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.     

Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification

This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0 mm (8814-3799 cm⁻¹), 10 mm (11,329-5944 cm⁻¹ and 5531-4490 cm⁻¹) and 20 mm (11,688-5952 cm⁻¹ and 5381-4679 cm⁻¹). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20 mm. Otherwise, PLS-DA shows the best results for classification of 10 mm cell data, which achieved a correct prediction rate of 100% in the test set.     

Classification of edible vegetable oils using square wave voltammetry with multivariate data analysis

This paper proposes a simple and non-expensive electroanalytical methodology for classification of edible vegetable oils with respect to type (canola, sunflower, corn and soybean) and conservation state (expired and non-expired shelf life). The proposed methodology employs an alcoholic extraction procedure followed by square wave voltammetry (SWV). Two chemometric methods were compared for classification of the resulting voltammograms, namely Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) with variable selection by the Successive Projections Algorithm (SPA). The results were evaluated in terms of errors in a set of samples not included in the modelling process. The best results were obtained with the SPA-LDA method, which correctly classified all samples in terms of type and conservation state.     

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance (¹H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the ¹H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices.     

Determination of lecithin and soybean oil in dietary supplements using partial least squares-Fourier transform infrared spectroscopy

Lecithin and soybean oil in dietary supplements were determined by Fourier transform infrared spectrometry transmission measurements in dichloromethane in combination with a partial least squares (PLS) regression. Two different PLS models were developed, using 16 synthetic mixtures of analytes in dichloromethane, making measurements in the spectral range from 931.8 to 1252.3 cm⁻¹ for lecithin and from 911.4 to 1246.9 cm⁻¹ and 1695.3 to 1774.5 cm⁻¹ for soybean oil. Seven products from the Spanish market with lecithin concentrations between 21.1% and 99.1% and soybean oil concentrations between 0% and 37.2% were analyzed by the proposed method and the data was compared to a chromatographic reference procedure obtaining accurate results. For samples spiked with amounts between 50 and 250 mg of lecithin and soybean oil recovery percentages between 98.0% and 102.1% and between 93.6% and 102.0% with an average precision of 0.35% and 0.41% were achieved for lecithin and soybean oil, respectively. This method can be applied for the quality control of dietary supplements.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

A colorimetric sensor array for detection and discrimination of biothiols based on aggregation of gold nanoparticles

Developments of sensitive, rapid, and cheap systems for identification of a wide range of biomolecules have been recognized as a critical need in the biology field. Here, we introduce a simple colorimetric sensor array for detection of biological thiols, based on aggregation of three types of surface engineered gold nanoparticles (AuNPs). The low-molecular-weight biological thiols show high affinity to the surface of AuNPs; this causes replacement of AuNPs’ shells with thiol containing target molecules leading to the aggregation of the AuNPs through intermolecular electrostatic interaction or hydrogen-bonding. As a result of the predetermined aggregation, color and UV–vis spectra of AuNPs are changed. We employed the digital mapping approach to analyze the spectral variations with statistical and chemometric methods, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). The proposed array could successfully differentiate biological molecules (e.g., cysteine, glutathione and glutathione disulfide) from other potential interferences such as amino acids in the concentration range of 10–800 μmol L⁻¹.     

Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data

The use of biofuels, such as bioethanol or biodiesel, has rapidly increased in the last few years. Near infrared (near-IR, NIR, or NIRS) spectroscopy (>4000 cm⁻¹) has previously been reported as a cheap and fast alternative for biodiesel quality control when compared with infrared, Raman, or nuclear magnetic resonance (NMR) methods; in addition, NIR can easily be done in real time (on-line). In this proof-of-principle paper, we attempt to find a correlation between the near infrared spectrum of a biodiesel sample and its base stock. This correlation is used to classify fuel samples into 10 groups according to their origin (vegetable oil): sunflower, coconut, palm, soy/soya, cottonseed, castor, Jatropha, etc. Principal component analysis (PCA) is used for outlier detection and dimensionality reduction of the NIR spectral data. Four different multivariate data analysis techniques are used to solve the classification problem, including regularized discriminant analysis (RDA), partial least squares method/projection on latent structures (PLS-DA), K-nearest neighbors (KNN) technique, and support vector machines (SVMs). Classifying biodiesel by feedstock (base stock) type can be successfully solved with modern machine learning techniques and NIR spectroscopy data. KNN and SVM methods were found to be highly effective for biodiesel classification by feedstock oil type. A classification error (E) of less than 5% can be reached using an SVM-based approach. If computational time is an important consideration, the KNN technique (E = 6.2%) can be recommended for practical (industrial) implementation. Comparison with gasoline and motor oil data shows the relative simplicity of this methodology for biodiesel classification.     

