Chemometric Models Research Articles

Contribution of multiway calibration to White Analytical Chemistry.

In this article, the compatibility between second- and third-order calibration and White Analytical Chemistry (WAC) is exposed and discussed. The WAC concept and the principles on which it is based are briefly presented. Multiway calibration methods, which consist of performing analyte(s) quantification by processing second- or higher-order instrumental data using chemometric models, are analyzed in light of their contribution to the whiteness of an analytical method. Special emphasis is directed towards the chemometric property called "second-order advantage," which offers the possibility of modeling sample interferences without either needing to remove them nor knowing their identity. With the purpose of illustrating to the analytical community the valuable contribution of multiway calibration to the development of white methodologies, some examples selected from our laboratory are here presented.

Raman‐Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations

ABSTRACTRaman spectroscopy, a robust and non‐invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell‐free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real‐time, on‐line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae‐based biopharmaceutical production.

Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production.

The present work focused on inline Raman spectroscopy monitoring of SARS-CoV-2 VLP production using two culture media by fitting chemometric models for biochemical parameters (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and viral titer). For that purpose, linear, partial least square (PLS), and nonlinear approaches, artificial neural network (ANN), were used as correlation techniques to build the models for each variable. ANN approach resulted in better fitting for most parameters, except for viable cell density and glucose, whose PLS presented more suitable models. Both were statistically similar for ammonium. The mean absolute error of the best models, within the quantified value range for viable cell density (375,000-1,287,500 cell/mL), cell viability (29.76-100.00%), glucose (8.700-10.500g/), lactate (0.019-0.400g/L), glutamine (0.925-1.520g/L), glutamate (0.552-1.610g/L), viral titer (no virus quantified-7.505 log10 PFU/mL) and ammonium (0.0074-0.0478g/L) were, respectively, 41,533 ± 45,273cell/mL (PLS), 1.63 ± 1.54% (ANN), 0.058 ± 0.065g/L (PLS), 0.007 ± 0.007g/L (ANN), 0.007 ± 0.006g/L (ANN), 0.006 ± 0.006g/L (ANN), 0.211 ± 0.221 log10 PFU/mL (ANN), and 0.0026 ± 0.0026g/L (PLS) or 0.0027 ± 0.0034g/L (ANN). The correlation accuracy, errors, and best models obtained are in accord with studies, both online and offline approaches while using the same insect cell/baculovirus expression system or different cell host. Besides, the biochemical tracking throughout bioreactor runs using the models showed suitable profiles, even using two different culture media.

Chemometric approaches for polysaccharide viscosity profiling

Traditional viscosity measurements for carrageenan are laborious, present practical and environmental challenges, and fail to provide structure-property understanding for application and manufacturing development. We hypothesize that integrating Size Exclusion Chromatography (SEC) with Multi-Angle Light Scattering (MALS) and online viscometry, combined with chemometric techniques, can develop a more efficient and environmentally friendly method for determining the apparent viscosity of carrageenan solutions.To test this hypothesis, predictive chemometric models were developed using SEC-MALS data for carrageenan extracted from four different seaweed species. By integrating SEC-MALS with Partial Least Squares (PLS) regression, key molecular parameters such as hydrodynamic radius, intrinsic viscosity, and molecular mass were identified as significant influencers of viscosity. The model for carrageenan from Eucheuma denticulatum yielded the lowest prediction error (RMSEP 8.4), while those for carrageenan extracted from Kappaphycus alvarezii or from several species of the Chondrus genus showed higher errors due to κ-carrageenan sensitivity. For carrageenan extracted from seaweed of the Gigartina genus, incorporating the root mean square radius resulted in a low prediction error of 10.This study concludes that integrating SEC-MALS with PLS regression effectively identifies key molecular parameters influencing carrageenan viscosity, enhancing structure-property understanding and providing a reliable analytical method for optimizing quality control and application in various industries.

