Search

The development of rapid, reliable, and environmentally friendly analytical methods for food analysis is increasingly required. In this sense, the combination of spectrofluorimetry and second-order calibration is a promising alternative to the determination of naturally fluorescent analytes in food matrices, as compared to the laborious and time-consuming chromatographic methods. This work combined the chemometric second-order method parallel factor analysis (PARAFAC) with spectrofluorimetry aiming to develop a new analytical method to quantify phenylalanine in honey samples. Honey samples of different botanical and geographical origins were diluted with deionized water, and their excitation-emission matrices were recorded between 280–350 nm (excitation) and 360–490 nm (emission). Due to the presence of matrix effect, second-order standard addition was employed. This methodology utilizes the second-order advantage, which allows to quantify an analyte in the presence of uncalibrated/unmodeled interferences. Phenylalanine concentrations were estimated in the range from 5.7 to 32.5 mg kg−1. Method validation was performed by verifying the agreement between the results of the developed method and from an independent methodology based on liquid chromatography. In addition, proper figures of merit were estimated for the proposed method, such as sensitivity, analytical sensitivity, limits of detection, and quantification.

The determination of alcohol content in beers is essential for the quality control of this beverage. This paper proposed and validated a new rapid and direct multivariate method for this aim using a portable near-infrared (NIR) spectrometer and partial least squares (PLS) regression. Reference values were obtained by a gas chromatography with flame ionization detection (GC-FID) method developed and validated for this purpose. Aiming at building a robust model, a great variety of beers, from different styles, brands, and breweries, was incorporated into the model. NIR spectra were recorded between 908 and 1676 nm for 92 beer samples, corresponding to a range from 3.2 to 10.9% (v/v) of alcohol content. PLS model provided accurate results with root-mean-square error of calibration (RMSEC) and prediction (RMSEP) of 0.5% and 0.6%, respectively. The developed method was validated through the estimate of figures of merit, such as linearity, trueness, precision, analytical sensitivity, bias, and residual prediction deviation (RPD). In addition, an elliptical joint confidence region was calculated to verify the linearity, and confidence intervals based on the standard prediction errors were estimated for the validation samples.

This paper described the development of a multivariate classification methodology to detect frauds in bovine meat based on mid-infrared spectroscopy and partial least squares discriminant analysis (PLS-DA). These frauds consisted of adding carrageenan, sodium chloride, and tripolyphosphate, ingredients that increase meat water holding capacity aiming to obtain economic gains. Meat pieces (fresh beef muscle) of the same bovine cut, M. semitendinosus, from different origins were injected with single to ternary mixtures of adulterants, and their purges were analyzed totaling 176 spectra. Multiclass PLS-DA models for specifically detecting each adulterant provided good results (correctly classification rates > 90%) only for tripolyphosphate. Nevertheless, a two-class PLS-DA model discriminating adulterated and non-adulterated meat provided high success rates (≥ 95%). Aiming to verify the model’s ability to detect other (non-trained) adulterant, this last model was combined with outlier detection in a soft version of a discriminant model that was able to correctly detect 100% of a new validation set consisting of 20 meat samples containing maltodextrin.

Color is an important sensory parameter required for the quality control of beers. A new multivariate image analysis method for the color determination of beers was proposed and validated. Reference color values were determined using the SRM (standard reference method) system, which is based on absorbance measurements at 430 nm. Digital images were obtained with an iPhone 7 smartphone. The obtained RGB histograms were used for building partial least squares (PLS) models. The developed method is direct, simple, and rapid, not requiring sample pretreatment steps as the reference method. Beer samples of different styles, brands, and brewery companies were obtained in a large variety, totalizing 128 samples and comprising a range from 3 to 130 SRM units. A global PLS model built with all the beer samples presented too large prediction errors for some samples in the lower part of the SRM scale (below 12 units). Thus, considering the requirement of dilution prescribed by the reference method for samples with absorbances higher than 1.0, two local calibration models were built: for high SRM range (above 12 units) and low SRM range (equal or below 12 units) samples. A previous PLS discriminant analysis (PLS-DA) model was used to assign the beer samples to these two classes, resulting in 78 and 50 samples in the high- and low-range models, respectively. These two models were validated according to the Brazilian and international guidelines, being considered linear, accurate, precise, and unbiased. Uncertainties were also calculated for estimating confidence intervals for the predictions of the validation samples. The developed method could be easily adapted in a mobile platform, spreading its use and opening the possibility for the commercial production of a dedicated equipment.

