Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists.

This Review summarizes how big (bio)chemical data (BBCD) can be analyzed with multivariate chemometric methods and highlights some of the important challenges faced by modern analytical researches. Here, the potential of chemometric methods to solve BBCD problems that are being encountered in chromatographic, spectroscopic and hyperspectral imaging measurements will be discussed, with an emphasis on their applications 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. Thereafter, analytical tools which can produce BBCD are presented as well as the theoretical background of chemometric methods and their limitations when they are applied to BBCD. Finally, the importance of chemometric methods for the analysis of BBCD in different chemical disciplines is highlighted with some examples. In this work, we have tried to cover many of the current applications of big data analysis in the (bio)chemistry field.

Chemometric techniques coupled with NMR for matabolic profiling of lettuce exposed to polycyclic aromatic hydrocarbones.

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.

Development of multi-response optimization and quadratic calibration curve for determination of ten pesticides in complex sample matrices using QuEChERS dispersive liquid-liquid microextraction followed by gas chromatography.

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.

Quantitative analysis of multiple high-resolution mass spectrometry images using chemometric methods: quantitation of chlordecone in mouse liver.

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.

Chemometrics comparison of gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry Daphnia magna metabolic profiles exposed to salinity.

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.

NMR- and GC/MS-based metabolomics of sulfur mustard exposed individuals: a pilot study.

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.

Pattern recognition analysis of chromatographic fingerprints of Crocus sativus L. secondary metabolites towards source identification and quality control.

Chromatographic fingerprinting is an effective methodology for authentication and quality control of herbal products. In the presented study, a chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and multivariate pattern recognition methods was used to establish a gas chromatography-mass spectrometry (GC-MS) fingerprint of saffron. For this purpose, the volatile metabolites of 17 Iranian saffron samples, collected from different geographical regions, were determined using the combined method of ultrasound-assisted solvent extraction (UASE) and dispersive liquid-liquid microextraction (DLLME), coupled with GC-MS. The resolved elution profiles and the related mass spectra obtained by an extended MCR-ALS algorithm were then used to estimate the relative concentrations and to identify the saffron volatile metabolites, respectively. Consequently, 77 compounds with high reversed match factors (RMFs > 850) were successfully determined. The relative concentrations of these compounds were used to generate a new data set which was analyzed by multivariate data analysis methods including principal component analysis (PCA) and k-means. Accordingly, the saffron samples were categorized into five classes using these techniques. The results revealed that 11 compounds, as biomarkers of saffron, contributed to the class discrimination and characterization. Eleven biomarkers including nine secondary metabolites of saffron (safranal, α- and ß-isophorone, phenylethyl alcohol, ketoisophorone, 2,2,6-trimethyl-1,4-cyclohexanedione, 2,6,6-trimethyl-4-oxo-2-cyclohexen-1-carbaldehyde, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one, and 2,6,6-trimethyl-4-hydroxy-1-cyclohexene-1-carboxaldehyde (HTCC)), a primary metabolite (linoleic acid), and a long chain fatty alcohol (nanocosanol) were distinguished as the saffron fingerprint. Finally, the individual contribution of each biomarker to the classes was determined by the counter propagation artificial neural network (CPANN) method.

Quality assessment of gasoline using comprehensive two-dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration.

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.

N-way partial least squares with variable importance in projection combined to GC × GC-TOFMS as a reliable tool for toxicity identification of fresh and weathered crude oils.

