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One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.

This work describes the development of methodology based on the hierarchical soft classification method by combining multivariate analysis techniques and Hyperspectral Near Infrared Images (HSI-NIR) to confirm identification of bloodstains on colored and printed fabrics. The term hierarchical is used to designate that the classification is done sequentially on smaller parts of the data such as first splitting the data into human and non-human etc. Human Blood (HB) and Animal Blood (AB) stains and stains from different commercial products (Common False Positives-CFP) were deposited on ten different fabrics of two types (five synthetic and five cotton-based) and hyperspectral imagens were acquired. The best pre-processing techniques were spectral smoothing (Savitzky-Golay filter, 11 point), Standard Normal Variate (SNV), and Generalized Least Squared Weighted (GLSW) performed in that sequence, to reduce the influence of the fabric on the model. Principal Component Analysis (PCA) models for samples prepared on synthetic fabrics demonstrated a great potential as a filter in discriminating blood samples from common false positives than the models built for samples prepared on cotton fabrics. This was done in a SIMCA-like fashion. The Partial Least Squared Discriminant Analysis (PLS-DA) model was used only to separate HB from AB samples for samples prepared on synthetic fabrics. For the samples prepared on cotton fabric, PLS-DA was also needed to discriminate some CFP from blood samples. PCA and PLS-DA were combined in a hierarchical structure to result in a single aggregated soft classification model. The hierarchical classification model built by a fusion of PCA and PLS-DA showed 100% sensitivity and 98% specificity in distinguishing between HB and Animal Blood and False Positive Samples in synthetic fabric. For stains prepared on cotton fabrics, the hierarchical model showed 95% sensitivity and 98% specificity.

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R$20, R$50 and R$100) banknotes, enabling a simple field analysis. NIR spectra (950–1650 nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R$20, R$50 and R$100 currencies. These results show that the use of the portable near-infrared with SIMCA or SPA-LDA models can be a completely effective, fast, and non-destructive way to identify authenticity of banknotes as well as permitting field analysis.

Identification of semen stains is a critical step in evidence analysis in cases of sexual assaults (rape, child molestation, sexual harassments, etc). Presumptive and confirmatory methodologies that are nondestructive and can be used at crime scenes are extremely needed to help forensic experts to collect useful evidences without compromising the integrity of the samples. In this work, we put forward the high potential of a fast, nondestructive and noninvasive methodology like Near Infrared Hyperspectral Imaging (HSI-NIR), together with adequate multivariate methodologies for exploratory data analysis and classification. Due to the high absorption of the substrate, an exhaustive data analysis is performed in order to test the most suitable tool for the detection and discrimination of semen stains in diverse fabrics. Samples of human semen, lubricants, breast milk and animal semen were placed on fabrics of different colors and compositions to create a stain and hyperspectral images were acquired. As identification tools, Principal Component Analysis and MCR-ALS were used. Moreover, Classification techniques such as PLS-DA, sPLS-DA and SVM-DA were used in order to differentiate human semen from other substances. Although the methodology developed depend on the substrate used, it has been proven that HSI-NIR can be used as presumptive and confirmatory method of analysis in the search for semen stains.

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 924 cm − 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.

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.

This article describes a non-destructive analytical method developed to solve forensic document examination problems involving crossed lines and obliteration. Different strategies combining confocal Raman imaging and multivariate curve resolution-alternating least squares (MCR-ALS) are presented. Multilayer images were acquired at subsequent depth layers into the samples. It is the first time that MCR-ALS is applied to multilayer images for forensic purposes. In this context, this method provides a single set of pure spectral ink signatures and related distribution maps for all layers examined from the sole information in the raw measurement. Four cases were investigated, namely, two concerning crossed lines with different degrees of ink similarity and two related to obliteration, where previous or no knowledge about the identity of the obliterated ink was available. In the crossing line scenario, MCR-ALS analysis revealed the ink nature and the chronological order in which strokes were drawn. For obliteration cases, results making active use of information about the identity of the obliterated ink in the chemometric analysis were of similar quality as those where the identity of the obliterated ink was unknown. In all obliteration scenarios, the identity of inks and the obliterated text were satisfactorily recovered. The analytical methodology proposed is of general use for analytical forensic document examination problems, and considers different degrees of complexity and prior available information. Besides, the strategies of data analysis proposed can be applicable to any other kind of problem in which multilayer Raman images from multicomponent systems have to be interpreted.