In the last years, there has been an important and growing concern about food authentication due to the increasing number of occurrences of new types of food frauds. Recently, some frauds have been reported describing the injection of non-meat ingredients, such as salts and polysaccharide compounds, into bovine meat in natura, aiming at increasing its water holding capacity (WHC) and obtaining economic fraudulent gains. Thus, this paper developed a simple and rapid analytical method based on a multivariate supervised classification model (partial least squares discriminant analysis, PLS-DA) and Raman spectroscopy for tackling this problem. Sixteen vacuum-packed pieces of the same cut, eye of the round (semitendinosus), of approximately 4 kg were obtained from different origins. According to an experimental design, each piece was divided into 11 parts, providing control and adulterated samples. Single, binary and ternary mixtures of adulterated samples were prepared by injecting NaCl, sodium tripolyphosphate and carrageenan in the meat pieces. A total of 165 samples were produced (54 controls and 111 adulterated) and their purges, the exudated liquid extracted from the meat after thawing, were obtained. Raman spectra of these purges were recorded between 1800 and 700 cm−1. The whole data set was split into 112 samples for the training set and 53 for the test set. The best PLS-DA model was built with 4 latent variables and successfully discriminated adulterated samples at relatively small rates of false negative and false positive results, which varied from 8.0 to 11.7%. As an additional validation step, confidence intervals were calculated by bootstrap algorithm.

Grape is the most consumed nectar in Brazil and a relatively expensive beverage. Therefore, it is susceptible to fraud by substitution with other less expensive fruit juices. Adulterations of grape nectars by the addition of apple juice, cashew juice, and mixtures of both were evaluated by using low-field nuclear magnetic resonance (LF-NMR) and supervised multivariate classification methods. Two different approaches were investigated using one-class (only unadulterated samples (UN) were modeled) and multiclass (three classes were modeled: UN, adulterated with cashew (CAS), and adulterated with apple (APP)) strategies. For the one-class approach, soft independent modeling of class analogy (SIMCA), one-class partial least squares (OCPLS), and data-driven SIMCA (DD-SIMCA) models were built. For the multiclass approach, partial least squares discriminant analysis (PLS-DA) and multiclass SIMCA models were built. The results obtained demonstrated good performances by all the one-class methods with efficiency rates higher than 93%. For the multiclass approach, the classification of samples containing only one type of adulterant presented efficiencies higher than 90% and 97% using SIMCA and PLS-DA, respectively. The classification of samples containing blends of two adulterants was satisfactory for the CAS class, but not for the APP class when applying PLS-DA. Nevertheless, multiclass SIMCA did not provide satisfactory predictions for either of these two classes.

This paper presents the combination of wooden-tip electrospray ionization mass spectrometry (WTESI-MS) and multivariate pattern recognition methods (principal component analysis, PCA and partial least squares discriminant analysis, PLS-DA) for the rapid and reliable discrimination, via chemical fingerprints, of garlic origin. A total of 312 garlic samples grown in different countries (Brazil, China, Argentina, Spain, and Chile) were studied. The methodology was based on a direct sampling approach, which relies on loading the sample by penetrating the garlic cloves with a pre-wetted wooden tip, followed by direct prompt analysis by WTESI-MS. Thus, no sample preparation is needed, which prevents the degradation of important metabolites and increases the analytical throughput. Parameters that affects the WTESI were optimized and the best performance in terms of signal stability and intensity was achieved using the positive ion mode. Most of the ions in WTESI mass spectra were assigned to amino acids, sugars, organosulfur compounds, and lipids. The discriminative model showed good performance (accuracy rates between 81.9% and 98.6%) and enabled identifying diagnostic ions for garlic samples from different origins. The differentiation and classification of garlic origin is of major importance as this food flavoring product is widely consumed, with worldwide trade representing billions of dollars every year, and is very often the subject of fraud.

