scholarly journals Evaluation of Discrimination Performance in Case for Multiple Non-Discriminated Samples: Classification of Honeys by Fluorescent Fingerprinting

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5351
Author(s):  
Elizaveta A. Rukosueva ◽  
Valeria A. Belikova ◽  
Ivan N. Krylov ◽  
Vladislav S. Orekhov ◽  
Evgenii V. Skorobogatov ◽  
...  
Keyword(s):  
Fluorescence Spectra ◽  
Fluorescent Sensor ◽  
Discrimination Performance ◽  
Principal Component ◽  
Correct Choice ◽  
Mahalanobis Distances ◽  
Confidence Ellipses ◽  
Different Sources ◽  
Total Sensitivity

In this study we develop a variant of fluorescent sensor array technique based on addition of fluorophores to samples. A correct choice of fluorophores is critical for the successful application of the technique, which calls for the necessity of comparing different discrimination protocols. We used 36 honey samples from different sources to which various fluorophores were added (tris-(2,2′-bipyridyl) dichlororuthenium(II) (Ru(bpy)32+), zinc(II) 8-hydroxyquinoline-5-sulfonate (8-Ox-Zn), and thiazole orange in the presence of two types of deoxyribonucleic acid). The fluorescence spectra were obtained within 400–600 nm and treated by principal component analysis (PCA). No fluorophore allowed for the discrimination of all samples. To evaluate the discrimination performance of fluorophores, we introduced crossing number (CrN) calculated as the number of mutual intersections of confidence ellipses in the PCA scores plots, and relative position (RP) characterized by the pairwise mutual location of group centers and their most distant points. CrN and RP parameters correlated with each other, with total sensitivity (TS) calculated by Mahalanobis distances, and with the overall rating based on all metrics, with coefficients of correlation over 0.7. Most of the considered parameters gave the first place in the discrimination performance to Ru(bpy)32+ fluorophore.

scholarly journals THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

2016 ◽  
Vol XLI-B1 ◽  
pp. 147-150
Author(s):  
Jian Yang ◽  
Wei Gong ◽  
Shuo Shi ◽  
Lin Du ◽  
Jia Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Laser Induced Fluorescence ◽  
Support Vector ◽  
Excitation Wavelength ◽  
Lidar System ◽  
Excitation Light

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

scholarly journals A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Varun Srivastava ◽  
Ravindra Kumar Purwar
Keyword(s):  
Neural Network ◽  
Ct Scan ◽  
Wavelet Decomposition ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Two Dimensional ◽  
Mr Images ◽  
Probabilistic Principal Component Analysis ◽  
Different Sources

This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The two-dimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.

Fluorescence Spectroscopy for Rapid Detection and Classification of Bacterial Pathogens

2009 ◽  
Vol 63 (11) ◽  
pp. 1251-1255 ◽  
Author(s):  
Miryeong Sohn ◽  
David S. Himmelsbach ◽  
Franklin E. Barton ◽  
Paula J. Fedorka-Cray
Keyword(s):  
Fluorescence Spectroscopy ◽  
Rapid Detection ◽  
Fluorescence Spectra ◽  
Pathogenic Bacteria ◽  
Bacterial Pathogens ◽  
Emission Spectra ◽  
Principal Component ◽  
Minimal Sample ◽  
Synchronous Scan

This study deals with the rapid detection and differentiation of Escherichia coli, Salmonella, and Campylobacter, which are the most commonly identified commensal and pathogenic bacteria in foods, using fluorescence spectroscopy and multivariate analysis. Each bacterial sample cultured under controlled conditions was diluted in physiologic saline for analysis. Fluorescence spectra were collected over a range of 200–700 nm with 0.5 nm intervals on the PerkinElmer Fluorescence Spectrometer. The synchronous scan technique was employed to find the optimum excitation (λex) and emission (λem) wavelengths for individual bacteria with the wavelength interval (Δλ) being varied from 10 to 200 nm. The synchronous spectra and two-dimensional plots showed two maximum λex values at 225 nm and 280 nm and one maximum λem at 335–345 nm (λem=λex + Δλ), which correspond to the λex=225 nm, Δλ=110–120 nm, and λex=280 nm, Δλ=60–65 nm. For all three bacterial genera, the same synchronous scan results were obtained. The emission spectra from the three bacteria groups were very similar, creating difficulty in classification. However, the application of principal component analysis (PCA) to the fluorescence spectra resulted in successful classification of the bacteria by their genus as well as determining their concentration. The detection limit was approximately 103–104 cells/mL for each bacterial sample. These results demonstrated that fluorescence spectroscopy, when coupled with PCA processing, has the potential to detect and to classify bacterial pathogens in liquids. The methology is rapid (<10 min), inexpensive, and requires minimal sample preparation compared to standard analytical methods for bacterial detection.