In forensic examination of questioned documents a type of casework often encountered are the frauds that occur by mean of addition and adulteration of parts of text or numbers on document. The goal of this work is to evaluate the performance of hyperspectral images (HSI) in the near (NIR) and middle (MIR) regions, combined with the unsupervised pattern recognition techniques Principal Component Analysis (PCA) and Projection Pursuit (PP) for a rapid, reliable and non-destructive identification of document falsifications by means of alterations and additions. Blind tests were conducted for this purpose. Sixteen black ink pens from different brands, models and ink types were employed to prepare the samples in two different ways: (i) for initial discrimination and method validation, straight lines of approximately 2 cm long were produced in white paper; (ii) for blind testing, three collaborators used any of the sixteen pens available and prepared genuine or altered/added numbers (in total 30 samples) in white paper and in bank check paper. Overall, PP analysis showed better results than PCA to discriminate the 120 pairs of ink lines in white paper using HSI-MIR (97.5% and 87.5%, respectively). It is important to mention that the 10.0% of pairs that were not discriminated by PCA analysis were discriminated by PP, which highlights the importance of the combined use of the two chemometric techniques. HSI-NIR in combination with PCA and PP analysis was able to solve 76.7% and 83.3% of the blind testing samples, respectively. When HSI-MIR was used in a complementary way to HSI-NIR, discrimination of blind test samples increased to 90%. Therefore, HSI-NIR and HSI-MIR combined with PCA and PP show great discrimination potential and provide objective examination of suspected fraudulent documents.

The determination of the chronological sequence of crossing ink lines is a recurrent issue in the forensic examination of questioned documents. This manuscript intends to evaluate the potential of hyperspectral imaging in the near infrared range (HSI-NIR) combined with multivariate data analysis for rapid, objective and nondestructive analysis of crossing ink lines made with black pens. Twenty-one black gel and ballpoint pens from different brands and models were employed to prepare crossing combinations in both orders in white office paper. An initial pre-selection and extraction step using the Principal Components Analysis (PCA) scores plot arranged as histograms was necessary for extracting the inks spectra. Then, Partial Least Squares-Discriminant Analysis (PLS-DA) was applied for selection of the most important variables. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in the augmented form was performed using both the raw and the pre-processed spectra. However, the pre-processing of spectra resulted in incorrect identification of pixels in the inks distribution maps. The MCR-ALS results obtained using the raw spectra allowed the correct determination of the order of crossings in only one pair of gel-gel pen crossings. The remainder were either impossible to discriminate ink from paper or the skipping of one pen ink line led to incorrect determinations in one of the orders. In spite of the practical advantages of the HSI-NIR for document examination and the different chemometric approaches considered for surpassing some of the spectral limitations, the results obtained demonstrate the difficulties of using this technology for application in real forensic cases.

The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed.

The objective of this work is assessing whether the combination of Raman spectroscopy and chemometric tools is appropriate to differentiate blue ballpoint pen inks. Fourteen commercial blue ballpoint pen inks from different brands and models were studied and Raman spectra were obtained on ink lines written on A4 sulfite paper. First, a study of the best Raman configurations, in terms of laser intensity used and acquisition mode, was carried out to ensure sufficient spectroscopic quality without damaging the sample. Chemometric methods were applied first to improve the definition of spectral bands and to suppress fluorescence contributions from the signal. Once the spectra were suitably preprocessed, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to explore whether the different inks could be distinguished from their Raman spectra. Almost all inks could be gradually differentiated, through successive PCA analyses or looking at the different levels of the dendrogram structure provided by HCA. From these exploratory results, a tree structure was constructed based on PCA and HCA results in order to reflect the degree of similarity among ink classes. This tree structure was used as the basis to develop hierarchical classification models based on partial least squares-discriminant analysis (PLS-DA). Correct classification of inks was achieved by these PLS-DA models built and the most important regions to identify the ink classes were detected using the variable importance in projection plots (VIPs). The combination of Raman spectroscopy and chemometrics has been proven to be a promising fast non-destructive tool to differentiate among very similar ink types in documents.

