Marcelo M. Sena

Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA.

This paper proposed a new screening method for the simultaneous detection of five common adulterants in raw cow milk by using attenuated total reflectance (ATR) mid infrared spectroscopy and multivariate supervised classification (partial least squares discrimination analysis - PLSDA). The method was able to detect the presence of the adulterants water, starch, sodium citrate, formaldehyde and sucrose in milk samples containing from one up to five of these analytes, in the range of 0.5-10% w/v. A multivariate qualitative validation was performed, estimating specific figures of merit, such as false positive and false negative rates, selectivity, specificity and efficiency rates, accordance and concordance. The proposed method does not need any sample pretreatment, requires a small amount of sample (30 μL), is fast and simple, being suitable for the control of raw milk in a dairy industry or for the quality inspection of commercialized milk.

Analysis of seized cocaine samples by using chemometric methods and FTIR spectroscopy

The aim of this article was to develop a chemometric methodology for determining the chemical profile of cocaine samples seized in Minas Gerais State, Brazil. The adulterant detection and the cocaine determination were performed by gas chromatography-mass spectrometry (GC-MS). Spectra of 91 samples were obtained by attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and used to build an exploratory principal component analysis (PCA) model. The first principal component (PC1) discriminated samples of more purity from the more diluted/adulterated ones, which were characterized by the presence of lidocaine, caffeine and benzocaine. PC2 discriminated the two chemical forms of cocaine, hydrochloride and base. In addition, two supervised discriminant partial least-squares models (partial least-squares discriminant analysis, PLS-DA) were developed for classifying the samples according to dilution (above and below 15% m/m) and chemical form, with a rate of success that varied between 83 and 97%. The classification models constitute a simple, rapid and non-destructive tool, of great value for both forensic experts and criminal investigators.

Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy

Concerns about meat authenticity are increasing recently, due to great fraud scandals. This paper analysed real samples (43 adulterated and 12 controls) originated from criminal networks dismantled by the Brazilian Police. This fraud consisted of injecting solutions of non-meat ingredients (NaCl, phosphates, carrageenan, maltodextrin) in bovine meat, aiming to increase its water holding capacity. Five physico-chemical variables were determined, protein, ash, chloride, sodium, phosphate. Additionally, infrared spectra were recorded. Supervised classification PLS-DA models were built with each data set individually, but the best model was obtained with data fusion, correctly detecting 91% of the adulterated samples. From this model, a variable selection based on the highest VIPscores was performed and a new data fusion model was built with only one chemical variable, providing slightly lower predictions, but a good cost/performance ratio. Finally, some of the selected infrared bands were specifically associated to the presence of adulterants NaCl, tripolyphosphate and carrageenan.

Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers.

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit.

Evaluation of transformer insulating oil quality using NIR, fluorescence, and NMR spectroscopic data fusion

Power transformers are essential components in electrical energy distribution. One of their most important parts is the insulation system, consisting of Kraft paper immersed in insulating oil. Interfacial tension and color are major parameters used for assessing oil quality and the system׳s degradation. This work proposes the use of near infrared (NIR), molecular fluorescence, and (1)H nuclear magnetic resonance (NMR) spectroscopy methods combined with chemometric multivariate calibration methods (Partial Least Squares - PLS) to predict interfacial tension and color in insulating mineral oil samples. Interfacial tension and color were also determined using tensiometry and colorimetry as standard reference methods, respectively. The best PLS model was obtained when NIR, fluorescence, and NMR data were combined (data fusion), demonstrating synergy among them. An optimal PLS model was calculated using the selected group of variables according to their importance on PLS projections (VIP). The root mean square errors of prediction (RMSEP) values of 2.9 mN m(-1) and 0.3 were estimated for interfacial tension and color, respectively. Mean relative standard deviations of 1.5% for interfacial tension and 6% for color were registered, meeting quality control requirements set by electrical energy companies. The methods proposed in this work are rapid and simple, showing great advantages over traditional approaches, which are slow and environmentally unfriendly due to chemical waste generation.

Development and analytical validation of a simple multivariate calibration method using digital scanner images for sunset yellow determination in soft beverages

This paper proposed a novel methodology for the quantification of an artificial dye, sunset yellow (SY), in soft beverages, using image analysis (RGB histograms) and partial least squares regression. The developed method presented many advantages if compared with alternative methodologies, such as HPLC and UV/VIS spectrophotometry. It was faster, did not require sample pretreatment steps or any kind of solvents and reagents, and used a low cost equipment, a commercial flatbed scanner. This method was able to quantify SY in isotonic drinks and orange sodas, in the range of 7.8-39.7 mg L(-1), with relative prediction errors lower than 10%. A multivariate validation was also performed according to the Brazilian and international guidelines. Linearity, accuracy, sensitivity, bias, prediction uncertainty and a recently proposed tool, the β-expectation tolerance intervals, were estimated. The application of digital images in food analysis is very promising, opening the possibility for automation.

