Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.     

基于多光谱特征融合技术的面粉掺杂定量分析方法

提出了一种基于拉曼光谱技术(Raman)和激光诱导击穿光谱技术(LIBS)的多光谱特征融合技术(MFFT),利用拉曼光谱中分子组分信息和激光诱导击穿光谱中原子组分信息之间的互补特性,采用自适应小波变换(AWT)-竞争性自适应加权(CARS)-偏最小二乘回归(PLS)建模技术,获取了面粉体系更为全面的特征信息。在多光谱特征融合技术中,首先采用AWT-CARS方法分别提取拉曼光谱和激光诱导击穿光谱中的特征变量,然后将两者的特征变量融合为一个向量,采用PLS方法构建MFFT模型,实现了面粉掺杂物的定量分析。通过对二氧化钛、硫酸铝钾等面粉掺杂体系建模分析,考察MFFT模型的有效性。结果表明,与单一拉曼光谱技术或激光诱导击穿光谱技术建立的预测模型相比,MFFT模型显著提升了模型的预测性能,二氧化钛和硫酸铝钾预测模型的线性相关系数分别从相对较差的Raman模型的0.884、0.877提升到0.981、0.980,其预测均方根误差分别从相对较差的Raman模型的0.151、0.154降低到0.069、0.068。表明多光谱特征融合技术可以准确提取Raman光谱中的分子信息和LIBS光谱中的元素信息,使其互为补充、互为校正,进而有效克服面粉基质对掺杂组分定量分析的干扰,显著提高模型的预测精度。     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

Application of Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics for authentication of cod-liver oil

Some vegetable oils such as canola (CaO), corn (CO), soybean (SO), and walnut (WO) oils have similar color with cod liver oil (CLO), therefore, the presence of these oils was difficult to detect using naked eye. For this reason, Fourier transform infrared (FTIR) spectroscopy using horizontal attenuated total reflectance (HATR) as sampling accessory and in the combination with chemometrics was developed for detection and quantification of these vegetable oils as adulterants in CLO. The quantification of vegetable oils was carried out by using multivariate calibrations of partial least squares (PLS) and principle component regression (PCR), while the classification between pure CLO and CLOs adulterated with CaO, CO, SO, and WO was performed using discriminant analysis (DA). PLS with FTIR normal spectra was more suitable compared with PCR for quantification purposes with coefficient of determination (R²) higher than 0.99 and root mean square error of calibration (RMSEC) in the range of 0.04-0.82% (v/v). The PLS model was further used to predict the levels of these vegetable oils in independent samples for validation/prediction purpose. The root mean square error of prediction (RMSEP) values obtained were of 1.75% (v/v) (CaO), 1.39% (v/v) (CO), 1.35% (v/v) (SO), and 1.37% (v/v) (WO), respectively. The classification using DA revealed that the developed method can classify CLO and that mixed with these vegetable oils using 9 principal components.     

Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.     

Chemometric Techniques Applied for Classification and Quantification of Binary Biodiesel/Diesel Blends

In this paper, three different types of biodiesel, which were synthesized from peanut, corn, and canola oils, were characterized by positive-ion electrospray ionization (ESI) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Different biodiesel/diesel blends containing 2–90% (V/V) of each biodiesel type were prepared and analyzed by near infrared spectroscopy (NIR). In the next step, the chemometric methods of hierarchical clusters analysis (HCA), principal component analysis (PCA), and support vector machines (SVM) were used for exploratory analysis of the different biodiesel samples, and the SVM was able to give the best classification results (correct classification of 50 peanut and 50 corn samples, and only one misclassification out of 49 canola samples). Then, partial least squares (PLS) and multivariate adaptive regression splines (MARS) models were evaluated for biodiesel quantification. Both methods were considered equivalent for quantification purposes based on the values smaller than 5% for the root mean square error of calibration (RMSEC) and root mean square of validation (RMSEP), as well as Pearson correlation coefficients of at least 0.969. The combination of NIR to the chemometric techniques of SVM and PLS/MARS was proven to be appropriate to classify and quantify biodiesel from different origins.     

