Extraction Method Research Articles

BEBOP VI. Enabling the detection of circumbinary planets orbiting double-lined binaries with the DOLBY method of radial–velocity extraction

ABSTRACT Circumbinary planets, orbiting both stars in a binary system, offer the opportunity to study planet formation and orbital migration in an environment different from that around single stars. However, despite the fact that $\gt 90~\% $ of binary systems in the solar neighbourhood are spectrally resolved double-lined binaries, there has been only one detection of a circumbinary planet orbiting a double-lined binary using the radial velocity method so far. Spectrally disentangling binary components is challenging due to blending of spectral lines and inaccuracies in spectral modelling. These inaccuracies add scatter to the measurements, which can hide the weak radial velocity signature of circumbinary exoplanets. We have obtained new high signal-to-noise, high-resolution spectra with the SOPHIE spectrograph, mounted on the 193 cm telescope at Observatoire de Haute-Provence (OHP), for six bright, double-lined binaries for which circumbinary exoplanet detection has been attempted in the past. To extract radial velocities, we use the DOLBY code, a recent method of spectral disentangling using Gaussian processes to model the time-varying components. We analyse the resulting radial velocities with a diffusive nested sampler to seek planets, and compute sensitivity limits. We do not detect any new circumbinary planet. However, we show that the combination of new data, new radial velocity extraction methods, and improved statistical methods to determine a data set’s sensitivity to planets leads to an approximately one order of magnitude improvement compared to previous results. This improvement brings us into the range of known circumbinary exoplanets and paves the way for future observation campaigns targeting double-lined binaries.

Efficient band reduction for hyperspectral imaging with dependency-based segmented principal component analysis

ABSTRACT In the context of hyperspectral image (HSI) analysis, a widely used feature extraction method, Principal Components Analysis (PCA) suffers from limitations such as wavelength bias and a lack of consideration for local spectral information. While various segmentation based PCA methods attempt to address these issues by incorporating local relationships, they still overlook band similarity beyond immediate neighbours. To address these challenges, this paper introduces a novel approach called dependency based segmented PCA (dPCA). This method employs hierarchical clustering-driven mutual information-based segmentation, facilitating more comprehensive feature extraction from HSI data. By utilizing this dependency-based segmentation, both global and local structures are effectively captured, providing enhanced details for classification tasks. The proposed dPCA is evaluated on four prominent HSI datasets in remote sensing for land use classification, and the experimental results underscore its superiority over conventional PCA, and other segmentation based PCA methods in terms of classification performance.

Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops

This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.

Method development, multi-residue determination, and dietary exposure risk assessment of plant growth regulators in homologous materials of medicine and food.

Residues of plant growth regulators (PGRs) in homologous materials of medicine and food threaten public health. This study aimed to develop a rapid, sensitive, and high-throughput method for simultaneously determining 16 PGR residues in homologous materials of medicine and food. Furthermore, the established method was applied to actual samples to assess the potential exposure risk of multi-PGR residues. A modified high-throughput quick, easy,cheap, effective, rugged, and safe (QuEChERS) method coupled with ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was developed and validated. The extraction solvent, type of extraction method, and subsequent purification techniques were investigated to achieve a better analysis of the target. Risk assessment was based on chronic dietary risk assessment. Ultrasonic extraction with 1% formic acid-acetonitrile was employed, and MgSO4 + NaAc was selected as the clean-up sorbent. The 16 PGRs showed a good linear relationship in the range of 1 ~ 200 μg/L (r ≥ 0.9960), with detection limits ranging from 0.3 to approximately 3 μg/kg. The recovery rate ranged from 65 to 109%, with RSD from 0.01 to 10% (n = 6). The total detection rate of 16 PGRs in the samples was 87%. The risk assessment indicated that the multi-residues of PGRs in homologous materials of medicine and food would not pose a potential risk to human health. This work provides a valuable reference for the monitoring of multiple PGRs. It has also improved our understanding of the possible exposure risk of PGR residues in homologous materials of medicine and food.

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

Highly Sensitive Molecular Diagnostic Platform for Scrub Typhus Diagnosis Using O. tsutsugamushi Enrichment and Nucleic Acid Extraction.

