Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.

Monitoring of crystallization processes by using infrared spectroscopy and multivariate methods

Väitöstutkimuksessa on tarkasteltuinfrapunaspektroskopian ja monimuuttujaisten aineistonkäsittelymenetelmien soveltamista kiteytysprosessin monitoroinnissa ja kidemäisen tuotteen analysoinnissa. Parhaillaan kiteytysprosessitutkimuksessa maailmanlaajuisesti tutkitaan intensiivisesti erilaisten mittausmenetelmien soveltamista kiteytysprosessin ilmiöidenjatkuvaan mittaamiseen niin nestefaasista kuin syntyvistä kiteistäkin. Lisäksi tuotteen karakterisointi on välttämätöntä tuotteen laadun varmistamiseksi. Erityisesti lääkeaineiden valmistuksessa kiinnostusta tämäntyyppiseen tutkimukseen edistää Yhdysvaltain elintarvike- ja lääkeaineviraston (FDA) prosessianalyyttisiintekniikoihin (PAT) liittyvä ohjeistus, jossa määritellään laajasti vaatimukset lääkeaineiden valmistuksessa ja tuotteen karakterisoinnissa tarvittaville mittauksille turvallisten valmistusprosessien takaamiseksi. Jäähdytyskiteytyson erityisesti lääketeollisuudessa paljon käytetty erotusmenetelmä kiinteän raakatuotteen puhdistuksessa. Menetelmässä puhdistettava kiinteä raaka-aine liuotetaan sopivaan liuottimeen suhteellisen korkeassa lämpötilassa. Puhdistettavan aineen liukoisuus käytettävään liuottimeen laskee lämpötilan laskiessa, joten systeemiä jäähdytettäessä liuenneen aineen konsentraatio prosessissa ylittää liukoisuuskonsentraation. Tällaiseen ylikylläiseen systeemiin pyrkii muodostumaan uusia kiteitä tai olemassa olevat kiteet kasvavat. Ylikylläisyys on yksi tärkeimmistä kidetuotteen laatuun vaikuttavista tekijöistä. Jäähdytyskiteytyksessä syntyvän tuotteen ominaisuuksiin voidaan vaikuttaa mm. liuottimen valinnalla, jäähdytyprofiililla ja sekoituksella. Lisäksi kiteytysprosessin käynnistymisvaihe eli ensimmäisten kiteiden muodostumishetki vaikuttaa tuotteen ominaisuuksiin. Kidemäisen tuotteen laatu määritellään kiteiden keskimääräisen koon, koko- ja muotojakaumansekä puhtauden perusteella. Lääketeollisuudessa on usein vaatimuksena, että tuote edustaa tiettyä polymorfimuotoa, mikä tarkoittaa molekyylien kykyä järjestäytyä kidehilassa usealla eri tavalla. Edellä mainitut ominaisuudet vaikuttavat tuotteen jatkokäsiteltävyyteen, kuten mm. suodattuvuuteen, jauhautuvuuteen ja tabletoitavuuteen. Lisäksi polymorfiamuodolla on vaikutusta moniin tuotteen käytettävyysominaisuuksiin, kuten esim. lääkeaineen liukenemisnopeuteen elimistössä. Väitöstyössä on tutkittu sulfatiatsolin jäähdytyskiteytystä käyttäen useita eri liuotinseoksia ja jäähdytysprofiileja sekä tarkasteltu näiden tekijöiden vaikutustatuotteen laatuominaisuuksiin. Infrapunaspektroskopia on laajalti kemian alan tutkimuksissa sovellettava menetelmä. Siinä mitataan tutkittavan näytteenmolekyylien värähtelyjen aiheuttamia spektrimuutoksia IR alueella. Tutkimuksessa