Analytical Techniques for the Authenticity Evaluation of Chokeberry, Blackberry and Raspberry Fruit Wines: Exploring FT-MIR Analysis and Chemometrics

The modern analytical technique of Fourier-transform mid-infrared spectroscopy (FT-MIR) has found its place in routine wine quality control. It allows rapid and nondestructive analysis, with easy sample preparation and without the need for chemical pretreatment or expensive reagents. The objective of this research was to apply these advantages to fruit wines in order to create a tool for the authentication of fruit wines produced from different fruit species (chokeberry, blackberry, and raspberry). The aim of this work was to establish a chemometric model from FT-MIR spectra and to find a “fingerprint” of specific fruit wines, enabling the classification of fruit wines by plant species. Physicochemical analysis of 111 Croatian fruit wine samples (38 liqueur fruit wines and 73 fruit wines) revealed content levels of the following parameters: alcoholic strength (5.0–15.2% vol.), total dry extract (60.4–253.3 g/L), total sugars (1.2–229.9 g/L), pH (3.13–4.98), total acidity (4.2–18.3 g/L) and volatile acidity (0.2–1.5 g/L). For statistical data processing, spectral ranges between 926 and 1450 cm−1 and between 1801 and 2951 cm−1 were used. The first principal component (PC1) explained 70.4% of the observed variation, and the second component (PC2) explained 16.7%, clearly separating chokeberry fruit wines from blackberry and raspberry fruit wines. Soft Independent Modeling Class Analogy (SIMCA) was performed following the development of a PCA model showing that the chokeberry and blackberry wine samples form clearly separated clusters. Key discriminators for classifying chokeberry vs. blackberry wines were identified at 1157, 1304, and 1435 cm−1, demonstrating high discrimination power (DP 26, 17, and 14, respectively). FT-MIR spectroscopy, in combination with chemometric methods, has shown promising potential for the authenticity assessment of fruit wines.

Looking for Actives in the Haystack: Merging HRMS2-Based Molecular Networking, Chemometrics, and 13C NMR-Based Dereplication Approaches.

The identification of bioactive natural products (NPs) in complex mixtures has become an important subject of contemporary NP research. In an attempt to address this challenge, the present work proposes an integrated strategy that combines tandem mass spectrometry (MS2)-based molecular networking (MN), a partial least-squares (PLS) chemometric model, as well as 13C NMR-based dereplication using MixONat software. In addition, an advanced glycation end product (AGEs) assay was used for activity evaluation. The approach was implemented on a Garcinia parvifolia bark extract that comprised a high content of prenylated xanthones and had previously shown a notable inhibitory effect on AGE formation. As a main result, the proposed strategy permitted the identification of potentially active metabolites within complex mixtures and their annotation with a higher level of confidence by NMR data. Overall, this comprehensive approach provides a powerful and efficient solution for the targeting and annotating of active compounds in complex NP mixtures.

Semi-quantitative chemometric models for characterization of mixtures of sugars using infrared spectral data

Sugars (saccharides) are sweet-tasting carbohydrates that are abundant in foods and play very important roles in living organisms, particularly as sources and stores of energy, and as structural elements in cellular membranes. They are desirable therapeutic targets, as they participate in multiple metabolic processes as fundamental elements. However, the physicochemical characterization of sugars is a challenging task, mostly due to the structural similarity shared by the large diversity of compounds of this family. The need for fast, accurate enough, and cost-effective analytical methods for these substances is of extreme relevance, in particular because of the recently increasing importance of carbohydrates in Medicine and food industry. With this in view, this work focused on the development of chemometric models for semi-quantitative analysis of samples of different types of sugars (glucose, galactose, mannitol, sorbose and fructose) using infrared spectra as data, as an example of application of a novel approach, where the Principal Component Analysis (PCA) score plots are used to estimate the composition (weight-%) of the mixtures of the sugars. In these plots, polygonal geometric shapes emerge in the vectorial space of the most significant principal components, that allow grouping different types of samples on the vertices, edges, faces and interior of the polygons according to the composition of the samples. This approach was applied successfully to mixtures of up to 5 sugars and shown to appropriately extract the compositional information from the hyper-redundant complex spectral data. Thought the method has been applied here to a specific problem, it shall be considered as a general procedure for the semi-quantitative analysis of other types of mixtures and applicable to other types of data reflecting their composition. In fact, the methodology appears as an efficient tool to solve three main general problems: (i) use hyper-redundant (in variables) data, as spectral information, directly and with minimum pre-treatment, to evaluate semi-quantitatively the composition of mixtures; (ii) do this for systems which produce data that can be considered rather similar; and (iii) do it for a number of substances present in the mixtures that might be greater than that usually considered in chemistry, which in general is limited to 3 components. In addition, this work also demonstrates that, similarly to the developed analysis based on the PCA score plots, the Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) chemometric method can also be used successfully for the qualitative (when used without any previous knowledge of the components present in the samples) or semi-quantitative (when the pure components spectral profiles are provided as references) analyses of mixtures of (at least) up to 5 distinct sugars.

