Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

Nickel adsorption from waters onto Fe3O4/sugar beet pulp nanocomposite

In this study the magnetic nanocomposite material was synthesized with Fe₃O₄ impregnated to sugar beet pulp using chemical precipitation technique. Ni(II) removal performance of magnetic nanocomposite was investigated under different environmental conditions such as contact time, adsorbent dose, pH, initial heavy metal concentration, etc. The experimental studies have shown that, 81.2% Ni(II) removal efficiency was achieved at optimal conditions (25 mg/L initial Ni(II) concentration at 40 minute contact time, 200 rpm shaking speed, 5 g/L nanocomposite dose and pH 6.6). Freundlich and Langmuir isotherm experiments were performed and correlation coefficients were determined as 94.5% and 99.4%, respectively. The maximum adsorption capacity of material was achieved as 9.36 mg/g. These findings indicate that the adsorption that takes place is a monolayer process. The results of the pseudo-second order kinetic model (R² = 0.9947) indicate the chemisorptions process is used for Ni(II) removal using the electrostatic interaction. Thermodynamic studies illustrated that Ni(II) adsorption onto nanocomposite are exothermic and causes a decrease in the entropy. The adsorption of Ni(II) ions is non-spontaneous except for at low temperature and low initial concentrations. Nanocomposite characterization was illuminated with XRD, FT-IR, BET, TGA, TEM, SEM/EDX analysis.

New sandwich-type polymeric potassium-dicyanoargentate(I) complex: synthesis, characterization and enzymatic activity

Coordination compounds containing dicyanoargentate(I) have remarkable biological potential due to their therapeutic antibacterial, antifungal, antibiofilm, and anticancer properties. In this study, a new dicyanoargentate(I)-based complex was synthesized and characterized by various procedures (elemental, thermal, FT-IR for complex) involving crystal analysis of the complex. In addition, the biological activity of this new compound on the acetylcholinesterase (AChE) enzyme, an important enzyme for the nervous system, was investigated. When the infrared (IR) spectrum of the complex is examined, the OH vibration peak resulting from H2O molecules in the structure at 3948-3337 cm-1 and at 2138 cm-1, along with a CN peak coordinated to Ag, can be seen, indicating that the mass remaining in the thermal degradation of the complex at 1000 ◦ C is the weight corresponding to the metal mixture consisting of K+Ag (calc.: 68.06). The crystal method revealed that the complex has a sandwich-like, polymeric chemical structure with layers formed by K+ cations and [Ag(CN)2H2O]- anions. Therefore, the AChE enzyme has potential therapeutic uses in improving ACh levels in brain cells, in reducing various side effects, and in improving cognitive impairment, especially in advanced Alzheimer's disease patients. In this study, the activity of this newly synthesized complex on AChE was also investigated. As a result of this research, [Ag(CN)2(H2O)K] had 0.0282 ± 0.010 μM Ki values against AChE. The compound was therefore a good inhibitor for the AChE enzyme. This type of compound can be used for the development of novel anticholinesterase drugs.