Kirill Laptinskiy

Optical imaging of fluorescent carbon biomarkers using artificial neural networks

Abstract. The principle possibility of extraction of fluorescence of nanoparticles in the presence of background autofluorescence of a biological environment using neural network algorithms is demonstrated. It is shown that the methods used allow detection of carbon nanoparticles fluorescence against the background of the autofluorescence of egg white with a sufficiently low concentration detection threshold (not more than 2  μg/ml for carbon dots and 3  μg/ml for nanodiamonds). It was also shown that the use of the input data compression can further improve the accuracy of solving the inverse problem by 1.5 times.

Neural Network Approaches to Solution of the Inverse Problem of Identification and Determination of the Ionic Composition of Multi-component Water Solutions

The studied inverse problem is determination of ionic composition of inorganic salts (concentrations of up to 10 ions) in multi-component water solutions by their Raman spectra. The regression problem was solved in two ways: 1) by a multilayer perceptron trained on the large dataset, composed of spectra of all possible mixing options of ions in water; 2) dividing the data set into compact clusters and creating regression models for each cluster separately. Within the first approach, we used supervised training of neural network, achieving good results. Unfortunately, this method isn’t stable enough; the results depend on data subdivision into training, test, and out-of-sample sets. In the second approach, we used algorithms of unsupervised learning for data clustering: Kohonen networks, k-means, k-medoids and hierarchical clustering, and built partial least squares regression models on the small datasets of each cluster. Both approaches and their results are discussed in this paper.

Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts by Artificial Neural Networks

The paper presents a study of aspects of using single and multiple output artificial neural networks to determine concentrations of inorganic salts in multicomponent water solutions by processing their Raman spectra. The dependence of the results on complexity of the inverse problem has been demonstrated. The results are compared for two data arrays including spectra of solutions of: (1) 5 salts composed of 10 different ions, and (2) 10 salts composed of 10 different ions.

Improvement of fidelity of molecular DNA computing using laser spectroscopy

This work is devoted to application of laser Raman spectroscopy to improve the fidelity of molecular computing by DNA strands. The developed method provides determination of concentrations of specific nitrogenous bases not lower than 0.03 g l−1 and the accuracy of determining the total concentration of DNA in solutions 0.02–0.04 g l−1 by the Raman spectra of DNA solutions. The method allows us to control the temperature of DNA solutions in the process of molecular computing with an accuracy better than 0.2 °C. The obtained results provide required control of the parameters of DNA solutions to increase the speed and accuracy of solving the problems in the results of molecular computing.

Use of neural network algorithms for elaboration of fluorescent biosensors on the base of nanoparticles

In this paper, the results of application of artificial neural networks for extraction of fluorescence contribution of nanoparticles used in biomedicine as biomarkers and drug carriers against the fluorescence background of inherent fluorophores of biological objects are presented. Principle possibility of solving this problem is shown. The used architectures of ANN allow detecting fluorescence of carbon dots against the background of proper fluorescence of egg protein with reasonably high accuracy-not worse than 0.002 mg/mL.

Use of Adaptive Methods to Solve the Inverse Problem of Determination of Composition of Multi-Component Solutions

This study considers solving the inverse problem of determination of salt or ionic composition of multi-component solutions of inorganic salts by their Raman spectra using artificial neural networks. From the point of view of data analysis, one of the key problems here is high input dimensionality of the data, as the spectrum is usually recorded in 1–2 thousand channels. The two main approaches used for dimensionality reduction are feature selection and feature extraction. In this paper, three feature extraction methods are compared: channel aggregation, principal component analysis, and discrete wavelet transformation. It is demonstrated that for neural network solution of the inverse problem of determination of salt composition, the best results are provided by channel aggregation.

Neural Network Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts

The paper presents a study into several aspects of solution of the inverse problem on determination of concentrations of components in a multi-component water solution of inorganic salts by processing Raman spectra of the solutions by perceptron type artificial neural networks. The studied aspects are: (1) determination of the optimal architecture of a multi-layer perceptron, (2) influence of the input dimensionality reduction by aggregation of adjacent spectral channels on the error of problem solution. The results are compared for two data arrays including spectra of solutions of: (1) 5 salts composed of 10 different ions (salt determination problem), and (2) 10 salts composed of 10 different ions (ion determination problem).

Determination of type and concentration of DNA nitrogenous bases by Raman spectroscopy

In this paper results of application of laser Raman spectroscopy for increasing reliability of molecular DNA computations are presented. It is shown that elaborated method provides the accuracy of determination of concentration of individual nitrogenous bases 0.03 g/l and the accuracy of determination of total DNA concentration in the solutions 0.04 g/l by Raman spectra.

Influence of hydrogen bonds on the colloidal and fluorescent properties of detonation nanodiamonds in water, methanol and ethanol

ABSTRACT This paper presents the results of research of influence of hydrogen bonds with different strength on fluorescence and colloidal properties of detonation nanodiamonds with surface carboxylic groups in the solvents. It is established that the colloidal properties of detonation nanodiamonds are almost independent on hydrogen bonds strength in water, methanol and ethanol. The fluorescent properties of detonation nanodiamonds are dependent on the type of solvent: the more intensive fluorescent properties correspond to weaker hydrogen bonds in solvents.

Artificial Neural Networks for Diagnostics of Water-Ethanol Solutions by Raman Spectra

The present paper is devoted to an elaboration of a method of diagnosis of alcoholic beverages using artificial neural networks: the inverse problem of spectroscopy – determination of concentrations of ethanol, methanol, fusel oil, ethyl acetate in water-ethanol solutions – was solved using Raman spectra. We obtained the following accuracies of concentration determination: 0.25% vol. for ethanol, 0.19% vol. for fusel oil, 0.35% vol. for methanol, and 0.29% vol. for ethyl acetate. The obtained results demonstrate the prospects of using Raman spectroscopy in combination with modern data processing methods (artificial neural networks) for the elaboration of an express non-contact method of detection of harmful and dangerous impurities in alcoholic beverages, as well as for the detection of counterfeit and low-quality beverages.