Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

Raman spectroscopy has proven to be a valuable tool for analyzing various types of forensic evidence such as traces of body fluids. In this work, Raman spectroscopy was employed as a nondestructive technique for the analysis of dry traces of oral fluid to differentiate between smoker and nonsmoker donors with the aid of advanced statistical tools. A total of 32 oral fluid samples were collected from donors of differing gender, age and race and were subjected to Raman spectroscopic analysis. A genetic algorithm was used to determine eight spectral regions that contribute the most to the differentiation of smokers and nonsmokers. Thereafter, a classification model was developed based on the artificial neural network that showed 100% accuracy after external validation. The developed approach demonstrates great potential for the differentiation of smokers and nonsmokers based on the analysis of dry traces of oral fluid.

Sensor array for qualitative and quantitative analysis of metal ions and metal oxyanion based on colorimetric and chemometric methods

Gold nanoparticle (AuNP)-based colorimetric sensor is sensitive for the detection of metal ions and metal oxyanion in aqueous solution. However, this method is usually not suitable for multi-objective analysis in complex mixture systems because it is suffering from interference of co-existents. In the present paper, we proposed a sensitive, flexible, low-cost, and multi-units sensor method for the qualitative and quantitative analysis of metal ions and metal oxyanion based on the global ultraviolet and visible (UV-Vis) spectra of amino acid-gold nanoparticles (amino acid-AuNPs) sensors in the range of 230-800 nm. Different amino acids (L-Histidine, L-Lysine, L-Methionine, D-Penicillium) which can prevent the aggregation of the AuNPs in NaCl solution, were investigated to build sensor arrays responding to different ions induced AuNPs aggregation. The UV-Vis spectra that Cd²⁺, Ba²⁺, Mn²⁺, Ni²⁺, Cu²⁺, Fe³⁺, Cr³⁺, Cr₂O₇^2-, Sn⁴⁺, Pb²⁺ induce amino acid-AuNPs displayed different characteristics and the ions were classified correctly by using partial least squares-discriminant analysis (PLS-DA). Taking the advantage of the multivariate analysis and sensor arrays, we simultaneously quantified the ions in binary and ternary mixture systems (Cr³⁺/Cr₂O₇^2-, Fe³⁺/Cd²⁺, Fe³⁺/Cr³⁺/Cr₂O₇^2-). Data fusion methods further improved the prediction accuracy of the chemometric models built on multi-amino acids-AuNPs sensors. The proposed method has a potential for analyzing metal ions and metal oxyanion in much more complex mixture systems.

Support vector machine classification trees based on fuzzy entropy of classification

The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTreeG). This classifier partitioned the data by finding gaps in the data space. For large and complex datasets, there may be no gaps in the data space confounding this type of classifier. A novel algorithm was devised that uses fuzzy entropy to find optimal partitions for situations when clusters of data are overlapped in the data space. Also, a kernel version of the fuzzy entropy algorithm was devised. A fast support vector machine implementation is used that has no cost C or slack variables to optimize. Statistical comparisons using bootstrapped Latin partitions among the tree classifiers were made using a synthetic XOR data set and validated with ten prediction sets comprised of 50,000 objects and a data set of NMR spectra obtained from 12 tea sample extracts.

Artificial neural network based identification of Campylobacter species by Fourier transform infrared spectroscopy

Two prototypes of artificial neural network (ANN), multilayer perceptron (MLP), and probabilistic neural network (PNN), were used to analyze infrared (IR) spectral data obtained from intact cells belonging to the species Campylobacter coli and Campylobacter jejuni. In order to establish a consistent identification and typing procedure, mid infrared spectra of these species were obtained by means of a Fourier transform infrared (FT-IR) spectroscope. FT-IR patterns belonging to 26 isolates subclassified into 4 genotypes were pre-processed (normalized, smoothed and derivatized) and grouped into training, verification and test sets. The two architectures tested (PNN, MLP) were developed and trained to identify or leave unassigned a number of IR patterns. Two window ranges (w(4), 1200 to 900 cm(-1); and w(5), 900 to 700 cm(-1)) in the mid IR spectrum were presented as input to the ANN models functioning as pattern recognition systems. No matter the ANN used all the training sets were correctly identified at subspecies level. For the test set, the four-layer MLP network was found to be specially suitable to recognize FT-IR data since it correctly identified 99.16% of unknowns using the w(4) range, and was fully successful in detecting atypical patterns from closely related Campylobacter strains and other bacterial species. The PNN network obtained lower percentages in assignation and rejection. Overall, ANNs constitute an excellent mathematical tool in microbial identification, since they are able to recognize with a high degree of confidence typical as well as atypical FT-IR fingerprints from Campylobacter spp.

Classification of lactate dehydrogenase of different origin by liquid chromatography-mass spectrometry and multivariate analysis

A method intended to serve as a multivariate quality control tool in the production of pharmaceutical proteins is presented. The method is based on multivariate analysis of peptide maps generated with liquid chromatography-mass spectrometry (LC-MS). Lactate dehydrogenase (LDH) from different species and tissues were used as model compounds in the study. The proteins were digested with Endoproteinase Lys-C before the LC-MS analysis. After data pretreatment of the peptide maps, successful classification of the LDHs were obtained by discriminant analysis with partial least squares regression and artificial neural networks. Further, principal component analysis was applied to visualize the relationships between the samples.