Supervised Pattern Recognition Methods Research Articles

Forensic Characterization of Mobile Phone Cases Using Infrared Spectroscopy with Deep Learning Methods

The fast and nondestructive identification of mobile phone cases is a crucial task for law enforcement personnel in the field of forensic science. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been employed in conjunction with feature extraction and deep learning algorithms to develop a method for distinguishing mobile phone cases. As preprocessing steps, multivariate scatter correction and standard normal variate were performed. Feature variables were extracted using the competitive adaptive reweighted sampling and shuffled frog leaping algorithm. Several supervised pattern recognition methods, namely ResNet50, VGG16, and multilayer perceptron (MLP), were utilized for constructing classifiers. The results indicated that the model’s performance based on multivariate scattering correction (MSC) with competitive adaptive re-weighted sampling (CARS) surpassed that of other approaches. Notably, the ResNet50 model exhibited superior discriminative ability compared to the VGG16 and MLP models, achieving 100% accuracy in the differentiation of all samples. This designed method represents a potentially rapid and nondestructive approach for identifying mobile phone cases in forensic science.

Influence of the use of sulfur dioxide, the distillation method, the oak wood type and the aging time on the production of brandies

Brandies are spirits produced from wine spirit and wine distillates. The original wines selected to be distilled to produce the wine spirits as well as the distillation method used determine, to a large extent, the organoleptic characteristics of the final products. The young wine spirits evolve during their aging in oak casks, this being another key stage that affects the chemical and sensorial characteristics of the final brandy. In this work, seven different brandies have been studied. They were obtained from wine produced with and without the addition of sulfur dioxide, during their fermentation, using different distillation methods (single, double or serial distillation using pot stills and continuous column distillation) and aged for 14 or 28 months in three different types of oak wood (Quercus alba, Quercus robur and Quercus petraea) previously toasted to two different grades (medium or light).The use of unsupervised pattern recognition methods (HCA and FA) determined that the addition of sulfur dioxide during the fermentation of the base wine has a major influence on the aromatic and phenolic profile of the aged distillates. On the other hand, by means of supervised pattern recognition methods such as LDA and ANNs, the most significant variables that would allow to discriminate between the classes of brandies identified in the study were evaluated. Thus, the results obtained should cast some light on the most significant variables to be taken into account regarding Brandy production processes if a better control over these production processes is to be achieved, so that more exclusive and better quality products are obtained.

Forensic Identification of 3,4-Methylendioxy-N-methylamphetamine (MDMA), Ketamine, and Benzodiazepine by Handheld Infrared Spectroscopy and Chemometrics

The development of simple and cost-effective analytical methods is indispensable for law enforcement officials in the rapid screening for large quantities of seized drugs. Handheld infrared spectroscopy has been used with Bayes discrimination analysis (BDA), k nearest neighbor (KNN), support vector machine (SVM), decision-making tree (DT), and random forest (RF) chemometrics to characterize 3,4-methylenedioxy-N-methylamphetamine (MDMA), ketamine, and benzodiazepine samples. Spectral data were pretreated by normalization, multivariate scatter correction, Savitzky-Golay smoothing algorithm, and automatic baseline correction. Principal component analysis (PCA) was employed to reduce the dimensionality of spectral data, while BDA, KNN, SVM, DT, and RF, as supervised pattern recognition methods, were implemented and their parameters were optimized to identify the samples. The results showed that SVM was optimal among the models, followed by DT, KNN, BDA, and RF. The optimal model, based upon polynomial kernel function, achieved an accuracy of 100% (training set) and 100% (test set) for all samples and was used to further distinguish the additives in MDMA, ketamine, and benzodiazepine samples. In the MDMA samples, 42 samples contained caffeine, 23 contained glucose, and 21 contained starch. In the ketamine samples, 34 contained caffeine, 13 contained glucose, and 12 contained phenacetin. In the benzodiazepine samples, estazolam, diazepam, lorazepam, clonazepam, and nitrazepam were identified. 5 samples were misidentified into other categories. This study demonstrated that handheld infrared spectroscopy with chemometrics is a reliable and desirable approach for the rapid and nondestructive identification of MDMA, ketamine, and benzodiazepine with high potential in practical applications.

Use of Multivariate Statistics in the Processing of Data on Wine Volatile Compounds Obtained by HS-SPME-GC-MS.