Development and validation of a multivariate calibration model for determination of dipyrone in oral solutions by near infrared spectroscopy

This work developed a new method for determination of dipyrone (DIP) in oral pharmaceutical formulations, through the use of near infrared (NIR) transflectance measurements and multivariate calibration. The studied range varied from 300.0 to 569.2 mg mL-1. The best PLS (partial least squares) model was obtained with two latent variables and the root mean square errors of calibration and prediction were 1.1 and 1.0 mg mL-1, respectively. The proposed method was validated in accordance with ANVISA, the Brazilian regulatory agency, and ICH, being considered selective, linear, precise, accurate and robust. By comparison with the main alternatives, iodimetric titration and HPLC, this method is simpler, non-destructive, does not use reagents or solvents and does not produce chemical waste. Besides, its rapidity is considered the major advantage over the other methods, since only about 50 s were spent per assay.

Calibration transfer from powder mixtures to intact tablets: A new use in pharmaceutical analysis for a known tool

Calibration transfer is commonly used for spectra obtained in different spectrometers or other conditions. This paper proposed the use of calibration transfer between spectra recorded for the same samples in different physical forms. A new method was developed for the direct determination of nevirapine in solid pharmaceutical formulations based on diffuse reflectance near infrared spectroscopy (NIRS) and partial least squares (PLS). This method was developed with 50 powder mixtures and then, successfully extended to the quantification in intact tablets by using calibration transfer with double window piecewise direct standardization (DWPDS). This chemometric strategy provided good results with a small number of tablet transfer samples, only seven, prepared out of the narrow range of active principle ingredients (API) content around the nominal value of the formulation (100%). The method was fully validated in the working range of 83.0-113.9% of nevirapine and the use of DWPDS allowed to significantly decreasing the root mean square error of prediction (RMSEP) from 4.8% (tablets predicted by a model built with only powder samples) to 2.6%. The range of relative errors decreased from -5.1/8.7% to -4.6/3.3%. Considering that the amount of raw materials demanded for preparing tablets is up to ten times higher than for powder mixtures, this type of application is of particular interest in pharmaceutical analysis. In the context of process analytical technology (PAT), the use of the same multivariate model in different steps of the production is very advantageous, saving time and labor.

Chemometric quality inspection control of pyrantel pamoate, febantel and praziquantel in veterinary tablets by mid infrared spectroscopy.

This paper describes the development and validation of a new multivariate calibration method based on diffuse reflectance mid infrared spectroscopy for direct and simultaneous determination of three veterinary pharmaceutical drugs, pyrantel pamoate, praziquantel and febantel, in commercial tablets. The best synergy interval partial least squares (siPLS) model was obtained by selecting three spectral regions, 3715-3150, 2865-2583, and 2298-1733 cm(-1), preprocessed by first derivative and Savitzky-Golay smoothing followed by mean centering. This model was built with five latent variables and provided root mean square errors of prediction (RMSEP) equal or lower than 0.69 mg per 100 mg of powder for the three analytes. The method was validated according the appropriate regulations through the estimate of figures of merit, such as trueness, precision, linearity, analytical sensitivity, bias and residual prediction deviation (RPD). Then, it was applied to three different veterinary pharmaceutical formulations found in the Brazilian market, in a situation of multi-product calibration, since the excipient composition of these commercial products, which was not known a priori, was modeled by an experimental design that scanned the likely content range of the possible constituents. The results were verified with high performance liquid chromatography with diode array detection (HPLC-DAD) and high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) and were in agreement with the predicted values at 95% confidence level. The developed method presented the advantages of being simple, rapid, solvent free, and about ten times faster than the HPLC ones.

Paper Spray Mass Spectrometry for the Forensic Analysis of Black Ballpoint Pen Inks

AbstractThis article describes the use of paper spray mass spectrometry (PS-MS) for the direct analysis of black ink writings made with ballpoint pens. The novel approach was developed in a forensic context by first performing the classification of commercially available ballpoint pens according to their brands. Six of the most commonly worldwide utilized brands (Bic, Paper Mate, Faber Castell, Pentel, Compactor, and Pilot) were differentiated according to their characteristic chemical patterns obtained by PS-MS. MS on the negative ion mode at a mass range of m/z 100–1000 allowed prompt discrimination just by visual inspection. On the other hand, the concept of relative ion intensity (RII) and the analysis at other mass ranges were necessary for the differentiation using the positive ion mode. PS-MS combined with partial least squares (PLS) was utilized to monitor changes on the ink chemical composition after light exposure (artificial aging studies). The PLS model was optimized by variable selection, which allowed the identification of the most influencing ions on the degradation process. The feasibility of the method on forensic investigations was also demonstrated in three different applications: (1) analysis of overlapped fresh ink lines, (2) analysis of old inks from archived documents, and (3) detection of alterations (simulated forgeries) performed on archived documents. Graphical Abstractᅟ