Scrub typhus is caused by the Gram-negative obligate intracellular bacterium Orientia tsutsugamushi, and this tick-borne disease is difficult to distinguish from other acute febrile illnesses as it typically presents with symptoms such as rash, crusting at the bite site, headache, myalgia, lymphadenopathy, and elevated liver transaminases. It can often be diagnosed clinically, but not all patients present with characteristic symptoms, so serological diagnosis and molecular techniques may be required. However, existing diagnostic tests often have low sensitivity and specificity, making early detection difficult. This study presents a nucleic acid extraction method using large volumes of plasma and buffy coat to increase sensitivity, as well as an improved detection method using two target genes. Using the I-PULL device, nucleic acids can be extracted from up to 4 mL of sample in 30 min, avoiding contamination. The extracted DNA detects two genes of O. tsutsugamushi, increasing sensitivity compared to single-gene detection. Clinical validation in 38 patient samples showed 100% specificity and 95.24% sensitivity for the single target gene, with specificity and sensitivity rising to 100% when both genes are analyzed. This molecular diagnostic platform can be useful for distinguishing scrub typhus from similar diseases.

Comparison of Three Different Extraction Methods on Schizonepeta tenuifolia (Benth.) Briq Essential Oil: Chemical Constituents and in Vitro and in Silico Biological Activities.

This study evaluated the effects of three extraction methods, hydrodistillation (HD), steam distillation (SD), and lipophilic solvent extraction (LSE), on the yield, chemical composition, and in vitro / in silico biological activities of Schizonepeta tenuifolia (Benth.) Briq essential oil (STEO). The highest yield of STEO was obtained by HD (0.750±0.040%), followed by SD (0.440±0.020%) and LSE (0.350±0.030%). Although their contents varied, menthone and pulegone predominated in HDEO, SDEO, and LSEO. HDEO displayed the strongest antioxidant ability, with a 2,2-diphenyl-1-picrylhydrazyl (DPPH) IC50 value of 14.164±0.090 mg/mL and 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonate) (ABTS) value of 0.326±0.023 mg/mL. SDEO demonstrated the highest antimicrobial activity against Staphylococcus aureus and Escherichia coli and HDEO the highest potent anti-inflammatory activity. The molecular docking of menthone and pulegone demonstrated strong binding to inflammatory targets, including prostaglandin-endoperoxide synthase 1 (PTGS1) and 2 (PTGS2) and tumor necrosis factor alpha (TNF-α). In short, the extraction method significantly affected the yield, composition, and biological activity of STEO.

Proteomic Analysis of Human Serum Proteins Adsorbed onto Collagen Barrier Membranes.

Collagen barrier membranes are frequently used in guided tissue and bone regeneration. The aim of this study was to analyze the signature of human serum proteins adsorbed onto collagen membranes using a novel protein extraction method combined with mass spectrometry. Native porcine-derived collagen membranes (Geistlich Bio-Gide®, Wolhusen, Switzerland) were exposed to pooled human serum in vitro and, after thorough washing, subjected to protein extraction either in conjunction with protein enrichment or via a conventional surfactant-based method. The extracted proteins were analyzed via liquid chromatography with tandem mass spectrometry. Bioinformatic analysis of global profiling, gene ontology, and functional enrichment of the identified proteins was performed. Overall, a total of 326 adsorbed serum proteins were identified. The enrichment and conventional methods yielded similar numbers of total (315 vs. 309), exclusive (17 vs. 11), and major bone-related proteins (18 vs. 14). Most of the adsorbed proteins (n = 298) were common to both extraction groups and included several growth factors, extracellular matrix (ECM) proteins, cell adhesion molecules, and angiogenesis mediators involved in bone regeneration. Functional analyses revealed significant enrichment of ECM, exosomes, immune response, and cell growth components. Key proteins [transforming growth factor-beta 1 (TGFβ1), insulin-like growth factor binding proteins (IGFBP-5, -6, -7)] were exclusively detected with the enrichment-based method. In summary, native collagen membranes exhibited a high protein adsorption capacity in vitro. While both extraction methods were effective, the enrichment-based method showed distinct advantages in detecting specific bone-related proteins. Therefore, the use of multiple extraction methods is advisable in studies investigating protein adsorption on biomaterials.