This review takes a snapshot of the main multivariate statistical techniques and methods used to process data on the concentrations of wine volatile molecules extracted by means of solid phase micro-extraction and analyzed using GC-MS. Hypothesis test, exploratory analysis, regression models, and unsupervised and supervised pattern recognition methods are illustrated and discussed. Several applications in the wine volatolomic sector are described to highlight different interactions among the various matrix components and volatiles. In addition, the use of Artificial Intelligence-based methods is discussed as an innovative class of methods for validating wine varietal authenticity and geographical traceability.

Non-destructive characterization and discrimination of vehicle bumpers fragments in forensic science using molecular spectral fusion analysis and chemometrics

A rapid and non-destructive approach for characterizing and discriminating trace evidences is one of the hotspots and focuses in forensic science. The vehicle bumpers fragments, as a trace evidence, are often found and extracted from the clothing of victims, the crashed vehicle, and the road at the scenes. Micro-laser Raman spectroscopy (MLRM) and attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been used with principal component analysis (PCA), Multi-layer perceptron neural network (MLPNN), and Fisher discriminant analysis (FDA) to propose a method for characterizing and discriminating 160 vehicle bumpers fragments from 8 manufacturers. The spectra were pre-treated by automatic baseline correction, multivariate scatter correction, standard normal variate and Savitzky-Golay algorithm. PCA was used to reduce the dimension and extraction of feature variables, while MLPNN, FDA, the several supervised pattern recognition methods, were used as algorithms of constructing classifiers. The results displayed that the discrimination ability of fusion spectra model was stronger than that of single spectra dataset. In MLPNN model, the order of it from high to low was feature-level fusion (93.5%) > data-level fusion (90.3%) > MLRM (74.2%) > ATR-FTIR (64.5%). In FDA model, the order of it from high to low was feature-level fusion (100%) > data-level fusion (93.5%) > MLRM (77.4%) > ATR-FTIR (61.3%). Fisher discriminant analysis model based on feature-level fusion dataset was the more optimal and practical method for discriminating the vehicle bumpers fragments. “Audi”, “Hyundai”, and “Chery” samples, the similar kind from different manufacturers, were also distinguished exactly, which was satisfactory. The designed method represented a potentially rapid and non-destructive approach for differentiation trace evidences in forensic science.

On the rapid and non-destructive approach for barbiturates, benzodiazepines, and phenothiazines determination and differentiation using spectral combination analysis and chemometric methods

The fast determination and differentiation of substance samples (tablets or powders) of medical drugs with high addiction potential by police or customs is an important forensic task. The sedative hypnotic drugs, due to their accessibility and low price, not only make some people from trying to use to becoming depended and addicted to them gradually, but also let some people be addicted to certain types of sedative hypnotic drugs due to the prolonged use. The attenuated total reflection (ATR)-Fourier transform infrared spectroscopy (FTIR) has been adopted with principal component analysis (KNN), Fisher discriminant analysis (FDA), and K nearest neighbor analysis (KNN) to develop a method for differentiating the target sedative hypnotic drugs of abuse. Automatic baseline correction, multivariate scatter correction, standard normal variate and Savitzky-Golay algorithm and the ATR correction were performed as pre-processing methods. PCA was used to reduce the infrared spectroscopy multi-dimensionality and extraction of feature variables. While FDA and KNN, the several supervised pattern recognition methods, were used as algorithms of constructing classifiers. The results showed that accuracy of combination spectral models was significantly higher than that of single models. The distinguishing ability of FDA model was superior to that of KNN model. First-second derivative model was better than that of other models in differentiating samples. All bands region model was more worthwhile than functional group or fingerprint region models. The optimal differentiation model was FDA model that based on all band region data of first-second derivative spectral combination. 13 different types of sedative hypnotic drugs were all differentiated exactly. The same kind of them from different places of origin were also distinguished accurately. The designed method represented a potentially rapid and non-destructive approach for determination and differentiation sedative hypnotic drugs.

A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies

The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

Comparative chemometric and quantitative structure-retention relationship analysis of anisotropic lipophilicity of 1-arylsuccinimide derivatives determined in high-performance thin-layer chromatography system with aprotic solvents

Numerous structurally different amides and imides including succinimide derivatives exhibit diverse bioactive potential. The development of new compounds requires rationalization in the design in order to provide structural changes that guarantee favorable physico-chemical properties, pharmacological activity and safety. In the present research, a comprehensive study with comparison of the chromatographic lipophilicity and other physico-chemical properties of five groups of 1-arylsuccinimide derivatives was conducted. The chemometric analysis of their physico-chemical properties was carried out by using unsupervised (hierarchical cluster analysis and principal component analysis) and supervised pattern recognition methods (linear discriminant analysis), while the correlations between the in silico molecular features and chromatographic lipophilicity were examined applying linear and non-linear Quantitative Structure-Retention Relationship (QSRR) approaches. The main aim of the conducted research was to determine similarities and dissimilarities among the studied 1-arylsuccinimides, to point out the molecular features which have significant influence on their lipophilicity, as well as to establish high-quality QSRR models which can be used in prediction of chromatographic lipophilicity of structurally similar 1-arylsuccinimides. This study is a continuation of analysis and determination of the physico-chemical properties of 1-arylsuccinimides which could be important guidelines in further in vitro and eventually in vivo studies of their biological potential.