Hydrogeochemical evolution and heavy metal characterization of groundwater from southwestern, Nigeria: An integrated assessment using spatial, indexical, irrigation, chemometric, and health risk models

This study examines the hydrogeochemical and heavy metal parameters of groundwater in Ojo District to determine its suitability for use, potential sources, and human health implications. Ten groundwater samples were assessed, and hydrogeochemical modelling was performed via the Aquachem software. The chemical ions were in the following order: EC > (107.78–448.65 μS/cm) > TDS (182.02–320.77 mg/l) > TH (46.22–182.45 mg/l) > pH (5.55–6.35); HCO3− (64.13–125.82 mg/l) > Na+ (36.87–96.49 mg/l) > Ca2+ (47.65–58.88 mg/l) > SO42− (19.94–53.67) > NO3− (15.55–44.25 mg/l) > Cl− (20.43–27.16 mg/l) > Mg2+ (11.09–16.87 mg/l) and K+ (2.55–7.86 mg/l). The concentrations of heavy metals in groundwater were in the range of: Fe (0.11–0.27 mg/l) > Mn (0.003–0.16 mg/l) > Ni (0.05–0.12 mg/l) > Zn (0.003–0.05 mg/l) > Pb (0.001–0.03 mg/l) > As (0.001–0.005 mg/l) > Cr (0.002–0.005 mg/l) > Cd (0.001–0.003 mg/l) and Cu (0.001–0.0002 mg/l), with Pb, Mn, and Ni exceeding their allowable limits. The Schoeller and Gibbs plots revealed that the major mechanisms controlling the aquifer groundwater in Ojo region are geological rock weathering and mineralization, with a minimal influence of saltwater intrusion. The piper trilinear diagram also revealed that none of the cation was dominant while the anions were strongly dominated by HCO3− (weak acids). The hydrogeochemical facies which describes the geochemical characteristics of the groundwater were classified into 3 types; “Ca2+–Mg+–HCO3– (65 %)”, “mixing zones (30 %)”, and “Na+–K+–Cl––HCO3– (5 %)”. The hydrogeochemical modelling revealed that the groundwater is characterized by forward cation exchange, while rock–water interactions (silicate dissolution) were heavily involved in the geochemical processes. The single pollution index showed that Pb, Ni, and Mn contributed significantly to contamination, and the multi–pollution indices showed that the groundwater was slightly-moderately polluted. The integrated groundwater quality index revealed that only 10 % were clean, 50 % were poor or moderately unclean, 30 % were highly unclean, and only 10 % were extremely unclean (unfit for utilization). The water pollution index showed that 70 % of the groundwater was good. The irrigation indices suggest that the groundwater would enhance soil quality and support plant growth. Multivariate analysis revealed that the groundwater is being influenced by geogenic factors and anthropogenic activities. The health risk assessment (Hazard Quotient and Hazard Index) showed that exposure of adults to the investigated groundwaters could result in noncarcinogenic adverse effects. The cancer risk values also exceeded the minimum limit (1.0 x 10−6) and thresholds (1.0 x 10−4) for adults, indicating the carcinogenic potential of the groundwater.

Chemometrics and analytical blank on the at-line monitoring of Zika-VLP production using near-infrared spectroscopy

The Zika disease caused by the Zika virus was declared a Public Health Emergency by the World Health Union (WHO), with microcephaly as the most critical consequence. Aiming to reduce the spread of the virus, biopharmaceutical organizations invest in vaccine research and production, based on multiple platforms. A crescent vaccine production approach is based on virus-like particles (VLP), for not having genetic material in its composition, hypoallergenic and non-mutant character. For bioprocess, it is essential to have means of real-time monitoring, which can be assessed using process analysis techniques such as Near-infrared (NIR) spectroscopy, that can be combined with chemometric methods, like Partial-Least Squares (PLS) and Artificial Neural Networks (ANN) for prediction of biochemical variables. This work proposes a biochemical Zika VLP upstream production at-line monitoring model using NIR spectroscopy comparing sampling conditions (with or without cells), analytical blank (air, ultrapure water), and spectra pre-processing approaches. Seven experiments in a benchtop bioreactor using recombinant baculovirus/Sf9 insect cell platform in serum-free medium were performed to obtain biochemical and spectral data for chemometrics modeling (PLS and ANN), composed by a random data split (80 % calibration, 20 % validation) for cross-validation of the PLS models and 70 % training, 15 % testing, 15 % validation for ANN. The best models generated in the present work presented an average absolute error of 1.59 × 105 cell/mL for density of viable cells, 2.37 % for cell viability, 0.25 g/L for glucose, 0.007 g/L for lactate, 0.138 g/L for glutamine, 0.18 g/L for glutamate, 0,003 g/L for ammonium, and 0.014 g/L for potassium.