prosessinaikaiset mittaukset toteutettiin in-situ reaktoriin sijoitettavalla uppoanturilla käyttäen vaimennettuun kokonaisheijastukseen (ATR) perustuvaa Fourier muunnettua infrapuna (FTIR) spektroskopiaa. Jauhemaiset näytteet mitattiin off-line diffuusioheijastukseen (DRIFT) perustuvalla FTIR spektroskopialla. Monimuuttujamenetelmillä (kemometria) voidaan useita satoja, jopa tuhansia muuttujia käsittävä spektridata jalostaa kvalitatiiviseksi (laadulliseksi) tai kvantitatiiviseksi (määrälliseksi) prosessia kuvaavaksi informaatioksi. Väitöstyössä tarkasteltiin laajasti erilaisten monimuuttujamenetelmien soveltamista mahdollisimman monipuolisen prosessia kuvaavan informaation saamiseksi mitatusta spektriaineistosta. Väitöstyön tuloksena on ehdotettu kalibrointirutiini liuenneen aineen konsentraation ja edelleen ylikylläisyystason mittaamiseksi kiteytysprosessin aikana. Kalibrointirutiinin kehittämiseen kuuluivat aineiston hyvyyden tarkastelumenetelmät, aineiston esikäsittelymenetelmät, varsinainen kalibrointimallinnus sekä mallin validointi. Näin saadaan reaaliaikaista informaatiota kiteytysprosessin ajavasta voimasta, mikä edelleen parantaa kyseisen prosessin tuntemusta ja hallittavuutta. Ylikylläisyystason vaikutuksia syntyvän kidetuotteen laatuun seurattiin usein kiteytyskokein. Työssä on esitetty myös monimuuttujaiseen tilastolliseen prosessinseurantaan perustuva menetelmä, jolla voidaan ennustaa spontaania primääristä ytimenmuodostumishetkeä mitatusta spektriaineistosta sekä mahdollisesti päätellä ydintymisessä syntyvä polymorfimuoto. Ehdotettua menetelmää hyödyntäen voidaan paitsi ennakoida kideytimien muodostumista myös havaita mahdolliset häiriötilanteet kiteytysprosessin alkuhetkillä. Syntyvää polymorfimuotoa ennustamalla voidaan havaita ei-toivotun polymorfin ydintyminen,ja mahdollisesti muuttaa kiteytyksen ohjausta halutun polymorfimuodon saavuttamiseksi. Monimuuttujamenetelmiä sovellettiin myös kiteytyspanosten välisen vaihtelun määrittämiseen mitatusta spektriaineistosta. Tämäntyyppisestä analyysistä saatua informaatiota voidaan hyödyntää kiteytysprosessien suunnittelussa ja optimoinnissa. Väitöstyössä testattiin IR spektroskopian ja erilaisten monimuuttujamenetelmien soveltuvuutta kidetuotteen polymorfikoostumuksen nopeaan määritykseen. Jauhemaisten näytteiden luokittelu eri polymorfeja sisältäviin näytteisiin voitiin tehdä käyttäen tarkoitukseen soveltuvia monimuuttujaisia luokittelumenetelmiä. Tämä tarjoaa nopean menetelmän jauhemaisen näytteen polymorfikoostumuksen karkeaan arviointiin, eli siihen mitä yksittäistä polymorfia kyseinen näyte pääasiassa sisältää. Varsinainen kvantitatiivinen analyysi, eli sen selvittäminen paljonko esim. painoprosentteina näyte sisältää eri polymorfeja, vaatii kaikki polymorfit kattavan fysikaalisen kalibrointisarjan, mikä voi olla puhtaiden polymorfien huonon saatavuuden takia hankalaa.

