity layer, and the next is porous silicon with a Bragg structure. After functionalization, different concentrations of target DNA were coupled to construct quantum dot-labelled probe DNA to bind specifically to the target DNA. With the Bragg device based on quartz glass forming an assembled microcavity structure, using a He-Ne laser as the detection light and collimation beam, the angle spectrum method is used to detect different concentrations of target DNA before and after biological reaction with quantum dot-labelled probe DNA and reflect the angle of minimum light intensity. The experimental results show that with increasing target DNA concentration, probe DNA concentration and angle, the detection limit is 13.25 pM. The angle-spectrum method has been successfully used to detect the assembled porous silicon microcavity. The angle-spectrum method has the advantages of spectrometer free and low cost.

Attenuated total reflection (ATR) – Fourier transform infrared (FTIR) spectroscopy with the pretreatment of converting the absorption spectrum into angle spectrum (CASAS) and partial least squares (PLS) was employed to rapidly and simultaneously determine the ingredients of cellulose slurries and solutions during lyocell production. ATR was employed to rapidly and conveniently collect the spectra of the swollen slurries and viscous solutions during production. CASAS was used to eliminate the influence on modeling by the optical path differences due to uneven ingredient distributions in the samples. The PLS models for the cellulose, water, and N-methylmorpholine N-oxide (NMMO) concentrations of the cellulose slurry/solution were constructed using the raw spectra, pretreated spectra by CASAS, and common pretreatment methods, respectively. The results show that CASAS eliminates interferences due to the differences in optical path and improves the performance of PLS model for each analyte. The standard errors of prediction (SEP) of models using CASAS for cellulose, NMMO, and water concentration of cellulose slurry were 0.368, 0.196 and 0.171, while the values for the cellulose solution were 0.442, 0.353, and 0.066, respectively. The standard errors of prediction (SEPs) of CASAS models are significantly lower than the reproducibility requirement of the reference method, 0.5. The reported approach using ATR-FTIR spectroscopy combined with CASAS and PLS is rapid and accurate for determining the ingredients of the cellulose slurries and solutions and offers potential for identifying similar samples. [ABSTRACT FROM AUTHOR]