Search

In the present contribution, a novel approach based on multivariate curve resolution and deep learning (DL) is proposed for quantitative mass spectrometry imaging (MSI) as a potent technique for identifying different compounds and creating their distribution maps in biological tissues without need for sample preparation. As a case study, chlordecone as a carcinogenic pesticide was quantitatively determined in mouse liver using matrix-assisted laser desorption ionization-MSI (MALDI-MSI). For this purpose, data from seven standard spots containing 0 to 20 picomoles of chlordecone and four unknown tissues from the mouse livers infected with chlordecone for 1, 5, and 10 days were analyzed using a convolutional neural network (CNN). To solve the lack of sufficient data for CNN model training, each pixel was considered as a sample, the designed CNN models were trained by pixels in training sets, and their corresponding amounts of chlordecone were obtained by multivariate curve resolution-alternating least-squares (MCR-ALS). The trained models were then externally evaluated using calibration pixels in test sets for 1, 5, and 10 days of exposure, respectively. Prediction

This review aims to demonstrate abilities to analyze Big (Bio)Chemical Data (BBCD) with multivariate chemometric methods and to show some of the more important challenges of modern analytical researches. In this review, the capabilities and versatility of chemometric methods will be discussed in light of the BBCD challenges that are being encountered in chromatographic, spectroscopic and hyperspectral imaging measurements, with an emphasis on their application to omics sciences. In addition, insights and perspectives on how to address the analysis of BBCD are provided along with a discussion of the procedures necessary to obtain more reliable qualitative and quantitative results. In this review, the importance of Big Data and of their relevance to (bio)chemistry are first discussed. Then, analytical tools which can produce BBCD are presented as well as some basics needed to understand prospects and limitations of chemometric techniques when they are applied to BBCD are given. Finally, the significance of the combination of chemometric approaches with BBCD analysis in different chemical disciplines is highlighted with some examples. In this paper, we have tried to cover some of the applications of big data analysis in the (bio)chemistry field. However, this coverage is not extensive covering everything done in the field., Hadi Parastar would like to thank Sharif University of Technology (SUT) of Iran for financial support for a short sabbatical leave at IDAEA-CSIC of Barcelona. Roma Tauler would like to thank the European Research Council for the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. 320737.

A multi-residue method using gas chromatography coupled with flame ionisation detector has been developed for simultaneous determination of 10 pesticides in milk. The methodology involved a sample ...

In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (dd-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%.

S. lanata has been traditionally used as a medicinal plant due to its various biological activities such as antioxidant activity. Therefore, identification and quality control studies of this plant are of great importance. To this end, gas chromatography (GC) combined with chemometrics was proposed for fingerprint analysis of S. lanata samples. This study sought to classify GC fingerprints of twenty-eight S. lanata samples from eight different regions of Iran and more importantly, to correlate fingerprints to the antioxidant activity to select S. lanata volatile antioxidant markers. S. lanata samples were classified into five and three classes using partial least squares-discriminant analysis (PLS-DA) according to their GC fingerprints and antioxidant peaks, respectively. The results of PLS regression (PLS-R) and variable importance in projection (VIP) showed that phenol, 2,4-bis (1,1-dimethylethyl)-, hexadecanoic acid- ethyl ester, vitamin E, Beta- sitosterol, and 1- monolinoleoylglycerol trimethylsily ether are volatile antioxidant markers of S. lanata samples.

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real datasets. Generalized analytical sensitivity is defined and calculated for neural networks as an additional figure of merit. Moreover, the dependence of convolutional neural networks on regularization dataset size is studied and compared with other conventional calibration methods.

In this work, high-performance thin-layer chromatography (HPTLC) coupled with multivariate image analysis (MIA) is proposed as a fast and reliable tool for authentication and adulteration detection of Iranian saffron samples based on their HPTLC fingerprints. At first, the secondary metabolites of saffron were extracted using ultrasonic-assisted solvent extraction (UASE) which was optimized using central composite design (CCD). Next, the RGB coordinates of HPTLC images were used for estimation of saffron origin based on principal component analysis (PCA). The PCA scores plot showed that saffron samples were clustered into two clear-cut groups which was 92% matched with the geographical origins of the samples. In the next step, common plant-derived adulterants of saffron including safflower, saffron style, calendula, and rubia were investigated with MIA analysis of HPTLC images using partial least squares-discriminant analysis (PLS-DA) at 5-35% (w/w) levels. The PLS-DA results showed proper classification of saffron and adulterants with sensitivity 99.14%, specificity 96.94%, error rate 1.96% and accuracy 98.04. Also, the effect of HPTLC injection volume on the performance of the proposed strategy was evaluated. The ability of the proposed method was then investigated by analyzing two additional sample sets including mixed samples of four plant-derived adulterants and adulterated commercial samples. Sensitivity and specificity of this model were 100% which confirmed its validity.