Classification of heroin, methamphetamine, ketamine and their additives by attenuated total reflection-Fourier transform infrared spectroscopy and chemometrics.

Drug crime is a prominent issue of concern from pole to pole. In order to seek higher profits, drug gangs often add diluents and adulterants to the drugs to disperse drug products Analysis of these additives would be greatly conducive to determine the origin of drug products for law enforcement departments. A method using attenuated total reflectance-Fourier transform infrared spectroscopy and chemometrics methods to classify the heroin hydrochloride, methamphetamine hydrochloride, ketamine hydrochloride and their five additives (caffeine, phenacetin, starch, glucose, and sucrose), was developed. The Baseline correction, multivariate scatter correction, standard normal variate and Savitzky-Golay algorithm were adopted to pre-process the spectral data. Several supervised pattern recognition methods including decision tree, Bayes discriminant analysis, and support vector machine were considered as algorithms of constructing classifiers. The results reveal that, repetitive and interfering data in original spectrum data could be eliminated by principal component analysis and factor analysis. F-measure, as a comprehensive evaluation index of precision rate and recall rate, was more objective than precision rate and recall rate to reflect the ability of model to distinguish samples. It should be used as one of the indicators to evaluate the model. The CHAID classification tree could be identified as priorities in the decision tree model, while the linear kernel could be considered as the optimal kernel in the support vector machine model. The classification ability of three hydrochloride mixtures based on Bayes discriminant analysis was better than that of another models. Bayes discriminant analysis model was the more useful and practical method for classifying the target drugs of abuse than that of decision trees and support vector machine. The designed approach represents a potentially simple, non-destructive, and rapid method of classifying hydrochloride mixtures.

Low risk of category misdiagnosis of rice syrup adulteration in three botanical origin honey by ATR-FTIR and general model.

This study is about the rice syrup adulteration determination in different botanical origin honey in the food product. Due to time-consuming and large risk of misdiagnosis, it is essential to establish a general model for adulteration detection regardless of the original category of honey. In this paper, infrared (IR) spectra combined with four supervised pattern recognition methods were employed to establish the general model for rice syrup adulteration detection in acacia, linden and jujube honey samples simultaneously. Moreover, Monte-Carlo sampling technology was executed to evaluate the models via the average accuracy, sensitivity and specificity. The first derivative-least squares support vector machines (Der-LS-SVM) gave an outstanding performance with higher accuracy (97.09%), higher sensitivity (96.64%), higher specificity (97.58%) and lower standard deviations after fifty trials. In addition, this study makes further efforts to control the quality of the honey product in the market on rice syrup adulteration.

Simple distinction of grapevine (Vitis vinifera L.) genotypes by direct ATR-FTIR

The identification of grapevine (Vitis vinifera L.) genotypes is conventionally a laborious activity that must be carried out by specialized staff. In this work a novel and simple method for differentiation of grapevine genotypes is presented. Direct measurements of leaves by attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) combined with chemometric methods were used for classification of six genotypes (five varieties and a pair of clones), viz. Cinsault, Gewurztraminer (clone 643), Moscatel de Alejandría, País, Pinot Noir (French clone 777), Pinot Noir (local clone ‘Valdivieso’). These were successfully classified and identified through supervised pattern recognition methods such as soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA). The error rate for spectra classification of test sets by both models was 0.08. The results demonstrate the advantages of using ATR-FTIR as a rapid and non-destructive tool that achieves accurate grapevine genotype differentiation.