FTIR spectroscopy coupled with multivariate classification methods to identify different powdered infant formulas adulterated with melamine and cyanuric acid

Fourier transform infrared spectroscopy (FTIR) is a nondestructive, simple, rapid, and cheap measurement technique for analysis of many multicomponent chemical systems, e.g., detection of adulterants in food samples. In this respect, this study proposes combining FTIR spectroscopy with multivariate classification methods for classification and discrimination of different samples of infant formulas adulterated by melamine or/and cyanuric acid. Different parametric and non-parametric multivariate classification methods including the linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and classification and regression tree (CART) approaches were used to classify the recorded FTIR data. Assessing the performance of the multivariate methods according to their sensitivity, specificity and percent of correct prediction results demonstrated that coupling FTIR spectroscopy with multivariate classification can be applied as a rapid and powerful technique to the simultaneous detection of melamine and cyanuric acid in powdered infant formulas. This combinatorial method is efficient for adulterant concentrations as low as 0.0001 w/w%.     

Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO₃. The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Néel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other.     

Determination of residual oil in diesel oil by spectrofluorimetric and chemometric analysis

Multivariate calibration (PLS), principal components analysis (PCA) and linear discriminant analysis (LDA), associated to synchronous spectrofluorimetry, were used to identify and quantify non-transesterified residual vegetable oil in diesel oil with the addition of 2% of biodiesel (B2). The addition of residual oil, one of the easiest ways of adultering fuel, damages engines and leads to tax evasion. Using this method, the samples of diesel oil, B2, and B2 contaminated with residual oil were classified correctly and separated into three well-defined groups. The quantification of residual oil in B2 was carried out in the 0-25% (w/w) band, RMSEC and RMSEP values ranging from 0.26 to 0.48% (w/w) and 1.6-2.6% (w/w), respectively. The method is highly sensitive and efficient to identify and quantify this type of adulterant in which 100% of the samples were correctly classified and the average relative error was approximately 4% in the range 0.5-25% (w/w).     

拉曼光谱结合背景扣除化学计量学方法用于汽油中MTBE含量的快速测定研究

应用便携式拉曼光谱仪测量了汽油样本的拉曼光谱,以自适应迭代惩罚最小二乘方法(airPLS)对光谱进行了背景扣除和平滑处理,并选取特征峰区间利用偏最小二乘方法(PLS)建立了预测甲基叔丁基醚(MT-BE)的校正模型。以训练集相关系数和拟合误差及测试集相关系数和预测误差作为判定依据,确定了最佳建模条件。最终训练集相关系数为0.996 0,拟合误差为0.316 1,测试集相关系数为0.996 6,预测误差为0.490 1。结果表明采用便携式拉曼光谱结合化学计量学方法处理,可以满足对汽油中MTBE含量快速检测的要求。     

Estimation of the composition of heparin mixtures from various origins using proton nuclear magnetic resonance and multivariate calibration methods

A multivariate calibration method for the characterization of heparin samples based on the analysis of (1)H nuclear magnetic resonance (NMR) spectral data is proposed. Heparin samples under study consisted of two-component or four-component mixtures of heparins from porcine, ovine and bovine mucosae and bovine lung. Although the (1)H NMR spectra of all heparin types were highly overlapping, each origin showed some particular features that could be advantageously used for the quantification of the components. These features mainly concerned the anomeric H, which appeared in the range 5.0-5.7 ppm and the peaks of acetamidomethyl protons at 2.0-2.1 ppm. The determination of the percentage of each heparin class depended on these differences and was carried out using partial least squares regression (PLS) as a calibration method. Prior to the PLS analysis, the spectral data were standardized using the internal standard peak (sodium 4,4-dimethyl-4-silapentanoate- 2,2,3,3- d (4), TSP) as the reference. The quantification of each heparin type in the samples using PLS models built with 4 or 5 components was satisfactory, with an overall prediction error ranging from 3% to 10%.     

Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra‐high performance liquid chromatography fingerprints and chemical pattern recognition

In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra‐high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS‐DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter‐propagation artificial neural network (CP‐ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS‐DA, SVMDA and CP‐ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS‐DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.     

Quick monitoring of coloring agents in highly consumed candies using multivariate calibration

Multivariate calibration are gaining popularity in assaying food matrices. Partial least squares is a powerful multivariate calibration method that used to build a quantitative relationship between measured variables and a property of interest (i.e., concentration) of the system under study. Partial least squares PLS calibration along with UV/vis spectral data was efficient to account for indirect food matrix and direct interference effects resulted from overlapping food dyes. PLS was able to quantify tartrazine TAT, allura red AR, sunset yellow SY and brilliant black BB that added to wide selection sugar-based candies. The results indicated that 70% of samples containing single dye while 8% containing TAT-SY mix and certain samples containing TAT + SY + AR + BB. Lollypops were found to contain high levels of AR (77–120 mg/kg) and TAT (56–166 mg/kg). The maximum adulteration was 50% observed in lollypops. PLS calibration was workable to predict colorants with prediction errors of 7%. Using PLS, dyes were detected down to 0.1 mg/L with acceptable accuracy and precision. PLS showed comparable performance with liquid chromatography for dyes quantification and can substitute laborious chromatography for quick detection of coloring agents in candies.     

基于拉曼光谱成像的食品中化学添加剂的无损检测

基于拉曼光谱成像技术对小麦粉中过氧化苯甲酰和L-抗坏血酸进行快速、 无损、 原位检测, 并对2种添加剂的空间分布进行了可视化研究. 采用实验室自行搭建的线扫描式拉曼光谱成像系统, 激发光源波长为785 nm, 有效光谱范围为0~2885.7 cm^-1. 分别在小麦粉中添加含量为0.1%~30%的过氧化苯甲酰和L-抗坏血酸, 对制备的样品进行拉曼光谱扫描, 选取感兴趣区域的光谱信号进行平均, 得到平均光谱代表该样品的拉曼信息. 分别选取过氧化苯甲酰和L-抗坏血酸的2个特征峰, 与该物质在小麦粉中的含量建立线性关系, 其决定系数R²分别为0.9828 和0.9912. 采集的特征波段拉曼图像经过自适应迭代重加权惩罚最小二乘(airPLS)方法扣除荧光背景后, 选取合适的特征峰强度作为阈值, 对校正拉曼图像进行二值化分析, 得到添加物的空间分布可视化图像. 该方法与点检测拉曼技术相比, 具有检测结果准确且检测时间较短的优势, 且可以实现不均匀样品中多种物质的同时检测与分布可视化.     

Sequential automated fusion/extraction chromatography methodology for the dissolution of uranium in environmental samples for mass spectrometric determination

The ability to predict the amount of time that a light petroleum mixture has been weathered could have many applications, such as aiding forensic investigators in determining the cause and intent of a fire. In our study, an evaporation chamber that permits control of airflow and temperature was constructed and used to weather a model nine-component hydrocarbon mixture. The composition of the mixture was monitored over time by gas chromatography and a variety of chemometric models were explored, including partial least squares (PLS), nonlinear PLS (PolyPLS) and locally weighted regression (LWR or loess). A hierarchical application of multivariate techniques was able to predict the time for which a sample had been exposed to evaporative weathering. A classification model based on partial least squares discriminant analysis (PLS-DA) could predict whether a sample was relatively fresh (< 12 h exposure time) or highly weathered (>20 h exposure time). Subsequent regression models for these individual classes were evaluated for accuracy using the root mean square error of prediction (RMSEP). Prior to regression model calculation, y-gradient generalized least squares weighting (GLSW) was used to preprocess the data by removing variance from the X-block, which was orthogonal to the Y-block. LWR was found to be the most successful regression method, whereby fresh samples could be predicted to within 40 min of exposure and highly weathered samples predicted to within 5.6h. These results suggest that our hierarchical chemometric approach may also allow us to estimate the age of more complicated light petroleum mixtures, such as gasoline.     

