Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples

The most common fraudulent practice in the vinegar industry is the addition of alcohol of different origins to the base wine used to produce wine vinegar with the objective of reducing manufacturing costs. The mixture is then sold commercially as genuine wine vinegar, thus constituting a fraud to consumers and an unfair practice with respect to the rest of the vinegar sector. A method based on near-infrared spectroscopy has been developed to discriminate between white wine vinegar and alcohol or molasses vinegar. Orthogonal signal correction (OSC) was applied to a set of 96 vinegar NIR spectra from both original and artificial blends made in the laboratory, to remove information unrelated to a specific response. The specific response used to correct the spectra was the extent of adulteration of the vinegar samples. Both raw and corrected NIR spectra were used to develop separate classification models using the potential functions method as a class-modeling technique. The previous models were compared to evaluate the suitability of near-infrared spectroscopy as a rapid method for discrimination between vinegar origin. The transformation of vinegar NIR spectra by means of an orthogonal signal-correction method resulted in notable improvement of the specificity of the constructed classification models. The same orthogonal correction approach was also used to perform a calibration model able to detect and quantify the amount of exogenous alcohol added to the commercial product. This regression model can be used to quantify the extent of adulteration of new vinegar samples.     

A chemometric approach to the detection of milk adulteration based on protein profiles determined by capillary electrophoresis

The general objective of this study was to utilize chemometrics in the interpretation of capillary electrophoresis milk protein profiles, for the detection of pasteurized milk adulteration with rehydrated milk powder or a rehydrated dairy-based milk substitute. The specific objectives were 1) to collect quantitative data on major casein and whey proteins in authentic and adulterated milks in a single CE analysis; and 2) to apply a pattern recognition procedure, Soft Independent Modeling of Class Analogies (SIMCA), on collected CE protein data, for the development of a statistical model useful in the detection of pasteurized milk adulteration. Authentic samples were fresh milk collected from various farms over a period of six months. Adulterated samples were authentic fresh milk partially or totally substituted with rehydrated milk powder or a rehydrated commercial milk substitute at different levels. Quantitative protein data obtained by capillary free zone electrophoresis for beta-lactoglobulin, alpha-lactalbumin, beta-casein, and alpha-casein of 86 samples, authentic and adulterated samples, were used as a training set to build a SIMCA multivariate statistical model. The detection of sample outliers was useful for the elimination of unusual samples and optimization of the multivariate model. From the 35 commercial pasteurized milks tested, which were treated as unknowns, a total of 14 samples (40%) were not assigned to the authentic or fresh milk group, meaning that these samples had some type of adulteration at the levels included in the training set (> 15%). Decision-making on detecting adulteration of unknown commercial pasteurized milk samples was eased since predictions were based on statistical probabilities.     

Expanding the analytical toolbox for identity testing of pharmaceutical ingredients: Spectroscopic screening of dextrose using portable Raman and near infrared spectrometers

In the pharmaceutical industry, dextrose is used as an active ingredient in parenteral solutions and as an inactive ingredient (excipient) in tablets and capsules. In order to address the need for more sophisticated analytical techniques, we report our efforts to develop enhanced identification methods to screen pharmaceutical ingredients at risk for adulteration or substitution using field-deployable spectroscopic screening. In this paper, we report our results for a study designed to evaluate the performance of field-deployable Raman and near infrared (NIR) methods to identify dextrose samples. We report a comparison of the sensitivity of the spectroscopic screening methods against current compendial identification tests that rely largely on a colorimetric assay. Our findings indicate that NIR and Raman spectroscopy are both able to distinguish dextrose by hydration state and from other sugar substitutes with 100% accuracy for all methods tested including spectral correlation based library methods, principal component analysis and classification methods.     

Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines

Near-infrared (NIR) spectroscopy is a non-destructive measurement technique for many chemical compounds that has proved its efficiency for laboratory and industrial applications (including petroleum industry). Motor oil classification is an important task for quality control and identification of oil adulteration. Type of motor oil base stock is a key factor in product price formation. In this paper we have tried to evaluate the efficiency of different methods for motor oils classification by base stock (synthetic, semi-synthetic and mineral) and kinematic viscosity at low and high temperature. We have compared the abilities of seven (7) different classification methods: regularized discriminant analysis (RDA), soft independent modelling of class analogy (SIMCA), partial least squares classification (PLS), K-nearest neighbour (KNN), artificial neural network - multilayer perceptron (ANN-MLP), support vector machine (SVM), and probabilistic neural network (PNN) - for classification of motor oils. Three (3) sets of near-infrared spectra (1125, 1010, and 1050 items) were used for classification of motor oils into three or four classes. In all cases NIR spectroscopy was found to be effective for motor oil classification when combined with an effective multivariate data analysis (MDA) technique. SVM and PNN chemometric techniques were found to be the most effective ones for classification of motor oil based on its NIR spectrum.     