In this study, N-way partial least squares (NPLS) is proposed to correlate comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC × GC-TOFMS) data of different aromatic oil fractions (fresh and weathered) to their toxicity values. Before NPLS modeling, since drift and wander of baseline interfere with information of sought analytes in GC × GC-TOFMS data, a novel method called two-dimensional asymmetric least squares is thus developed for comprehensive correction of the baseline contributions in both chromatographic dimensions. The algorithm is termed comprehensive because it functions to correct the entire chromatogram in both dimensions and it preserves the separation information in both dimensions. In this method, a smoother is combined with asymmetric weighting of deviations from the (smooth) trend to get an effective baseline estimator in both chromatographic dimensions. After baseline correction, the NPLS model was calibrated with 20 oil fractions and evaluated by leave-one-out cross-validation. The number of latent variables was chosen on the basis of minimum root mean squares error of cross validation (RMSECV), and it was 7 (RMSECV = 0.073). The developed NPLS model was able to accurately predict the toxicity effects in the five oil fractions as prediction sets which were independent of 20 oil fractions in calibration set (RMSEP = 0.0099 and REP = 11.38 %). Finally, the newly developed n-way variable importance in projection (NVIP) was used for identification of the most influential chemical components on the toxicity values of different oil fractions. According to the high NVIP values in both chromatographic dimensions and their corresponding mass spectra, alkyl substituted three- and four-ring aromatic hydrocarbons were identified. It is concluded that multivariate chemometric methods (e.g., NPLS) combined to non-target analysis using GC × GC-TOFMS is a viable strategy to be used for analytical identification in fuel oil studies, with a potential to reduce the number of fractionation steps needed to obtain necessary chromatographic and mass spectral information.

Multivariate curve resolution of hyphenated and multidimensional chromatographic measurements: a new insight to address current chromatographic challenges.

In this Feature, the capabilities and versatility of multivariate curve resolution methods are discussed in light of the current challenges in chromatographic measurements, with special emphasis on hyphenated and multidimensional chromatographic analysis. This Feature provides insights and perspectives on recommended chemometric strategies to improve the qualitative and quantitative chromatographic information gathered from analytical determinations of complex natural samples.

Mutual information concept for evaluation of separation quality in hyphenated chromatographic measurements.

A new method for the evaluation of separation quality in hyphenated chromatographic measurements based on the information-theoretic concept of mutual information (MI) is developed. The MI values for the purest spectra selected using the simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and orthogonal projection approach (OPA) methods are calculated according to the differential information entropy (Shannon entropy). To calculate the MI values for more than two variables, the Kozachenko-Leonenko (KL) estimation of the Shannon entropy is used. The MI values of the purest spectra can reliably reflect the degree of peak overlap in the chromatographic direction. Herein, the developed method is employed on different simulated and real GC-MS and HPLC-DAD datasets (i.e., chromatographic segments and chromatographic fingerprints) to evaluate the potential of this new method. Inspection of the results showed that minimization of MI values is a good criterion for comprehensive evaluation of separation quality in hyphenated chromatographic measurements and to reach to the best chromatographic separation. Additionally, the performance of this method is compared with the previously developed overlap index (OVI) criterion and classical univariate criteria, such as ΣRs and ΠRs, which showed an improvement in all cases. As demonstrated by simulated and real chromatographic data, the MI index gives not only a comprehensive criterion for evaluation of separation quality, but also provides reliable information for the purity assessment of compounds of interest. Furthermore, the MI index can be used as a reliable criterion for multivariate optimization of hyphenated chromatographic measurements.

Chemometrics-assisted effect-directed analysis of crude and refined oil using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry.

An effect-directed analysis (EDA) of fresh and artificially weathered (evaporated, photooxidized) samples of North Sea crude oil and residual heavy fuel oil is presented. Aliphatic, aromatic, and polar oil fractions were tested for the presence of aryl hydrocarbon receptor (AhR) agonist and androgen receptor (AR) antagonist, demonstrating for the first time the AR antagonist effects in the aromatic and, to a lesser extent, polar fractions. An extension of the typical EDA strategy to include an N-way partial least-squares (N-PLS) model capable of relating the comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) data set to the bioassay data obtained from normal-phase LC fractions is proposed. The predicted AhR binding effects in the fresh and artificially weathered aromatic oil fractions facilitated the identification of alkyl-substituted three- and four-ring aromatic systems in the active fractions through the weighting of their contributions to the observed effects. A N-PLS chemometric model is demonstrated as a potentially useful strategy for future EDA studies that can streamline the compound identification process and provide additional reduction of samples' complexity. The AhR binding effects of the suspected compounds predicted by N-PLS and identified by GC × GC-TOFMS were confirmed using quantitative structure-activity relationship (QSAR) estimates.