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, as well as to correctly classify chicken fillets according to the growth conditions of the chickens with good accuracy. In all cases, the random subspace discriminant ensemble (RSDE) method significantly outperformed other common classification methods such as partial least squares-discriminant analysis (PLS-DA), artificial neural network (ANN) and support vector machine (SVM) with classification accuracy of >95%. This study shows that handheld NIR coupled with machine learning algorithms is a useful, fast, non-destructive tool to identify the authenticity of chicken meat. By comparing and combining different protocols to measure the NIR spectra (i.e., through packaging and directly on meat), we show the possibilities for both consumers and food inspection authorities to check the authenticity and origin of packaged chicken fillet.

In the present contribution, the capability of isotopic ratio mass spectrometry (IRMS) for saffron authentication and detection of four common plant-derived adulterants (marigold flower, safflower, rubia, and saffron style) was investigated. For this purpose, 62 authentic saffron samples were analyzed by elemental analyzer-IRMS (EA-IRMS) and gas chromatography-combustion-IRMS (GC-C-IRMS). In this regard, EA-IRMS and GC-C-IRMS isotope fingerprints of carbon-13 and nitrogen-15 isotopes of saffron components were provided and then analyzed by chemometric methods. Principal component analysis (PCA) showed two different behaviors regarding two main regions. Then, a representative saffron sample was provided to study adulteration. On this matter, binary mixtures of saffron and adulterants were prepared at five different weight percentages (5%, 10%, 15%, 25%, and 35%) and analyzed by EA-IRMS and GC-C-IRMS. Data-driven soft independent modeling of class analogy (DD-SIMCA) was used to model authentic saffron samples and find a boundary between authentic and adulterated samples with a sensitivity of 100% by GC-C-IRMS. After that, discriminant models of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least squares-discriminant analysis (PLS-DA) were tested to find the best discrimination line and also detection of the lowest level of adulterants. Among different models, the QDA model outperformed other methods and showed the ability to predict adulterants at 5% w/w level with 100% accuracy and precision. Finally, the developed QDA model was successfully used to discriminate a set of mixed samples of saffron and four adulterants as well as some commercial samples.

In the present contribution, the effects of different contaminants of emerging concerns (CECs), including parabens, drugs, and polycyclic aromatic hydrocarbons (PAHs) on antioxidant activity of Lactuca sativa L. in different concentration levels (10–500 µg L−1) were evaluated using ultraviolet–visible (UV–Vis) spectrophotometry combined with chemometric techniques. The extracts of lettuce samples were investigated for the antioxidant activity (AA), total phenolic content (TPC), and total flavonoid content (TFC) after 39 days of planting and 14 days of exposure. Then, the spectroscopic data were arranged in two different data matrices, including (i) the control lettuce samples and PAHs exposed samples and (ii) control samples and parabens/drugs exposed samples. The data were then analyzed using unsupervised pattern recognition techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) and supervised pattern recognition method of partial least squares-discriminant analysis (PLS-DA). The PCA and HCA results suggested that samples belong to different groups. Then, the PLS-DA model was applied to indicate discrimination between types of organic pollutants. The PLS-DA results showed proper discrimination between control and exposed samples with an accuracy > 90%. It is concluded that the TFC, AA, and TPC values in controls were increased under the specific exposure circumstances.

The objective of the present work is development of joint approximate diagonalization of eigenmatrices (JADE) as a member of independent component analysis (ICA) family, for the analysis of gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) data to address incomplete separation problem occurred during the analysis of complex sample matrices. In this regard, simulated GC-MS and GC×GC-MS data sets with different number of components, different degree of overlap and noise were evaluated. In the case of simultaneous analysis of multiple samples, column-wise augmentation for GC-MS and column-wise super-augmentation for GC×GC-MS was used before JADE analysis. The performance of JADE was evaluated in terms of statistical parameters of lack of fit (LOF), mutual information (MI) and Amari index as well as analytical figures of merit (AFOMs) obtained from calibration curves. In addition, the area of feasible solutions (AFSs) was calculated by two different approaches of MCR-BANDs and polygon inflation algorithm (FACPACK). Furthermore, JADE performance was compared with multivariate curve resolution-alternating least squares (MCR-ALS) and other ICA algorithms of mean-field ICA (MFICA) and mutual information least dependent component analysis (MILCA). In all cases, JADE could successfully resolve the elution and spectral profiles in GC-MS and GC×GC-MS data with acceptable statistical and calibration parameters and their solutions were in AFSs. To check the applicability of JADE in real cases, JADE was used for resolution and quantification of phenanthrene and anthracene in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC-MS. Surprisingly, pure elution and spectral profiles of target compounds were properly resolved in the presence of baseline and interferences using JADE. Once more, the performance of JADE was compared with MCR-ALS in real case. On this matter, the mutual information (MI) values were 1.01 and 1.13 for resolved profiles by JADE and MCR-ALS, respectively. In addition, LOD values (μg/mL) were respectively 1.36 and 1.24 for phenanthrene and 1.26 and 1.09 for anthracene using MCR-ALS and JADE which showed outperformance of JADE over MCR-ALS.