This work developed an analytical method to differentiate conventional and omega-3 fat acids enriched eggs by Raman spectroscopy and multivariate supervised classification with Partial Least Squares Discriminant Analysis (PLS-DA). Forty samples of enriched eggs and forty samples of different types of common eggs from different batches were used to build the model. Firstly, gas chromatography was employed to analyze fatty acid profiles in egg samples. Raman spectra of the yolk extracts were recorded in the range from 3100 to 990 cm−1. PLS-DA model was able to correctly classify samples with nearly 100% success rate. This model was validated estimating appropriate figures of merit. Predictions uncertainties were also estimated by bootstrap resampling. The most discriminant Raman modes were identified based on VIP (variables importance in projection) scores. This method has potential to assist food industries and regulatory agencies for food quality control, allowing detecting frauds and enabling faster and reliable analyzes.

A teaching experiment on supervised pattern recognition in chemometrics was proposed in this tutorial to introduce partial least squares discriminant analysis (PLS-DA). A new approach of the experiment published in the first tutorial of this series was revisited and employed to the classification of edible vegetable oils. The spectra of olive, canola, soybean and corn oils were obtained using an attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectrometer in the range of 600 to 4000 cm-1. The combination of ATR-FTIR and PLS-DA classification method was able to correctly classify 100% of the validation samples. The Matlab commands, routines and functions were presented, and a didactic explanation of the concepts and interpretation of the data was provided. Made available in DSpace on 2020-06-23T11:13:01Z (GMT). No. of bitstreams: 1 Experimento-didatico-de-quimiometria-para-classificacao-de-oleos-vegetais-comestiveis-2020.pdf: 3697819 bytes, checksum: 05d368657c156ed3e2b9190d911f5515 (MD5) Previous issue date: 2020

During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.

Spectrofluorimetry combined with multiway chemometric tools were applied to discriminate pure Aroeira honey samples from samples adulterated with corn syrup, sugar cane molasses and polyfloral honey. Excitation emission spectra were acquired for 232 honey samples by recording excitation from 250 to 500 nm and emission from 270 to 640 nm. Parallel factor analysis (PARAFAC), partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA (UPLS-DA) and multilinear PLS-DA (NPLS-DA) methods were used to decompose the spectral data and build classification models. PLS-DA models presented poor classification rates, demonstrating the limitation of the traditional two-way methods for this dataset, and leading to the development of three-way classification models. Overall, UPLS-DA provided the best classification results with misclassification rates of 4% and 8% for the training and test sets, respectively. These results showed the potential of the proposed method for routine laboratory analysis as a simple, reliable, and affordable tool.

This paper combined attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), multivariate calibration with partial least squares (PLS), and different variable selection methods for the development of models to determine Robusta-Arabica coffee blends in the analytical range from 0.0 to 33.0% w/w. Ground samples of different origins were roasted at three different levels: light, medium, and dark. Specific models were built for each roasting level, and a robust model was also obtained including all the samples. Mid infrared spectra were recorded in the wavenumber range between 4000 and 800 cm−1 for the 120 samples used in the models. Four variable selection methods were tested: genetic algorithm (GA), ordered predictors selection (OPS), successive projections algorithm (SPA), and interval PLS (iPLS). The best results were obtained using GA and OPS, decreasing root mean square errors of prediction (RMSEP) in 44–68% as compared to full spectra models. The best robust model was obtained with OPS, providing RMSEP of 1.8% w/w. The number of selected variable in the optimized models varied from 6.5 to 17.0% of the total number of original variables. This demonstrated the importance of selecting a limited number of wavenumbers richer in information specifically related to the analytes. All the methods were validated by estimating appropriate figures of merit and considered accurate, linear, sensitive, and unbiased.

