مجله دانش آزمایشگاهی ایران
سال دوازدهم شماره 1 (پیاپی 45، بهار 1403)

Amber is known as jewel with organic origin, which was formed during the fossilization of the resin of some plants. The resin of plants in the early stages of petrification is called copaline. There are, however, some synthetic organic materials such as Bakelite, with amber-like appearance and are a counterfeit for amber. Main characteristics of natural ambers include: having a higher melting point and refractometry index, not dissolving in some organic solvents such as acetone, blue fluorescence under ultraviolet light and having a pleasant resinous smell in heat. Most ambers have been found from the Cretaceous, Eocene to Miocene sediments, and the largest numbers of ambers discovered is from the Baltic basin. Having some well-preserved fossils, and presence of gas, liquid and solid inclusions, cause to increase the valuable of amber in the paleontological studies. Simple physical tests as well as the use of Nuclear Magnetic Resonance (NMR) or Pyrolysis Gas Chromatography/Mass Spectrometry (Pyrolysis-GC-MS) are useful analysis methods in the amber detection.

Nonlinear Raman scattering spectroscopy is an optical method used to detect vibrational modes of target molecules, enabling the analysis and detection of chemical species at the molecular scale. When combined with a confocal optical microscope, this system can scan the sample surface point-by-point and record the molecular information present. By processing the raw spectral data and spatial coordinates, two-dimensional hyperspectral images are generated, containing nonlinear Raman scattering information for each pixel. Advanced statistical methods in artificial intelligence and machine learning are then used to extract information about the spatial distribution of molecules and chemical phases with high sensitivity and accuracy. In this study, an integrated optical setup consisting of a confocal optical microscope and a Raman spectroscopic system was used to scan the surface of a heterogeneous polymer sample at 3900 points using a visible laser with a wavelength of 532 nanometers. The recorded hyperspectral images were then processed using artificial intelligence and machine learning statistical methods to extract a molecular map of the spatial distribution of polymers in the sample.

After performing any type of measurement, a number of data (numbers) are always obtained, which must be discovered or categorized in order to be able to analyze them. To do this, first, the method of data distribution should be determined. Researchers usually try to find out which data distributions are closest to mathematical functions, so that they can have a correct analysis of the nature of the distribution and calculations on it. In fact, one of the most important statistical distributions is called "normal distribution". Checking the normality of the distribution of test results is a prerequisite for many statistical tests, and one of the softwares to perform this check is minitab software. In this research, in order to check the normality of the results obtained from measuring the vertical rebound of a size 5 rubber outdoor soccer ball, the Anderson Darling method was used with the help of minitab software. The parameter used to judge the normality of data distribution is p-value. According to the obtained result, the p-value ≥ 0.05, which indicates the normality of data distribution.

The service companies that provide the task of operating the power grids of industrial cities obtain the electricity they need from the electricity markets or gas power plants. To minimize the company's cost, an objective function is introduced considering the energy supply constraints of consumers, gas unit constraints, and recycling system constraints. The algorithm used for optimization is the heuristic learning-learning algorithm, and finally, the simulation results of the introduced algorithm have been compared with the genetic algorithm. The codes related to the simulation are presented in the software of the article and the relevant results show that the teaching-learning algorithm can find the optimal solution to the problem faster than the genetic algorithm.

Gluten is one of food ingredients that is widely used in food industry because of its technological and organoleptic properties. Celiac is an autoimmune enteropathy disease caused by permanent intolerance to gluten. When genetically predisposed patients are exposed to gluten, which is the major protein of wheat, barley, rye, and related grains, a specific immune response is activated in their body. Currently, gluten-free diets are the only treatment for celiac patients. Eliminating high gluten foods containing wheat, barley, and rye reduces symptoms, improves quality of life and even reduces mortality. For this purpose, in order to determine and confirm the absence of gluten in the products and foods it is necessary to carry out a control test. ELISA has been approved by the American Chemical Society as a conventional method for gluten detection. ELISA is able to detect naturally and heat-treated gluten and measure it quantitatively. ELISA assay can estimate the quantitative amount of gliadins and hordeins in based on wheat and barley processed and unprocessed products, as well as the gluten content of hydrolyzed products.

Many approaches have been proposed in the literature to increase productivity and eliminate structural defects of nanofibers. They have focused more on increasing the number of jets through needle modification, using multiple needles, and needleless electrospinning, each of which has advantages and disadvantages. It is true that needleless electrospinning has solved the problem of needle clogging and low production speed; But the problem of rapid evaporation of volatile solvents, which leads to a decrease in accuracy and lack of reproducibility in the manufacturing process, has not yet been completely solved. Today, the production of nanofibers has become very important due to their many applications in various fields in the world. Although the production of nanofibers in the laboratory is easy, but when it comes to production on an industrial scale, it seems a bit difficult due to the difficult conditions of controlling environmental and machine factors that have a direct effect on the structure and properties of nanofibers.