[EN] Hyperspectral images in the near infrared range (HSI-NIR) were evaluated as a nondestructive method to detect fraud in documents. Three different types of typical forgeries were simulated by (a) obliterating text, (b) adding text and (c) approaching the crossing lines problem. The simulated samples were imaged in the range of 928 2524 nm with spectral and spatial resolutions of 6.3 nm and 10 mm, respectively. After data pre-processing, different chemometric techniques were evaluated for each type of forgery. Principal component analysis (PCA) was performed to elucidate the first two types of adulteration, (a) and (b). Moreover, Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) was used in an attempt to improve the results of the type (a) obliteration and type (b) adding text problems. Finally, MCR-ALS and Partial Least Squares Discriminant Analysis (PLS-DA), employed as a variable selection tool, were used to study the type (c) forgeries, i.e. crossing lines problem. Type (a) forgeries (obliterating text) were successfully identified in 43% of the samples using both the chemometric methods (PCA and MCR-ALS). Type (b) forgeries (adding text) were successfully identified in 82% of the samples using both the methods (PCA and MCR-ALS). Finally, type (c) forgeries (crossing lines) were successfully identified in 85% of the samples. The results demonstrate the potential of HSI-NIR associated with chemometric tools to support document forgery identification, INCTAA (Processes no. : CNPq 573894/2008-6; FAPESP 2008/57808-1), NUQAAPE, FACEPE, CNPq, CAPES, Spanish Ministry of Science and Innovation MICINN (grant DPI2011-28112-C04-02).

Attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy associated to linear discriminant analysis (LDA) was employed to perform classification of blue pen ink according to types and brands, in a nondestructive way. To build a representative data set, blue pens of 3 types, namely ballpoint (5 brands), roller ball (2 brands) and gel (3 brands) were purchased from local dealers. Ten different pens, representing the best seller of each brand, were purchased, making a total of 100 pens. Circular areas were painted five times with each pen and spectra were taken in 2 different locations, using a Universal Attenuated Total Reflectance accessory (UATR), within the range of 4000 to 650 cm− 1. Three types of paper were employed: two brands of A4 sulfite paper (paper 1 and paper 2) and one recycled paper (paper 3). The genetic algorithm (GA), stepwise formulation (SW) and successive projections algorithm (SPA) were employed to select spectral variables employed in LDA. LDA models were built using the blue pen ink spectra obtained from paper 1. Three test sets were employed using the blue pen ink spectra obtained from papers 1, 2 and 3, in order to evaluate the influence of the paper on the predictions. The LDA models used to classify the pens according to their type (gel, rollerball and ballpoint) achieved a correct classification rate of 100% in the test set composed of blue pen ink spectra obtained from paper 1, using GA and SPA. Using SW, the rate achieved was 99.5%. For paper 2, SPA, GA and SW provided 100%, 97.3% and 93.8% of correct classification, respectively. For paper 3, SPA, GA and SW achieved a correct prediction rate of 100%, 100% and 94.9%, respectively. LDA models for classifications of pens according to their brand were 100% correct in their classification when the test set was composed of blue pen ink spectra obtained from papers 1 and 2. For the test set composed of blue pen ink spectra obtained from paper 3, LDA-SPA, LDA-GA and LDA-SW classified them correctly at 91.3%, 100% and 100%, respectively. The method developed was able to differentiate successfully all brands of pen used on each type of paper and could be a helpful tool for detection and confirmation of counterfeits in documents of legal importance.

The versatility of the flow-batch technique has been taken advantage of to implement an extremely simple and fast water hardness screening system. The proposed approach addresses the demand for simple YES/NO responses on water hardness quality control. It is based on monitoring the well-known quantitative reaction of the calcium/magnesium ions with EDTA, in the presence of the Eriocrhome Black T indicator, by monitoring the absorbance of the medium at 650 nm after addition of fixed amounts of the titrant. Performance curves were employed to measure the reliability of the method and to calculate cut-off statistic values. As a real-life problem, river and dam waters from Paraiba, Brazil, were screened and the results were validated by a reference procedure. The high sample throughput (up to 120 samples per hour), low reagent and sample consumption and disposal, high reliability and easy handling make the screening system very promising for routine applications. Advantageously, the same system assembly can still be used to perform quantitative analysis of target-screened water samples.