This paper describes the development of an optical sensor device using a smartphone and a homemade dark chamber built with recycled materials. This low cost instrument was employed in the development of multivariate image regression methods for the determination of the azo dye allura red in hard candies. To build the models, 238 candy samples of four flavors and different brands and batches were used. Firstly, a multivariate calibration model using RGB histograms and partial least squares (PLS) was built. This model provided high prediction errors, which were attributed to the presence of textural variations in the images. Then, a more complex image analysis methodology that incorporates spatial information, and consists of preprocessing by a two-dimensional fast Fourier transform followed by multi-way calibration with N-way PLS, provided better results, decreasing the prediction errors around 25–35%. The final model was submitted to a complete multivariate analytical validation, being considered precise, linear, sensitive and unbiased. The analytical range was established between 22.9 and 78.8 mg kg −1 of allura red. Root mean square errors of calibration (RMSEC) and prediction (RMSEP) of 4.8 and 6.1 mg kg −1 were estimated. The developed method is simple, rapid, and nondestructive.

Due to the general increase in drug trafficking crime rates, a high amount of drug samples is continuosly seized and requires forensic analysis. In order to cover the demand for this great amount of samples in forensic investigations, non-destructive, fast and direct analysis methods are desirable. A new supervised classification method using PLS-DA ( partial least squares discriminant analysis ) and ATR-FTIR ( attenuated total reflectance Fourier transform infrared spectroscopy ) was developed to identify NPS (new psychoactive substances) drugs in blotter papers. A multivariate model was built to classify NBOMe, 2C-H, LSD, MAL (methallylescaline) and discriminate them of blank papers. A submodel was also built to discriminate 25B-NBOMe, 25C-NBOMe and 25I-NBOMe inside NBOMe class. Both models were validated through the estimate of specific figures of merit. The average of reliability rate (RLR) was 88.9%, accordance (ACC) was 91.1% and concordance (CON) was 86.1%. For the NBOMe submodel RLR was 82.2%, ACC was 100% and CON was 94.4%. The model presented high correct classification rates for all the classes, with the exception of LSD, possibly due to its lower concentration on seized blotters. The proposed method has potential to be used on blotter screening routine. The analysis is cost-effective, rapid, 2 min per sample, and utilizes ATR-FTIR, a technique whose use is increasing on forensic laboratories around the world.

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0–33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100–4950 cm−1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

Perfumes are cosmetic products with high added value and worldwide consumption, which make them a potential target for counterfeiting. A novel, simple and rapid method was developed for the differentiation of samples of authentic and counterfeit perfumes by employing paper spray mass spectrometry (PS-MS) combined with multivariate supervised classification models: Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). Samples of authentic (n = 29, consisting of 10 different brands from several batches and from the same producer) and seized counterfeit (n = 31) perfumes were analysed by PS-MS in the positive ionization mode and within a mass range of m/z 150–1000. An initial unsupervised exploratory model (Principal Component Analysis – PCA) provided a rough visual separation between the two classes. In contrast, PLS-DA and SIMCA provided good predictions, with low false positive and false negative rates for both models. The interpretation of informative vectors, i.e. regression coefficients and Variable Importance in Projection (VIP) scores obtained from the PLS-DA model allowed the detection of diagnostic ions for authentic and counterfeit samples. Some of the most discriminant ions for counterfeit perfumes were suggested to be attributed to compounds with allergenic properties.

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.

Ischemic stroke is a neurovascular disorder caused by reduced or blockage of blood flow to the brain, which may permanently affect motor and cognitive abilities. The diagnostic of stroke is performed using imaging technologies, clinical evaluation, and neuropsychological protocols, but no blood test is available yet. In this work, we analyzed amino acid concentrations in blood plasma from poststroke patients in order to identify differences that could characterize the stroke etiology. Plasma concentrations of sixteen amino acids from patients with chronic ischemic stroke (n = 73) and the control group (n = 16) were determined using gas chromatography coupled to mass spectrometry (GC-MS). The concentration data was processed by Partial Least Squares-Discriminant Analysis (PLS-DA) to classify patients with stroke and control. The amino acid analysis generated a first model able to discriminate ischemic stroke patients from control group. Proline was the most important amino acid for classification of the stroke samples in PLS-DA, followed by lysine, phenylalanine, leucine, and glycine, and while higher levels of methionine and alanine were mostly related to the control samples. The second model was able to discriminate the stroke subtypes like atherothrombotic etiology from cardioembolic and lacunar etiologies, with lysine, leucine, and cysteine plasmatic concentrations being the most important metabolites. Our results suggest an amino acid biosignature for patients with chronic stroke in plasma samples, which can be helpful in diagnosis, prognosis, and therapeutics of these patients.

