Prediction of the physicochemical properties of gasoline by comprehensive two-dimensional gas chromatography and multivariate data processing

The estimation of physicochemical parameters such as distillation points and relative densities still plays an important role in the quality control of gasoline and similar fuels. Their measurements according to standard ASTM procedures demands specific equipments and are time and work consuming. An alternative method to predict distillation points and relativity density by multivariate analysis of comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) data is presented here. Gasoline samples, previously tested according to standard methods, were used to build regression models, which were evaluated by external validation. The models for distillation points were built using variable selection methods, while the model for relativity density was built using the whole chromatograms. The root mean square prediction differences (RMSPD) obtained were 0.85%, 0.48%, 1.07% and 1.71% for 10, 50 and 90% v/v of distillation and for the final point of distillation, respectively. For relative density, the RMSPD was 0.24%. These results suggest that GC×GC-FID combined with multivariate analysis can be used to predict these physicochemical properties of gasoline.     

Study of the similarity between distribution maps of concentration in near-infrared spectroscopy chemical imaging obtained by different multivariate calibration approaches

Nowadays, near-infrared spectroscopy chemical imaging (NIR-CI) has been widely used in pharmaceutical analysis since it provides important surface information about the samples. In this work the information of NIR-CI at the pixel level was compared through calculation of the similarity between distribution maps of concentration obtained by different multivariate calibration approaches. The comparison was performed by using four different multivariate methods (MCR, MLR, CLS and PLS) in analysis of carbamazepine pharmaceutical formulations. For global determination, all models developed showed RMSEP below 1.9% (w/w) for active principal ingredient (API) and better than 4.6% (w/w) for excipients. Also, the distribution maps obtained by PLS, CLS and MCR showed great similarity for all compounds of the formulation as well with concentrations in the tablets. However, comparing the distribution maps obtained by MLR with those from the other chemometric tools, a lower similarity was observed. Thus, this fitted model does not ensure, by itself, that the images obtained are reliable or accurate. The paper also compares the distribution maps of concentrations obtained from all constituents present in the pharmaceutical formulation with their respective micrographs.     

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.     

Evolving factor analysis-based method for correcting monitoring delay in different batch runs for use with PLS: On-line monitoring of a transesterification reaction by ATR-FTIR

In this work, the base-catalyzed transesterification of soybean oil with ethanol was monitored on-line using mid-infrared spectroscopy (MIRS) and the yield of fatty acid ethyl esters (biodiesel) was obtained by (1)H NMR spectroscopy. The MIRS monitoring carried out for 12min, was performed using a cylindrical internal reflectance cell of PbSe in the range of 3707-814cm(-1) with eight co-added scans. Two different data treatment strategies were used: in the first, the models were built using the raw data and in the other, evolving factor analysis (EFA) was used to overcome the sensor time delay due to the on-line analysis, producing significantly better results. In addition, models based on partial least squares (PLS) using three batches for calibration and another for validation were compared with models with just one batch for calibration and three for validation. The models were compared between each other using root mean square error of prediction (RMSEP) and root mean square prediction difference (RMSPD), obtaining relative errors under 3%.     

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new “leave one out” method, so that the number of original variables resulted further reduced.     

Determination of alcohol content in beverages using short-wave near-infrared spectroscopy and temperature correction by transfer calibration procedures

This paper reports the utilization of short-wave near-infrared (SW-NIR) transmission spectroscopy for rapid and conclusive analysis of alcoholic content (% v/v) in beverages. This spectral region is interesting because common visible diode array spectrometers can be utilized, reducing time and costs in comparison with traditional near-infrared or mid-infrared instruments. A correction of external temperature influence is necessary, and for this purposes two calibration transfer procedures were compared: piecewise direct standardization (PDS) and orthogonal signal correction (OSC). The RMSEP found for the alcoholic content model at 20 °C was 0.13% v/v and, after application of transfer calibration procedures at other temperatures (15, 25, 30 and 35 °C) using the model built at 20 °C, errors of the same order of magnitude were obtained.     

