Mixture Samples Research Articles

Simultaneous bond-selective deuterium-based isotopic labeling sensing with disposable ultra-miniature CARS fiber probe.

Deuterium-based isotopic labeling is an important technique for tracking cellular metabolism with the Raman signals analysis of low-wavenumber (LW) C-D bonds and high-wavenumber (HW) C-H bonds. We propose and demonstrate a disposable ultra-miniature fiber probe to detect LW and HW coherent anti-Stokes Raman scattering (CARS) spectra for deuterated compounds simultaneously and bond-selectively sensing. The 10.78 µm diameter disposable fiber probe, comprised of focusing taper as fiber probe head and time-domain walk-off eliminating fiber section with designed length, realizes wide-frequency-interval dual Stokes pulse delivering and focusing. The fiber probe enables quantitative concentration determination with resolution down to 11 mM. The chemical vibration modes of LW region C-D bonds and HW region C-H bonds of the mixture samples of organic compounds and their deuterated counterparts in a simulated cell are simultaneously excited and characterized. The CARS disposable fiber probe introduces a promising handle for in vivo biochemical detection based on isotopic labeling sensing.

Analysis of mixture samples of uranium dioxide and boron compounds by using thermal neutron prompt gamma activation analysis

Analysis of mixture samples of uranium dioxide and boron compounds by using thermal neutron prompt gamma activation analysis

Justification of the choice of excipients during the development of the composition of “Pastinocard” tablets

The aim of the work is to justify the choice of excipients as components for the cardioprotective medicine “Pastinocard” tablets based on the domestic raw material of cultivated parsnip herb thick extract (CPHTE). Materials and methods. Experimental test samples of mixtures of the plant substance of domestic origin CPHTE with groups of excipients, approved for medical use, were used as the object of research. Results. We conducted experimental research by a mathematical planning method called the Greek–Latin square. In this study, we investigated how 16 different auxiliary substances affected the pharmacotechnological properties of tableting masses and tablets made from PPTEG with cardioprotective properties. Our goal was to determine the optimal composition and manufacturing process for a medication, specifically “Pastinocard” tablets. We also developed project plans for manufacturing regulations and quality control methods, which we successfully tested in the industrial production setting at Pharmaceutical Company “Zdorovye”, LTD in Kharkiv. Conclusions. The formulation for cardioprotective tablets, designated as “Pastinocard”, has been both theoretically justified and experimentally developed by domestic CPHTE raw materials. These research findings have been instrumental in crafting the regulatory documentation for quality control and the production processes of the resulting tablet medication.

Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge.

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra.

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems.

Diagnostic development using net analyte signal for pure component modeling approaches

The use of calibration-free or minimal calibration chemometric modeling approaches has garnered increased interest as the application of process analytical technologies (PAT) has expanded in the pharmaceutical industry. Pure component models such as iterative optimization technology (IOT) algorithms require only pure component spectra and no mixture samples as model inputs. However, the deployment of pure component models for PAT is limited by the lack of model diagnostics to establish confidence in model predictions and identify outliers. In the presented research, a novel diagnostic for use with IOT, called NAS-T, is proposed based on comparing the shape of net analyte signals (NAS) between the pure components and a sample mixture. Circular statistics were used in the analysis of NAS-T. The new diagnostic was applied in parallel with the partial least squares (PLS) Hotelling's T2 diagnostic to identify outlier near-infrared spectral samples from a set of pharmaceutical powder mixtures. The outliers detected by the NAS-T diagnostic included both chemical interferences (contamination) and physical interferences (process variations). Preprocessing the mixture sample spectra produced trends in the model diagnostics that were consistent between IOT and PLS. These results support the adoption of IOT and other pure component models for PAT applications in the pharmaceutical industry.

