Independent Modeling Of Class Analogy Research Articles

Classification and quantification of sucrose from sugar beet and sugarcane using optical spectroscopy and chemometrics.

Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380nm. Spectral differences were observable in the ultraviolet-visible (UV-Vis) region from 200 to 600nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV-Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky-Golay were most acceptable with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV-Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries' agricultural policies.

Authentication and detection of common adulterants in clove buds (Syzygium aromaticum L.) powders and oils using near IR combined to multivariate analysis

This study investigates the use of NIR diffuse reflectance spectroscopy with multivariate analysis for quality control and authentication of clove buds powders as well as their oils. Unsupervised and supervised chemometric analysis techniques, including principal component analysis (PCA), data driven soft independent modeling of class analogy (DD-SIMCA) were implemented to authenticate clove and its oil and distinguish them from their adulterants. The SIMCA model showed 100% sensitivity and specificity values in detecting adulterants in clove powder samples and clove oil samples. This showcases the accuracy and reliability of the DD-SIMCA approach in accurately distinguishing between different classes.The models were validated using test samples, and the absence of noise modeling was confirmed through permutation. PLS regression analysis was utilized to measure levels of adulterants in clove powder and clove oil samples. The models produced good results, with RMSEC values ranging from 0.68 to 1.5% for clove oil and from 0.96 to 1.27% for clove powder. External validation was performed and the limits of quantitation ranged from 1.8% to 4.9% for clove powder and clove oil. The models can detect sample adulteration and ensure authenticity. The results showed that the suggested method and models can be used to detect sample adulteration, ensure authenticity, and have high sample throughput. The method had several advantages, including simplicity, speed of analysis, with no sample preparation needed.

Chemical and physical studies of metallic alloy-based old Indian coins with LIBS coupled with multivariate analysis

The present work aims to demonstrate the capabilities of Laser-induced Breakdown Spectroscopy (LIBS) coupled with a multivariate technique for rapid quantification and classification of old Indian coins made of various alloys. Thirteen old Indian coins in different years of circulation, (1922–1986) were selected for the study. The concentrations were determined by Calibration free LIBS (CF-LIBS) method. The concentration of cuprum (Cu) is negligible, and aluminum (Al) is maximum in the first five coins, and vice-versa in the remaining eight coins. Two different multivariate methods, Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) have been used to classify and identify the coins. PCA classified all thirteen samples into four main alloy categories. The discernment of unknown samples to their probable class membership of alloy was performed using SIMCA. The surface hardness (Brinell hardness number) is linearly correlated with the plasma temperature and LIBS intensity ratios. The sample surface of the first and fifth coin belongs to Al-alloy, having the least surface hardness, and it became harder for Cu–Ni alloy, Ni-brass alloy, and bronze alloy. The hardness of the surface is more for bronze sample twelve. It is also observed that the plasma temperature increases monotonically with the Brinell hardness number. This analysis provides valuable information on fabrication methodology and explains large diversification in the elementary composition of old coins.

Multi-block DD-SIMCA as a high-level data fusion tool

Multi-block classification method based on the Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) is presented. A high-level data fusion approach is used for the joint analysis of data collected with the help of different analytical instruments. The proposed fusion technique is very simple and straightforward. It uses a Cumulative Analytical Signal which is a combination of outcomes of the individual classification models. Any number of blocks can be combined. Although the high-level fusion eventually leads to a rather complex model, the analysis of partial distances makes it possible to establish a meaningful relationship between the classification results and the influence of individual samples and specific tools.Two real world examples are used to demonstrate the applicability of the multi-block algorithm and the consistency of the multi-block method with its predecessor, a conventional DD-SIMCA.