scholarly journals THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

2016 ◽  
Vol XLI-B1 ◽  
pp. 147-150 ◽  
Author(s):  
Jian Yang ◽  
Wei Gong ◽  
Shuo Shi ◽  
Lin Du ◽  
Jia Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Laser Induced Fluorescence ◽  
Support Vector ◽  
Excitation Wavelength ◽  
Lidar System ◽  
Excitation Light

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Chemometric Analysis for the Classification of some Groups of Drugs with Divergent Pharmacological Activity on the Basis of some Chromatographic and Molecular Modeling Parameters

2018 ◽  
Vol 21 (2) ◽  
pp. 125-137
Author(s):  
Jolanta Stasiak ◽  
Marcin Koba ◽  
Marcin Gackowski ◽  
Tomasz Baczek
Keyword(s):  
Correlation Analysis ◽  
Pharmacological Activity ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Cardiovascular Drugs ◽  
New Drugs ◽  
Analgesic Drugs ◽  
Starting Point ◽  
Chromatographic Parameters

Aim and Objective: In this study, chemometric methods as correlation analysis, cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) have been used to reduce the number of chromatographic parameters (logk/logkw) and various (e.g., 0D, 1D, 2D, 3D) structural descriptors for three different groups of drugs, such as 12 analgesic drugs, 11 cardiovascular drugs and 36 “other” compounds and especially to choose the most important data of them. Material and Methods: All chemometric analyses have been carried out, graphically presented and also discussed for each group of drugs. At first, compounds’ structural and chromatographic parameters were correlated. The best results of correlation analysis were as follows: correlation coefficients like R = 0.93, R = 0.88, R = 0.91 for cardiac medications, analgesic drugs, and 36 “other” compounds, respectively. Next, part of molecular and HPLC experimental data from each group of drugs were submitted to FA/PCA and CA techniques. Results: Almost all results obtained by FA or PCA, and total data variance, from all analyzed parameters (experimental and calculated) were explained by first two/three factors: 84.28%, 76.38 %, 69.71% for cardiovascular drugs, for analgesic drugs and for 36 “other” compounds, respectively. Compounds clustering by CA method had similar characteristic as those obtained by FA/PCA. In our paper, statistical classification of mentioned drugs performed has been widely characterized and discussed in case of their molecular structure and pharmacological activity. Conclusion: Proposed QSAR strategy of reduced number of parameters could be useful starting point for further statistical analysis as well as support for designing new drugs and predicting their possible activity.

Fingerprint of PGI Mantova Cucumis melo by ICP-MS and Chemometric Analysis

2020 ◽  
Vol 17 (1) ◽  
pp. 94-104
Author(s):  
Antonio F. Mottese ◽  
Maria R. Fede ◽  
Francesco Caridi ◽  
Giuseppe Sabatino ◽  
Giuseppe Marcianò ◽  
...  
Keyword(s):  
Cucumis Melo ◽  
Principal Component ◽  
Inorganic Elements ◽  
Discrimination Power ◽  
Icp Ms ◽  
High Discrimination ◽  
Strong Relation ◽  
Trace Element Contents ◽  
Safe Food

Background and Objectives: In this work, yellow and green varieties of Cucumis melo fruits belonging to different cultivars were studied. In detail, three Sicilian cultivars of winter melons tutelated by TAP (Traditional agro-alimentary products) labels were considered, whereas asun protected the Calabrian winter melon was studied too. With the aim to compare the selective uptakes of inorganic elements among winter and summer fruits, the “PGI Melone Mantovano” was investigated. The purpose of this work was to apply the obtained results i) to guarantee the quality and healthiness of fruits, ii) to producers defend, iii) to help the customers in safe food purchase. Method: All samples were analyzed by ICP-MS and the obtained results, subsequently, were subjected to Cluster analysis (CA), Principal component analysis (PCA) and Canonical discriminant analysis (CDA). Results: CA results were generally in agreement with samples origin, whereas the PCA elaboration has confirmed the presence of a strong relation between fruit origins and trace element contents. In particular, two principal components justified the 57.32% of the total variance (PC1= 40.95%, PC2= 16.37%). Finally, the CDA approach has provided several functions with high discrimination power, confirmed by the correct classification of all samples (100%). Conclusions: CA, PCA and CDA could represent an integrated to label to discriminate the origin of agri-food products and, thus, protect and guarantee their healthiness.

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