Adaptive construction approach for virtual samples of hot mix asphalt based on 3-D structural characterization of laboratory compact specimens

Marshall and Superpave gyratory compactor (SGC) methods are widely applied in pavement engineering. Laboratory samples derived from the two methods have great difference in aggregate structure, which should be adequately characterized and considered to generate virtual samples of Marshall and Superpave mixture. To characterize the aggregate structure in realistic samples of asphalt mixture, six laboratory-compacted samples, three Marshall and three SGC, are prepared under SMA-13 gradation and then three-dimensionally (3-D) reconstructed. Next, the statistical indicators of the aggregate structure are extracted, analyzed, and characterized to develop an algorithm for adaptive construction of virtual samples conforming to the structural characteristics of realistic samples. Four virtual samples, two Marshall and two SGC, are generated to verify the control effect of the proposed approach on the aggregate structure to simulate the internal structure of realistic samples. Results indicate that virtual samples capture close structural characteristics with laboratory samples of Marshall and SGC, which validates the effectiveness of the proposed approach.

Backfill thermal properties improvement using granular phase change materials and graphite

Phase change materials (PCMs) are ideal for thermal energy storage due to latent heat release or absorption at target temperatures. Hence, adding PCMs into backfill materials of ground heat exchangers for example, can enhance the borehole thermal energy storage capacity and the corresponding shallow geothermal system performance. However, the heat transfer efficiency between the borehole and surrounding ground might be reduced because of the low thermal conductivity of PCMs. Incorporating additives with high thermal conductivity can reduce the lowering effect of PCMs on the overall thermal conductivity of the mixture while maintaining a desirable thermal capacity. This work mixes encapsulated PCMs (EPCMs), graphite and recycled glass fines, aiming to develop green backfill materials with excellent thermal properties. The heat capacity and thermal conductivity of the mixtures with different fractions of the components are measured in a laboratory. The measured data agree with simulated results. Better thermal properties can be achieved with specific fractions of components, and the findings could help geothermal systems design for better energy utilisation.
 Geothermal energy is one of the promising renewable energy sources and its utilisation can be an alternative to fossil fuels. A geothermal system requires a ground source heat exchanger with pipes installed inside a borehole, and backfill materials filling the gap between the pipe heat exchanger and the ground. Thus, the thermal properties of backfill materials are essential to the geothermal energy extraction process and influence the corresponding shallow geothermal system performance [2]. PCMs are used to improve the thermal storage capacity of backfill materials because they can absorb and release heat at a relatively constant temperature [1]. To avoid problems of leakage and volume change of PCMs during a phase change, encapsulated PCMs (EPCMs) are better suited in backfill materials [5]. However, mixtures including PCMs may hinder heat transfer because of their low thermal conductivity. Therefore, other additives with high thermal conductivity can be used to resist the low heat transfer rate [3]. Even though the thermal properties of backfill materials are fundamental to the geothermal energy extraction process, their dependence on the amount of added PCMs and other additives has not been investigated yet. This work mixes EPCMs, graphite and recycled glass fines, aiming to develop green backfill materials with excellent thermal properties. The heat capacity (), thermal conductivity () and thermal diffusivity () of the mixtures with different fractions of the components are measured in a laboratory.
 SEM images of materials used, recycled glass fines, EPCMs and graphite, are shown in Figure 1(a). Cylindrical containers with a diameter of 44 mm and a height of 57 mm are used to prepare samples of mixtures. Each sample has the controlled volume fractions of components indicated by Figure 1(b) while keeping a constant overall porosity of 0.47. A decagon KD2 Pro thermal analyzer with a dual thermal needle measures the thermal properties of samples kept at 4 °C when EPCMs are in the solid phase and 45 °C when EPCMs are in the liquid phase. The geometrical mean model is used for theoretical estimations of values for comparison against experimental results [4].
 