In this study, QuEChERS combined with dispersive liquid-liquid microextraction is developed for extraction of ten pesticides in complex sample matrices of water and milk. In this regard, effective factors of proposed extraction technique combined with gas chromatography with flame ionization detector were designed, modeled, and optimized using central composite design, multiple linear regression, and Nelder-Mead simplex optimization. Later, univariate calibration model for ten pesticides was developed in concentration range of 0.5-100 ng/mL. Surprisingly, quadratic calibration behavior was observed for some of the pesticides. In this regard, Mandel's test was used for evaluating linearity and types of calibration equation. Finally, four pesticides followed linear calibration curve with sensitivity (0.23-0.66 mL/ng), analytical sensitivity (0.20-0.32), regression coefficient (0.988-0.995), limit of detection (0.39-1.83 ng/mL), and limit of quantitation (1.30-6.10 ng/mL) and six of them followed quadratic calibration curve with sensitivity (0.18-0.93 mL/ng), analytical sensitivity (0.25-0.86), regression coefficient (0.944-0.999), limit of detection (0.59-1.92 ng/mL), and limit of quantitation (1.96-6.40 ng/mL). The calculated limits of detection were below the maximum residue limits according to European Union pesticides database of European Commission. Finally, the proposed analytical method was used for determination of ten pesticides in water and milk samples.

In this work, the potential of near-infrared (NIR) and mid-infrared (MIR) spectroscopy along with chemometrics was investigated for authentication and adulteration detection of Iranian saffron samples. First, authentication of one-hundred saffron samples was examined by principal component analysis (PCA). The results showed the NIR spectroscopy can better predict the origin of samples than the MIR. Next, partial least squares-discriminant analysis (PLS-DA) was developed to detect four common plant-derived adulterants (i.e., saffron style, calendula, safflower, and rubia). In all cases, PLS-DA classification figures of merit in terms of sensitivity, specificity, error rate and accuracy were satisfactory for both NIR and MIR datasets. The built models were then successfully validated using test set and also commercial samples. Finally, partial least squares regression (PLSR) was used to estimate the amount of adulteration. In this case, only NIR showed a good performance with regression coefficients (R2) in range of 0.95-0.99.

Metabolomics is the science of studying small molecules (metabolites) in biological systems with the aim of getting insight into cells, biofluids and organisms. Chemometric methods are powerful tools to address data problems generated in metabolomic studies and to extract valuable information. This review focuses mainly on a range of chemometric methods used for processing of metabolomics data generated from gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). Herein, essential skills used for preprocessing of raw data, multivariate resolution, pattern recognition, variable selection and identification of metabolites are thoroughly discussed to tackle metabolomic problems. Additionally, different metabolites extraction methods from various biological samples are discussed prior to chemometric analysis. To present a clear picture, different examples form the literature between 2010 and 2020 are used to show each of the mentioned topics.

In the present work, a new approach based on external parameter orthogonalization combined with support vector machine (EPO-SVM) is proposed for processing of attenuated total reflectance-Fourier transform mid-infrared (ATR-FT-MIR) spectra with the goal of solving authentication problem in saffron, the most expensive spice in the world. First, one-hundred authentic saffron samples are clustered by principal component analysis (PCA) with EPO as the best preprocessing strategy. Then, EPO-SVM is used for the detection of four commonly used plant-derived adulterants (i.e. safflower, calendula, rubia, and style) in binary mixtures (saffron and each of plant adulterants) and its performance is compared with other common classification methods. The obtained results showed that the EPO-SVM approach has a much better classification accuracy (>95%) than other methods (accuracy

