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The concentration analyzer with high precision and wide range is the core device for monitoring the fermentation process. In this work, we designed and proposed a lowcost three-electrode glucose analyzer based on a self-made screen-printed enzyme biosensor chip, which has a Prussian blue (PB) nanocubic structure and leads to high sensitivity of 117.31 μAmM-1cm-2. The hardware design of the glucose analyzer can be divided into five critical parts, including digital, signal treatment system, power supply, motor-driven and the host computer. The signal treatment system is used to collect, convert and amplify the weak current signal generated by the biosensor. The digital circuit of the central processing unit of the analyzer is designed using the STM32F407ZET6. Besides, an external analog-to-digital converter is used to achieve high precision A/D conversion. The stability of the potentiostat is ensured by designing the precision power supply, hardware filtering, and algorithm filtering. The experimental results show that the glucose analyzer has a wide linear detection range from 1g/L to 120g/L and the coefficient of variation at 1g/L is 0.038, which exhibits excellent performance in stability and detection accuracy. The analyzer can be applied in the future for in-situ measurement of glucose concentration for its wide-range and high-precision detection capabilities.

Octane number is an important indicator for gasoline quality. Its real-time detection can guide gasoline reconciliation devices, and increases productivity. Raman spectra has a strong absorption to the carbon and hydrogen which are high in the gasoline. Raman analysis octane number possess on-line, fast, non-destructive detection comparing with other motor octane number and research octane number. This octane number detection system based on Raman spectra includes on-line detection system, Raman spectra data pre-processing, and prediction model. On-line detection system includes source, Raman spectrometer, and data acquisition system. Raman spectra data pre-processing uses Savitzky-Golay smoothing filter and standard orthogonal transformation. Octane number prediction model uses principal component analysis method.