A flow-batch system was developed for the determination of Fe(III) in estuarine waters with high variability in salinity. The method is based on the catalytic effect of iron(III) on the oxidation rate of N , N -dimethyl- p -phenylenediammonium dichloride (DmPD) by hydrogen peroxide and the formed product is spectrophotometrically monitored at 554 nm. A controlled addition of sodium chloride to every assayed sample is accomplished for in-line individual salinity matching. The proposed system processes about 30 samples h −1 and yields reproducible results. Relative standard deviations were estimated as −1 Fe; ca. 0.5 mol l −1 Cl). Synthetic samples (15.0 μg l −1 Fe; 0.25–1.0 mol l −1 NaCl) were efficiently processed, and no significant differences in results were found at a probability level of 99.7%. The method works for the full range of salinities. Only 120 μg DmPD are consumed per determination. The analytical curve is linear up to about 60 μg l −1 Fe ( r >0.999; n =5) and the detection limit is 5 μg l −1 Fe. Results are in agreement with graphite furnace atomic absorption spectrometry.

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2% w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples.

The feasibility of flow-batch systems to carry out procedures exploiting feedback mechanisms is discussed, and a flow-batch analyzer is proposed for spectrophotometric determination of aluminum in plant digests. As eriochrome cyanine R (ECR) was used as chromogenic reagent and considering the pronounced pH dependence of absorbance of the Al-ECR, in-line individual pH conditioning was performed. For pH matching, the m-cresol purple indicator was monitored in order to reach the required acidity for every assayed sample. The proposed system handles about 15 samples per hour, requires only 60 μg ECR per determination, and yields reproducible results (R.S.D. 0.999, n=6) was achieved within 0.25 and 6.00 mg l−1 Al (dry basis). Results are in agreement with inductively coupled plasma atomic emission spectrometry.

A flow-batch system allowing in-line individual sample matrix matching is proposed for analysis of sample lots with high variability in acidity. The feasibility of the approach is demonstrated in the spectrophotometric determination of total nitrogen in Kjeldahl digests, using a column with a slightly soluble reagent (AgCl). The solutions are sequentially injected by means of an 8-port selecting valve and processed in a mixing chamber that is also used as a monitoring unit. The system yields reproducible results (r.s.d. usually2.5%) and the sampling rate is 14 samples/h. The analytical curve is linear within 1.00 and 6.00% N (dry basis), and the regression coefficient is0.999 (n = 6). Results are in agreement with certified values of standard reference materials and with results obtained by conductometry.

In titrimetric procedures, the number of data points can be reduced when the analyte concentration is roughly known. This aspect is emphasised in automated titrations where a foresight of the analyte concentration can be fast and easily achieved by processing known amounts of sample and titrant, and evaluating the titrant excess. This prior assay permits a previous definition of the different titrant amounts to be further added. The feasibility of this strategy is demonstrated in designing a flow-batch titrator for spectrophotometric determination of iron in steels, iron ores and similar. The method involves titration of ferrous ions by permanganate performed inside a 400ml reaction chamber assembled in the manifold. The proposed system handles about 20 samples per hour, requires 94mg KMnO4 per determination and yields precise results (R.S.D. ca. 1.5%, nD6) for 1.0‐1010 3 mol l 1 Fe in the injectate. Accuracy was assessed by running a standard reference material and different samples already analysed by a manual titrimetric procedure. © 2000 Elsevier Science B.V. All rights reserved.

Several procedures for the determination of clinical–biochemical parameters are based on commercial kits supplied with only one standard solution because linear calibration graphs are involved. When these procedures are carried out in flow analysis non-linearity of the analytical graph may be observed due to time restrictions. Since at least three standard solutions are then needed, direct implementation of commercial kits in flow injection analysers can be hindered. To overcome these drawbacks, a computer controlled flow injection system exploiting concentration gradients and requiring only one standard solution is proposed for non-linear calibration. Limitations due to non-linearity of the analytical graph were overcome using a dye solution to calibrate the concentration gradient of the injected sample and standard solution. As an application, the spectrophotometric kinetic determination of total protein in blood serum based on commercial kits was selected. The system handled 60 samples per hour and required 17 μl and 1.0 ml of sample and biuret reagent, respectively, per determination. No baseline drift was observed during 8 h operating periods. Within the 40–80 g l −1 range, the overall relative standard deviation of results was estimated as 3.0% ( n = 5). The paired t -test was used and no statistic differences at the 95% probability level between the results for the reference and the proposed procedures was found.

