scholarly journals Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy

2021 ◽  
Vol 4 ◽  
pp. 45-52
Author(s):  
Cia Min Lim ◽  
Manus Carey ◽  
Paul N. Williams ◽  
Anastasios Koidis
2017 ◽  
Vol 7 (8) ◽  
pp. 661 ◽  
Author(s):  
Canan Aksoy ◽  
Meltem Maras Atabay ◽  
Engin Tirasoglu ◽  
Ezgi Taylan Koparan ◽  
Aysel Kekillioglu

Background: Macro-element content profiles in propolis that have been previously used in traditional folk medicine have provided enough information to develop a classification of the geological origin of propolis. Within this study, we aim to contribute our research to existing literatüre, particularly through our use of EDXRF spectroscopy, which has not been used to study propolis before. The results of the study led us to conclude that the residues of heavy metals were a limited concentration in Turkish propolis samples.Objective: The purpose of this study was to investigate the macro-element profiles in Turkish propolis from 18 different cities of Turkey.  Methods: The macro-element of 22 raw propolis samples were investigated using Energy-Dispersive X-ray fluorescence spectrometry.Results: Turkish Propolis was discovered to be rich with minerals of potassium, sodium which could be more beneficial in human nutrition. Potassium content was at a relatively higher level than other elements in these samples, while calcium content was at  alower level in those samples from various regions of Turkey.Conclusion: The elements of propolis that we studied were distinctive enough to make the discrimination of propolis from different locations in Turkey possible. The quantification by energy-dispersive X-ray fluorescence spectrometry procedures provided good resolution of multi-element analysis in propolis samples.Keywords: Propolis; element analysis; energy-dispersive X-ray fluorescence spectrometre 


Author(s):  
N.K.R. Smith ◽  
K.E. Hunter ◽  
P. Mobley ◽  
L.P. Felpel

Electron probe energy dispersive x-ray microanalysis (XRMA) offers a powerful tool for the determination of intracellular elemental content of biological tissue. However, preparation of the tissue specimen , particularly excitable central nervous system (CNS) tissue , for XRMA is rather difficult, as dissection of a sample from the intact organism frequently results in artefacts in elemental distribution. To circumvent the problems inherent in the in vivo preparation, we turned to an in vitro preparation of astrocytes grown in tissue culture. However, preparations of in vitro samples offer a new and unique set of problems. Generally, cultured cells, growing in monolayer, must be harvested by either mechanical or enzymatic procedures, resulting in variable degrees of damage to the cells and compromised intracel1ular elemental distribution. The ultimate objective is to process and analyze unperturbed cells. With the objective of sparing others from some of the same efforts, we are reporting the considerable difficulties we have encountered in attempting to prepare astrocytes for XRMA.Tissue cultures of astrocytes from newborn C57 mice or Sprague Dawley rats were prepared and cultured by standard techniques, usually in T25 flasks, except as noted differently on Cytodex beads or on gelatin. After different preparative procedures, all samples were frozen on brass pins in liquid propane, stored in liquid nitrogen, cryosectioned (0.1 μm), freeze dried, and microanalyzed as previously reported.


2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ivan Lorencin ◽  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Anđela Blagojević ◽  
Tijana Šušteršić ◽  
...  

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.


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