Effects of acid, alkaline and enzymatic extraction methods on functional, structural and antioxidant properties of dietary fiber fractions from quince (Cydonia oblonga Miller)

In this study, quince soluble dietary fiber (SDF) and insoluble dietary fiber (IDF) were obtained by acid extraction, enzyme extraction and alkaline extraction methods. The acid extracted DF displayed higher results compared to enzyme and alkaline extraction methods in terms of water holding capacity (15.97 g/g SDF), oil holding capacity (1.05 g/g SDF) and nitrite ion adsorption capacity (92.83 mg/g SDF). The antioxidant activity and phenolic content of acid extracted IDF were significantly higher than the other quince DFs. In addition, quince DFs exhibited in vitro hypoglycaemic activity, exhibiting high glucose adsorption capacity (237 mg/g) and α-amylase inhibition activity (82 %). Similarly, acid extracted SDF of quince showed in vitro hypolipidemic activity, with cholesterol adsorption capacity of 155 mg/g and lipase inhibition activity of 36 %. The structures and thermal properties of quince DFs were characterized by FT-IR and TGA. Quince DFs with high functional properties might be suitable agents for functional food formulations, such as meat products, low-calorie fruit bars, flour mixtures, etc.

Evaluation of protein extraction protocols for MALDI-TOF Biotyper analysis of mycobacteria

Infections caused by Mycobacterium tuberculosis and nontuberculous mycobacteria represent a significant global threat and medical concern. Therefore, accurate and reliable methods must be employed to identify mycobacteria rapidly. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a technique that compares the cellular protein profiles of unknown isolates with reference mass spectra in a database to identify microorganisms. However, the thick and waxy lipid layer, which is rich in mycolic acids and is present in mycobacterial cells, makes protein extraction challenging. To identify the optimal protocol for correctly identifying bacilli using MALDI-TOF mass spectrometry, this study compared four different cellular protein extraction methods. Four strains of M. bovis BCG were selected as representatives of slow-growing mycobacteria, while three strains of fast-growing mycobacteria were also included: M. peregrinum, M. smegmatis, and M. farcinogenes. The extraction method that proved most effective was the extraction of inactivated cells with chloroform and methanol, which partially delipidates the cells. These cells were then extracted with formic acid, as is standard practice for protein extraction. The advantage of this method is that it allows the parallel analysis of cellular lipids and proteins from a single sample. It is therefore important to optimize mycobacterial protein extraction for MALDI-TOF MS analysis in clinical microbiology laboratories.

Optimized biomedical entity relation extraction method with data augmentation and classification using GPT-4 and Gemini.

Despite numerous research efforts by teams participating in the BioCreative VIII Track 01 employing various techniques to achieve the high accuracy of biomedical relation tasks, the overall performance in this area still has substantial room for improvement. Large language models bring a new opportunity to improve the performance of existing techniques in natural language processing tasks. This paper presents our improved method for relation extraction, which involves integrating two renowned large language models: Gemini and GPT-4. Our new approach utilizes GPT-4 to generate augmented data for training, followed by an ensemble learning technique to combine the outputs of diverse models to create a more precise prediction. We then employ a method using Gemini responses as input to fine-tune the BioNLP-PubMed-Bert classification model, which leads to improved performance as measured by precision, recall, and F1 scores on the same test dataset used in the challenge evaluation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-viii/track-1/.

A sustainable and clean method for vanadium extraction from shale ore by suspension oxidation roasting: A comparative study of leaching behavior

Vanadium (V) is an important rare metal element that is widely applied in a variety of fields. It is necessary to develop an efficient method for the utilization of V-bearing shale ore. In this work, we investigated the mechanism of enhanced vanadium extraction from shale ore by suspension oxidation roasting (SOR) by comparing the leaching behavior between traditional direct acid leaching and SOR-acid leaching. After being roasted, the V leaching rate obviously increased from 60.33 % to 83.20 % while the H2SO4 dosage, leaching temperature, and leaching time decreased 20 wt%, 30 °C, and 5 h, respectively. The non-appearance of CaF2 alleviated environment pollution from wastewater. Roasting activation improved the chemical reaction activity and transformed the dense structure into a loose and porous structure which contributed to improving the diffusion rate of acid into particles and the solutions. Kinetic studies shown that SOR contributed to decrease the activation energy of leaching system and solid layer or solution boundary layer diffusion contributed to the control of roasted product leaching rate.