The resistance of Annona muricata roots to the nematode Meloidogyne javanica was investigated by nuclear magnetic resonance (NMR)-based metabolomics in combination with principal component analysis (PCA). Metabolic changes in roots exposed and not exposed to the nematode were evaluated and compared. In addition, the presence of nematicidal compounds in the root extracts was investigated through in vitro assay against Meloidogyne javanica second-stage juveniles. Plants exposed to nematodes showed significant changes in their metabolism after 24 h. Several resistance-related metabolites, including dopamine, xanthine and aromatic compounds, could be identified in the roots with the joint analysis of 1D/2D NMR and the loadings of PC3 (17.8%). A.muricata root chloroform extract, containing mainly acetogenins, has shown nematostatic activity against M. javanica, suggesting that a pre-formed defense mechanism can support the reported resistance. For the first time, metabolomic studies allowed to identify induced and pre-formed defense mechanisms and their related metabolites in Annona muricata.

This paper describes a robust multivariate model for quantifying and characterizing blends of Robusta and Arabica coffees. At different degrees of roasting, 120 ground coffee blends (0.0–33.0%) were formulated. Spectra were obtained by two different techniques, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and paper spray mass spectrometry (PS-MS). Partial least squares (PLS) models were built individually with the two types of spectra. Nevertheless, better predictions were obtained by low and medium-level data fusion, taking advantage from the synergy between these two data sets. Data fusion models were improved by variable selection, using genetic algorithms (GA) and ordered predictors selection (OPS). The smallest prediction errors were provided by OPS low-level data fusion model. The number of variables used for regression was reduced from 2145 (full spectra) to 230. Model interpretation was performed by assigning some of the selected variables to specific coffee components, such as trigonelline and chlorogenic acids.

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 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.

Several new psychoactive substances (NPS) have reached the illegal drug market in recent years, and ecstasy-like tablets are one of the forms affected by this change. Cathinones and tryptamines have increasingly been found in ecstasy-like seized samples as well as other amphetamine type stimulants. A presumptive method for identifying different drugs in seized ecstasy tablets (n = 92) using ATR-FTIR (attenuated total reflectance – Fourier transform infrared spectroscopy) and PLS-DA (partial least squares discriminant analysis) was developed. A hierarchical strategy of sequential modeling was performed with PLS-DA. The main model discriminated four classes: 5-MeO-MIPT, methylenedioxyamphetamines (MDMA and MDA), methamphetamine, and cathinones. Two submodels were built to identify drugs present in MDs and cathinones classes. Models were validated through the estimate of figures of merit. The average reliability rate (RLR) of the main model was 96.8% and accordance (ACC) was 100%. For the submodels, RLR and ACC were 100%. The reliability of the models was corroborated through their spectral interpretation. Thus, spectral assignments were performed by associating informative vectors of each specific modeled class to the respective drugs. The developed method is simple, fast, and can be applied to the forensic laboratory routine, leading to objective results reports useful for forensic scientists and law enforcement.

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior There is no any doubt about the importance of food fraud control, as it has implications in food safety and in consumer health. Focusing on fruit beverages, some types of adulterations have been detected more frequently, such as substitution with less expensive fruits. A methodology based on attenuated total reflectance Fourier-transform mid-infrared spectroscopy (ATR-FTIR) and multivariate classification was applied to detect whether grape nectars were adulterated by substitution with apple juice or cashew juice. A total of 126 samples were obtained and analyzed. Two strategies were proposed: one-class and multiclass approaches. Soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and partial least squares density modeling (PLS-DM) were used to build the models. Among them, PLS-DA presented the best performance with a sensitivity and specificity of nearly 100%. The multiclass strategy was preferred if the adulterants to be studied are known because it provides additional information.