Evaluation of climatic factors on the yield and chemical composition of the essential oil of Myrocarpus frondosus

The species Myrocarpus frondosus is native to southern Brazil, and it is known as cabreúva. It is a large tree, and its wood is used as a reservoir for distilled spirits, especially Cachaça. In this pioneering work, the essential oil was extracted monthly from the leaves of M. frondosus, for a period of 1 year. The essential oil was extracted by using hydrodistillation and the determination of the chemical composition of essential oil was performed by using the chromatographic techniques GC-FID and GC–MS. Thirty-three compounds were identified in the essential oil and bicyclogermacrene was the major compound. Through multivariate analysis, it was determined that β-caryophyllene, limonene and terpinen-4-ol were directly proportional to climatic factors. The yield of the oil was directly proportional to temperature and radiation.     

Quantitative analysis by comprehensive two-dimensional gas chromatography using interval Multi-way Partial Least Squares calibration

A new approach for target quantitative analysis for comprehensive two-dimensional gas chromatography (GC × GC), interval Multi-way Partial Least Square (iNPLS) is presented and evaluated in this paper. In iNPLS, the two-dimensional chromatogram is split in small sections; each of these pieces is treated as an independent new chromatogram. Separated conventional NPLS calibration models for the concentration of the target analyte are built for each of the pieces of the whole chromatogram, and the best model is selected for quantitative analysis. An algorithm for iNPLS running on MatLab platform was written, preliminarily evaluated with using solutions of model compounds with different chemical properties and subsequently applied to quantify some allergens in perfume samples. The results were found to be adequate, and good precision and accuracy was obtained even for poorly resolved peaks.     

Standardization of SPE signals in multicomponent analysis of three benzimidazolic pesticides by spectrofluorimetry

Piecewise direct standardization (PDS) is applied to multivariate standardization of fluorescence signals using partial least squares (PLS) and principal component regression (PCR) as the calibration models. The multivariate standardization was used to transfer spectra obtained after a step of solid phase extraction (SPE) to spectra registered in pure solvent in the determination of carbendazim, fuberidazole and thiabendazole in water samples. The influential parameters, such as tolerance, window size and the number of samples of the standardization subset were optimized by means of the root mean squared error of prediction (RMSEP). Similar RMSEP values were obtained by PLS and PCR using the optimized influential parameters in the standardization. However, better predictions of the compounds were obtained in test set by the PLS model.     

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.     

Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: A review

This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC–MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC × GC-FID) and coupled to mass spectrometry (GC × GC–MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.     

Simultaneous determination of albumin and immunoglobulin G with fluorescence spectroscopy and multivariate calibration

A method is proposed for the simultaneous determination of albumin and immunoglobulin G (IgG1) with fluorescence spectroscopy and multivariate calibration with partial least squares regression (PLS). The influence of some instrumental parameters were investigated with two experimental designs comprising 19 and 11 experiments, respectively. The investigated parameters were excitation and emission slit, detection voltage and scan rate. When a suitable instrumental setting had been found, a minor calibration and test set were analysed and evaluated. Thereafter, a larger calibration of albumin and IgG1 was made out of 26 samples (0-42 μg ml⁻¹ albumin and 0-12.7 μg ml⁻¹ IgG1). This calibration was validated with a test set consisting of 14 samples in the same concentration range. The precision of the method was estimated by analysing two test set samples for six times each. The scan modes tested were emission scan and synchronous scan Δ60 nm. The results showed that the method could be used for determination of albumin and IgG1 (albumin, root mean square error of prediction (RMSEP) <2, relative standard error of prediction (RSEP) <6% and IgG1, RMSEP <1, RSEP <8%) in spite of the overlapping fluorescence of the two compounds. The estimated precision was relative standard deviation (R.S.D.) <1.7%. The method was finally applied for the analysis of some sample fractions from an albumin standard used in affinity chromatography.     