Phage communities in household-related biofilms correlate with bacterial hosts

The average American spends 93% of their time in built environments, almost 70% of that is in their place of residence. Human health and well-being are intrinsically tied to the quality of our personal environments and the microbiomes that populate them. Conversely, the built environment microbiome is seeded, formed, and re-shaped by occupant behavior, cleaning, personal hygiene and food choices, as well as geographic location and variability in infrastructure. Here, we focus on the presence of viruses in household biofilms, specifically in showerheads and on toothbrushes. Bacteriophage, viruses that infect bacteria with high host specificity, have been shown to drive microbial community structure and function through host infection and horizontal gene transfer in environmental systems. Due to the dynamic environment, with extreme temperature changes, periods of wetting/drying and exposure to hygiene/cleaning products, in addition to low biomass and transient nature of indoor microbiomes, we hypothesize that phage host infection in these unique built environments are different from environmental biofilm interactions. We approach the hypothesis using metagenomics, querying 34 toothbrush and 92 showerhead metagenomes. Representative of biofilms in the built environment, these interfaces demonstrate distinct levels of occupant interaction. We identified 22 complete, 232 high quality, and 362 medium quality viral OTUs. Viral community richness correlated with bacterial richness but not Shannon or Simpson indices. Of quality viral OTUs with sufficient coverage (614), 532 were connected with 32 bacterial families, of which only Sphingomonadaceae, Burkholderiaceae, and Caulobacteraceae are found in both toothbrushes and showerheads. Low average nucleotide identity to reference sequences and a high proportion of open reading frames annotated as hypothetical or unknown indicate that these environments harbor many novel and uncharacterized phage. The results of this study reveal the paucity of information available on bacteriophage in indoor environments and indicate a need for more virus-focused methods for DNA extraction and specific sequencing aimed at understanding viral impact on the microbiome in the built environment.

An electrocardiogram signal classification using a hybrid machine learning and deep learning approach

An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection.

Oxygen exposure decreases the yield of high-molecular-weight DNA from some anaerobic bacteria and bacterial communities during DNA extraction

ObjectivesThe central challenge in third-generation sequencing lies in meeting the requirements for DNA quality (integrity and purity) and quantity. Therefore, novel improvements in DNA extraction methods are needed to satisfy these requirements. We reasoned that in anaerobic microbial communities, the presence of certain strict anaerobes containing oxygen-activated DNase activity might contribute substantially to the poor integrity of extracted metagenomic DNA (or genomic DNA from some pure cultures) if exposed to air. MethodsTo test this hypothesis, we developed an enhanced genomic and metagenomic DNA isolation technique that we applied to a specifically chosen set of both strict and aerotolerant anaerobes, as well as to the hindgut microbiota of a herbivorous marine fish. ResultsConsidering the quality (or degradation) of extracted DNA obtained under anaerobic versus aerobic conditions, we found that DNA extracted aerobically from cells of some strict anaerobes showed more degradation of high molecular weight DNA than analogous preparations under anaerobic conditions. In contrast, with the selected aerotolerant anaerobes, no discernible difference was found between the molecular sizes of DNA extracted aerobically and anaerobically. Metagenomic DNA extracted from the fish hindgut microbiota showed higher yields and better quality under anaerobic conditions compared to aerobic conditions. ConclusionOur study effectively demonstrates the advantages of our improved extraction protocol in anaerobic conditions. This is evident through the improved quality of extracted DNA. Such findings may be valuable for studies, especially metagenomic studies, where the quality and quantity of DNA are crucial for downstream analysis.