In the present study, an analytical strategy was developed using reduced graphene oxide (rGO) as an effective sorbent in dispersive micro-solid phase extraction (DMSPE) for simultaneous determination of seven polycyclic aromatic hydrocarbons (PAHs) combined with gas chromatography (GC-FID). rGO was synthesized using modified Hummer’s method and characterized using scanning electron microscope (SEM), atomic force microscope (AFM) and Raman spectroscopy. A rotatable central composite design (CCD) combined with multiple linear regression (MLR) and analysis of variance (ANOVA) was used for designing, modelling and optimization of the extraction procedure. In this regard, principal component analysis (PCA) was used to develop a general response using the scores on the first PC instead of GC peak areas of seven PAHs to develop a global model. Finally, optimum DMSPE conditions were found as 0.7 mL sorbent volume, 0.05 mg mL−1 sorbent amount, 2.2 mL sample volume, 0.5 mL solvent extraction volume, 7.8 w w−1 salt concentration and 20 min vortex time. The optimized method was then used for identification and quantification of target PAHs in standard and real samples using partial least squares regression (PLSR). Multivariate analytical figures of merit (AFOMs) for PLS model including sensitivity (SEN), analytical sensitivity (γ), limit of detection (LOD) and limit of quantitation (LOQ) were calculated by focusing on estimation of signal and concentration which were in the range of (0.37–1.19), (0.87–2.49), (2.39–10.36 ng mL−1) and (7.17–21.36 ng mL−1), respectively. Thoroughly, multivariate analytical figures of merit were better than univariate ones. Finally, the proposed strategy was successfully tested for the analysis of real oil-contaminated sea water samples. The relative recovery (RR) and relative standard deviation (RSD) were in the range of (9.6–96.86) and (1.54–28.44), respectively.

Treated waste water (TWW) quality varies due to the occurrence of polycyclic aromatic hydrocarbons (PAHs) up to low μg L−1. In this study, a non-targeted metabolomic analysis was performed on lettuce (Lactuca sativa L) exposed to 4 PAHs by irrigation. The plants were watered with different concentrations of contaminants (0–100 μg L−1) for 39 days under controlled conditions and then harvested, extracted and analyzed by nuclear magnetic resonance (NMR). Different chemometric tools based on principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are proposed for the analysis of the complex data sets generated in the different exposure experiments. Furthermore, Analysis of Variance Simultaneous Component Analysis (ASCA) of changes on metabolite peaks showed significant PAHs concentration and exposure time-dependent changes, clearly differentiating between exposed and non-exposed samples.

In the present work, the abilities of five different variable selection methods including recursive partial least squares (rPLS), variable importance in projection (VIP), selectivity ratio (SR), significance multivariate correlation (sMC), and PLS loading weights were evaluated on the supervised classification of gas chromatographic fingerprints of saffron using PLS-discriminant analysis (PLS-DA). In this regard, eighty-three saffron samples analyzed by gas chromatography-flam ionization detector (GC-FID), were used as a case study. The GC-FID chromatograms of saffron samples were baseline corrected and aligned using asymmetric least squares (AsLS) and correlation optimized warping (COW) methods, respectively. Then, the whole digital profiles of preprocessed chromatograms were normalized to internal standard (I.S.), mean-centered, pareto-scaled and finally modeled by PLS-DA to classify saffron samples according to their cultivation areas. Afterwards, performance of different variable selection methods (i.e., rPLS, VIP, SR, sMC and loading weights) for choosing the most important variables (i.e., retention time points) in GC-FID fingerprints, were compared in terms of the model's interpretability and predictability. The results indicated that although different variable selection methods could select different subset of variables, but, prediction ability of all the models were still acceptable. The best model performance was achieved when the result of all variable selection methods were taken into account. Finally, nine secondary metabolites of saffron suggested by almost all selection methods were chosen as the saffron biomarkers.

The present contribution is devoted to develop multivariate analytical figures of merit (AFOMs) as a new metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS). In this regard, new definition of sensitivity (SEN) is extended to GC×GC-MS data and then, other multivariate AFOMs including analytical SEN (γ), selectivity (SEL) and limit of detection (LOD) are calculated. Also, two frequently used second- and third-order calibration algorithms of multivariate curve resolution-alternating least squares (MCR-ALS) as representative of multi-set methods and parallel factor analysis (PARAFAC) as representative of multi-way methods are discussed to exploit pure component profiles and to calculate multivariate AFOMs. Different GC×GC-MS data sets with different number of components along with various levels of artifacts are simulated and analyzed. Noise, elution time shifts in both chromatographic dimensions, peak overlap and interferences are considered as the main artifacts in this work. Additionally, a new strategy is developed to estimate the noise level using variance-covariance matrix of residuals which is very important to calculate multivariate AFOMs. Finally, determination of polycyclic aromatic hydrocarbons (PAHs) in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC-MS is considered as real case to confirm applicability of the proposed metric in real samples. It should be pointed out that the proposed strategy in this work can be used for other types of comprehensive two-dimensional chromatographic (CTDC) techniques like comprehensive two dimensional liquid chromatography (LC×LC).