CHEMOMETRIC EVIDENCE FOR SR AND RB ISOTOPES DUE TO THE SPECIFIC SOIL CHEMISTRY IN DIFFERENT GEOGRAPHICAL REGIONS

Strontium and rubidium are the commonly used metals for isotope-ratio analysis. Moreover, this geochemical marker varies between different rock types and formations. The 87Sr/86Sr ratio has been shown to vary widely in surface rocks, so any Sr released into soils, rivers, and groundwater has an isotopic signature that reflects its source. Sr and Rb isotopes have also been used to trace agricultural products, which have incorporated Sr, along with Ca, from soils incorporating the Sr-isotope ratios of the underlying rocks. The specific conditions in the soil represent characteristic conditions of the environment which is reflected in a certain way in the plants. Despite uncertainty about the organic compounds in a sample, the content of selected elements (trace and rare earth elements, REEs) reflects the growing conditions in the environment. For that instance, in the present research, we will give focus on the inorganic compounds’ identifications, due to the more stable response to the lithogenic nature of the soil-plant interaction. For the present study, the target isotopes were 88Sr, 87Sr, 86Sr, and 85Rb. The isotope analysis was conducted with the application of ICP-MS, following the protocol provided in the EPA METHOD 6020. In the validation process, no significant interference occurred that could affect the sensitivity of the measurement of the selected isotopes. Data analysis has been applied to the comparative issues between Sr and Rb content in soil from North Macedonia and selected regions in China. Moreover, the same chemometric model was applied for data analysis for selected plant species for both regions. Data analyses has improved the significant differences within the element contents and ratios as well between both geographical regions.

Short wave-near infrared spectroscopy for predicting soluble solid content in intact mango with variable selection algorithms and chemometric model

Mango sweetness is one of the most prominent internal quality constituents that attract consumer's attention. The current common methods used to determine the sweetness of mango have significant disadvantages in that labor-intensive, time-consuming, and damaging techniques. This research aims to predict sweetness in mangoes using short wave-near infrared (SW-NIR) spectroscopy in the ranges 900–1650 nm, along with variable selection algorithms and chemometric model. A total of 120 mango samples were used to collect the spectra using a fibre module SW-NIR spectrometer. The partial least squares (PLS) regression with several spectral preprocessing methods was employed to develop the calibration model, and the best preprocessing technique was selected. The Savitzky–Golay second-derivative preprocessing technique performed better among the other preprocessing techniques with a correlation coefficient of prediction (rpred) of 0.74 and standard error of prediction (SEP) is 0.78 %Brix. After that, two variable selection techniques were used to select effective wavelength variables, including regression coefficient and successive projections algorithm (SPA). For SSC prediction in the range 900–1650 nm, the SPA-PLS model obtained a rpred of 0.78 and SEP of 0.67 %Brix. The current study unequivocally shows that the proposed SW-NIR spectroscopy coupled with a suitable chemometrics method can evaluate mango sweetness nondestructively.

A novel coal quality index and its estimation using diffuse reflectance spectroscopy