 The thermal conductivity () of the mixtures increases with increasing graphite volume while decreasing with increasing EPCMs volume, as shown in Figure 1(c). The difference between under solid EPCMs and liquid EPCMs is limited. The heat capacity () rises with increasing EPCMs volume but declines with the increase of graphite volume, and it is higher when EPCMs are liquid than solid. It can be observed that a trade-off between the improvement of and the reduction of exists. Hence, the thermal diffusivity (), which depends on the ratio of to , is also investigated as presented in Figure 1(d). It decreases with the increase of EPCMs volume but grows with increasing graphite volume. Besides, it is higher under solid EPCMs than that under liquid EPCMs. This indicates the backfilling materials have a faster thermal response to the change of surrounding temperature under solid EPCMs.

Discrete particle methods for granular bentonite material simulation

Bentonite clays are strategic materials to ensure the sealing capacity of engineered barrier systems (EBS) in deep geological repositories of high-level radioactive waste. Formerly installed in the form of compacted blocks, in recent years, there has been growing interest in EBS design using granular bentonite materials (GBM) due to improved construction processes [1]. GBM range from the powdered and granulated forms, both natural and obtained from crushed blocks, to compressed bentonite pellets, and the mixtures thereof. The effect of thermo-hydro-chemo-mechanical (THCM) conditions on the evolving gap-filling performance following installation has been investigated in full-scale experiments [2] and in laboratory-scale tests [3], with particular focus on bentonite pellet-powder mixtures [4, 5, 6].
 Numerical modelling of coupled THCM expansive soil behaviour is widespread using continuum methods, namely the finite element method [7, 8], applicable up to full-scale systems even though at the expense of resolution. At the laboratory scale, discrete particle-based methods accounting for the granular nature of bentonite materials in the early stages of hydration provide insight into pellet shape and local effects on the mechanical behaviour of dry pellet-powder systems as well as hydro-mechanical effects on the swelling capacity of GBM systems.
 The compressibility of dry pellet packings and pellet-powder mixture samples is modelled by the discrete element method (DEM). This method has been applied to study the swelling behaviour of pellets represented by spherical particles [9]. The DEM is a powerful tool for reproducing complex granular shapes by non-spherical particle surfaces [10] and also with clumps of spheres (Figure 1(a)), allowing to define mixed material and mechanical contact properties within the pellets. The deformation and fissuring of pellets under oedometer compression is tracked using a cohesive particle model [11], including damage for interparticle contacts in tension, which captures deformation associated phenomena such as surface spalling and pellet reorganisation occurring at stress levels below the diametral fracture. In addition, bentonite powder is represented by loose packings of upscaled spheres matching
 the experimental porosity of the fillings, interacting by a classical linear spring-dashpot contact model resulting in the reduced overall compressibility observed in pellet-powder mixtures [5].
 Flow through GBM systems is modelled by the new discrete particle-based XMm method [12]. In the XMm, the material is discretised into a set of bentonite units (BU) or particles acting as representative elementary volumes of a homogeneous continuum inside which the mass balance equations are solved at the megastructural (X), macrostructural (M), and microstructural (m) pore levels (Figure 1(b)). Moreover, the mechanical compatibility of the system is obtained from the coupling between strain increments and centroid displacements in each BU, where the contact stress and overlap between neighbouring particles are updated similarly to the DEM approach. The hydro-mechanical problem is posed as a set of ordinary differential equations (ODE) to be solved for each BU, and thus the efficiency of the XMm is a compromise between the typically reduced computational cost of ODE solvers and the number of BU considered. The XMm has been used to simulate the hydration of a powder sample with low heterogeneity [4], successfully capturing the evolution of the displacement field upon saturation in isochoric conditions. This method is applied to reproduce the confined hydration of a heterogeneous pellet-powder system [6]. Assuming 2D axisymmetry, the XMm allows defining different initial material properties in each BU introducing the heterogeneity of the sample. The evolving swelling pressure is reproduced as well as the hydration velocities at the different structural levels, showing the decreasing heterogeneity of the mixture.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures.

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.