In the present study, the profiling of 17 fatty acids (FAs) in avocado seed and pulp was investigated. The fatty acids were extracted with vortex-assisted extraction, methyl esterified and finally preconcentrated by dispersive liquid–liquid microextraction. The preconcentrated fatty acid methyl esters (FAMEs) were analyzed using gas chromatography–mass spectrometry (GC–MS) to obtain qualitative and quantitative information. The GC–MS data were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) method to overcome general chromatographic problems such as overlapped peaks, background interference and peak shifts. The calibration data were prepared using pure analytical information obtained by MCR-ALS. The linear dynamic ranges and regression coefficients (R 2) for FAMEs were in the range of 0.19–65 mg L−1 and 0.990–0.999, respectively. The relative standard deviation (RSD%) for determination of FAs in avocado seed and pulp was 0.17–8.84 and 0.64–17.93, respectively. The main FAs in the avocado pulp were: oleic acid (74.25 g Kg−1), linoleic acid (26.87 g Kg−1), palmitic acid (26.02 g Kg−1), palmitoleic acid (1.22 g Kg−1) and stearic acid (0.05 g Kg−1). And, the main FAs in the avocado seed were: linoleic acid (1.09 g Kg−1), palmitic acid (0.47 g Kg−1), oleic acid (0.33 g Kg−1), linolenic acid (0.12 g Kg−1), and palmitoleic acid (0.04 g Kg−1).

Sulfur mustard (SM) is a potent alkylating agent and its effects on cells and tissues are varied and complex. Due to limitations in the diagnostics of sulfur mustard exposed individuals (SMEIs) by noninvasive approaches, there is a great necessity to develop novel techniques and biomarkers for this condition. We present here the first nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC/MS) metabolic profiling of serum from and healthy controls to identify novel biomarkers in blood serum for better diagnostics. Of note, SMEIs were exposed to SM 30 years ago and that differences between two groups could still be found. Pathways in which differences between SMEIs and healthy controls are observed are related to lipid metabolism, ketogenesis, tricarboxylic acid (TCA) cycle and amino acid metabolism.

Fuzzy C-means clustering (FCM) is proposed as a promising method for the clustering of chromatographic fingerprints of complex samples, such as essential oils. As an example, secondary metabolites of 14 citrus leaves samples are extracted and analyzed by gas chromatography-mass spectrometry (GC-MS). The obtained chromatographic fingerprints are divided to desired number of chromatographic regions. Owing to the fact that chromatographic problems, such as elution time shift and peak overlap can significantly affect the clustering results, therefore, each chromatographic region is analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to address these problems. Then, the resolved elution profiles are used to make a new data matrix based on peak areas of pure components to cluster by FCM. The FCM clustering parameters (i.e., fuzziness coefficient and number of cluster) are optimized by two different methods of partial least squares (PLS) as a conventional method and minimization of FCM objective function as our new idea. The results showed that minimization of FCM objective function is an easier and better way to optimize FCM clustering parameters. Then, the optimized FCM clustering algorithm is used to cluster samples and variables to figure out the similarities and dissimilarities among samples and to find discriminant secondary metabolites in each cluster (chemotype). Finally, the FCM clustering results are compared with those of principal component analysis (PCA), hierarchical cluster analysis (HCA) and Kohonon maps. The results confirmed the outperformance of FCM over the frequently used clustering algorithms.

In this study, a multivariate-based strategy was developed for simultaneous determination of thirteen carcinogenic polycyclic aromatic hydrocarbons (PAHs) in water samples using gold nanoparticles (AuNPs) as solid-phase extraction (SPE) sorbent combined with gas chromatography (GC). The extraction technique is based on the strong affinity between citrate-capped AuNPs and PAHs. Furthermore, characterization of AuNPs was performed by UV-vis spectroscopy and transmission electron microscopy (TEM) techniques. A rotatable central composite design (CCD) combined with multiple linear regression (MLR) was used for designing the extraction procedure and developing models using the GC peak areas of 13 PAHs. Moreover, multi-response optimization using the Derringer desirability function was utilized to find optimum conditions, which were 7.22 min adsorption vortex time, 5 μL of 1,3-propanedithiol as desorption solvent, 44 μL methanol, 15 μL n-nonane as acceptor solvent and 9.63 min desorption vortex time. The optimized method was then used for identification and quantification of target PAHs in standard and spiked samples using partial least squares regression (PLSR). Different variable selection methods including PLS regression vector (RV), variable importance in projection (VIP) and selectivity ratio (SR) were tested and RV showed the best performance. Finally, the proposed strategy was successfully tested for the analysis of spiked water samples (i.e., from tap, well and farm).

Comprehensive two-dimensional gas chromatography and flame ionization detection combined with unfolded-partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two-dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root-mean square error of leave-one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996-0.998, root-mean square error of prediction of 0.005-0.010 and relative error of prediction of 1.54-3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70-85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set.