A monosegmented flow system involving sample and titrant sequential injections and the establishment of a tandem plug between two air bubbles is proposed for automated titrations. The end point is found after successive variations of the sample and titrant volumes, determined by an algorithm based on the one-dimensional optimization method of Fibonacci. The set up includes a multi-port solenoid valve with four inlets and one outlet. The feasibility of the system was demonstrated in the spectrophotometric determination of the total acidity in vinegar with NaOH and phenolphthalein used as the titrant and indicator, respectively. The sample throughput was ca. 30 h-1 and consumption of the sample and titrant was about 0.7 ml. The results were in agreement with the expected values, with relative errors lower than ±0.4%. After 12 successive analyses of typical samples with a 47.1 - 53.4 total acidity, expressed as g l-1 acetic acid, the relative standard deviations of the results were estimated within the 1.2 - 2.1% range.

An automated flow-injection titrator to perform spectrophotometric determinations of acidity in wine samples is described. The proposed titrator exploits concentration gradients generated in the flow-injection system and, in contrast to the automated flow titrators previously proposed, it does not require analytical curves based on several standard solutions of tartaric acid. Only one single standard solution, the proper titrant, is directly used for gradient calibration and calculating of the analyte concentrations. A simple laboratory-made photometer that uses an LED (Light-Emitting Diode) as light source and a phototransistor as detector was built in to this titrator. Yellow LED was usually used for spectrophotometric measurements because m-cresol purple (7.4 < pH < 9.0 yellow–purple transition) was selected as acid–base indicator. The system is fully controlled by microcomputer and the software for control and for acquisition and treatment data was written in C language. A good agreement between results yielded by the proposed titrator and those produced by a reference method of the Association of Official Analytical Chemistry (AOAC) was obtained and no statistical difference between results at the 95% probability level was confirmed by applying the paired t-test. An overall relative accuracy of about 0.9% and a mean relative standard deviation lower than 1.5% were obtained with nine replicate titrations. The proposed titrator allows 72 determinations per hour to be carried out, consuming just 200 µl of the sample and 1.5 ml of the titrant.

An automated flow-rate meter easy to construct and operate is described. This is contralled by a micracomputer connected to an analog/digitai converter interface and by a contrai software. It uses two acrylic flow cells, each comprising a bicolor light-emitting diode anda phototransistor piaced on opposite sides. The performance of this flow-rate meter was evaiuated by appiying it to three flow manifoids: one monosegmented flow titration system by binary sampling; one conventionai monosegmented flow system, and one singie-iine FIA system. The FIA system flow rate was assessed by means of a Vanderslice's expression. The time range of measurement of the flow rates was of 3.5-19.5 seconds and relative errars of 0.2-2.6% as well as relative stanCorrespondence to: Mario Cesar U. Araujo. © 1999 John Wiley & Sons, Inc. CCC 0895-7533/99/020097-08 dard deviations fram 0.4 to 3.1 % were attained. The flow-rate meter allows one to optimize precision and velocity of measurements just by changing one of its parameters. Moreover, the flow-rate meter can be used with flow systems, therefore ailowing one to perform on-line measurements, which facilitates a quick and effective contrai of the channeis flow rate. © 1999 John Wiley & Sons, Inc. Lab Robotics and Automation 11: 97-104, 1999

This paper describes use of gradients of concentration generated in flow injection (FI) systems to perform determinations based on points where the concentration of titrant and analyte are at stoichiometric ratio. Two procedures were developed. In one procedure the titrant is injected in a FI manifold and merges with the sample which is continuously pumped towards the detector. In the other procedure the sample is injected and merged with the titrant which is continuously pumped. Both techniques make use of concentration gradients of the sample or titrant generated in FI manifolds that contain a mixing chamber. This gradient is calibrated employing only one standard solution (usually the titrant) in order to convert any detector signal, obtained in the elapsed time after injection, to instantaneous concentration values. The flow system is microcomputer controlled and data are treated to locate points where the concentration of titrant and analyte are at the stoichiometric ratio. These points are found in abrupt changes of the signal × concentration curves obtained in the presence of the reaction. The method has been evaluated for determination of Fe(II) and acetic acid by spectrophotometric and conductimetric detection, respectively. Results show a mean relative standard deviation lower than 1%, an average accuracy of 1% and a high sampling processing capability (40 to 60 samples per hour).