This chapter presents some novel approaches that can be applied in food analysis, such as data fusion and multivariate curve resolution (MCR) and discusses preprocessing and unsupervised classification methods. Chemometrics is the application of multivariate statistics in chemistry and is defined as the chemical discipline that uses mathematical and statistical methods to design or select optimal measurement procedures and experiments and provide maximum chemical information by analyzing chemical data. Hierarchical cluster analysis (HCA) is an unsupervised method often employed in exploratory data analysis and pattern recognition. The Euclidean distance is the most used measure for HCA. One of the most employed methods to extract relevant information in experimental data and pattern recognition is Principal component analysis. In the milk dataset, the complete-linkage was applied, which is one of the simplest linkage criteria. In multivariate calibration, several variables per sample are available, such as in sensor arrays, spectra, voltammograms, and chromatograms.

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated.

Sweet sorghum biomass is gaining importance as feedstock for second generation bioethanol production. Consequently, breeding programs are seeking to improve the quality of this feedstock in order to increase the productivity, with the generation of a great number of samples to be analyzed. Thus, this paper developed rapid and low cost methods based on partial least squares (PLS) and near infrared reflectance spectroscopy for determining cellulose, hemicellulose, lignin and theoretical ethanol yield (TEY) in sorghum biomass. The models were built with 957 samples, obtained from more than 100 hybrids and inbred strains, in the ranges of 21.4–49.1% w/w, 18.4–34.8% w/w, 1.8–11.5% w/w and 221–412 L t − 1 for cellulose, hemicellulose, lignin and TEY, respectively. These models presented root mean square errors of prediction of 1.5%, 1.7%, 0.8% and 12 L t − 1 (and ranges of relative errors of prediction between − 5.3 and 6.5%, − 9.8 and 12.2%, − 28.8 and 37.6%, and − 5.6 and 6.1%), respectively. The methods were submitted to a complete multivariate analytical validation in accordance with the Brazilian and international guidelines, and considered accurate, linear, sensitive and unbiased. Finally the stability of these methods was monitored for approximately six months by developing appropriate control charts.

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.

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.

The potential of near-infrared spectroscopy (NIRS) combined with partial least squares discriminant analysis (PLS-DA) to develop a screening method for distinguishing uninfested from infested sorghum (Sorghum bicolour (L.) Moench) grains was for the first time investigated. A total of 108 sorghum grain samples from thirty-six different genotypes were infested with seventy Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) unsexed adult insects during seventy days. More 101 uninfested sorghum grain samples from these same genotypes were used to build models. Principal components analysis (PCA) allowed slightly discriminating between the two classes along principal component two. A PLS-DA model presented perfect classification rates, with sensitivity and specificity equal to 100% for the test set. In addition, the model showed high accuracy, and accordance and concordance (precision) both equal to 100%. These results showed that the combination of NIRS with PLS-DA provided a rapid, cost-effective and non-invasive way to detect insect infestation in sorghum grains.

Constant supervision is required to ensure the quality control in extra virgin olive oil, a quite expensive product with worldwide consumption. In this paper, a rapid, simple and efficient method based on the application of the partial least squares (PLS) approach to electrospray ionization mass spectrometry (ESI-MS) data was developed for determining adulteration of extra virgin olive oil with four adulterant oils (soybean, corn, sunflower and canola). Each model was built with 40 adulterated samples (from 0.5 to 20.0% w/w), which were prepared using commercial oils. These models presented root mean square errors of prediction of 1.7% w/w for soybean, 1.0% w/w for corn, 1.4% w/w for sunflower and 1.0% w/w for canola. The methods were subjected to a complete multivariate analytical validation in accordance with the Brazilian and international guidelines, and were considered accurate, linear, sensitive and unbiased. So, it can be envisaged that this methodology has the potential to be applied in quality control of extra virgin olive oil samples.

Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)-MS] is used to obtain fingerprints of aqueous-methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS-DA) protocol aiming at discriminating the above-mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS-DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1-7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils.