Benchtop volatilomics supercharged: How machine learning based design of experiment helps optimizing untargeted GC-IMS gas phase metabolomics.

Gas chromatography-ion mobility spectrometry (GC-IMS) plays a significant role in both targeted and non-targeted analyses. However, the non-linear behavior of IMS and its complex ion chemistry pose challenges for finding optimal experimental conditions using existing methodologies. To address these issues, integrating machine learning (ML) strategies offers a promising approach. In this study, we propose a hybrid strategy, combining design of experiment (DOE) and machine learning (ML) for optimizing GC-IMS conditions in non-targeted volatilomic/flavoromic analysis, with saffron volatiles as a case study. To begin, a rotatable circumscribed central composite design (CCD) is used to define five influential GC-IMS factors of sample amount, headspace temperature, incubation time, injection volume, and split ratio. Subsequently, two ML models are utilized: multiple linear regression (MLR) as a linear model and Bayesian regularized-artificial neural network (BR-ANN) as a nonlinear model. These models are employed to predict the response variables of total peak areas (PAs) and the number of detected peaks (PNs) in GC-IMS. The findings show that there is a direct correlation between the factors in GC-IMS and the PNs, suggesting that MLR is a suitable approach for building a model in this scenario. However, the PAs exhibit nonlinear behavior, suggesting that BR-ANN is better suitable to capture this complexity. Notably, Derringer's desirability function is utilized to integrate the PAs and PNs, and in this scenario, MLR demonstrates satisfactory performance in modeling the GC-IMS factors.

Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer.

Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within metabolic networks, metabolites often exhibit high correlation and collinearity. To address this challenge, self-organizing maps (SOMs) of Kohonen maps and counter propagation-artificial neural networks (CP-ANN) were employed in this study to model proton nuclear magnetic resonance spectroscopic (1HNMR) data from control samples and breast cancer (BC) patients. Blood serum samples from a control group (n=24) and BC patients (n=18) were used to extract metabolites using methanol and chloroform solvents in optimum extraction conditions. The 1HNMR data was preprocessed by performing phase, baseline, and shift corrections. Subsequently, the preprocessed data was modeled using Kohonen network as an unsupervised technique and CP-ANN as a supervised technique. In this regard, the model built with CP-ANN successfully distinguished between the two classes with an accuracy of 100 % for both group and sensitivity of 96 % and 100 % for control group and BC patients, respectively. Additionally, CP-ANN algorithm demonstrated predictive capabilities by accurately classifying test samples with 90 % sensitivity, 98 % specificity, and 96 % accuracy for control group and 100 % sensitivity, 90 % specificity, and 96 % accuracy for BC patients. Furthermore, analysis of the resulting topological map revealed 14 significant variables (biomarkers) such as sarcosine, lysine, trehalose, tryptophan, and betaine that effectively differentiated between healthy individuals and BC patients.

Automated 2D peak detection in gas chromatography-ion mobility spectrometry through persistent homology.