Determination of biogenic amines in apples and wine with 8-phenyl-(4-oxy-acetic acid N-hydroxysuccinimide ester)-4, 4-difluoro-1,3,5,7-tetramethyl-4-bora-3a,4a-diaza-s-indacene by high performance liquid chromatography

A solvent free, fast and environmentally friendly photoacoustic-infrared-based methodology (PAS-FTIR) was developed for the determination of Mancozeb in agrochemicals. This methodology was based on the direct measurement of the transmittance spectra of solid samples and a multivariate calibration model to determine the active ingredient concentration. The proposed partial least squares (PLS) model was made using nine standards prepared by mixing different amounts of kaolin and Mancozeb, with concentrations between 5.43 and 88.10% (w/w).A hierarchical cluster analysis was made in order to classify the samples in terms of similarity in the PAS-FTIR spectra. From their spectra different commercially available fungicide samples were classified in four groups, attending to the presence of other active ingredients co-formulated with Mancozeb. Different PLS models were applied for the analysis of each group of samples.So, for samples containing copper oxychloride (group 1), the information in the spectral range from 1543 to 1474 and 1390 to 1269 cm⁻¹ was employed. For samples co-formulated with Fosetyl-Al (group 2) the range between 3334 and 3211 cm⁻¹, corrected with a single point baseline located at 3055 cm⁻¹, was used. For samples containing Metalaxyl (group 3) it was used the information in the spectral range from 1543 to 1474 cm⁻¹ was used to determine Mancozeb. Finally, the range between 1456 and 1306 cm⁻¹ was used for Mancozeb determination in samples containing Cymoxanil (group 4).The PLS factors used for Mancozeb determination depends on the PLS model employed. 3, 2, 2 and 3 factors were used for Mancozeb determination in commercially available pesticides for groups 1, 2, 3 and 4, respectively. The mean accuracy errors found were 3.1, 2.1, 2.5 and 3.0% for groups 1, 2, 3 and 4, respectively. The developed PAS-FTIR methodology does not consume any solvent, as no sample preparation is necessary it improves the laboratory efficiency without sacrifice the accuracy and avoids the contact of the operator with toxic substances.     

Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions

This study compares results obtained with several chemometric methods: SIMCA, PLS2-DA, PLS2-DA with SIMCA, and PLS1-DA in two infrared spectroscopic applications. The results were optimized by selecting spectral ranges containing discriminant information. In the first application, mid-infrared spectra of crude petroleum oils were classified according to their geographical origins. In the second application, near-infrared spectra of French virgin olive oils were classified in five registered designations of origins (RDOs). The PLS-DA discrimination was better than SIMCA in classification performance for both applications. In both cases, the PLS1-DA classifications give 100% good results. The encountered difficulties with SIMCA analyses were explained by the criteria of spectral variance. As a matter of fact, when the ratio between inter-spectral variance and intra-spectral variance was close to the F_c (Fisher criterion) threshold, SIMCA analysis gave poor results. The discrimination power of the variable range selection procedure was estimated from the number of correctly classified samples.     