The Optimization of the Hot Water Extraction of the Polysaccharide-Rich Fraction from Agaricus bisporus

The optimization of extraction parameters, including the process time, temperature, and liquid-to-solid ratio, was conducted in order to obtain the polysaccharide-rich fraction from the lyophilized Agaricus bisporus fruiting body. The efficiency of extraction for polysaccharides and antioxidant activity was determined by analyzing the extracts for total carbohydrate content, the reducing sugars content, and the antioxidant activity employing DPPH, ABTS, and hydroxyl radical scavenging assays. The results showed that all parameters, except for the extraction time, impacted differently on the extraction efficiency of polysaccharides and antioxidant activity. The highest total carbohydrate content was observed at the longest process time, highest temperature, and a liquid-to-solid ratio of 118 mL/g. To minimize the reducing sugar level, a lower temperature is required, while the highest antioxidant activity requires a moderate temperature and the lowest liquid-to-solid ratio. The optimization of antioxidant activity by means of the DPPH and H2O2 method failed, which shows that the specific mechanism of polysaccharides as antioxidants needs further investigation. The aqueous extraction method demonstrated to be an efficient and simple approach to recover the potentially bioactive polysaccharide fractions from Agaricus bisporus that are also active as antioxidants.

New Refined Experimental Analysis of Fungal Growth in Degraded Bio-Based Materials

When exposed to different building environmental conditions, bio-composite materials, such as hemp mortars, represent a risk of mold proliferation. This later plays a critical role in the biodeterioration of the materials when their physical properties are locally modified by the natural aging process. The primary objectives of the present work are first to assess the evolution of the surface of contaminated mortar; second, to investigate an accurate DNA extraction method that could be used for both bio-composite mortars and their fiber sources collected in situ; then, to understand the process of the proliferation of mold strains on both hemp shives and hemp mortar; and finally, to compare mold strains present in these phases to show their relationship to mold contamination and their impact on human health. In situ hemp mortar contamination behavior was investigated in the region of Pau (France) two months after hemp mortar application in extreme conditions (high humidity, low temperature, no aeration), which did not match the standard conditions under which hemp mortar must be used. The SEM observations and FTIR and pH analyses highlighted the decrease in pH level and the presence of organic matter on the mortar surface. DNA sequencing results showed that hemp shives were the main source of fungal contamination of hemp mortar. A mold population analysis showed that the most dominant phylum was Ophistokonta, which represented 83.6% in hemp shives and 99.97% in hemp mortar. The Acrostalagmus genus representatives were the most abundant, with 42% in hemp shives and 96% in hemp mortar. The interconnection between the mold strain characteristics (particularly the ability to grow in extreme environments) and the presence of hemp mortar was emphasized.

Classification and extraction method of hidden dangers along railway lines based on semantic segmentation network

ABSTRACT Foreign objects invading high-speed railway lines can cause danger. One existing solution is to use remote sensing images to analyse the dangerous areas along the railway line, thereby providing a certain amount of investigation time. Considering the spatial and temporal resolution characteristics of existing remote sensing technologies in identifying floating objects and the reality of rapid land use changes, this paper identifies areas on the ground where floating objects may be generated by using semantic segmentation techniques oriented to remotely sensed imagery and provides early warnings to staff along the route. However, these regions that need to be analysed have different semantics and scales. To address these challenges, this paper proposes a Dual-branch Parallel Fusion Network (DPFNet) based on Transformer, aimed at enhancing multi-class semantic segmentation in remote sensing images. To leverage global contextual information, we introduce a Swin Transformer-based backbone network, which employs self-attention to capture a comprehensive scene context, facilitating better segmentation by considering the entire scene’s context. For multi-scale semantic features, we propose one approach that involves independent branching feature expression and a Multi-scale Feature Space Fusion Module (MFSFM). The former enriches multi-scale information, while the latter fuses features across different levels to capture diverse semantic features. Experimental results demonstrate that DPFNet can effectively identify the hidden danger area, and the fusion of multi-scale features makes the network more accurately identify and segment the risk area of different sizes, improving the segmentation accuracy and robustness, and is of great significance to the formation of the ‘prevention’ as the core of the railway safety operation.

