Wyniki wyszukiwania - Biblioteka Nauki

1

Spectrophotometric determination of nitrite using phenosafranine

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Geetha K. , Balasubramanian N.

Chemia Analityczna

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2001

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tom Vol. 46, No. 4

579-588

EN

A simple and sensitive spectrophotometric method for the determination of nitrite is described. Diazotisation of the primary amino group in the dye phenosafranine resulted in a decrease in absorbance at 520 nm. This decrease in absorbance is directly proportional to nitrite concentration obeying Beer's law in the range 0-12 ug of nitrite in a final aqueous volume of 25 ml with a relative standard deviation of 2.9% at 8ug of nitrite (n = 10). Sensitivity of the method is enhanced by extracting the dye into 3-methyl-1 -butanol under alkaline condition, followed by the addition of methanolic sulfuric acid. Beer's law is obeyed over the range 0-1ug of nitrite at 530 nm, after extraction with a relative standard deviation of 1.9% at 0.6ug of nitrite (n = 10). The developed method has been applied for the determination of residual NO(2) in the laboratory fume cupboard after fixing it as nitrite in suitable absorber solution. Nitrate and nitrite contents in analytical grade chemicals, medicated tooth paste, soil and water samples were determined. The effects of interfering gases and other ions on the determination of nitrite are described. Reliability of the method was established by parallel determination using a diazocoupling reaction and by studying recovery of added nitrite and nitrate.

PL

Opisano prostą i czułą spektrofotometryczną metodą oznaczania azotynów. Diazowanie pierwszorzędowej grupy aminowej barwnika fenosafraniny powoduje zmniejszenie wartości absorbancji przy 520 nm. Zmniejszenie to jast proporcjonalne do stężenia azotynów i wykazuje zgodność z prawem Beera w zakresie 0-12 ug azotynów w końcowej objętości 25 ml (RSD = 2.9% dla 8ug azotynów, n = 10). Czułość metody zwiększa się po ekstrakcji barwnika 3-mety lo-1 -butanolem ze środowiska alkalicznego i dodaniu metanolowego roztworu kwasu siarkowego. Po ekstrakcji, zgodność z prawem Beera stwierdzono w zakresie 0-1 ug azotynów przy 530 nm (RSD = l .9% dla 0.6 mg azotynów, n = 10). Opracowaną metodę zastosowano do oznaczania NO(2) w dymie z wyciągu laboratoryjnego po absorpcji gazu w odpowiednim roztworze. Zawartość azotanów i azotynów oznaczano także w odczynnikach chemicznych, próbkach wody i gleby oraz w leczniczej paście do zębów. Opisano wpływ różnych jonów i gazów na wyniki oznaczania azotynów. Wiarygodność wyników opracowanej metody sprawdzono metodą diazowania i sprzęgania oraz metodą dodawania wzorca (azotyny i azotany) określono dokładność metody.

2

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

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Sathya D. J. , Geetha K.

Polish Journal of Medical Physics and Engineering

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2017

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tom Vol. 23, Iss. 4

81--88

EN

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

3

Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques

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Janaki S. D. , Geetha K.

Polish Journal of Medical Physics and Engineering

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2019

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tom Vol. 25, Iss. 2

131--139

EN

Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.