A novel strategy for implementing the standard addition method in flow analysis is described. It requires only one standard solution and provides several addition levels using a single injection. Data acquisition and treatment are performed by means of a microcomputer running a software in C-language. A mathematical model to account for the changes in sample matrix due to differences in dispersion associated with the different addition levels is derived. It corrects also deviations related to the asymmetry of the merging zones process. Model systems are critically examined for potassium determination in synthetic samples by flame photometry, to which ethanol or glycerol are added to promote different matrix effects. As application, the determination of copper in spirits by flame atomic absorption spectrometry was selected. The proposed system handles about 60 samples per hour and yields precise results (R.S.D. usually < 5%) regardless of the ethanol content of the assayed samples. Comparison with an alternative procedure involving sample evaporation and manual water dilution revealed systematic deviations usually < 3%.

Laser induced breakdown spectroscopy (LIBS) is further developed for the detection of gunshot residue (GSR) on the hands of a suspected shooter. To develop the procedure, 114 samples were obtained by pressing a small piece of adhesive tape against the dorsal region of the hands of non-shooters, of shooters just after firing a weapon, and of shooters after washing their hands with soap and water. The tapes were analyzed directly in a time-resolved echelle based LIBS system, assembled in the laboratory, and 20 single laser pulses, spread uniformly on the tape surface, were shown to be enough to ensure the detection of true positives. The spectra obtained by LIBS from the tape present a signature with emission lines that are attributed to barium and lead. After the spectral data was submitted to a pattern recognition method SIMCA (Soft Independent Modeling of Class Analogy), shooters and non-shooters were corrected classified. The method based on LIBS and SIMCA has been demonstrated to be non-destructive of crime evidence and enabled discrimination between the samples collected from non-shooter volunteers and shooter volunteers before and after washing their hands. A espectroscopia de emissão em plasma induzido por laser (LIBS) foi avaliada para a detecção de resíduos de disparo de arma de fogo (GSR) nas mãos de um suspeito. Para desenvolver o procedimento, 114 amostras foram obtidas pressionando um pequeno pedaço de fita adesiva contra a região dorsal das mãos de não-atiradores, atiradores após o disparo e de atiradores após terem lavado as mãos com sabão e água. As fitas foram analisadas diretamente em um equipamento LIBS baseado em um policromador echelle com resolução temporal construído em laboratório. Os espectros foram obtidos a partir da aplicação de um único pulso de laser em 20 locações diferentes, espalhadas uniformemente sobre a superfície da fita, para assegurar uma amostragem eficiente na detecção de resíduos de disparo. Os espectros mostram uma assinatura com linhas de emissão características de bário e chumbo. Quando os dados espectrais foram submetidos à técnica de reconhecimento de padrão SIMCA (Modelagem Independente e Flexível por Analogia de Classe), atiradores e não-atiradores foram corretamente classificados. O método baseado em LIBS e SIMCA demonstrou ser não-destrutivo da evidência do crime e permitiu discriminar as amostras coletadas de voluntários não-atiradores e atiradores, mesmo após a lavagem das mãos.