Application of NIR spectroscopy coupled with DD-SIMCA class modelling for the authentication of pork meat

Rearing system, breed, geographical origin, and animal welfare are some of the more recent quality features able to drive consumer’s choice in meat and meat products purchase. The same aspects are marketing leverage from the producer’s side. However, there is a need to develop rapid analytical strategies to check the compliance of declared quality with actual quality features. To face with this issue, this work aims at test the feasibility of using the fat portion as a marker of authenticity. To this end, fat samples from extensively and intensively reared pigs have been collected and analysed by NIRS (Near Infra-Red Spectroscopy). Then, after multivariate exploration, the Data Driven variant of Soft Independent Modelling of Class Analogy (DD-SIMCA) was used to develop models for the target class. The type of signal pre-processing and the number of principal components (PCs) used strongly affected the performance of the classification. Excellent results were obtained with SNV pre-treated data and 4 PCs that allowed to reach 100% sensitivity and specificity in calibration and validation.

A green method for the authentication of sugarcane spirit and prediction of density and alcohol content based on near infrared spectroscopy and chemometric tools

Cachaça is a Brazilian beverage obtained from the fermentation of sugarcane juice (sugarcane spirit) and is considered one of the most consumed alcoholic beverages in the world with a strong economic impact on the northeastern Brazil, more specifically in the Brejo. This microregion produces sugarcane spirits with high quality associated to edaphoclimatic conditions. In this sense, analysis for sample authentication and quality control that uses solvent-free, environmentally friendly, rapid and non-destructive methods is advantageous for cachaça producers and production chain. Thus, in this work commercial cachaça samples using near-infrared spectroscopy (NIRS) were classified based on geographical origin using one-class classification Data-Driven in Soft Independent Modelling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) and predicted quality parameters of alcohol content and density based on different chemometric algorithms. A total of 150 sugarcane spirits samples were purchased from the Brazilian retail market being 100 from Brejo and 50 from other regions of Brazil. The one-class chemometric classification model was obtained with DD-SIMCA using the Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as preprocessing algorithm and sensibility was 96.70 % and specificity 100 % in the spectral range 7,290–11,726 cm−1. Satisfactory results were obtained in the model constructs for density and the chemometric model, iSPA-PLS algorithm with baseline offset as preprocessing, obtained root mean square errors of prediction (RMSEP) of 0.0011 mg/L and Relative Error of Prediction (REP) of 0.12 %. The chemometric model for alcohol content prediction used the iSPA-PLS algorithm with Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as algorithm as preprocessing obtaining RMSEP and REP of 0.69 and 1.81 % (v/v), respectively. Both models used the spectral range from 7,290–11,726 cm−1. The results reflected the potential of vibrational spectroscopy coupled with chemometrics to build reliable models for identifying the geographical origin of cachaça samples for predicting quality parameters in cachaça samples.

Comparative study of the efficacy of lens extraction and laser peripheral iridotomy in primary anterior chamber angle closure

PURPOSE. To compare the anatomical and functional effectiveness of lensectomy (LE) and laser peripheral iridotomy (LPI) in patients with primary anterior chamber angle closure.METHODS. This prospective study included 120 patients aged 41 to 80 years (60 eyes — primary angle closure (PAC), 30 — primary angle closure suspects (PACs), 30 eyes — without ophthalmic pathology). 30 PAC eyes were treated using LE with intraocular lens implantation, and 30 eyes with LPI. All subjects underwent swept source optical coherence tomography (SS-OCT). The following parameters were analyzed: spherical equivalent (SE), uncorrected and corrected distance visual acuity, intraocular pressure (IOP), Shaffer grade of angle opening, lens opacity, goniosyne chiae, choroidal thickness in the macular region, axial length (AL) of the eye, anterior chamber depth (ACD), lens vault (LV), iris curvature (ICurv), iris thickness at 750 µm from scleral spur (IT750), angle opening distance (AOD500, AOD750), iridotrabecular space area (TISA500, TISA750). Along with standard descriptive statistics methods, machine learning methods were used, including Data Driven Soft Independent Modelling of Class Analogies (DD-SIMCA).RESULTS. Every third eye with PAC had reached control group values after LE (DD-SIMCA specificity 0.67), which was not the case after LPI (DD-SIMCA specificity 1.0) with the probability of getting into the control group estimated as 0.01. After LE, all parameters of the anterior chamber angle did not significantly differ from the norm (p>0.05 for all), while ACD, ICurv even exceeded the norm (p=0.000). After LPI, there was a deepening of the ACD (from 2.34±0.28 mm to 2.36±0.280 mm, p=0.000) and a decrease in LV (from 0.864±0.120 µm to 0.843±0.110 µm, p=0.000), however, the result comparable to control was achieved only in ICurv (p=1.000). After LE in PAC, all parameters, including uncorrected visual acuity, SE, ACD, LV, iris profile, Shaffer grade of angle opening, and AOD500, AOD750, TISA500, TISA750 parameters in the superior and inferior sectors had advantages over those in PACs without treatment (p<0.05). After LPI, an improvement in a number of pa rameters was also achieved compared to LPI: ICurv, Shaffer grade of angle opening, AOD500, AOD750, TISA500, TISA750 in the superior sectors and AOD500 in the inferior sector (p><0.05).CONCLUSION. The effectiveness of LE in PAC is higher than LPI due to the better postoperative anterior chamber topography and lower IOP. Both treatments, especially LE, improve these parameters compared to PACs. Lens extraction is the treatment of choice at the early stages of primary angle closure.