Sellun kuivauskoneen ajettavuusongelman monimuuttujainen tarkastelu

Tässä työssä on tutkittu sellun kuivauskoneella koivusellua kuivattaessa esiintyvää ajettavuusongelmaa. Tähän ongelmaan on etsitty ratkaisua monimuuttujamenetelmien avulla. Työn kirjallisuusosassa on lyhyesti käyty läpi sellun kuivauskoneiden historia lieriöviirakoneista nykyaikaisiin kaksoisviirasovelluksiin. Lisäksi kirjallisuusosassa on käsitelty tässä työssä käytettyjen monimuuttujamenetelmien perusteet ja käyty esimerkin omaisesti läpi joitakin kemometrian sovelluksia puunjalostusteollisuudessa. Työn kokeellisessa osassa on haettu tiedonkeruujärjestelmästä massan ominaisuuksista ja kuivauskoneen ajoparametreistä koostuvaa dataa vuoden 1999 ajalta. Tästä datasta on tehty PCA-mallit vuoden 1999 jokaisen kuukauden datasta ja diskriminoiva PLS-malli ajettavuuden kannalta hyvästä ja huonosta jaksosta koostuvasta datasta. Kuivauskoneelta on koottu dataa myös ottamalla kiertovesi- ja selluarkkinäytteitä. Kiertovesinäytteiden analyysituloksista on tehty PCA-mallit ja lisäksi analyysitulokset on tarkasteltu yksimuuttujaisesti. Selluarkkinäytteistä on määritetty UV-spektrit ja niistä on tehty PCA-mallit, jotta spektreistä saataisiin mahdollisimman paljon informaatiota. Työssä on havaittu yhdeksi selitykseksi ajettavuusongelmalle kuivauskoneen kiertovesien likaantuminen. Kiertovesiä ja massarainan pintakemiaa tulisi kuitenkin tutkia laajemmassa mittakaavassa kuin tämän työn puitteissa on ollut mahdollista tutkia.

Motorcycle helmets: what about their coating?

In traffic accidents involving motorcycles, paint traces can be transferred from the rider's helmet or smeared onto its surface. These traces are usually in the form of chips or smears and are frequently collected for comparison purposes. This research investigates the physical and chemical characteristics of the coatings found on motorcycles helmets. An evaluation of the similarities between helmet and automotive coating systems was also performed.Twenty-seven helmet coatings from 15 different brands and 22 models were considered. One sample per helmet was collected and observed using optical microscopy. FTIR spectroscopy was then used and seven replicate measurements per layer were carried out to study the variability of each coating system (intravariability). Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were also performed on the infrared spectra of the clearcoats and basecoats of the data set. The most common systems were composed of two or three layers, consistently involving a clearcoat and basecoat. The coating systems of helmets with composite shells systematically contained a minimum of three layers. FTIR spectroscopy results showed that acrylic urethane and alkyd urethane were the most frequent binders used for clearcoats and basecoats. A high proportion of the coatings were differentiated (more than 95%) based on microscopic examinations. The chemical and physical characteristics of the coatings allowed the differentiation of all but one pair of helmets of the same brand, model and color. Chemometrics (PCA and HCA) corroborated classification based on visual comparisons of the spectra and allowed the study of the whole data set at once (i.e., all spectra of the same layer). Thus, the intravariability of each helmet and its proximity to the others (intervariability) could be more readily assessed. It was also possible to determine the most discriminative chemical variables based on the study of the PCA loadings. Chemometrics could therefore be used as a complementary decision-making tool when many spectra and replicates have to be taken into account. Similarities between automotive and helmet coating systems were highlighted, in particular with regard to automotive coating systems on plastic substrates (microscopy and FTIR). However, the primer layer of helmet coatings was shown to differ from the automotive primer. If the paint trace contains this layer, the risk of misclassification (i.e., helmet versus vehicle) is reduced. Nevertheless, a paint examiner should pay close attention to these similarities when analyzing paint traces, especially regarding smears or paint chips presenting an incomplete layer system.