In this study, a chemometric strategy was developed for analysis of gas chromatographic (GC) and infrared spectroscopic (FT-IR) fingerprints of nine crude oil samples from the main oil wells of Iran to classify them and to find their origins. In this regard, a fractionation method based on saturated, aromatic, resin, and asphaltene (SARA) test was used. Then, these fractions were analyzed by GC-FID and GC–MS. Also, nine crude oil samples were analyzed by FT-IR. The obtained GC fingerprints were aligned using correlation optimized warping (COW) and auto-scaled, and then analyzed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Evaluation of PCA scores plot (explaining 89.35% of variance for two PCs) and HCA dendrogram showed that aromatic fractions belong to three classes. The clustering results of aliphatic and resin fractions also showed the presence of three clusters but with different samples due to the difference in their composition. The results of unsupervised classification were then used as a starting point for partial least squares-discriminant analysis (PLS-DA) and counter propagation-artificial neural network (CP-ANN). On the other side, FTIR fingerprints of crude oil samples were also clustered using PCA and HCA. Evaluation of PCA scores plot (explaining 95.15% of variance for two PCs) and HCA dendrogram showed that samples belong to three clusters. These initial results were used as a starting point for PLS-DA and CP-ANN. Accuracy of PLS-DA was 0.890 for calibration and 0.818 for test sets. Also, CP-ANN accuracy values were 1.000 and 0.818 for train and test sets, respectively.

In this work, a chemometrics-based strategy is developed for quantitative mass spectrometry imaging (MSI). In this regard, quantification of chlordecone as a carcinogenic organochlorinated pesticide (C10Cll0O) in mouse liver using the matrix-assisted laser desorption ionization MSI (MALDI-MSI) method is used as a case study. The MSI datasets corresponded to 1, 5 and 10 days of mouse exposure to the standard chlordecone in the quantity range of 0 to 450 μg g-1. The binning approach in the m/z direction is used to group high resolution m/z values and to reduce the big data size. To consider the effect of bin size on the quality of results, three different bin sizes of 0.25, 0.5 and 1.0 were chosen. Afterwards, three-way MSI data arrays (two spatial and one m/z dimensions) for seven standards and four unknown samples were column-wise augmented with m/z values as the common mode. Then, these datasets were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) using proper constraints. The resolved mass spectra were used for identification of chlordecone in the presence of a complex background and interference. Additionally, the augmented spatial profiles were post-processed and 2D images for each component were obtained in calibration and unknown samples. The sum of these profiles was utilized to set the calibration curve and to obtain the analytical figures of merit (AFOMs). Inspection of the results showed that the lower bin size (i.e., 0.25) provides more accurate results. Finally, the obtained results by MCR for three datasets were compared with those of gas chromatography-mass spectrometry (GC-MS) and MALDI-MSI. The results showed that the MCR-assisted method gives a higher amount of chlordecone than MALDI-MSI and a lower amount than GC-MS. It is concluded that a combination of chemometric methods with MSI can be considered as an alternative way for MSI quantification.

The performances of gas chromatography with mass spectrometry and of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry are examined through the comparison of Daphnia magna metabolic profiles. Gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with mass spectrometry were used to compare the concentration changes of metabolites under saline conditions. In this regard, a chemometric strategy based on wavelet compression and multivariate curve resolution–alternating least squares is used to compare the performances of gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry for the untargeted metabolic profiling of Daphnia magna in control and salinity-exposed samples. Examination of the results confirmed the outperformance of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry over gas chromatography with mass spectrometry for the detection of metabolites in D. magna samples. The peak areas of multivariate curve resolution–alternating least squares resolved elution profiles in every sample analyzed by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry were arranged in a new data matrix that was then modeled by partial least squares discriminant analysis. The control and salt-exposed daphnids samples were discriminated and the most relevant metabolites were estimated using variable importance in projection and selectivity ratio values. Salinity de-regulated 18 metabolites from metabolic pathways involved in protein translation, transmembrane cell transport, carbon metabolism, secondary metabolism, glycolysis, and osmoregulation. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, Hadi Parastar would like to thank Sharif University of Technology (SUT) of Iran for financial support for a short sabbatical leave at IDAEA-CSIC. The research leading to these results received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. 320737

The occurrence of contaminants of emerging concern (CECs) in irrigation waters (up to low μg L-1) and irrigated crops (ng g-1 in dry weight) has been reported, but the linkage between plant morphological changes and plant metabolomic response has not yet been addressed. In this study, a non-targeted metabolomic analysis was performed on lettuce (Lactuca sativa L) exposed to 11 CECs (pharmaceuticals, personal care products, anticorrosive agents and surfactants) by irrigation. The plants were watered with different CEC concentrations (0-50 μg L-1) for 34 days under controlled conditions and then harvested, extracted, derivatised and analysed by comprehensive two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer (GC × GC-TOFMS). The resulting raw data were analysed using multivariate curve resolution (MCR) and partial least squares (PLS) methods. The metabolic response indicates that exposure to CECs at environmentally relevant concentrations (0.05 μg L-1) can cause significant metabolic alterations in plants (carbohydrate metabolism, the citric acid cycle, pentose phosphate pathway and glutathione pathway) linked to changes in morphological parameters (leaf height, stem width) and chlorophyll content. © 2017 The Author(s)., The authors gratefully acknowledge the financial support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) under project AGL2014-59353-R. C. H. and Dr V. M. would like to acknowledge an FPI fellowship (BES-2012-055067) and a Ramon y Cajal contract (RYC-2013-12522) from the MEIC. The authors would also like to thank the Polytechnic University of Catalonia (UPC) for allowing them to use the Agropolis agriculture station. Dr H. P. would like to thank Sharif University of Technology (SUT) for financial support for a short sabbatical leave at IDAEA-CSIC.