The effects of initial water saturation on retrograde condensation in natural porous media: An in situ experimental investigation of three-phase displacements

In this study, state-of-the-art imaging technology is integrated with a miniature core-flooding system to map pore-scale fluid occupancy during the retrograde condensation process in the presence of brine and in a natural porous medium. Two depletion experiments were performed using a three-component synthetic gas condensate mixture in a Fontainebleau sandstone core sample and after establishing different levels of initial water saturations. The results are analyzed to investigate the impact of water saturation on condensate formation, accumulation, and mobilization and to shed light on the relevant three-phase flow dynamics. The results provide the first direct pore-scale observation of gas, condensate, and brine residing in the pore space. The micro-scale fluid occupancy maps illustrated that the presence of water in the pores delays the pressure at which condensate nuclei form, slows down the initial growth of nuclei, and partially impedes the accumulation of condensate. The higher the water saturation is, the lower the amount of condensate will be accumulated. As the pore pressure decreases, the condensate clusters develop significantly in the pore space triggering chains of displacement events between various pairs of fluids. The key mechanisms include gas-to-brine/condensate and condensate-to-gas/brine. The displacement events eventually reduce the brine saturation and increase the gas and condensate saturations in the pore space. The condensate saturation increases monotonically as the pore pressure reduces and reaches a maximum value. Quantitative pore fluid occupancy data also reconfirm the visual observations and demonstrate that condensate accumulation is associated with displacing gas and brine from small- and medium-sized pore elements.

Verification Gas Mixtures are the Metrological Basis of Life Safety

The article pays attention to the influence of air pollution on the safety of human life. The issue of production and application of domestic standard samples of calibration gas mixtures as a metrological basis in the process of ensuring the safety of life is considered. General information is provided on standard samples of calibration gas mixtures – metrological purpose, types, methods of production, packaging. The article provides an analysis of the nomenclature of produced calibration gas mixtures mastered by the Karaganda branch of the Republican State Enterprise «Kazakhstan Institute of Standardization and Metrology». Special attention is paid to the component composition of the standard samples produced. The demand for these standard samples in the country has been investigated. The prospects for further expansion of production are given.

Identification of variety-specific metabolites of basil by high performance thin layer chromatography-assisted metabolic profiling techniques

The technological advances of analytical instrumentation and techniques has laid the ground for the rapid expansion of metabolomics or in a wider sense, untargeted analysis applied to life sciences themes. However, the objective of identifying all existing metabolites within organisms remains a daunting challenge. All analytical techniques exhibit varying degrees of sensitivity and versatility for the detection of metabolites and none of the existing analytical platforms can be expected to be ideal for exhaustive chemical profiling. Planar liquid chromatography, and in particular, high performance thin layer chromatography (HPTLC), has been used for chemical profiling of natural products in conjunction with metabolomics. HPTLC has specific advantages which include its ability to generate reliable chemical fingerprinting data and facilitate preparative work for metabolite isolation during later stages of metabolomics analysis. In this study, we investigated the chemical profiles of four commercially available basil cultivars, namely Dolly, Emily, Keira, and Rosie. We used HPTLC as the primary analytical tool for the separation of basil cultivars based on detected metabolites, and then compared the results with those obtained with other analytical platforms. We identified the characteristic marker compounds of each basil cultivar from the HPTLC plates and validated their potential using LC-MS and GC-MS analyses as a metabolomics tool. Firstly, we compared the HPTLC data of the four cultivars, obtained with two systems that used silica gel 60 and two mobile phases composed of toluene-EtOAc (8:2, v/v) and EtOAc-formic acid-acetic acid-water (100:11:11:27, v/v), with 1H NMR data to evaluate their separation power. Despite providing lower resolution and detecting fewer compounds, the HPTLC separation power was comparable, and in some cases even better than that of 1H NMR. Additionally, we investigated the potential of HPTLC as a tool for chemical fingerprinting and demonstrated its suitability for preparative purposes that are essential for identifying metabolites in mixture analysis. Metabolites were easily isolated from sample mixtures, and identified with the assistance of GC-MS, LC-MS, and TLC-densitometry.. Several marker compounds were thus identified, including 2,4 di-tertbutylphenol, palmitic acid, hexadecanamide, 9-octadecenamide, squalene, hentriacontane, methyl 3-(3,5-ditert‑butyl‑4-hydroxyphenyl)propanoic acid, sagerinic acid, and cyanidin-3-O-sophoroside.