Classification of the Biopolymer Sodium Pentosan Polysulfate by Infrared Spectroscopy

Summary Sodium pentosan polysulfate (NaPPS) is a glycosaminoglycan that is of increasing interest due to its medical properties. It has been investigated for the treatment of osteoarthritis, HIV and Prion based diseases. This work describes an investigation into the application of infrared spectroscopy (IR) for the differentiation between sources of NaPPS. Multivariate techniques such as principle components analysis were applied to detect differences between the IR and near IR (NIR) spectra and to classify the biopolymers based on their manufacturer. This study compared two samples of NaPPS from different manufacturers. Principle components analysis (PCA) together with soft independent modeling of class analogies (SIMCA) was used to successfully classify the different samples. Clear differentiation between all batches was achieved using PCA and class distances using first derivative spectra (500–1800 cm^–1).Presented at: International Symposium on Separation and Characterization of Natural and Synthetic Macromolecules, Amsterdam, The Netherlands, February 5–7, 2003     

A simple method for water discrimination based on an light emitting diode (LED) photometer

This work describes the use of a multi-LED photometer for discrimination of mineral water samples, employing chromogenic reagents and chemometric techniques. Forty-five water samples (including 7 different brands of mineral water and samples of deionised, distilled and tap waters) were analysed in a monosegmented flow system, using three different chromogenic reagents (murexide, PAR and eriochrome black T) in a pH 10.0 NH₃/NH₄⁺ buffer in separate injections. Measurements were performed at 470, 500, 525, 562, 590, 612, 636 and 654 nm. Analyses were carried out using PCA, employing data sets including absorbance values obtained with one, two or all three reagents, which comprise 8, 16 or 24 variables, respectively. The best result was obtained with the data set from murexide and eriochrome black T, providing a clear distinction between 9 groups (distilled and deionised waters were classified in the same group). Based on the loading values, it was possible to select four wavelengths (470, 500, 590 and 654 nm) that provided a similar discrimination. With the use of these four LED, an HCA was performed, providing discrimination between 8 groups at a similarity level of 0.88. A model based on SIMCA allowed correctly classifying 94% of the samples. The discrimination between different groups is due to the metal ion contents in the water samples, mainly calcium and magnesium. Therefore, the use of common complexing reagents, such as murexide and erichrome black T, a multi-LED photometer and chemometric techniques provide an easy and simple method for water discrimination.     

Application of UV–Vis,near-infrared and mid-infrared spectroscopy to the study of Mn-bearing humites

The combination of electronic and vibrational spectra has been applied to correlate the spectral properties, with composition, structure and cation substitutions such as Mn, Fe, Ca and Zn for Mg in humites: norbergite, alleghanyite, leucophoenicite and sonolite with increasing number of silicate layers, 1, 2, 3 and 4. The observation of two broad bands in the visible range, near 550 and 450 nm (18 180 and 22 220 cm⁻¹) and one sharp band around 410 nm (24 390 cm⁻¹) is characteristic of Mn²⁺ in alleghanyite and leucophoenicite. The study of UV–Vis (electronic) spectral features confirms Mn as a major substituent in these two samples. Cation impurities like Zn and Ca as revealed from EDX analysis might be the cause for the absence of Mn-type spectrum in sonolite. The first observation is the near-infrared spectra of all four minerals in the first fundamental overtone OH-stretching mode are different and each mineral is characterized by its NIR spectrum. The feature in the range 7180–6600 cm⁻¹ [1393–1515 nm or 1.39–1.52 μm] corresponds to the overtones of OH stretching vibrational modes of the humite groups observed in their IR spectra over the range, 3680–3320 cm⁻¹. The infrared spectra of the hydrous components of OH and SiO₄ groups in the mineral structure act as an aid to distinguish the minerals of the humite mineral group. A band at 541 cm⁻¹ is assigned to MnO stretching mode.     