Inférence de source de traces d'essence retrouvées dans des débris d'incendie: Evaluation de la contribution de la chromatographie en phase gazeuse couplée à la spectrométrie de masse à rapport isotopique

Lorsque de l'essence est employée pour allumer et/ou propager un incendie, l'inférence de la source de l'essence peut permettre d'établir un lien entre le sinistre et une source potentielle. Cette inférence de la source constitue une alternative intéressante pour fournir des éléments de preuve dans ce type d'événements où les preuves matérielles laissées par l'auteur sont rares. Le but principal de cette recherche était le développement d'une méthode d'analyse de spécimens d'essence par GC-IRMS, méthode pas routinière et peu étudiée en science forensique, puis l'évaluation de son potentiel à inférer la source de traces d'essence en comparaison aux performances de la GC-MS. Un appareillage permettant d'analyser simultanément les échantillons par MS et par IRMS a été utilisé dans cette recherche. Une méthode d'analyse a été développée, optimisée et validée pour cet appareillage. Par la suite, des prélèvements d'essence provenant d'un échantillonnage conséquent et représentatif du marché de la région lausannoise ont été analysés. Finalement, les données obtenues ont été traitées et interprétées à l'aide de méthodes chimiométriques. Les analyses effectuées ont permis de montrer que la méthodologie mise en place, aussi bien pour la composante MS que pour l'IRMS, permet de différencier des échantillons d'essence non altérée provenant de différentes stations-service. Il a également pu être démontré qu'à chaque nouveau remplissage des cuves d'une station-service, la composition de l'essence distribuée par cette station est quasi unique. La GC-MS permet une meilleure différenciation d'échantillons prélevés dans différentes stations, alors que la GC-IRMS est plus performante lorsqu'il s'agit de comparer des échantillons collectés après chacun des remplissages d'une cuve. Ainsi, ces résultats indiquent que les deux composantes de la méthode peuvent être complémentaires pour l'analyse d'échantillons d'essence non altérée. Les résultats obtenus ont également permis de montrer que l'évaporation des échantillons d'essence ne compromet pas la possibilité de grouper des échantillons de même source par GC-MS. Il est toutefois nécessaire d'effectuer une sélection des variables afin d'éliminer celles qui sont influencées par le phénomène d'évaporation. Par contre, les analyses effectuées ont montré que l'évaporation des échantillons d'essence a une forte influence sur la composition isotopique des échantillons. Cette influence est telle qu'il n'est pas possible, même en effectuant une sélection des variables, de grouper correctement des échantillons évaporés par GC-IRMS. Par conséquent, seule la composante MS de la méthodologie mise en place permet d'inférer la source d'échantillons d'essence évaporée. _________________________________________________________________________________________________ When gasoline is used to start and / or propagate an arson, source inference of gasoline can allow to establish a link between the fire and a potential source. This source inference is an interesting alternative to provide evidence in this type of events where physical evidence left by the author are rare. The main purpose of this research was to develop a GC-IRMS method for the analysis of gasoline samples, a non-routine method and little investigated in forensic science, and to evaluate its potential to infer the source of gasoline traces compared to the GC-MS performances. An instrument allowing to analyze simultaneously samples by MS and IRMS was used in this research. An analytical method was developed, optimized and validated for this instrument. Thereafter, gasoline samples from a large sampling and representative of the Lausanne area market were analyzed. Finally, the obtained data were processed and interpreted using chemometric methods. The analyses have shown that the methodology, both for MS and for IRMS, allow to differentiate unweathered gasoline samples from different service stations. It has also been demonstrated that each new filling of the tanks of a station generates an almost unique composition of gasoline. GC-MS achieves a better differentiation of samples coming from different stations, while GC-IRMS is more efficient to distinguish samples collected after each filling of a tank. Thus, these results indicate that the two components of the method can be complementary to the analysis of unweathered gasoline samples. The results have also shown that the evaporation of gasoline samples does not compromise the possibility to group samples coming from the same source by GC-MS. It is however necessary to make a selection of variables in order to eliminate those which are influenced by the evaporation. On the other hand, the carried out analyses have shown that the evaporation of gasoline samples has such a strong influence on the isotopic composition of the samples that it is not possible, even by performing a selection of variables, to properly group evaporated samples by GC-IRMS. Therefore, only the MS allows to infer the source of evaporated gasoline samples.