Edibility and species discrimination of wild bolete mushrooms using FT-NIR spectroscopy combined with DD-SIMCA and RF models

Mushroom poisoning is a highly publicized food safety concern, and commercial fraud by adulterating cheap species in the wild mushroom supply chain often occurs. To safeguard the lives and property of consumers, it is necessary to develop accurate methods to discriminate the edibility and species of wild mushrooms. Near-infrared (NIR) spectroscopy has become a popular analytical tool due to its key advantages. This work aimed to investigate the feasibility of discriminating the edibility and species of wild bolete mushrooms using NIR spectroscopy-based two-dimensional correlation spectroscopy (2DCOS) combined with Red-Green-Blue (RGB) image analysis and multivariate analysis methods. The results showed that the data driven version of soft independent modeling of class analogy (DD-SIMCA) model had excellent sensitivity (0.98) but the specificity was only 0.59. This model was not suitable to accurately discriminate the edibility of boletes, but can be used for screening a potentially poisonous species of boletes (Caloboletus calopus). As for species discrimination, random forests (RF) model had great classification and generalization ability and the accuracy of the training set and test set was 97.22% and 100%, respectively. Therefore, FT-NIR spectroscopy combined with DD-SIMCA and RF models had the potential to discriminate the edibility and species of wild bolete mushrooms.

Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods.

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas ('non-Zhejiang'). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R2 reaching 0.9979, while that of the second derivative was the lowest with an R2 of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

Classification of instant coffees based on caffeine content and roasting degree using NIR spectrometry and multivariate analysis

Instant coffee is a beverage obtained from the dehydration of roasted coffee extract. Its quality depends on several factors, such as the type of bean and grinding, drying decaffeination, and roasting degree processes. In this work, NIR spectrometry and multivariate classification tools were combined to develop, for the first time, methods for authenticating and checking the compliance of decaffeinated instant coffees in order to ensure consumer food safety, as well as discriminating between regular instant coffees according to the roasting degree (traditional and extra-strong) for monitoring the production chain of regular instant coffees. First, one-class methods were built and validated to authenticate decaffeinated instant coffees and to in order to alert caffeine-sensitive consumers to potential risks. For this purpose, two near-infrared (NIR) instruments (benchtop and portable) and Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OC-PLS) were used. After Partial Least Squares – Discriminant Analysis (PLS-DA), Successive Projections Algorithm for Interval Selection in Partial Least Squares – Discriminant Analysis (iSPA-PLS-DA), and the same NIR instruments were used to build and validate multiclass methods to discriminate regular instant coffees according to the roasting degree (traditional and extra-strong). The result for the classification of decaffeinated and roasting degree instant coffees was 100% and 98% correct classification rate using DD-SIMCA and iSPA-PLS-DA, respectively. Therefore, the combination of NIR spectroscopy and multivariate classification techniques proved valuable and fast tools for checking the quality of instant coffees.

