Electrochemical Determination of Uric Acid at CdTe Quantum Dot Modified Glassy Carbon Electrodes.

Cyclic voltammetry and differential pulse voltammetry were used to investigate the electrochemical behavior of uric acid (UA) at a CdTe quantum dot (QD) modified the glassy carbon electrode (GCE). CdTe QDs, as new semiconductor nanocrystals, can greatly improve the peak current of UA. The anodic peak current of UA was linear with its concentration between 1.0×10(-6) and 4.0×10(-4) M in 0.1 M pH 5.0 phosphate buffer solution. The LOD for UA at the CdTe electrode (1.0×10(-7) M) was superior to that of the GCE. In addition, we also determined the effects of scan rate, pH, and interferences of UA for the voltammetric behavior and detection. The results indicated that modified electrode possessed excellent reproducibility and stability. Finally, a new and efficient electrochemical sensor for detecting UA was developed.

Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN.

Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.

Determination of purine contents in different parts of pork and beef by high performance liquid chromatography.

Determination of adenine, hypoxanthine, guanine and xanthine in different parts of pork and beef using high performance liquid chromatography was described. Chromatographic separation was carried out on Waters Atlantis T3 column (4.6 mm × 250 mm × 5 µm) with column temperature at 30 °C. The mobile phase contained 99% 10.0 mmol/L ammonium formate solution at pH 3.6 and 1.0% methanol. Chromatography was achieved at a flow rate of 1.0 mL/min and detection wavelength at 254 nm. The results indicated that total purine amounts in pork rump and beef sirloin were higher than those in other parts (P<0.05). The principal purine bases were hypoxanthine and adenine, and hypoxanthine content was the most highest in all samples (P<0.05). As pork rump and beef sirloin contain considerable amounts of total purine and uricogenic purine base, we suggest that excess consumption of them be avoid, whereas pork loin chop and beef rib eye are more suitable for a low-purine diet.