In this work, a novel smartphone application entitled “ MVC app ” is developed to perform different multivariate calibration methods. This app is designed for chemists who are not expert in programming or in advanced statistics. The developed application can use any Android-powered device as an environment for running. It is an easy to use app which can simply install in your smartphone and play. Different multivariate calibration methods, such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) are included in this app. As an instance, for performing PLS modeling, first calibration and validation data sets are imported (via USB or Wi-Fi). Then, the number of latent variables (LVs) is chosen using leave-one-out cross-validation (LOO-CV). Afterwards, PLS model is built and the user can review the modeling results. In this regard, figures of merit (FOMs) of models, such as root-mean square error of prediction (RMSEP), standard error of prediction (SEP), bias and relative error (RE) and other parameters can be viewed for each analyte. Furthermore, various plotting options are included for each model. All of these options are available just by touching the screen, with no complexity that almost every chemist can use.

In the present research, ultrasonic-assisted emulsification-microextraction (USAEME) coupled with gas chromatography-mass spectrometry (GC-MS) has been proposed for analysis of thirteen environmental protection agency (EPA) polycyclic aromatic hydrocarbons (PAHs) in aqueous samples. Tetrachloroethylene was selected as extraction solvent. The main parameters of USAEME affecting the efficiency of the method were modeled and optimized using a central composite design (CCD). Under the optimum conditions (9μL for extraction solvent, 1.15% (w/v) NaCl (salt concentration) and 10min for ultrasonication time), preconcentration factor (PF) of the PAHs was in the range of 500-950. In order to have a comprehensive analysis, multivariate curve resolution-alternating least squares (MCR-ALS) as a second-order calibration algorithm was used for resolution, identification and quantification of the target PAHs in the presence of uncalibrated interferences. The regression coefficients and relative errors (REs, %) of calibration curves of the PAHs were in the satisfactory range of 0.9971-0.9999 and 1.17-6.59%, respectively. Furthermore, analytical figures of merit (AFOM) for univariate and second-order calibrations were obtained and compared. As an instance, the limit of detections (LODs) of target PAHs were in the range of 1.87-18.9 and 0.89-6.49ngmL(-1) for univariate and second-order calibration, respectively. Finally, the proposed strategy was used for determination of target PAHs in real water samples (tap and hookah waters). The relative recoveries (RR) and the relative standard deviations (RSDs) were 68.4-109.80% and 2.15-6.93%, respectively. It was concluded that combination of multivariate chemometric methods with USAEME-GC-MS can be considered as a new insight for the analysis of target analytes in complex sample matrices.

There are, to date, few general answers to fundamental questions related to the interactions of nanoparticles (NPs) with living cells. Studies reported in the literature have delivered only limited principles about the nano–bio interface and thus the biological behavior of NPs is yet far from being completely understood. Combining computational tools with experimental approaches in this regard helps to precisely probe the nano–bio interface and allows the development of predictive and descriptive relationships between the structure and the activity of nanomaterials. In the present contribution, a nano-quantitative structure-activity relationship (nano-QSAR) model has been statistically established using the Partial Least Squares Regression (PLSR) model. Also, variable importance on PLS projections (VIP) has been used to find the most responsible factors in NP exocytosis. Physicochemical properties of a set of different sized gold NPs with different surface coatings were strongly correlated to their exocytosis in macrophages. The results suggest that among the pool of physicochemical properties defined as nano-descriptors, charge density and surface charge seem to be the paramount factors leading to higher exocytosis values. Furthermore, charge accumulation and circularity of NPs are in the next level of priority among other nano-descriptors. The regression based nano-QSAR model reported here is satisfactory in both statistical quality and interpretability. The results could serve as a quantitative framework for better understanding the mechanisms that govern the interactions at the nano–bio interface.