Laser Ablation Molecular Isotopic Spectrometry: Parameter influence on boron isotope measurements

Laser Ablation Molecular Isotopic Spectrometry (LAMIS) was recently reported for optical isotopic analysis of condensed samples in ambient air and at ambient pressure. LAMIS utilizes molecular emissions which exhibit larger isotopic spectral shits than in atomic transitions. For boron monoxide ¹⁰BO and ¹¹BO, the isotopic shifts extend from 114 cm⁻¹ (0.74 nm) to 145–238 cm⁻¹ (5–8 nm) at the B²Σ⁺ (v = 0) → X²Σ⁺ (v = 2) and A²Π_i (v = 0) → X²Σ⁺ (v = 3) transitions, respectively. These molecular isotopic shifts are over two orders of magnitude larger than the maximum isotopic shift of approximately 0.6 cm⁻¹ in atomic boron. This paper describes how boron isotope abundance can be quantitatively determined using LAMIS and how atomic, ionic, and molecular optical emission develops in a plasma emanating from laser ablation of solid samples with various boron isotopic composition. We demonstrate that requirements for spectral resolution of the measurement system can be significantly relaxed when the isotopic abundance ratio is determined using chemometric analysis of spectra. Sensitivity can be improved by using a second slightly delayed laser pulse arriving into an expanding plume created by the first ablation pulse.     

Multivariate monitoring of soybean oil ethanolysis by FTIR

In this work, an analytical procedure was developed to monitor the ethanolysis of degummed soybean oil (DSO) using Fourier-transformed mid-infrared spectroscopy (FTIR) and methods of multivariate analysis such as principal component analysis (PCA) and partial least squares regression (PLS). The triglycerides (reagents) and ethyl esters (products) involved in ethanolysis were shown to have similar FTIR spectra. However, when the FTIR spectra derived from seven standard mixtures of triolein and ethyl oleate were treated by PCA at the region that represents the CO stretching vibration of ester groups (1700-1800 cm⁻¹), only two principal components (PC) were shown to capture 99.95% of the total spectral variance (92.37% for the former and 7.58% for the latter PC). This observation supported the development of a multivariate calibration model that was based on the PLS regression of the FTIR data. The prevision capability of this model was measured against 40 reaction aliquots whose ester content was previously determined by size exclusion chromatography. Only small discrepancies were observed when the two experimental data sets were treated by linear regression (R²=0.9837) and these deviations were attributed to the occurrence of non-modeled transient species in the reaction mixture (reaction intermediates), particularly at short reaction times. Therefore, the FTIR/PLS model was shown to be a fast and accurate method to predict reaction yields and to follow the in situ kinetics of soybean oil ethanolysis.     

Comparison of near-infrared and Raman spectroscopy for the determination of the density of polyethylene pellets

In this study, we compare near-infrared (NIR) and Raman spectroscopy for the determination of the density of linear low density polyethylene (PE) (in a pellet form). As generally known, Raman spectral features are more selective than those of NIR for most chemical samples. NIR spectroscopy has been more extensively used for the quantitative analysis of polymers, but Raman spectroscopy is the better choice as long as the problem of reproducibility of Raman measurements (especially for solid samples), mostly resulting from insufficient sample representation due to probing only localized chemical information and the sensitivity of sample placement with regard to the focal plane, can be overcome. To improve sample representation and reproducibility of Raman measurements, we have employed the wide area illumination (WAI) Raman scheme, capable of illuminating a laser onto a large sample area (28.3 mm²) for Raman spectral collection (a 6-mm laser spot with a focal length of 248 mm). Diffuse reflectance NIR spectra of PE pellets were collected using a sample moving system which allowed for the scanning of large areas. The prediction error was 0.0008 g cm⁻³ for Raman spectroscopy and 0.0011 g cm⁻³ for NIR spectroscopy. The harmonization of inherently selective Raman features and a reproducible spectral collection with correct sample representations using the WAI scheme led to an accurate determination of the density of the PE pellets.     

Discriminating olive and non-olive oils using HPLC-CAD and chemometrics

This work presents a method for an efficient differentiation of olive oil and several types of vegetable oils using chemometric tools. Triacylglycerides (TAGs) profiles of 126 samples of different categories and varieties of olive oils, and types of edible oils, including corn, sunflower, peanut, soybean, rapeseed, canola, seed, sesame, grape seed, and some mixed oils, have been analyzed. High-performance liquid chromatography coupled to a charged aerosol detector was used to characterize TAGs. The complete chromatograms were evaluated by PCA, PLS-DA, and MCR in combination with suitable preprocessing. The chromatographic data show two clusters; one for olive oil samples and another for the non-olive oils. Commercial oil blends are located between the groups, depending on the concentration of olive oil in the sample. As a result, a good classification among olive oils and non-olive oils and a chemical justification of such classification was achieved.     