Matrix solid-phase dispersion combined with micro-fractionation bioactivity evaluation screening polymethoxylated flavones from Citrus peels.

Polymethoxyflavones were a unique class of natural and safe flavonoids containing two or more methoxy groups, which were also the most abundant edible part in Citrus peel. The optimum condition in the process of selective extraction of polymethoxylated flavones from Citrus peel by matrix solid-phase dispersion (MSPD) was as follows: SBA-15 as adsorbent, ethyl acetate as eluent, the mass ratio of adsorbent to sample 1:1, and the mixture of sample and adsorbent was ground for 3min. Twelve antioxidants were successfully screened by micro-fractionation bioactivity evaluation assay, in which four of them were flavonoid glycosides, seven of them were polymethoxylated flavones, and one was phenylpropanoid. 1-sinapoly-β-D-glucopyranoside (1) was reported for the first time in Citrus peel. And antioxidant capacity of 1-sinapoly-β-D-glucopyranoside, 5, 7, 8, 3', 4', 5'-hexamethoxyflavone (6), hexamethoxyflavone (11), and 5, 6, 7, 4'-tetramethoxyflavone (7) were reported for the first time. Nobiletin (compound 8), 3, 5, 6, 7, 8, 3', 4'-heptamethoxyflavone (9) and tangeretin (10) were isolated and purified by countercurrent chromatography combined with preparative liquid chromatography. Antioxidant activity evaluation indicated that the three isolated polymethoxylated flavones owned similar antioxidant activity. This study indicated that MSPD combined with micro-fractionation bioactive evaluation was efficient in screening bioactive compounds for rapid extraction and effective pinpointing bioactive substances in natural products.

B-031 Digital Elisatotal 25-Hydroxyvitamin D

Abstract Evaluation of digital ELISA using a high-resolution computational optical system for measurement of total 25-hydroxyvitamin D Background Biomarkers such as vitamin D occur at very low concentrations in blood, and sensitive detection methods are required in order to measure them. It is difficult to accurately measure low-concentration biomarkers using conventional enzyme-linked immunosorbent assays (ELISA) with fluorescence staining. By developing a digital ELISA (DELISA, Small Machines, Seoul, Korea) platform, a low concentration substance was realized by analyzing the digitalized signal through a high-resolution computational optical system in a microfluidic chip. We evaluated DELISA for measurement of total 25-hydroxyvitamin D (25OHD) in a fluidic chip. Methods The sample with monoclonal antibody bead conjugates mixture was incubated for 20 min at 37°C, then 30 uL sample mixture was applied to inlet of the chip. After additional incubation for 2 min at room temperature and 2 min after sample application, 120 uL of washing buffer was applied onto the washing hole and the chip was incubated for 10 min. 25OHD molecules in the sample bond to magnetic beads coupled with anti-25OHD at the end of the membrane. Chips were inserted into DELISA, and 25OHD results were derived by analysis of the computational method of microscopic imaging, proportional to the concentration of 25OHD. Assay performance characteristics of the limit of detection (LoD), precision and analytical measurement interval (AMI) using Vitamin D Depleted, Low and High Serum (Molecular Depot, San Diego, CA) were determined according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. 87 Samples for method comparison studies were obtained from routine clinical samples submitted for 2 OHD determination or from apparently healthy normal volunteers. The study was approved by the Institutional Review Board of Dongguk University Ilsan Hospital (202207024). Results LoD was 10.3 ng/mL and within-laboratory precision was 11, 12, and 10% coefficient of variation at 14.1, 56.6, and 113.2 ng/mL, respectively. The best fit polynomial was a cubic equation from 14.1 to 113.2 ng/mL and the difference between the third-order equation and the first-order equation at Level 3 and 4 concentrations was outside the non-linearity limit. Comparison with chemiluminescent assay (Elecsys Vitamin D total II, Roche Diagnostics, Mannheim, Germany) yielded a 0.904 of correlation coefficient and 0.921 slope in the range from 7.0 to 57.9 ng/mL. Conclusions The DELISA 25OHD assay has a suitable LoD and precision, however, it is necessary to improve the AMI at high concentration values and correlation with the automated analyzer.