Characterization of Heroin and Its Additives by Attenuated Total Reflection (ATR) – Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis

A method using attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy and multivariate analysis is reported to classify heroin hydrochloride and five common additives: caffeine, phenacetin, starch, glucose, and sucrose. Baseline correction, multivariate scatter correction, standard normal variables and Savitzky-Golay algorithm using smoothing with a polynomial order of three and a window size of seven points were adopted to preprocess the spectral data. Several supervised pattern recognition methods including radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and linear fitting analysis (LFA) were used as algorithms to construct the classifiers. The use of a characteristic spectrum had lower complexity, required reduced modeling time, and was able to achieve the goal of rapid classification. Mixtures of heroin hydrochloride with caffeine, heroin hydrochloride and phenacetin, heroin hydrochloride with sucrose, heroin hydrochloride and starch, and heroin hydrochloride with glucose were distinguished with accuracy values of 100%, 100%, 88.89%, 77.78% and 66.67%, respectively. The fitting ability of the quadratic polynomial function was superior than using a linear model. A linear model was shown to be optimal when the additive was phenacetin, although the quadratic function was more superior when the additive was glucose. The developed method represents a simple, nondestructive, and rapid approach to classify mixtures of heroin hydrochloride with its common additives, caffeine, glucose, phenacetin, starch and sucrose.

Pattern recognition-based Raman spectroscopy for non-destructive detection of pomegranates during maturity.

In this study, the feasibility of utilizing Fourier transform Raman spectroscopy, combined with supervised and unsupervised pattern recognition methods was considered, to distinguish the maturity stage of pomegranate "Ashraf variety" during four distinct maturity stages between 88 and 143days after full bloom. Principal component analysis (PCA) as an unsupervised pattern recognition method was performed to verify the possibility of clustering of the pomegranate samples into four groups. Two supervised pattern recognition techniques namely, partial least squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) were also used as powerful supervised pattern recognition methods to classify the samples. The results showed that in all groups of samples, the Raman spectra of the samples were correctly clustered using PCA. The accuracy of the SIMCA classification for differentiation of four pomegranate groups was 82%. Also, the overall discriminant power of PLS-DA classes was about 96%, and 95% for calibration and validation sample sets, respectively. Due to the misclassification among different classes of immature pomegranates, that was lower than the expected, it was not possible to discriminate all the immature samples in individual classes. However, when considering only the two main categories of "immature" and "mature", a reasonable separation between the classes were obtained using supervised pattern recognition methods of SIMCA and PLS-DA. The SIMCA based on PCA modeling could correctly categorize the samples in two classes of immature and mature with classification accuracy of 100%.

Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350–1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R2 = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers.

Synthesis-identification integration: One-pot hydrothermal preparation of fluorescent nitrogen-doped carbon nanodots for differentiating nucleobases with the aid of multivariate chemometrics analysis

Most of the conventional multidimensional differential sensors currently need at least two-step fabrication, namely synthesis of probe(s) and identification of multiple analytes by mixing of analytes with probe(s), and were conducted using multiple sensing elements or several devices. In the study, we chose five different nucleobases (adenine, cytosine, guanine, thymine, and uracil) as model analytes, and found that under hydrothermal conditions, sodium citrate could react directly with various nucleobases to yield different nitrogen-doped carbon nanodots (CDs). The CDs synthesized from different nucleobases exhibited different fluorescent properties, leading to their respective characteristic fluorescence spectra. Hence, we combined the fluorescence spectra of the CDs with advanced chemometrics like principle component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA), to present a conceptually novel “synthesis-identification integration” strategy to construct a multidimensional differential sensor for nucleobase discrimination. Single-wavelength excitation fluorescence spectral data, single-wavelength emission fluorescence spectral data, and fluorescence Excitation-Emission Matrices (EEMs) of the CDs were respectively used as input data of the differential sensor. The results showed that the discrimination ability of the multidimensional differential sensor with EEM data set as input data was superior to those with single-wavelength excitation/emission fluorescence data set, suggesting that increasing the number of the data input could improve the discrimination power. Two supervised pattern recognition methods, namely KNN and SIMCA, correctly identified the five nucleobases with a classification accuracy of 100%. The proposed “synthesis-identification integration” strategy together with a multidimensional array of experimental data holds great promise in the construction of differential sensors.

Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learning.