Separação de espectros simulados e de luminescência total através do método generalizado de anulação do posto (GRAM)

A multivariate curve resolution method, "GENERALIZED RANK ANNIHILATION METHOD (GRAM)", is discussed and tested with simulated and experimental data. The analysis of simulated data provides general guidelines concerning the condition for uniqueness of a solution for a given problem. The second-order emission-excitation spectra of human and animal dental calculus deposits were used as an experimental data to estimate the performance of the above method. Three porphyrinic spectral profiles, for both human and cat, were obtained by the use of GRAM.

Algoritmo genético em química

Genetic algorithm is an optimization technique based on Darwin evolution theory. In last years its application in chemistry is increasing significantly due the special characteristics for optimization of complex systems. The basic principles and some further modifications implemented to improve its performance are presented, as well as a historical development. A numerical example of a function optimization is also shown to demonstrate how the algorithm works in an optimization process. Finally several chemistry applications realized until now is commented to serve as parameter to future applications in this field.

Quimiometria I: calibração multivariada, um tutorial

The aim of this work is to present a tutorial on Multivariate Calibration, a tool which is nowadays necessary in basically most laboratories but very often misused. The basic concepts of preprocessing, principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) are given. The two basic steps on any calibration procedure: model building and validation are fully discussed. The concepts of cross validation (to determine the number of factors to be used in the model), leverage and studentized residuals (to detect outliers) for the validation step are given. The whole calibration procedure is illustrated using spectra recorded for ternary mixtures of 2,4,6 trinitrophenolate, 2,4 dinitrophenolate and 2,5 dinitrophenolate followed by the concentration prediction of these three chemical species during a diffusion experiment through a hydrophobic liquid membrane. MATLAB software is used for numerical calculations. Most of the commands for the analysis are provided in order to allow a non-specialist to follow step by step the analysis.

Avaliação do uso de métodos quimiométricos em análise de solos

One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.

Aplicação de métodos quimiométricos na especiação de Cr(VI) em solução aquosa

The equilibria, the spectra and the identities of the species of Cr(VI) that are present in aqueous solution have long been an active subject of discussion in the literature. In this paper, three different chemometric methodologies are applied to sets of UV/Visible spectra of aqueous Cr(VI) solutions, in order to solve a chemical system where there is no available information concerning the composition of the samples nor spectral information about the pure species. Imbrie Q-mode factor analysis, followed by varimax rotation and Imbrie oblique projection, were used to estimate the composition of Cr(VI) equilibrium solutions and, by combining these results with the k-matrix method, to obtain the pure spectra of the species. Evolving factor analysis and self modeling curve resolution were used to confirm the number of the species and the resolution of the system, respectively. Sets of 3.3×10-4 and 3.3×10-5 mol L-1 Cr(VI) solutions, respectively, were analyzed in the pH range from 1 to 12. Two factors were identified, which were related to the chromate ion (CrO4(2-)) and bichromate ion (HCrO4-). The pK of the equilibrium was estimated as 5.8.

Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva

The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way.

Estudo do coeficiente de partição octanol-água de bifenilas policloradas (PCBs) utilizando parâmetros topológicos

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.

PARAFAC: uma ferramenta quimiométrica para tratamento de dados multidimensionais. Aplicações na determinação direta de fármacos em plasma humano por espectrofluorimetria

Since the last decade, the combined use of chemometrics and molecular spectroscopic techniques has become a new alternative for direct drug determination, without the need of physical separation. Among the new methodologies developed, the application of PARAFAC in the decomposition of spectrofluorimetric data should be highlighted. The first objective of this article is to describe the theoretical basis of PARAFAC. For this purpose, a discussion about the order of chemometric methods used in multivariate calibration and the development of multi-dimensional methods is presented first. The other objective of this article is to divulge for the Brazilian chemical community the potential of the combination PARAFAC/spectrofluorimetry for the determination of drugs in complex biological matrices. For this purpose, two applications aiming at determining, respectively, doxorrubicine and salicylate in human plasma are presented.