Analysis of metals and phosphorus in biodiesel B100 from different feedstock using a Flow Blurring multinebulizer in inductively coupled plasma-optical emission spectrometry

A simple and fast method for determining the content of Na, K, Ca, Mg, P, and 20 heavy metals in biodiesel samples with inductively coupled plasma optical emission spectrometry (ICP OES) using a two-nozzle Flow Blurring^® multinebulizer prototype and on-line internal standard calibration, are proposed. The biodiesel samples were produced from different feedstock such as sunflower, corn, soybean and grape seed oils, via a base catalyst transesterification. The analysis was carried out without any sample pretreatment. The standards and samples were introduced through one of the multinebulizer nozzles, while the aqueous solution containing yttrium as an internal standard was introduced through the second nozzle. Thus, the spectral interferences were compensated and the formation of carbon deposits on the ICP torch was prevented. The determination coefficients (R²) were greater than 0.99 for the studied analytes, in the range 0.21–14.75 mg kg⁻¹. Short-term and long-term precisions were estimated as relative standard deviation. These were acceptable, their values being lower than 10%. The LOQ for major components such as Ca, K, Mg, Na, and P, were within a range between 4.9 ng g⁻¹ for Mg (279.553 nm) and 531.1 ng g⁻¹ for Na (588.995 nm), and for the other 20 minor components they were within a range between 1.1 ng g⁻¹ for Ba (455.403 nm) and 2913.9 ng g⁻¹ for Pb (220.353 nm). Recovery values ranged between 95% and 106%.     

FTIR investigation of spray-dried milk protein concentrate powders

Fourier transform infrared (FTIR) spectroscopy was used to examine the conformation of proteins in spray-dried milk protein concentrate (MPC) powders and to determine if the spectral changes could be related to nitrogen solubility of these powders. MPC samples (83–92% protein, dry basis) were prepared using a range of processing conditions and stored for 4 weeks at 21 °C. FTIR spectra were collected in the mid infrared (MIR) region between 4000 and 600 cm⁻¹. FTIR data was pre-processed to remove physical effects causing discrimination between samples using firstly second derivatives and normalization and secondly the extended multiplicative scatter correction (EMSC) technique. The FTIR spectral changes were subsequently assessed using second derivative spectroscopy and principal components analysis (PCA) in the amide I and II regions (1700–1400 cm⁻¹) and the fingerprint region (1800–700 cm⁻¹). PCA analysis showed that the different powder preparations could be separated on scores plots but the separation was not related to nitrogen solubility per se. However, changes in nitrogen solubility of individual MPC powders during storage could be correlated to changes in FTIR spectra. PCA analysis of FTIR spectra could generally discriminate between MPC powders that had lost significant nitrogen solubility (9–20%) and those in which nitrogen solubility was preserved on storage. There were changes in intensity and/or position of bands at 1630 cm⁻¹ when the solubility of a stored sample decreased substantially. The results of this work also show that EMSC data pre-processing for these samples gives comparable results when compared with more complicated data pre-processing for the removal of physical effects.     

Identification of catecholamine neurotransmitters using fluorescence sensor array

A nano-based sensor array has been developed for identification and discrimination of catecholamine neurotransmitters based on optical properties of their oxidation products under alkaline conditions. To produce distinct fluorescence response patterns for individual catecholamine, quenching of thioglycolic acid functionalized cadmium telluride (CdTe) quantum dots, by oxidation products, were employed along with the variation of fluorescence spectra of oxidation products. The spectral changes were analyzed with hierarchical cluster analysis (HCA) and principal component analysis (PCA) to identify catecholamine patterns. The proposed sensor could efficiently discriminate the individual catecholamine (i.e., dopamine, norepinephrine, and l-DOPA) and their mixtures in the concentration range of 0.25–30 μmol L⁻¹. Finally, we found that the sensor had capability to identify the various catecholamines in urine sample.