Assessment of infrared spectroscopy and multivariate techniques for monitoring the service condition of diesel-engine lubricating oils

This paper presents two methodologies for monitoring the service condition of diesel-engine lubricating oils on the basis of infrared spectra. In the first approach, oils samples are discriminated into three groups, each one associated to a given wear stage. An algorithm is proposed to select spectral variables with good discriminant power and small collinearity for the purpose of discriminant analysis classification. As a result, a classification accuracy of 93% was obtained both in the middle (MIR) and near-infrared (NIR) ranges. The second approach employs multivariate calibration methods to predict the viscosity of the lubricant. In this case, the use of absorbance measurements in the NIR spectral range was not successful, because of experimental difficulties associated to the presence of particulate matter. Such a problem was circumvented by the use of attenuated total reflectance (ATR) measurements in the MIR spectral range, in which an RMSEP of 3.8 cSt and a relative average error of 3.2% were attained.     

Chemical composition and evaluation of antinociceptive activity of the essential oil of Stevia serrata Cav. from Guatemala

The composition and the antinociceptive activity of the essential oil of Stevia serrata Cav. from a population located in the west highlands of Guatemala were evaluated. A yield of 0.2% (w/w) of essential oil was obtained by hydrodistillation of the dried aerial parts of the plant. The essential oil analysed by GC-FID and GC-MS showed a high content of sesquiterpenoids, with chamazulene (60.1%) as the major component and 91.5% of the essential oil composition was identified. To evaluate antinociceptive activity in mice, the essential oil of S. serrata Cav. was administered as gavage, using three different doses. In the formalin test, the animals were pre-treated with oral doses of the essential oil before the administration of formalin. Oral administration of S. serrata Cav. essential oil produced a marked antinociceptive activity. Therefore, the plant could be domesticated as a source of essential oil rich in chamazulene for developing medicinal products.     

The use of FT-NIR for API content assay in organic solvent media: a single calibration for multiple downstream processing streams

The use of near infrared spectroscopy (NIRS) in downstream solvent based processing steps of an active pharmaceutical ingredient (API) is reported. A single quantitative method was developed for API content assessment in the organic phase of a liquid–liquid extraction process and in multiple process streams of subsequent concentration and depuration steps. A new methodology based in spectra combinations and variable selection by genetic algorithm was used with an effective improvement in calibration model prediction ability. Root mean standard error of prediction (RMSEP) of 0.05 in the range of 0.20–3.00% (w/w) was achieved. With this method, it is possible to balance the calibration data set with spectra of desired concentrations, whenever acquisition of new spectra is no longer possible or improvements in model's accuracy for a specific selected range are necessary. The inclusion of artificial spectra prior to genetic algorithms use improved RMSEP by 10%. This method gave a relative RMSEP improvement of 46% compared with a standard PLS of full spectral length.     

Interdonato lemon from Nizza di Sicilia (Italy): chemical composition of hexane extract of lemon peel and histochemical investigation

Considering that the determination of authenticity and of the geographical origin of food is a very challenging issue, in this study we studied by means of histological and histochemical analyses the famous Sicilian lemon known as ‘Interdonato Lemon of Messina PGI’. Since the protected geographical indication Interdonato lemon of Messina possesses high organoleptic properties, the composition of the hexane extract of lemon peel was determined by HRGC and HRGC–MS analyses and compared with that of lemon of different cultivars. The results obtained are informative of the oil’s quality and explain the variation of the lemon essential oil composition. Given the fundamental economic implications of any fraud, the aim of this study was to determine a fingerprint able to evaluate the authentication of the geographic origin in such way to prevent frauds in national and international markets.     