Detection of adulteration in acetonitrile

To address the increasing concern that acetonitrile may be intentionally adulterated to meet the shortfall in global supplies resulting from a downturn in its manufacturing, three analytical techniques were examined in this study. Gas Chromatography with Thermal Conductivity Detection (GC-TCD), Near Infrared (NIR) spectroscopy and Fourier Transform Infrared (FT-IR) spectroscopy were assessed for their ability to detect and quantify potential adulterants including water, alternative organic solvents, and by-products associated with the production of acetonitrile. The results of the assessment of the three techniques for acetonitrile adulteration testing are discussed.     

Identification of adulteration in milk by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

The development is described of a rapid, simply and accurate analytical method aimed at evaluating both the presence of cow milk in either raw ewe and water buffalo milk samples employed in industrial processes and the addition of powdered milk to samples of fresh raw milk, using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). The presence of adulteration is defined by evaluating the protein patterns coming from the most abundant whey proteins, alpha-lactalbumin and beta-lactoglobulin, used as molecular markers. As no pretreatment of the milk samples is required and owing to the speed and ease of use of MALDI-MS the proposed analytical protocol can be used as a routine strategy for the identification of possible adulteration of the raw fresh milk samples that the dairy industry receives from producers every day.     

Adulteration of Essential Oils: A Multitask Issue for Quality Control. Three Case Studies: Lavandula angustifolia Mill., Citrus limon (L.) Osbeck and Melaleuca alternifolia (Maiden & Betche) Cheel

The quality control of essential oils (EO) principally aims at revealing the presence of adulterations and at quantifying compounds that are limited by law by evaluating EO chemical compositions, usually in terms of the normalised relative abundance of selected markers, for comparison to reference values reported in pharmacopoeias and/or international norms. Common adulterations of EO consist of the addition of cheaper EO or synthetic materials. This adulteration can be detected by calculating the percent normalised areas of selected markers or the enantiomeric composition of chiral components. The dilution of the EO with vegetable oils is another type of adulteration. This adulteration is quite devious, as it modifies neither the qualitative composition of the resulting EO nor the marker’s normalised percentage abundance, which is no longer diagnostic, and an absolute quantitative analysis is required. This study aims at verifying the application of the two above approaches (i.e., normalised relative abundance and absolute quantitation) to detect EO adulterations, with examples involving selected commercial EO (lavender, bergamot and tea tree) adulterated with synthetic components, EO of different origin and lower economical values and heavy vegetable oils. The results show that absolute quantitation is necessary to highlight adulteration with heavy vegetable oils, providing that a reference quantitative profile is available.     

Identification of adulteration in water buffalo mozzarella and in ewe cheese by using whey proteins as biomarkers and matrix-assisted laser desorption/ionization mass spectrometry

A rapid and accurate method to identify bovine and ewe milk adulteration of fresh water buffalo mozzarella cheese by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) is described. The differentiation among mozzarella made from water buffalo milk and from mixtures of less expensive bovine and, more recently, ewe milk with water buffalo milk is achieved using whey proteins, alpha-lactalbumin and beta-lactoglobulins as molecular markers. It is worth noting that the method proposed here is, to our knowledge, the first strategy able to characterize possible fraudulent additions of ewe milk in samples of water buffalo milk devoted to the production of water buffalo mozzarella cheese. In addition, a linear relationship was found between the relative response of the molecular ion and the abundance of the analysed whey proteins. This demonstrates that this approach can be used to determine the amount of bovine or ovine milk added to water buffalo milk employed for mozzarella cheese production. Furthermore, this method also appears suitable for the analysis of ewe cheese. Hence these findings open the way to a new field for mass spectrometry in the evaluation of possible fraudulence in dairy industry production.     