BACKGROUND: Gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful analytical technique which has gained widespread use in a variety of fields. Detecting peaks in GC-IMS data is essential for chemical identification. Topological data analysis (TDA) has the ability to record alterations in topology throughout the entire spectrum of GC-IMS data and retain this data in diagrams known as persistence diagrams. To put it differently, TDA naturally identifies characteristics such as mountains, volcanoes, and their higher-dimensional equivalents within the original data and measures their significance. RESULTS: In the present contribution, a novel approach based on persistent homology (a flagship technique of TDA) is suggested for automatic 2D peak detection in GC-IMS. For this purpose, two different GC-IMS data examples (urine and olive oil) are used to show the performance of the proposed method. The outputs of the algorithm are GC-IMS chromatogram with detected peaks, persistence plot showing the importance (intensity) of the detected peaks and a table with retention times (RT), drift times (DT), and persistence scores of detected peaks. The RT and DT can be used for identification of the peaks and persistence scores for quantitation. Additionally, watershed segmentation is applied to the GC-IMS images to index individual peaks and segment overlapping compounds allowing for a more accurate identification and quantification of individual peaks. SIGNIFICANCE: Inspection of the results for GC-IMS datasets showed the accurate and reliable performance of the proposed strategy based on persistent homology for automatic 2D GC-IMS peak detection for qualitative and quantitative analysis. In addition, this approach can be easily extended to other types of hyphenated chromatographic and/or spectroscopic data.

Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Solving chromatographic challenges in comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry using multivariate curve resolution-alternating least squares.

Multivariate curve resolution-alternating least squares (MCR-ALS) analysis is proposed to solve chromatographic challenges during two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) analysis of complex samples, such as crude oil extract. In view of the fact that the MCR-ALS method is based on the fulfillment of the bilinear model assumption, three-way and four-way GC × GC-TOFMS data are preferably arranged in a column-wise superaugmented data matrix in which mass-to-charge ratios (m/z) are in its columns and the elution times in the second and first chromatographic columns are in its rows. Since m/z values are common for all measured spectra in all second-column modulations, unavoidable chromatographic challenges such as retention time shifts within and between GC × GC-TOFMS experiments are properly handled. In addition, baseline/background contributions can be modeled by adding extra components to the MCR-ALS model. Another outstanding aspect of MCR-ALS analysis is its extreme flexibility to consider all samples (standards, unknowns, and replicates) in a single superaugmented data matrix, allowing joint analysis. In this way, resolution, identification, and quantification results can be simultaneously obtained in a very fast and reliable way. The potential of MCR-ALS analysis is demonstrated in GC × GC-TOFMS analysis of a North Sea crude oil extract sample with relative errors in estimated concentrations of target compounds below 6.0 % and relative standard deviations lower than 7.0 %. The results obtained, along with reasonable values for the lack of fit of the MCR-ALS model and high values of the reversed match factor in mass spectra similarity searches, confirm the reliability of the proposed strategy for GC × GC-TOFMS data analysis.

Quantitative Mass Spectrometry Imaging Using Multivariate Curve Resolution and Deep Learning: A Case Study.

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 R2 for all three data sets ranged from 0.93 to 0.96, which was superior to support vector machine (SVM) and partial least-squares (PLS). The trained CNN models were finally used to predict the amount of chlordecone in mouse liver tissues, and their results were compared with MALDI-MSI and GC-MS methods, which were comparable. Inspection of the results confirmed the validity of the proposed method.

Comparison of a portable Vis-NIR hyperspectral imaging and a snapscan SWIR hyperspectral imaging for evaluation of meat authenticity.

The performance of visible-near infrared hyperspectral imaging (Vis-NIR-HSI) (400-1000 nm) and shortwave infrared hyperspectral imaging (SWIR-HSI) (1116-1670 nm) combined with different classification and regression (linear and non-linear) multivariate methods were assessed for meat authentication. In Vis-NIR-HSI, total accuracies in the prediction set for SVM and ANN-BPN (the best classification models) were 96 and 94 % surpassing the performance of SWIR-HSI with 88 and 89 % accuracy, respectively. In Vis-NIR-HSI, the best-obtained coefficient of determinations for the prediction set (R2p) were 0.99, 0.88, and 0.99 with root mean square error in prediction (RMSEP) of 9, 24 and 4 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. In SWIR-HSI, the best-obtained R2p were 0.86, 0.77, and 0.89 with RMSEP of 16, 23 and 15 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. The results ascertain that Vis-NIR-HSI coupled with multivariate data analysis has better performance rather than SWIR-HIS.

Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks.

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.