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).     

Application of FT-Raman Spectroscopy and Chemometric Analysis for Determination of Adulteration in Milk Powder

In this work, FT-Raman spectroscopy is explored as a rapid technique for the assessment of the milk powder quality. Based on information provided by Raman spectra of samples adulterated with starch and whey, a quantitative method is developed to identify the fraud, using Partial Least Squares regression (PLS). In regression models using PLS the results are satisfactory, and such models can be used to identify and quantify samples presenting whey and starch in milk powder at concentrations of 2.32% and 1.64% (w/w), respectively. In the whey determination, the obtained values in the PLS model of the new samples are compared with those obtained by the spectrophotometric method of acid ninhydrin. This result shows that there is no significant difference with the 95% level of confidence between the values provided by the PLS regression method and the acid ninhydrin. The present work shows Raman spectroscopy as an analytical tool which can be used in quality control of milk powder, even in fraud processes, and the calculated figures of merit such as sensitivity, accuracy, limit of detection and limit of quantification clearly demonstrate this potential use. Although the multivariate models developed are not strictly quantitative, especially for low concentrations, they can be used as screening methods for routine analysis, as showed by this work.     

Simultaneous Anodic Stripping Voltammetric Determination of Pb and Cd,Using a Vibrating Gold Microwire Electrode,Assisted by Chemometric Techniques

Simultaneous anodic stripping voltammetric determination of Pb and Cd is restricted on gold electrodes as a result of the overlapping of these two peaks. This work describes the quantitative determination of a binary mixture system of Pb and Cd, at low concentration levels (up to 15.0 and 10.0 µg L^?1 for Pb and Cd, respectively) by differential pulse anodic stripping voltammetry (DPASV; deposition time of 30 s), using a green electrode (vibrating gold microwire electrode) without purging in a chloride medium (0.5 M NaCl) under moderate acidic conditions (HCl 1.0 mM), assisted by chemometric tools. The application of multivariate curve resolution alternating least squares (MCR‐ALS) for the resolution and quantification of both metals is shown. The optimized MCR‐ALS models showed good prediction ability with concentration prediction errors of 12.4 and 11.4 % for Pb and Cd, respectively. The quantitative results obtained by MCR‐ALS were compared to those obtained with partial least squares (PLS) and classical least squares (CLS) regression methods. For both metals, PLS and MCR‐ALS results are comparable and superior to CLS. For Cd, as a result of the peak shift problem, the application of CLS was unsuitable. MCR‐ALS provides additional advantage compared to PLS since it estimates the pure response of the analytes signal. Finally, the built up multivariate calibration models, based either in MCR‐ALS or PLS regression, allowed to quantify concentrations of Pb and Cd in surface river water samples, with satisfactory results.     

Detection of counterfeit Scotch whisky samples using mid-infrared spectrometry with an attenuated total reflectance probe incorporating polycrystalline silver halide fibres

Two methods of analysis were developed to permit detection of counterfeit Scotch whisky samples using a novel attenuated total reflectance (ATR) diamond-tipped immersion probe for mid-infrared (MIR) spectrometry. The first method allowed determination of the ethanol concentration (35–45% (v/v)) in situ without dilution of the samples; the results obtained compared well with the supplied concentrations (average relative error of 1.2% and 0.8% for univariate and multivariate partial least squares (PLS) calibration, respectively). The second method involved analysis of dried residues of the whisky samples and caramel solutions on the diamond ATR crystal; principal component analysis (PCA) of the spectra was used to classify the samples and investigate the colorant added. Seventeen test whisky samples were successfully categorised as either authentic or counterfeit in a blind study when both MIR methods were used.     

Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy: effect of gasoline composition

Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.