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

Analyse thermogravimetrique et fraudes sur les origines dans la fabrication des fromages

It has been performed a research of a quick method in order to verify adulterations in the manufacture of cheeses. In particular we have studied “Idiazabal”, produced in Basque County of the milk of “latxa” sheep, which is spread on this area. The principal and more common adulteration is caused by the mixture of sheep's milk with cow's milk, with the corresponding lost of quality. In this way, TG has been applied to several samples of probable fraudulent milk. TG and DTG curves and their respective standards bring a qualitative index of the presence or absence of cow's milk. Seeing the results, it can be deduced that the method is valid and quick for the qualitative analysis of milk mixtures.     

Screening of adulteration in packaging biocomposites by infrared spectroscopy and chemometrics

Fourier transform infrared spectroscopy coupled with chemometrics was employed to detect packaging polylactic acid-based biocomposite samples adulterated with polypropylene (PP) 30–45% and linear low-density polyethylene 2–10%. Principal component analysis, soft independent modeling of class analogy (SIMCA) and partial least square discriminate analysis (PLS-DA) chemometric techniques were utilized to classify samples in different classes. Totally, 362 samples were modeled in three different classes (two adulterated and one non-adulterated). The obtained results revealed that PLS-DA is the most suitable chemometric approach for prediction of probable adulteration in biocomposite samples with reliable specificity and selectivity. It could provide 99% correct class prediction rate between non-adulterated biocomposite samples and adulterated ones, while SIMCA methods provided 73.33% prediction accuracy in classification.     

Illuminating the flesh of bone identification – An application of near infrared spectroscopy

Near infrared (NIR) reflectance spectroscopy coupled with chemometric analysis was evaluated as a non-destructive tool to discriminate skull bone samples from different animal species. In total 70 skull bones from animals of three classes (mammalians, avian and reptiles) were scanned in the wavelength range between 950 to 1650 nm. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyse the NIR spectra of the skull samples. Correct classification rates of 96% and 81% were obtained for the classification of skull bone samples according to avian and mammalian classes, respectively. Overall, a 91% correct classification rate was obtained for the classification of skull samples according to the class (mammalian and avian). This study demonstrates the potential of NIR spectroscopy coupled with chemometric as data processing, as a means of a rapid, non-destructive classification technique for skull bone samples.     

近红外光谱结合CARS变量筛选方法用于液态奶中蛋白质与脂肪含量的测定

采用CARS(Competitive adaptive reweighted sampling)变量筛选方法建模,显著提高了液态奶中蛋白质与脂肪近红外模型的预测精度。用蒙特卡罗采样(Monte-Carlo sampling)方法先剔除奇异样本,再对光谱进行中心化与Karl Norris滤波降噪处理,通过CARS方法筛选出与样本性质密切相关的变量,建立预测蛋白质与脂肪含量的偏最小二乘法(PLS)校正模型,并与未选变量的PLS模型进行比较。以定标集相关系数(r2)及交互验证均方残差(RMSECV)和预测误差均方根(RMSEP)作为判定依据,确定了蛋白质与脂肪的最佳建模条件。蛋白质与脂肪校正模型的相关系数分别为0.975 0、0.995 1,RMSECV分别为0.194 8、0.136 3,RMSEP分别为0.113 3、0.140 1,预测结果优于未选变量的PLS模型及其他选变量方法,有效简化了模型,适于液态奶中脂肪和蛋白质的快速、无损检测。     

High-sensitivity chromatographic detector incorporating three-dimensional charge coupled device fluorimetry

Food authentication has been evolving continually to situations that were basically governed by a global market trend. Analytical techniques have been developed or modified to give plausible solutions to the devious adulterations at each moment. Classical tests have largely been replaced with newer technical procedures, most of which are based on gas chromatography, with some being based on high-performance liquid chromatography. Determination of trans-fatty acid and sterolic composition, together with sterol-dehydration products, have been used most frequently used to detect contamination and adulteration. Sophisticated new adulterations, e.g., olive oil with hazelnut oil, represent a new challenge for the next millennium, although suggestive proposals for detecting these kinds of adulterations are emerging with the contribution of databases and mathematical algorithms.     