This work developed and validated a new multivariate diffuse reflectance near infrared method for direct determination of hydrochlorothiazide in powder pharmaceutical samples. The best partial least squares (PLS) model was obtained in the spectral region from 1640 to 1780 nm, with mean centered data preprocessed by first derivative and Savitzky–Golay smoothing followed by vector normalization. This model was built with 4 latent variables and provided a root mean square error of prediction of 1.7%. The method was validated according to the appropriate regulations in the range from 21.25 to 29.00 mg of hydrochlorothiazide per 150 mg of powder (average mass tablet), by the estimate of figures of merit, such as accuracy, precision, linearity, analytical sensitivity, capability of detection, bias and residual prediction deviation (RPD). The concept of net analyte signal (NAS) was used to estimate some figures of merit and to plot a pseudo-univariate calibration curve. The results for determinations in powdered manufactured tablets were in agreement with those of the official high performance liquid chromatographic method (HPLC). Finally, the method was extrapolated for determinations in intact tablets, providing prediction errors smaller than ± 9%. The developed method presented the advantage of being about fifteen times faster than the reference HPLC method.

This 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 Abstract ᅟ.

A direct method based on the application of paper spray mass spectrometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied to the discrimination of authentic and counterfeit samples of blended Scottish whiskies. The developed methodology employed the negative ion mode MS, included 44 authentic whiskies from diverse brands and batches and 44 counterfeit samples of the same brands seized during operations of the Brazilian Federal Police, totalizing 88 samples. An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the counterfeit whiskies in PC2. In spite of the samples heterogeneity, a robust, reliable and accurate PLS-DA model was generated and validated, which was able to correctly classify the samples with nearly 100% success rate. The use of PS-MS also allowed the identification of the main marker compounds associated with each type of sample analyzed: authentic or counterfeit.

This study developed and validated a method for moisture determination in artisanal Minas cheese, using near-infrared spectroscopy and partial-least-squares. The model robustness was assured by broad sample diversity, real conditions of routine analysis, variable selection, outlier detection and analytical validation. The model was built from 28.5-55.5% w/w, with a root-mean-square-error-of-prediction of 1.6%. After its adoption, the method stability was confirmed over a period of two years through the development of a control chart. Besides this specific method, the present study sought to provide an example multivariate metrological methodology with potential for application in several areas, including new aspects, such as more stringent evaluation of the linearity of multivariate methods.

This paper proposed and validated robust diffuse reflectance near-infrared methods for the direct determination of fat and moisture in cow mozzarella cheeses using partial least squares regression. They were developed under the realistic conditions of routine analysis in a state laboratory of quality inspection control and were used for analyzing a great variety of mozzarella samples manufactured by different manufacturing procedures and originating from the whole state of Minas Gerais, Brazil (more than 100 different producers). A robust methodology was implemented, including the detection of outliers and the harmonization of the multivariate concepts with the traditional univariate guidelines. The models were constructed in the ranges from 38.7 to 58.0 % w/w on dry basis for fat and from 41.5 to 55.1 % w/w for moisture, providing root mean square errors of prediction of 2.1 and 0.9 %, respectively. Both methods were validated through the estimation of figures of merit, such as linearity, trueness, precision, analytical sensitivity, ruggedness, bias, and residual prediction deviation. Once the methods were adopted, their performances were monitored for approximately 1 year through control charts and were considered satisfactorily stable with prediction errors within the established limits. Beyond these specific methods, it was also pursued to present a complete methodology for multivariate analytical validation, an important aspect for the implementation of near-infrared spectroscopy methods in the routine of food quality inspection.

This paper proposes a new method for determination of amoxicillin in pharmaceutical suspension formulations, based on transflectance near infrared (NIR) measurements and partial least squares (PLS) multivariate calibration. A complete methodology was implemented for developing the proposed method, including an experimental design, data preprocessing by using multiple scatter correction (MSC) and outlier detection based on high values of leverage, and X and Y residuals. The best PLS model was obtained with seven latent variables in the range from 40.0 to 65.0 mg mL(-1) of amoxicillin, providing a root mean square error of prediction (RMSEP) of 1.6 mg mL(-1). The method was validated in accordance with Brazilian and international guidelines, through the estimate of figures of merit, such as linearity, precision, accuracy, robustness, selectivity, analytical sensitivity, limits of detection and quantitation, and bias. The results for determinations in four commercial pharmaceutical formulations were in agreement with the official high performance liquid chromatographic (HPLC) method at the 99% confidence level. A pseudo-univariate calibration curve was also obtained based on the net analyte signal (NAS). The proposed chemometric method presented the advantages of rapidity, simplicity, low cost, and no use of solvents, compared to the principal alternative methods based on HPLC.