Effect of glycerol concentration on levels of toxicants emissions from water-pipe tobacco smoking (WTS)

Glycerol, flavorings and sweeteners constitute approximately 70% of water-pipe tobacco smoking (WTS) mixtures. Tobacco mixture combustion produces smoke toxins (e.g. carbonyl compounds), of which the type and amount are highly dependable on tobacco mixture formula. While glycerol in tobacco mixture contribute to enhanced smoking experience, its’ combustion produces toxicants such as acrolein. According to WHO, there are no approved international upper limits regulations on WTS ingredients. This study aims to assess toxicant emission levels corresponding to increasing glycerol concentration in WTS mixtures, which may aid in developing tobacco regulations towards harm reduction.MethodsLaboratory experimental study. Using laboratory water-pipe smoking machine, levels of toxicant emissions in the smoke from WTS mixture samples containing varying glycerol concentrations were measured using High-performance Liquid Chromatography (HPLC). Smoke from 5 consecutive smoking cycles with 35 puffs each (ISO 22486 standard) was led through a trapping system as described in the Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) recommended method No. 74 (Determination of selected carbonyls in mainstream cigarette smoke by HPLC). Trapped carbonyls were then analysed by HPLC with a DAD detector.ResultsAcrolein emission is associated with glycerol addition in WTS mixture indicated by lab-made samples throughout all glycerol concentrations (10%, 20%, 40% and 60%), and brand samples with glycerol concentrations 10% to 20%. However, brand samples showed no increase in acrolein emission corresponding to the increase in glycerol concentrations from 20% to 60%.ConclusionThe effect of glycerol addition in waterpipe tobacco on acrolein emission varies between products. Tobacco fillers, additives and contents quality and other factors may affect toxicant emission levels. Therefore, regulatory recommendations towards defining upper limits of content concentrations require further investigations regarding potential confounders in acrolein emissions and health effects of market-available glycerol concentrations in waterpipe tobacco smoking.

Application of inverted multivariate calibrations to determination of the total content of phenols

The total content (cΣ) of toxic phenols in water is usually determined using the procedures which include the introduction of a group reagent, measurement of the generalized signal (AΣ) at a selected wavelength, and assessment of cΣ in terms of C6H5OH. The use of diazotized sulfanilic acid (DSA) as a group reagent leads to the errors of cΣ determination which do not exceed 30% thus ensuring more adequate estimates of cΣ than measuring phenolic index. We suppose that further reduction of errors in group analysis would be possible with multiwavelength measurements and multivariate calibrations but these promising techniques have not been used yet for the determination of total phenol. To check up this assumption, model multicomponent colored solutions of the known composition were prepared and analyzed. These model mixtures contained simultaneously up to 5 different phenols with a total concentration ranged from 15 to 70 μmol/L. After converting all phenols to corresponding azo-dyes their generalized signals were measured at m wavelengths in the UV region of the spectrum 10 minutes after mixing the solutions. The results of group analysis were calculated with the multivariate calibrations; the recalculation of AΣ values to standard substance concentration we used only for comparison. The inverted multivariate calibrations were calculated with AΣ values of n model mixtures which formed a training set. For optimized conditions (m = 7, n = 10) the systematic error of cΣ prediction is less than 13 % rel., that is half of errors for total index calculation. Therefore, inverted multivariate calibrations can be rather useful to control the total content of phenolics in natural and waste waters (instead of the total indices). However, systematic errors raised sharply when the analyzed sample contained some individual phenols which were absent in samples of the training set; in such cases the errors can increase up to 80 % rel. To maximize the correctness of corresponding techniques, it is desirable to elucidate beforehand the qualitative composition of phenol mixtures in water samples under study and take it into account when the multivariate calibration is used.