The potentials of partial least squares regression (PLSR) and multivariate curve resolution alternating least squares (MCR-ALS) are evaluated for the simultaneous determination of diclofenac (DCF), naproxen (NAP), mefenamic acid (MEF) and carbamazepine (CBZ) as target analytes and gemfibrozil (GEM) as an interference in synthetic and real environmental samples. The analysis of first-order UV-Vis spectra is performed using PLSR with different variable selection methods, which include variable importance in projection (VIP), recursive weighted partial least squares (rPLS), regression coefficient (RV) and uninformative variable elimination (UVE), and using MCR-ALS with correlation constraint (MCR-ALS-CC). The obtained statistical parameters in terms of relative error (RE), regression coefficient (R2) and root mean square error (RMSE) were satisfactory for the calibration and validation sets. Furthermore, in real environmental samples, the obtained statistical parameters of PLSR using VIP and rPLS as well as MCR-ALS-CC were reasonable considering the heavy overlap of the target analytes and complex sample matrices. In general, PLSR showed a better performance for the determination of analytes in samples that are free of interference or contain calibrated interference(s). On the other hand, MCR-ALS-CC allowed the accurate determination of analytes in the presence of unknown interferences and more complex sample matrices.

Multivariate curve resolution-alternating least squares (MCR-ALS) as a second-order calibration algorithm was proposed for the simultaneous analysis of eighteen fatty acid methyl esters (FAMEs) in a standard mixture and pomegranate seed sample using vortex-assisted extraction-dispersive liquid–liquid microextraction (VAE-DLLME), followed by gas chromatography-mass spectrometry (GC-MS). The chemometric resolution, identification and quantification of the target FAMEs in the standard mixture and real sample (i.e., the pomegranate seed) were carried out successfully in the presence of some uncalibrated interferences. The lack of fit (LOF) and reverse match factor (RMF) were used for the evaluation of the MCR-ALS results of the calibration samples. The LOF (%) and RMF values were in the ranges of 7.24–24.36 and 708–977, respectively. In addition, the regression coefficients (R2) and relative errors (REs, %) of the calibration curves of different FAs were in the satisfactory range of 0.9934–0.9989 and 3.70–7.45, respectively. The application of the proposed strategy to the pomegranate seed extract showed that linoleic acid, oleic acid, palmitic acid, stearic acid and cis-11-eicosenoic acid were the main fatty acids in the pomegranate seed with concentrations of 9098.0, 4873.0, 3147.0, 1960.0 and 1019.0 mg kg−1, respectively, and relative standard deviations (RSD, %) between 0.15 and 11.57. It is concluded that MCR-ALS combined with VAE-DLLME-GC-MS is a fast and simple strategy for the qualitative and quantitative analysis of complex samples such as natural products.

In recent years, there has been an increasing interest in the analysis of the major active components of saffron owing to their significant role in various industries, such as food, medicine and perfume. In other words, analysis of the major active components of saffron can give complete information about its chemical composition, which can be used as a reliable index for the quality control of different saffron samples (i.e., natural and commercial). The aim of the present work was to develop a simple, low cost, efficient and comprehensive strategy for the extraction and analysis of the bioactive components of saffron. In this regard, ultrasonic-assisted solvent extraction (UASE) combined with ultrasonic-assisted emulsification microextraction (USAEME) is proposed for the extraction and preconcentration of the bioactive constituents of saffron. The extracted components are then analyzed using reverse-phase high-performance liquid chromatography with diode array detector (RP-HPLC-DAD). The effective parameters on the efficiency of extraction procedure are optimized using multivariate chemometric techniques. As a consequence, the optimum extraction parameters were 79.6 mg of a saffron sample, 1.1 mL of the extraction solvent (water), 62.7 μL of the preconcentration solvent (chloroform) and 18.6 min sonication time. In optimum extraction conditions, the relative standard deviations (RSDs) were below 1.0% (n = 3) for all the components. Moreover, the enrichment factors were higher than 10 for most components. Finally, the developed analytical method is used as a reliable method for the quality control of fifteen commercial saffron samples purchased from different markets. To do this, two multivariate clustering methods, principal component analysis (PCA) and k-means, are used for determining similarities and dissimilarities between standard and commercial saffron samples, according to their HPLC fingerprints. It is concluded that the proposed method is a fast, simple, accurate and unbiased method for analyzing the bioactive components of saffron and fingerprinting commercial saffron samples, obtaining a more complete set of information from data than conventional methods.

The extracted Iranian olive fruit essential oil was analyzed using gas chromatography-mass spectrometry. Multivariate curve resolution-alternative least squares approach was used to overcome the problem of background, baseline offset and overlapping/embedded peaks in gas chromatography-mass spectrometry. The analysis of gas chromatography-mass spectrometry data revealed that 90 components exist in the olive fruit essential oil. However, with the help of multivariate curve resolution-alternative least squares this number was extended to 141 components with concentrations higher than 0.01%. Major constituents in the olive fruit oil are 2-dodecenal (16.88%), 2-undecenal (10.71%), ethyl oleate (8.18%), and eremophilene (5.20%).