ABSTRACT An essential component of coal’s efficient use and large-scale environmental impact management is evaluating its quality. This study introduces a novel quality indexing technique that incorporates multiple coal properties, providing a comprehensive measure of coal quality. Conventional methods for assessing coal quality, such as proximate and ultimate analyses, are often time-consuming, costly, and require extensive sample preparation, prompting the need for a more efficient approach. In this study, we used the minimum dataset (MDS) approach to estimate a comprehensive coal quality index (CQI) and show that the diffuse reflectance spectroscopy (DRS) may be used as a rapid, nondestructive technique for estimating CQI as a single quality indicator for coal. A total of 212 coal samples of different ranks and grades are collected from the Tertiary and Gondwana coal basins of India. Ten chemical properties of coal were measured following the conventional laboratory-based methods. The spectral responses of the coal samples were measured across the visible-NIR-SWIR (350–2500 nm) range. The MDS approach was adopted while estimating the CQI from the lab-based coal quality parameters. We also examined different chemometric models for estimating CQI values from spectroscopic data. Results showed that the feature selection-based partial-least-square regression (PLSRFS) model may be used to estimate CQI with coefficient of determination (R2) values as high as 0.7 with root-mean-squared error (RMSE) of 0.06. While CQI serves as a single coal quality parameter, the capability to use the DRS approach as a rapid and noninvasive sensing approach for coal quality assessment provides new opportunity to characterize coal quality in an instant and cost-effective manner, especially, when a large number of coal samples have to be tested. The integration of the CQI with DRS could facilitate better decision-making in coal utilization and management, leading to optimized performance and reduced environmental impact.

The Time Is Ripe: Olive Drupe Maturation Can Be Simply Evidenced by a Miniaturized, Portable and Easy-to-Use MicroNIR Green Sensor

The analytical study described in this work, based on NIR spectroscopy with a handheld device, allowed the development of a chemometric prediction model that has been validated for the objective evaluation of the ripening of olive drupes. The miniaturized, portable NIR spectrometer is proposed here as an easy-to-use sensor able to estimate the best harvesting time for ripening of olive drupes. The MicroNIR/chemometrics approach was developed for on-site identification of olive drupe ripening directly on plants, avoiding collection and successive laboratory analysis steps. A supporting parallel characterization by chromatographic techniques validated the spectroscopic prediction. The novelty of this approach consists in the possibility of investigating the olive drupe maturation point by collecting spectra in the near-infrared region and processing them using a chemometric model. The fast and accurate device allows one to easily follow the spectrum profile changes of olive drupes during ripening, thus preserving the fruits from being harvested too early or too late. The results of this study demonstrate the possibility of using the MicroNIR/chemometrics approach to determine the optimal ripening time of olives regardless of the plant variety, age and cultivation location. The results consequently demonstrated that the MicroNIR/chemometrics approach can be proposed as a new method to perform on-site evaluation of ripening by a single-click device. It can be conveniently used by any operator, who does not necessarily have to be expert but must simply be trained to use spectroscopy and a prediction model.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming. This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences. Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models. The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence. These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

Raman laser intensity and sample clarification on biochemical monitoring over Zika-VLP upstream stages

In the current biopharmaceutical scenario, constant bioprocess monitoring is crucial for the quality and integrity of final products. Thus, process analytical techniques, such as those based on Raman spectroscopy, have been used as multiparameter tracking methods in pharma bioprocesses, which can be combined with chemometric tools, like Partial Least Squares (PLS) and Artificial Neural Networks (ANN). In some cases, applying spectra pre-processing techniques before modeling can improve the accuracy of chemometric model fittings to observed values. One of the biological applications of these techniques could have as a target the virus-like particles (VLP), a vaccine production platform for viral diseases. A disease that has drawn attention in recent years is Zika, with large-scale production sometimes challenging without an appropriate monitoring approach. This work aimed to define global models for Zika VLP upstream production monitoring with Raman considering different laser intensities (200 mW and 495 mW), sample clarification (with or without cells), spectra pre-processing approaches, and PLS and ANN modeling techniques. Six experiments were performed in a benchtop bioreactor to collect the Raman spectral and biochemical datasets for modeling calibration. The best models generated presented a mean absolute error and mean relative error respectively of 3.46 × 105 cell/mL and 35 % for viable cell density (Xv); 4.1 % and 5 % for cell viability (CV); 0.245 g/L and 3 % for glucose (Glc); 0.006 g/L and 18 % for lactate (Lac); 0.115 g/L and 26 % for glutamine (Gln); 0.132 g/L and 18 % for glutamate (Glu); 0.0029 g/L and 3 % for ammonium (NH4+); and 0.0103 g/L and 2 % for potassium (K+). Sample without conditioning (with cells) improved the models' adequacy, except for Glutamine. ANN better predicted CV, Gln, Glu, and K+, while Xv, Glc, Lac, and NH4+ presented no statistical difference between the chemometric tools. For most of the assessed experimental parameters, there was no statistical need for spectra pre-filtering, for which the models based on the raw spectra were selected as the best ones. Laser intensity impacts quality model predictions in some parameters, Xv, Gln, and K+ had a better performance with 200 mW of intensity (for PLS, ANN, and ANN, respectively), for CV the 495 mW laser intensity was better (for PLS), and for the other biochemical variables, the use of 200 or 495 mW did not impact model fitting adequacy.