Evaluation of VOCs from fungal strains, building insulation materials and indoor air by solid phase microextraction arrow, thermal desorption–gas chromatography-mass spectrometry and machine learning approaches

Solid phase microextraction Arrow and thermal desorption–gas chromatography–mass spectrometry allowed the collection and evaluation of volatile organic compounds (VOCs) emitted by fungal cultures from building insulation materials and in indoor air. Principal component analysis, linear discriminant analysis and supported vector machine were used for visualization and statistical assessment of differences between samples. In addition, a screening tool based on the soft independent modelling of class analogies (SIMCA) was developed for identification of fungal contamination of indoor air. Ten different fungal strains, incubated under ambient and microaerophilic conditions, were analyzed for time period ranging from 5 to 29 days after inoculation resulting in a total of 140 samples. In addition, the effect of additives on the fungal growing media was studied. The total number of compounds and concentration values were used for the evaluation of the results. Clear differences were observed for VOC profiles emitted by different fungal strains by exploiting long chain alcohols (3-octanol, 1-hexanol and 2-octen-1-ol) and sesquiterpenes (farnesene, cuprene). The analysis of glass-wool and cellulose based building insulation materials (3 samples) gave clear differences, mainly for oxygenated compounds (ethyl acetate and hexanal) and benzenoids (benzaldehyde). Moreover, the comparison of indoor air and insulation materials collected from a house with fungal indoor air problems indicated that 42% of the VOCs were found in both samples. The analysis of 52 indoor air samples demonstrated clear differences in their VOC profiles, especially for hydrocarbons, and between control (44 samples) and indoor air problem houses (8 samples). Finally, the SIMCA model enabled to recognize differences between control and fungi contaminated houses with a prediction capacity over 84%.

Botanical Authentication Using One-Class Modeling.

Authentication methods are necessary to guarantee the integrity of botanical supplements and their ingredients. In 2012, AOAC International published "Guidelines for Validation of Botanical Identification Methods" however these guidelines proved rather cumbersome. Develop a simpler method for validatation based on one-class modeling that only considers authentic materials. One-class modeling uses chemometric analysis based on soft independent modeling of class analogy and the specific pre-processing steps of sample vector normalization and autoscaling. Any unknown sample can be judged authentic or adulterated based on its agreement with the profile of the authentic samples. The sensitivity and accuracy of one-class modeling is improved using sample vector normalization and autoscaling. The limit of detection for any variable is statistically predictable. One-class modeling offers a simple approach to authentication and is applicable to any non-targeted analytical method. Only the characteristics of the authentic samples are necessary and no specification of an adulterant is necessary. One-class modeling offers a simple approach to authentication and is easily implemented using any chemometrics platform.

Fourier Transform Infrared Spectroscopy and Chemometrics for the Rapid Screening of Economically Motivated Adulteration of Honey Spiked With Corn or Rice Syrup

Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1–16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.

Determination of Possible Adulteration and Quality Assessment in Commercial Honey.

This study aims to predict several quality traits in commercial honey samples simultaneously and to reveal possible honey adulteration using a field-deployable portable infrared spectrometer without any sample preparation. A total of one hundred and forty-seven commercial honey samples were purchased from local and online markets in Turkey and the United States of America (USA), and their soluble solids (°Brix), pH, free acidity, moisture, water activity (aw), glucose, fructose, sucrose, and hydroxymethyl furfural (HMF) contents were determined using reference methods. The HMF (n = 11 samples) and sucrose (n = 21) concentrations were higher than the regulatory limits in some tested samples. The exceeding HMF content may imply temperature abuse during storage and prolonged storing. On the other hand, high sucrose content may indicate possible adulteration with commercial sweeteners. Therefore, soft independent modeling of class analogies (SIMCA) analysis was conducted to reveal this potential sweetener adulteration in the samples, and the SIMCA model was able to identify all the flagged samples. The suggested FT-IR technique may be helpful in regulatory bodies in determining honey authenticity issues as well as assessing the quality characteristics of honey samples in a shorter period and at a lower cost.