Calendula Flowers (Calendulae officinalis flos): Methods to Obtain Fractions of Biologically Active Substances

INTRODUCTION. The pharmaceutical industry aims at developing sustainable resource-saving technologies for manufacturing medicines. Approaches to the effective use of herbal drugs include applying technologies yielding compounds with various pharmacological effects in one production cycle. To date, no standardised herbal drug processing technology has been designed to produce calendula flower-based medicinal products with various pharmacological effects. The Member States of the Eurasian Economic Union (EAEU) process calendula flowers by extraction methods yielding one fraction of biologically active substances per cycle (either only flavonoids or only carotenoids).AIM. This article aimed to investigate the possibility of stepwise processing of calendula flowers to obtain biologically active substances with different polarities (carotenoids, flavonoids, and polysaccharides) per cycle.MATERIALS AND METHODS. The authors extracted carotenoids using hexane. The study included additional thermal pretreatment of the herbal drug. Carotenoids and flavonoids were quantified by spectrophotometry, and polysaccharides were quantified by gravimetric analysis. After hexane removal, the dry oily residue was dissolved in oil with mechanical stirring to obtain the oil extract. The study included a three-step processing method that comprised hexane extraction (extraction of carotenoids with a low-polarity organic solvent), extraction with a water–organic solvent system (extraction of flavonoids), and aqueous extraction (precipitation of polysaccharides).RESULTS. The lipophilic extracts obtained by single hexane extraction or by single hexane extraction and heat treatment had the maximum carotenoid content (~4%), which was 10 times the carotenoid content in the extracts obtained using a water– organic solvent system. Oil extracts were prepared from the dry residue left after removing hexane by distillation at its boiling point. The content of biologically active substances in the oil extracts was comparable to that in the intact herbal drug. Water–organic solvent extraction yielded 68.5% more flavonoids from the herbal drug extracted with hexane than from the intact herbal drug. In contrast, aqueous extraction applied to the intact herbal drug yielded 1.7 times the amount of flavonoids obtained by aqueous extraction preceded by hexane extraction. The water-soluble polysaccharide fraction extracted from the intact herbal drug had 10 times the flavonoid content observed in the fraction extracted from the processed herbal drug. Each subsequent processing step decreased the total polysaccharide content and the content of individual polysaccharide fractions and increased their purity. The ratio of polysaccharide fractions remained unchanged across all processing methods tested.CONCLUSIONS. Stepwise processing of calendula flowers ensured that one processing cycle provided three fractions containing increased amounts of carotenoids, flavonoids, and polysaccharides. Relative to the intact herbal drug, there was a 3.3-fold increase in the yield of carotenoids, and the yields of flavonoids and polysaccharides increased by 44.8 and 41.3%, respectively. The resulting product had a higher degree of purity than the products obtained by pretreatment and double hexane extraction or by using the intact herbal drug. The stepwise processing method is advisable for the production of medicines that are enriched with a specific group of biologically active substances or contain smaller amounts of impurities.

A novel integrated soft sensing model for online cement clinker quality monitoring based on fuzzy fine-grained classification

The content of free calcium oxide (f-CaO) in cement clinker is an important index for cement quality. For the problems of multiple working conditions and unbalanced distribution of sample labels in cement clinker production, a soft sensing method of cement clinker f-CaO content based on fuzzy fine-grained classification (FF) is proposed. First, a divide-and-conquer strategy is used to divide the samples into high, medium, and low subsets according to cement clinker f-CaO and extract the fine-grained features under diverse types of multiple production conditions. Second, fuzzy classification based on the membership function is used in the FF model to solve the uncertainty of the sample categories. To ensure the rationality of the classification, the fuzzy membership rule is combined with a convolutional neural network to implement the fuzzy classification method. Finally, different feature extraction methods are proposed to be selected according to the data size of various categories of samples. After experimental validation, the evaluation metrics of RMSE decreased by 4.5 % and R2increased by 17.8 % for the direct classification model compared to the single model. The RMSE of the fuzzy classification model over the direct classification model was again reduced by 2.34 % and R2was again improved by 7.24 %, showing the effectiveness of the proposed soft measurement model.