Quantitative prediction of minced chicken gel strength under ultrasonic treatment by NIR spectroscopy coupled with nonlinear chemometric tools evaluated using APaRPs

Achieving the ideal gel strength is essential for desired texture in minced chicken products. This study developed a rapid, non-destructive method using near-infrared (NIR) spectroscopy and nonlinear chemometric modeling to predict minced chicken gel strength under ultrasonic treatment. Initially, minced chicken samples were subjected to high-intensity ultrasound for 0–50 min. This was followed by heat-induced gelation. Gel strength was conventionally measured, and NIR spectra were collected. Nonlinearity between gel strength and spectral data was confirmed using augmented partial residual plots (APaRPs). Subsequently, nonlinear support vector machine (SVM) and extreme learning machine (ELM) models were developed using full NIR spectra and variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighting (CARS), and genetic algorithms (GA). GA proved most effective for enhancing model performance, achieving the highest predicted coefficient of determination (Rp2 = 0.8772) with the ELM model, demonstrating potential for rapid, non-destructive prediction of minced chicken gel strength quality.

Applications of an Electrochemical Sensory Array Coupled with Chemometric Modeling for Electronic Cigarettes.

This study investigates the application of an eNose (electrochemical sensory array) device as a rapid and cost-effective screening tool to detect increasingly prevalent counterfeit electronic cigarettes, and those to which potentially hazardous excipients such as vitamin E acetate (VEA) have been added, without the need to generate and test the aerosol such products are intended to emit. A portable, in-field screening tool would also allow government officials to swiftly identify adulterated electronic cigarette e-liquids containing illicit flavorings such as menthol. Our approach involved developing canonical discriminant analysis (CDA) models to differentiate formulation components, including e-liquid bases and nicotine, which the eNose accurately identified. Additionally, models were created using e-liquid bases adulterated with menthol and VEA. The eNose and CDA model correctly identified menthol-containing e-liquids in all instances but were only able to identify VEA in 66.6% of cases. To demonstrate the applicability of this model to a commercial product, a Virginia Tobacco JUUL product was adulterated with menthol and VEA. A CDA model was constructed and, when tested against the prediction set, it was able to identify samples adulterated with menthol 91.6% of the time and those containing VEA in 75% of attempts. To test the ability of this approach to distinguish commercial e-liquid brands, a model using six commercial products was generated and tested against randomized samples on the same day as model creation. The CDA model had a cross-validation of 91.7%. When randomized samples were presented to the model on different days, cross-validation fell to 41.7%, suggesting that interday variability was problematic. However, a subsequently developed support vector machine (SVM) identification algorithm was deployed, increasing the cross-validation to 84.7%. A prediction set was challenged against this model, yielding an accuracy of 94.4%. Altered Elf Bar and Hyde IQ formulations were used to simulate counterfeit products, and in all cases, the brand identification model did not classify these samples as their reference product. This study demonstrates the eNose's capability to distinguish between various odors emitted from e-liquids, highlighting its potential to identify counterfeit and adulterated products in the field without the need to generate and test the aerosol emitted from an electronic cigarette.

Chemometric Modeling Revealed Oleic and Linoleic Acids as Varietal Biomarkers for Six Sesame Varieties-In Vitro and UHPLC Analyses.