Comprehensive Metabolomic Fingerprinting Combined with Chemometrics Identifies Species- and Variety-Specific Variation of Medicinal Herbs: An Ocimum Study.

Identification of plant species is a crucial process in natural products. Ocimum, often referred to as the queen of herbs, is one of the most versatile and globally used medicinal herbs for various health benefits due to it having a wide variety of pharmacological activities. Despite there being significant global demand for this medicinal herb, rapid and comprehensive metabolomic fingerprinting approaches for species- and variety-specific classification are limited. In this study, metabolomic fingerprinting of five Ocimum species (Ocimum basilicum L., Ocimum sanctum L., Ocimum africanum Lour., Ocimum kilimandscharicum Gurke., and Hybrid Tulsi) and their varieties was performed using LC-MS, GC-MS, and the rapid fingerprinting approach FT-NIR combined with chemometrics. The aim was to distinguish the species- and variety-specific variation with a view toward developing a quality assessment of Ocimum species. Discrimination of species and varieties was achieved using principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA), data-driven soft independent modelling of class analogy (DD-SIMCA), random forest, and K-nearest neighbours with specificity of 98% and sensitivity of 99%. Phenolics and flavonoids were found to be major contributing markers for species-specific variation. The present study established comprehensive metabolomic fingerprinting consisting of rapid screening and confirmatory approaches as a highly efficient means to identify the species and variety of Ocimum, being able to be applied for the quality assessment of other natural medicinal herbs.

Discrimination of different edible vegetable oils based on GC-IMS and SIMCA

ABSTRACT Ion mobility spectrometry coupled to gas-chromatographic (GC-IMS) is tested regarding their ability to analyze various refined edible vegetable oils, including sunflower seed, rapeseed, sesame, soybean, peanut, corn, camellia, linseed, walnut, coconut, grape seed and extra virgin olive oils. GC-IMS assay displays peak difference of each edible vegetable oil in three-dimensional information at retention time in gas phase and at ion mobility rate in IMS. Moreover, 74 main peak intensities are extracted and imported into Excel for analysis. Then, chemometric methods are employed to establish discriminant models. The results show that based on Kennards-Stone (KS), the prediction accuracy of the soft independent modeling of class analogy (SIMCA) is perfect. Therefore, the GC-IMS system is shown to be an effective method to classification of edible vegetable oils.

Application of Near Infrared Spectroscopy to Monitor the Quality Change of Sour Cherry Stored under Modified Atmosphere Conditions.

Determining and applying ‘good’ postharvest and quality control practices for otherwise highly sensitive fruits, such as sour cherry, is critical, as they serve as excellent media for a wide variety of microbial contaminants. The objective of this research was to report two series of experiments on the modified atmosphere storage (MAP) of sour cherries (Prunus cerasus L. var. Kántorjánosi, Újfehértói fürtös). Firstly, the significant effect of different washing pre-treatments on various quality indices was examined (i.e., headspace gas composition, weight loss, decay rate, color, firmness, soluble solid content, total plate count) in MAP-packed fruits. Subsequently, the applicability of near infrared (NIR) spectroscopy combined with chemometrics was investigated to detect the effect of various storage conditions (packed as control or MAP, stored at 3 or 5 °C) on sour cherries of different perceived ripeness. Significant differences were found for oxygen concentration when two perforations were applied on the packages of ‘Kántorjánosi’ (p < 0.01); weight loss when ‘Kánorjánosi’ (p < 0.001) and ‘Újfehértói fürtös’ (p < 0.01) were packed in MAP; SSC when ‘Újfehértói fürtös’ samples were ozone-treated (p < 0.05); and total plate count when ‘Kántorjánosi’ samples were ozone-treated (p < 0.01). The difference spectra reflected the high variability in the samples, and the detectable effects of different packaging. Based on the investigations with the soft independent modelling of class analogies (SIMCA), different packaging and storage resulted in significant differences in most of the cases even on the first storage day, which in many cases increased by the end of storage. The soft independent modelling of class analogies proved to be suitable for classification with apparent error rates between 0 and 0.5 during prediction regardless of ripeness. The research findings suggest the further correlation of NIR spectroscopic and reference parameters to support postharvest handling and fast quality control.