Chemical composition and antimicrobial activity of the essential oil of Tanacetum walteri (Anthemideae-Asteraceae) from Iran

The composition and antimicrobial activity of the essential oil of Tanacetum walteri were studied. Aerial flowering parts of plant were collected from North Khorasan Province of Iran and the essential oil was isolated by hydrodistillation and analysed by GC-FID and GC-MS. Antimicrobial activity of the essential oil was determined by disc diffusion and MIC and MBC determination. Thirty-five compounds were identified in the oil of T. walteri accounting for 94.4% of the total oil. Thymol (22.5%), 1,8-cineole (8.2%), umbellulone (6.9%), α-bisabolol (6.3%) and camphor (5.3%) were as the principal constituents. The highest antimicrobial activity of the essential oil was observed against Staphylococcus aureus, Enterococcus faecalis and Klebsiella pneumoniae with MIC value of 0.63 mg/mL. The inhibitory effect of the essential oil of T. walteri could be attributed mainly to the high levels of phenolic compound thymol and oxygenated terpenes in essential oil.     

Improvement of GC‐MS Analysis of Shahrbabak Ziziphora tenuior Essential Oil by Using Multivariate Curve Resolution Approaches

Essential oil of aerial parts of Ziziphora tenuior growing in Shahrbabak in central Iran are isolated by hydrodistillation. Due to complexity of essential oils, there are fundamental problems such as co‐elution in their direct gas chromatography‐mass spectrometry analysis. These problems can result in low similarity matches in MS library search, so that true identification and determination of individual components may fail. In the present work, each component was identified and determined using GC‐MS coupled with multivariate curve resolution (MCR) techniques. In this way, more information along with higher accuracy and precision can be extracted from pure experimental GC‐MS data. The number of identified components found increased from 37 in direct similarity search to 80 in GC‐MS/MCR method. To identify each individual component, similarity search and Kovat's retention index comparison were implemented. The results found showed that pulegone (38.3%), 3′,5′‐dihydroxyacetophenone (22.83%), isomenthone (7.06%), 2‐methyl‐5‐(1‐methylethyl)‐phenol (3.41%), limonene (2.59%) and 2‐acetyl‐4,4‐dimethyl‐cyclopent‐2‐enone (2.49%) were the most abundant components. The reported compounds accounted for 94.39% of total content of the essential oil. A characteristic feature of the Iranian Ziziphora tenuior is the absence of piperitenone in its constituents compared with the oil of other Ziziphora species from Turkey.     

A study of robustness with multivariate calibration. Application to the polarographic determination of benzaldehyde

A procedure to evaluate the robustness of an analytical method when there are changes in some experimental variables, when using multivariate calibration, is proposed. The procedure consists of analysing the root mean square error of prediction (RMSEP) as a response to a Plackett–Burman experimental design, through which the influence of several experimental factors on the prediction capability of the multivariate partial least squares (PLS) models built is studied. Two different ways of analysing the experimental design response are considered: establishing the residual variance with replicates and using Lenth's method. The proposed methodology has been applied to estimate the robustness of the polarographic determination of benzaldehyde when PLS calibration is used.     

Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

In near-infrared (NIR) measurements, some physical features of the sample can be responsible for effects like light scattering, which lead to systematic variations unrelated to the studied responses. These errors can disturb the robustness and reliability of multivariate calibration models. Several mathematical treatments are usually applied to remove systematic noise in data, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). New mathematical treatments, such as orthogonal signal correction (OSC) and direct orthogonal signal correction (DOSC), have been developed to minimize the variability unrelated to the response in spectral data. In this work, these two new pre-processing methods were applied to a set of roasted coffee NIR spectra. A separate calibration model was developed to quantify the ash content and lipids in roasted coffee samples by PLS regression. The results provided by these correction methods were compared to those obtained with the original data and the data corrected by derivation, SNV and MSC. For both responses, OSC and DOSC treatments gave PLS calibration models with improved prediction abilities (4.9 and 3.3% RMSEP with corrected data versus 7.1 and 8.3% RMSEP with original data, respectively).