A Preliminary Study on the Use of Highly Aromatic Pyrolysis Oils Coming from Plastic Waste as Alternative Liquid Fuels.

In this work, the low-temperature pyrolysis of a real plastic mixture sample collected at a WEEE-authorised recycling facility has been investigated. The sample was pyrolysed in a batch reactor in different temperature and residence time conditions and auto-generated pressure by following a factorial design, with the objective of maximising the liquid (oil) fraction. Furthermore, the main polymers constituting the real sample were also pyrolysed in order to understand their role in the generation of oil. The pyrolysis oils were characterised and compared with commercial fuel oil number 6. The results showed that in comparison to commercial fuel oil, pyrolysis oils coming from WEEE plastic waste had similar heating values, were lighter and less viscous and presented similar toxicity profiles in fumes of combustion.

A novel mixture sampling strategy combining latin hypercube sampling with optimized one factor at a time method: A case study on mixtures of antibiotics and pesticides

Global sensitivity analysis in conjunction with quantitative high-throughput screening presents a novel technique for identifying the key components that induce the toxicities of mixtures. However, the mixtures currently designed with this method suffer from unequal frequency sampling, repeated mixtures, and only odd factor levels being considered. Accordingly, we use latin hypercube sampling to generate the starting points of the trajectories to achieve equal frequency sampling and non-repeated mixtures, as well as apply different one factor at a time methods for factors with odd and even levels to achieve suitability for factors with both odd and even levels. This method is called LHS-OAT. LHS-OAT was successfully applied to design 110 equal-frequency and non-repeated mixtures consisting of six antibiotics and four pesticides. It was found that four factors, roxithromycin (A5), tetracycline (A6), dichlorvos (P1), and demeton-S (P3), induce the toxicities of mixtures, and A5 and P1 in the Shaying River Basin have risk quotients ≥ 1. Additionally, we developed the toxicity deviation ratio to correct the risk quotients of interacting mixtures for effective risk assessments. This study provides a rational and effective method for mixture design that accurately identifies the important factors that induce the toxicities of mixtures.

Rapid authentication of intact saffron stigma thorough the package using Vis-SWNIR hyperspectral imaging coupled with chemometrics

This study presents a rapid method for detecting and authenticating intact saffron stigma through its packaging using visible-short wavelength near-infrared (Vis-SWNIR) hyperspectral imaging (HSI) in conjunction with chemometrics. A dataset containing 38 authentic saffron samples was utilized, and HSI images were captured within the spectral range of 400–950 nm. Multivariate curve resolution-alternating least squares (MCR-ALS) was used to obtain pure spatial and spectral profiles of the components. The resolved spatial profiles were then subjected to principal component analysis (PCA) to identify potential patterns in the authentic samples. Binary mixtures of pooled saffron samples and adulterants, including safflower, calendula, and saffron style, were prepared at varying levels. Data-driven soft independent modeling of class analogy (DD-SIMCA) was employed to establish a boundary between authentic and adulterated samples, resulting in robust classification performance with a sensitivity of 95 % and specificity of 100 %. Partial least squares-discriminant analysis (PLS-DA) was used for binary and multi-class classification of the MCR-ALS resolved spatial profiles. The performance of PLS-DA was evaluated using sensitivity, specificity, and accuracy measures, which exceeded 83.4 % for the calibration, cross-validation, and prediction sets across three different saffron packaging methods. These findings provide further evidence for the validity and efficacy of the proposed method.