Application of infrared spectroscopy in combination with chemometric algorithms for determining the presence of styrene-butadiene block copolymer in bitumen products

At present polymer-bitumen binders (PBB), including those containing styrene-butadiene copolymer (SBC), are widely used for improving the performance of asphalt concrete coverings. Qualitative detection of the polymer in the petroleum bitumen composition is a difficult research task owing to the low content of the polymer in the PBB composition. Existing methods for determining the polymer in petroleum bitumen are based on a complex multi-stage extraction or on measuring the specific characteristic viscosity of the polymer in the analyzed PBB. These methods are time-consuming and labor-intensive, and require application of a number of specific organic solvents and reference standards. The present work considers the possibility of applying infrared spectroscopy of disturbed total internal reflection (ATR) with subsequent processing of spectra using chemometric algorithms in the study of PBB containing SBC. Application of the principal component analysis (PCA), as well as one-class classification method, the method of soft independent modeling of class analogy (DD-SIMCA), is illustrated. The use of infrared spectroscopy in combination with chemometric data processing methods greatly simplifies and accelerates the research task of detection SBS in bitumen products. The objective of this work was to solve the classification and identification problem in the course of studying bitumen products, i.e. attributing/non-attributing a bitumen product to PBB with SBS.

MACHINE LEARNING-BASED VIRTUAL SCREENING, MOLECULAR DOCKING, DRUG-LIKENESS, PHARMACOKINETICS AND TOXICITY ANALYSES TO IDENTIFY NEW NATURAL INHIBITORS OF THE GLYCOPROTEIN SPIKE (S1) OF SARS-CoV-2

To identify natural bioactive compounds (NBCs) as potential inhibitors of spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed greater affinity with the S1 (affinity energy &lt; -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15 and NBC27 had better results in ADMET predictions. These had similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.

An intelligent extraction approach of feature information from three-dimensional DAD chromatogram for integrated quality control of traditional Chinese medicines: Gardenia jasminoides root as an example.

Traditional Chinese medicine (TCM) fingerprinting, which has the characteristics of holism and ambiguity, is a conventional strategy for the holistic quality control of TCMs. However, the fingerprinting of TCMs at the current stage generally adopts a single wavelength or few wavelengths, lacking the effective utilization of diode-array detector (DAD) chromatogram data. This study proposes an intelligent extraction approach of feature information from a three-dimensional DAD chromatogram to establish a novel bar-form-diagram (BFD) for integrated quality control of TCMs. The BFD was automatically established by the chromatographic and spectral information of a complex hybrid system in a DAD chromatogram. This covered the peak areas of target compositions at the optimal absorption wavelength. Taking 27 batches of Gardenia jasminoides root as samples, the BFD combined with chemometrics was applied for assessing the quality of samples completely, which improved the accuracy of origin classification using hierarchical cluster analysis, principal component analysis, soft independent modeling of class analogy and orthogonal partial least squares discriminant analysis. Single-wavelength fingerprinting and BFD used 23 and 38 common peaks as variables respectively, and the adjusted rand index results of the single wavelength and BFD were 0.559 and 0.819, respectively. Compared with the ergodic methods of each single wavelength, the peak recognition method in this study improved the operation speed from 180 s to 4 s and the computational complexity. The established BFD approach performed more abundant characteristic information of chemical components of TCMs and more accurate origin classification ability, and it had great advantages in the overall quality control of TCMs.