Pakistan once considered self-sufficient for edible oil production now became the major importer with 88.6% imports and producing only the minor portion. Scientific negligence in oil seed crops led to a dramatic decrease in edible oil production depending mainly on only the imports. Sesamum indicum L., "Queen of Oil seeds" with 50-55% oil, is cultivated in various geographical regions of Pakistan, but farmers are not considering this crop because of insufficient knowledge, poor crop management practices, and low yielding varieties. This study was conducted to check the nutritional, biochemical, antioxidant, and yield potentials of six major varieties, i.e., TS-5, TH-6, Til-18, NIAB-Mil, NIAB-Pearl, and NS-16, and to compare the nutritionals, oil quality, and oil yield potential of these varieties. Field experiment was conducted, and various crop growth biomarkers were analyzed. Chlorophyll content and superoxide dismutase activity were found to be highest in NIAB-Mil followed by NIAB-Pearl and comparable to those of Til-18, while APX, Cat, and GPX activity was found to be highest in Til-18 with 25.6 and 5.9 and 6.02 mg/g, respectively. Seed antioxidant parameters showed a mixed response, but NIAB-Mil, NIAB pearl, and Til-18 were found to be highest in all antioxidant parameters. UHPLC analysis of seed oil resulted in a total of 14 triacylglycerols (TAGs), and principal component analysis and OPLS-Da analysis showed seven TAG biomarkers responsible for the separation of sesame varieties. Til-18 was found to be highest in oil content (53.3%) more abundant with oleic acid, while NIAB-Pearl, NIAB-Mil, and NS-16 were found to be abundant with linoleic acid, both considered as potential TAG biomarkers for sesame oil separation. This study concluded that, in general, Til-18 variety is more resistant with high nutritional status, high antioxidant activity, and oil yielding variety, followed by NIAB-Mil and NIAB-Pearl.

An integrated approach for discrimination of Magnoliae officinalis cortex before and after being processed by ginger juice combining LC/MS, GC/MS, intelligent sensors, and chemometrics.

Magnoliae officinalis cortex (MOC) is an important traditional Chinese medicine (TCM), and both raw and stir-fried MOC were commonly used in clinic. This study aimed to discriminate MOC and MOC stir-fried with ginger juice (MOCG) using an integrated approach combining liquid chromatography/mass spectrometry (LC/MS), gas chromatography/mass spectrometry (GC/MS), intelligent sensors, and chemometrics. The sensory characters of the samples were digitalized using intelligent sensors, i.e., colorimeter, electronic nose, and electronic tongue. Meanwhile, the chemical profiles of the samples were analyzed using LC/MS and GC/MS methods. Chemometric models were constructed to discriminate samples of MOC and MOCG based on not only the sensory data but also the chemical data. The differential sensory characters (L* and b* from colorimeter, ANS from electronic tongue, W1S and W2S from electronic nose) and the differential chemical compounds (26 and 11 compounds from LC/MS and GC/MS, respectively) were discovered between MOC and MOCG. Furthermore, twelve differential compounds showed good relations with differential sensory characters. Finally, artificial neural network models were established to discriminate samples of MOC and MOCG, in which W1S, W2S, ANS, b*, and 10 differential compounds were among the top 10 important variables, respectively. Samples of MOC and MOCG can be discriminated not only by the digitalized data of color, taste, and scent detected by intelligent sensors but also by chemical information obtained from LC/MS and GC/MS using chemometrics. The variations in sensory characters and chemical compounds between MOC and MOCG partially resulted from the Maillard reaction products and the oxidation of some compounds in the stir-frying process.

Evaluation of spectroscopic techniques for on-site drug testing of festival seizures.

Despite the fact that drugs of abuse are illegal, a drug-free festival still remains an utopia in most settings. For law enforcement purposes, it is necessary to rapidly determine whether controlled substances are involved. On-site testing is a challenging task because drugs appear in different physical forms and concentrations. The aim of this study was to compare the performance of two spectroscopic techniques, Raman and Fourier transform-infrared (FT-IR), for the testing of drug seizures at a dance festival. First, samples were measured through packaging with Raman. Subsequently, homogenized samples were analysed with FT-IR. For MDMA tablets, a chemometric model was applied on the FT-IR spectra for the dose estimation. After the festival, results were confirmed in the forensic laboratory with gas chromatography coupled with mass spectrometry (GC-MS) and gas chromatography with flame ionization detection (GC-FID). In total, 166 samples of which 90 tablets, 53 powders, 16 crystals and 7 liquids were analysed. MDMA, cocaine and ketamine were the top three drugs seized. The Raman technique was suitable for powders and crystals (sensitivity of 100% and 81%, respectively). However, in comparison with FT-IR, Raman performance was lower for the analysis of liquids (sensitivity of 67%) and 'ecstasy'-like tablets (sensitivity of 41%). Overall, sensitivities above 95% were obtained with FT-IR. The MDMA doses of the tablets, determined on-site, ranged between 52 mg and 336 mg MDMA hydrochloride. For a quick identification of a variety of drugs on-site, the combination of Raman and FT-IR is recommended. It should be emphasized that optimized settings, in-house libraries and analysis by trained operators are essential to obtain correct results.