Capillary electrophoresis-mass spectrometry - a fast and reliable tool for the monitoring of milk adulteration

The development of a rapid, simple and accurate analytical method aimed at the detection and quantification of bovine milk in either ovine or caprine milk samples by means of CE-MS analyses of whey proteins with high-ionic strength and presence of acidic running buffer is described. The high-ionic strength buffer was used in order to minimize the problems with the adsorption of the proteins onto the fused-silica capillary wall. The acidic running electrolyte, pH 1.9, was used to support the production of positive ions in electrospray. Highly linear dependences of the ratio of the sum of non-bovine beta-lactoglobulins (ovine or caprine) to the total beta-lactoglobulins in milk mixture (bovine plus ovine or bovine plus caprine) vs. the volume percentage of added bovine milk in ovine (or caprine) milk were obtained. This technique allowed the fast and reliable evaluation of milk adulteration. The amount of bovine milk added into the "non-bovine" ones can be well within the concentration range of 5-95%.     

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular—for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76 ± 0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm—partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)—are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis.     

A novel strategy to verification of adulteration in alcoholic beverages based on Schlieren effect measurements and chemometric techniques

A novel strategy to evaluation of adulteration in alcoholic beverages based on the measurement of the Schlieren effect using an automated FIA system with photometric detection is proposed. The assay is based on the Schlieren effect produced when beverage samples are injected in a single-line FIA system that uses water as carrier stream and a light-emitting diode-phototransistor photometer controlled by microcomputer as detector. The flow system presents limited mixing conditions which make possible to create gradients of refractive index (Schlieren effect) in the injected sample zone. These gradients are reproducible, characteristic of each alcoholic beverage and undergo specific modifications when adulterations with water or ethanol are imposed. Schlieren effect data of brandies, cachaças, rums, whiskies and vodkas were treated by SIMCA to elaborate class models applied in the evaluation of alcoholic beverages adulteration. Samples of the original matrix of each sort of beverages were adulterated in laboratory by adding water, methanol and ethanol in levels of 5% and 10% (v/v). These samples were used as test set to validate SIMCA class models. The verification of authenticity using Schlieren effect measurements presented good results making possible to identify 100% of the beverages samples adulterated in laboratory and 93% of the actual adulterated alcoholic beverages with confidence levels of 95%. As principal advantage, the automated system does not use reagents to carry out the analysis.     

The use of multivariate modelling of near infra-red spectra to predict the butter fat content of spreads

In order to obtain a rapid method that can detect adulteration of butter fats with cheaper vegetable fats, the use of NIR spectroscopy and multivariate modelling was explored. For model building and validation, an extensive set of samples was collected, consisting of 152 butter samples, 42 oils and 200 blends thereof. Variations in butter fat composition are reflected in distinct NIR spectral regions. Principal components analysis and partial least square discriminant analysis was used to inspect the variation within the sample set. As reference values for training partial least squares models, butter fat levels as declared by suppliers were taken, as well as C4:0 fatty acid levels as measured directly by GC. All samples were used for training, except for 100 blends, which were used later for validation. Different pre-processing and PLS approaches were explored, resulting in models that had a RMSEPs for butter fat and C4:0 fatty acid level in the range of 4.3-8.2 and 0.33-0.38% (w/w), respectively. The performance of NIR in assessment of C4:0 fatty acid levels is lower as for GC, but this disadvantage is outweighed by shorter measurement times and the lower skill levels required. Furthermore NIR is able to assess overall levels of butter fat, in addition to the indirect indicator provided by the C4:0 fatty acid level.     

Near-Infrared Chemometric Approach to Exhaustive Analysis of Rice Straw Pretreated for Bioethanol Conversion

We report a simple analytical procedure combining near-infrared (NIR) spectroscopy with multivariate analysis to detect the saccharification efficiency of pretreated rice straw. Three types of sample preparation methods were tested to develop a powerful calibration model, with the disk sample used as the standard protocol. From the spectra dataset of NaOH-treated biomass, we obtained a good calibration for the saccharification ratio and some major structural components by partial least-squares regression. Adding dataset from hot water and dilute sulfuric acid pretreatments to NaOH sample dataset, an acceptable calibration model to predict the saccharification ratio as well as the glucose, xylose, and lignin contents was generated. NIR has a great potential for rapid screening of saccharification efficiency of pretreated biomass, which would allows us to control the quality of processing toward better bioethanol production.     

The application of NMR and MS methods for detection of adulteration of wine,fruit juices,and olive oil. A review

This review covers two important techniques, high resolution nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), used to characterize food products and detect possible adulteration of wine, fruit juices, and olive oil, all important products of the Mediterranean Basin. Emphasis is placed on the complementary use of SNIF-NMR (site-specific natural isotopic fractionation nuclear magnetic resonance) and IRMS (isotope-ratio mass spectrometry) in association with chemometric methods for detecting the adulteration.