Identification of botanical origin of Chinese unifloral honeys by free amino acid profiles and chemometric methods

The amino acid contents of five floral sources Chinese honeys(jujube, rape, chaste, acacia, and lungan) were measured using reversed phase high-performance liquid chromatography(RP-HPLC). The results showed that proline was the main amino acid in most of the analyzed samples. Phenylalanine presents at the highest content in chaste honey samples, and the total amino acid contents of chaste honeys were also significantly higher than those of other honey samples. Based on the amino acid contents, honey samples were classified using chemometric methods(cluster analysis(CA), principal component analysis(PCA), and discriminant analysis(DA)). According to the CA results, chaste honeys could be separated from other honeys, while the remaining samples were correctly grouped together when the chaste honey data were excluded. By using DA, the overall correct classification rate reached 100%. The results revealed that amino acid contents could potentially be used as indicators to identify the botanical origin of unifloral honeys.

Synthesis of hollow Prussian blue cubes as an electrocatalyst for the reduction of hydrogen peroxide

A cubic Prussian blue （PB） with the hollow interior was successfully synthesized by direct dissociation followed by a controlled self-etching process. The etching process also made hollow Prussian blue （HPB） a porous structure. SEM, TEM and XRD were employed to confirm the structure and morphology of the prepared materials. Then HPB and chitosan （CS） were deposited on a glassy carbon electrode （GCE）, used to determine H202. The amperometric performance of HPB/CS/GCE was investigated. It was found that the special structure of HPB exhibits enhanced performance in the H202 sensing.

A Hydrogen Peroxide Sensor Based on Silver Nanoparticles Biosynthesized by Bacillus subtilis

In this study, silver nanoparticles （AgNPs） were biosynthesized by Bacillus subtilis and used to construct a nonenzymatic hydrogen peroxide （H202） sensor, Scanning electron microscopy, transmission electron microscopy and energy dispersive spectroscopy confirmed that the AgNPs were prepared successfully with spherical morphol- ogy. The electrochemical properties of the resulted sensor were investigated by cyclic voltammetry, chronoam- perometry and electrochemical impedance spectroscopy. It was found that the sensor exhibited good electrocatalytic activity towards H202 reduction with a wider linear range from 0.05 to 120 mmol.L-1, a detection limit of 8 gmol.L-1 and a fast response time less than 2 s. The sensor exhibited good selectivity for H202 determination in the presence of glucose, acetaminophen, ascorbic acid and uric acid.

A MnOOH-Polyaniline Nanocomposite Modified Gold Electrode for Electrochemical Sensing of Nitrite

A novel non-enzymatic nitrite sensor was fabricated by immobilizing MnOOH-PANI nanocomposites on a gold electrode （Au electrode）. The morphology and composition of the nanocomposites were investigated by transmission electron microscopy （TEM） and Fourier transform infrared spectrum （FTIR）. The electrochemical results showed that the sensor possessed excellent electrocatalytic ability for NO2 oxidation. The sensor displayed a linear range from 3.0 μmol·L^-1 to 76.0 mmol·L^-1 with a detection limit of 0.9 μmol·L^-1 （S/N=3）, a sensitivity of 132.2 μA·L·mol^-1·cm^-2 and a response time of 3 s. Furthermore, the sensor showed good reproducibility and long-term stability. It is expected that the MnOOH-PANI nanocomposites could be applied for more active sensors and used in practice for nitrite sensing.

Force tracking control for electrohydraulic servo system based on adaptive neuro-fuzzy inference system(ANFIS)controller

Purpose–The purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system(EHSS).Specifically,the adaptive neuro-fuzzy inference system(ANFIS)is selected to improve the control performance for EHSS.Design/methodology/approach–Two types of input–output data were chosen to train the ANFIS models.The inputs are the desired and actual forces,and the output is the current.The first type is to set a sinusoidal signal for the current to produce the actual driving force,and the desired force is chosen as same as the actual force.The other type is to give a sinusoidal signal for the desired force.Under the action of the PI controller,the actual force tracks the desired force,and the current is the output of the PI controller.Findings–The models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller.The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot,rise time and mean absolute error and show adaptivity to different tracking conditions,including sinusoidal signal tracking and sudden change signal tracking.Originality/value–This paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.