Quantifying cocaine in apprehended samples is important to the Brazilian Federal Police because the concentration can indicate the origin of the drug and consequently the traffic route. In addition to the other risks of using this drug is the variability in cocaine concentration, which makes large doses lethal. Gas Chromatography with a Flame Ionization Detector (GC-FID) and a Mass Selective Detector (GC-MS) are the techniques usually employed, but these systems are not available in all police laboratories, due to the relatively high cost. In the present work, a flow-system procedure for the spectrophotometric determination of cocaine using cobalt thiocyanate as a complexing reagent was developed. In this reaction, two phases are formed: the superior (pink) contains an excess of cobalt thiocyanate solution and the lower layer (blue) contains the complex cocaine-cobalt thiocyanate. Samples and reagent are inserted through a sequential-injection valve between two air bubbles inside a reaction chamber. An optic fiber sensor connected to the chamber recorded the absorbance at 630 nm signal. The detection and quantification limits were 29.4 mg L(-1) and 98 mg L(-1), respectively. Relative standard deviation was 4.9% for solutions containing 400 mg L(-1) (n=10), with stable baselines. The analytical throughput was 12 determinations per hour.

A flow-batch manifold coupled to a flame atomic absorption spectrometer was evaluated to assess the iron content by the internal standard method in hydrated ethanol used as fuel in automotive industry. For this assessment official methods require calibration procedures with matrix matching, making it difficult to obtain accurate results for samples adulterated by the addition of water. Nickel was selected as the internal standard since it is usually absent in samples and because it requires similar conditions of atomization. After procedure optimization, which requires about 4.25 mL of sample and standard per measurement, it was possible to get linear analytical response for iron concentrations between 0.12 and 1.40 mg L −1 and a detection limit of 0.04 mg L −1 . Eighteen samples were collected randomly from fuel stations in Pernambuco (Brazil) and iron concentration was determined using the proposed procedure. Comparison of results obtained (0.20–1.50 mg L −1 ) showed a mean standard error of 3.9%, with 3.8% and 2.3% calculated for the mean variation coefficients of the proposed method and the reference procedure, respectively. For adulterated samples (0.12–0.64 mg L −1 ), the mean standard error was 4.8% when compared with the standard addition method. These results allowed concluding that the proposed procedure is adequate to accomplish the determination of iron in ethanol fuel in a large scale basis with a sampling rate of about 10 h −1 .

In most instrumental analysis, the analyte concentration is usually obtained the by Analytical Curve Method (ACM) or Standard Addition Method (SAM). Thus, it is important for the analyst to select the most appropriate method, to seek the best conditions of analysis, and to provide parameters of analytical performance. A strategy to do so is proposed in this paper in conjunction with MATLAB software to implement it. The proposed strategy was applied to copper determination by atomic absorption spectrometry in Brazilian sugarcane spirits termed 'Cachaca' and SAM was chosen as the most appropriate method. To select the best experimental design for SAM, the influence of some factors, such as the number of standard additions and concentration levels, the location of the levels and the average concentration of the standard additions were demonstrated. The design with six standard additions, four concentration levels located near the inferior and superior levels and the average concentration of the standard additions closer to zero yielded SAM with an adequate compromise between precision, cost and time of analysis. The uniform distribution of concentration levels, usually used in routine analysis, is not a good design regarding precision. On the other hand, it is adequate when the linear range is unknown. Generally, the proposed strategy can be applied to different instrumental techniques and samples, which aim to improve their analytical performance.

A low inner volume (ca. 64 ml) probe was built up in an injector-commutator in order to behave as a photometric leaping detector in flow analysis. It comprises a bicolour light-emitting diode (BLED), as a source of pulsed radiation in the red and green visible region, and two phototransistors as transducers. Sample injection, detector relocation, analytical signal recording, data treatment and definition of the spectral working range were computer-controlled. The feasibility of the system was initially demonstrated in the flow-injection speciation of iron, and the overall standard deviation of results was estimated as ± 1.6 and ± 1.4% for 1.6–4.0 mg l−1Fe(II) or total iron after eightfold processing of synthetic samples. The system was further applied to drug analysis: the mean deviations of results for typical samples were estimated as ± 5.2 and ± 3.3%, and the relative standard deviation as ± 1.6 and ± 1.3% for Fe(II) and total iron, respectively. Results were compared with those obtained by a conventional spectrophotometric procedure and no statistic differences at the 95% confidence level were found. In relation to an earlier system with multi-site detection, the proposed system is more stable, presenting low drift with a relative standard deviation of 0.026% and 0.039% for measurements (n=120 during 4 h of observation) with green and red emission. It is also faster with a sampling rate of 133 h−1and carryover problems are not found. The possibility of compensating the Schlieren noise by dual-wavelength spectrophotometry is discussed.