Deficit schizophrenia is characterized by neurocognitive impairments and changes in the patterning of IgA/IgM responses to plasma tryptophan catabolites (TRYCATs). In the current study, supervised pattern recognition methods, including logistic regression analysis (LRA), Support Vector Machine (SVM), and Soft Independent Modeling of Class Analogy (SIMCA), were used to examine whether deficit schizophrenia is a discrete diagnostic class with respect to Consortium To Establish a Registry for Alzheimer's disease (CERAD) and Cambridge Neuropsychological Test Automated Battery (CANTAB) tests and IgA/IgM responses to noxious (NOX) and generally more protective (PRO) TRYCATs. We recruited patients with (n = 40) and without (n = 40) deficit schizophrenia and healthy volunteers (n = 40). The combined use of TRYCAT and CERAD features strongly segregates deficit from nondeficit schizophrenia and healthy controls. Three out of the top five most important features in LRA, SVM and SIMCA agreed, namely two different NOX/PRO TRYCAT ratios and false memory recall. SIMCA shows that deficit schizophrenia is significantly separated from nondeficit schizophrenia and controls with as top 6 features IgA responses to picolinic acid, IgM responses to 3-OH-kynurenine and kynurenic acid, and impairments in Word List Memory and Verbal Fluency Tests and Mini-Mental State Examination. Nevertheless, nondeficit schizophrenia was not significantly separated from controls. The results show that schizophrenia is not a unitary disease with mere continuous differences in severity of illness between apparent subtypes. Deficit schizophrenia is a qualitatively distinct class defined by neuroimmune (autoimmune responses to TRYCATs) and neurocognitive (episodic and semantic memory) features coupled or not with clinical (negative) symptoms.

Evaluation and identification of blood stains with handheld NIR spectrometer

This work describes a noninvasive, nondestructive methodology for the confirmatory and in situ identification of dry blood stains deposited in different substrates using a hand-held near infrared (NIR) spectrometer. Different supervised pattern recognition methods were evaluated and compared for the correct identification of human blood. Human and animal blood stains and stains from different commercial products (perceived as common false positives) that had been deposited on different substrates were analyzed directly by NIR spectroscopy. The best pre-processing used was Standard Normal Variate (SNV) and normalization by range, which was obtained by evaluating the best classification for human blood with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA showed 100% correct classification for porcelain and glass substrates but 80% for metal and 90% for ceramic substrates. Genetic Algorithm - Linear Discriminant Analysis (GA-LDA) showed better classification performance (100%) than Successive Projection Algorithm - Linear Discriminant Analysis (SPA-LDA), in which one false positive and one false negative were obtained. Partial-Least Squares Discriminant Analysis (PLS-DA) correctly classified the human blood and other stains in all substrates.

Screening analysis of garlic-oil capsules by infrared spectroscopy and chemometrics

The search for a healthier life has led to an increase in the consumption of natural products with certain functional and/or nutritional properties while avoiding the collateral and harmful effects of medicines. In this context, garlic oil, in the form of gelatinous capsules, contains various substances considered beneficial to health, to reduce cholesterol and blood pressure, in the treatment of influenza and diabetes and to prevent the development of tumors and cardiovascular diseases. Currently, garlic oil is classified by ANVISA (National Health Surveillance Agency) as a “new food”, denomination given to food or substances without historical consumption in the country, or foods with substances already consumed and that may be added or used at much higher levels than currently observed in food. While there is no established law ensuring safety and quality of garlic oil commercialized in capsules, the development of simple and rapid methods of analysis to verify the authenticity of these products is of great importance. In this paper, we propose a screening analysis method to do this, using spectroscopy in the mid-infrared region and supervised pattern recognition methods, aimed at classifying garlic oil capsules by concentration levels. For this purpose, spectra of 136 samples from 11 brands of commercial capsules of and 123 solutions of garlic oil in soybean oil (0.00%, 0.25%, 0.50%, 0.75%, 1.00%, 1.25%, 1.50% and 1.75% (m/m)) were obtained using an attenuated total reflectance accessory. The spectra obtained were subjected to different preprocessing (Standard Normal Variate - SNV, Multiplicative Scatter Correction – MSC, and derivation by Savistzky Golay filters - SG) and uninformative spectral regions were eliminated. The supervised pattern recognition methods Partial Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) with variable selection by the Successive Algorithm Projections (SPA) were used to build models capable for classifying commercial capsules into three classes: class 1 (low dosage) from 0.00% to 0.50%, class 2 (medium dosage, usual) from 0.75% to 1.25% and class 3 (high dosage) from 1.50% to 1.75%. The PLS-DA provided, in the validation stage, a correct classification rate of 81.3% against 95.9% of the SPA-LDA method. In the SPA-LDA classification, only the 924cm−1 variable was selected. This is where a band from the deformation of the vinyl groups of the sulfides and vinyl dithiins, main constituents of garlic oil, was observed. For the 136 capsules tested a rate of 88.2% agreement with the information contained on their labels was obtained. The high number of capsules classified in the wrong classes suggests that their labels had presented incorrect information about their concentration levels. The screening methodology proposed in this work is a simple, fast and effective tool that can be useful to investigate the content of garlic oil gelatinous capsules.