Search

A U-shaped fixed bed reaction system was used to conduct pyrolysis experiments on 35 types of cigar tobaccos from eight different regions at home and abroad. The produced volatiles were analayzed in real-time by photoionization mass spectrometry (PI-MS). The principal component analysis (PCA) method was used to statistically analyze the obtained MS datas. The results showed that a total of 74 shared pyrolysis products were identified in the 35 types of cigar tobaccos, mainly hydrocarbons, phenols, ketones, alcohols, esters, and nitrogen-containing substances. There were significant differences in the composition of pyrolysis products between domestic and foreign cigar tobaccos. Especially, the domestic cigar coats had a much higher content of nicotine than the foreign cigar coats, whereas the relative content of fragrant components such as methylcyclopentenone, 2,3-dimethyl-2-cyclopentenone, and 2,5-dimethylcyclopentenone in the pyrolysis products of domestic cigar coats was lower than that of foreign cigar coats. The relative content of fragrant components such as 3-methylpyridine, 2,5-dimethylcyclopentenone, and (+)-limonene in the pyrolysis products of domestic cigar chips was lower than that of foreign cigar chips. In addition, the characteristic pyrolysis products of cigar tobaccos screened by PCA were consistent with the results of routine chemical composition analysis of cigar tobaccos, confirming the accuracy of the experimental analysis results. [ABSTRACT FROM AUTHOR]

Copyright of Tobacco Science & Technology is the property of Tobacco Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When using near-infrared (NIR) techniques combined with machine learning to identify the origin of apples, the construction and updating of discrimination models is essential. However, when constructing and updating the models, due to the high dimensionality of NIR data and the redundancy of the updated sample set can lead to irrational sample partitioning and the selected updated samples are not effective enough, which in turn affects the prediction performance of the NIR models. Therefore, in this study, local linear reconstruction combined with spectral information entropy (LLR–SIE) as the sample selection method is proposed. The two traditional methods, the Kennard-Stone (KS) and the sample set portioning based on joint x–y distance (SPXY) are used to compare and verify the superiority of the proposed method. Discrimination models was constructed after dividing the sample set using LLR–SIE method, KS method and SPXY method, and the results of the divided sample set were visually expressed. And using the above three methods, the updated samples set were selected for updating the initial discrimination models, and the spectra features of the updated samples set selected based on different methods were demonstrated. The results show that the models constructed using the LLR–SIE method after sample partitioning obtained an accuracy of 92.7% and 91% in predicting the two batches of samples, and reached a double high in both recall and precision, with both metrics above 0.9. This improves the prediction accuracy by at least 4% relative to the accuracy obtained from the models constructed after dividing the samples set using KS. The prediction accuracy of the models constructed after dividing the sample set using the SPXY method was 91% and 86.5%, and the recall and precision were low. In terms of model updating, when different numbers of updated samples are used for model updating, the accuracy obtained from updating the discrimination models by selecting updated samples using the LLR–SIE method is higher than that obtained by updating the model using the KS method and the SPXY method. After 150 updated samples were selected, the use of the LLR–SIE method resulted in the updated model prediction accuracy of 90% on the F-Measure metric. The results proved that the prediction accuracy of the model built after sample partitioning using the LLR–SIE method is better than that built after partitioning by the two traditional methods. In addition, with the same number of updated samples selected, the updated samples selected using the LLR–SIE method are more effective compared to the traditional methods, resulting in higher comprehensive evaluation indexes of the updated model. The LLR–SIE method combined with the NIR technique can provide a new solution to the problem of model construction and updates. [ABSTRACT FROM AUTHOR]

Introduction: Blackheart is one of the most common physiological diseases in potatoes during storage. In the initial stage, black spots only occur in tissues near the potato core and cannot be detected from an outward appearance. If not identified and removed in time, the disease will seriously undermine the quality and sale of the-entire batch of potatoes. There is an urgent need to develop a method for early detection of blackheart in potatoes. Methods: This paper used visible-near infrared (Vis/NIR) spectroscopy to conduct online discriminant analysis on potatoes with varying degrees of blackheart and healthy potatoes to achieve real-time detection. An efficient and lightweight detection model was developed for detecting different degrees of blackheart in potatoes by introducing the depth-wise convolution, pointwise convolution, and efficient channel attention modules into the ResNet model. Two discriminative models, the support vector machine (SVM) and the ResNet model were compared with the modified ResNet model. Results and discussion: The prediction accuracy for blackheart and healthy potatoes test sets reached 0.971 using the original spectrum combined with a modified ResNet model. Moreover, the modified ResNet model significantly reduced the number of parameters to 1434052, achieving a substantial 62.71% reduction in model complexity. Meanwhile, its performance was evidenced by a 4.18% improvement in accuracy. The Grad-CAM++ visualizations provided a qualitative assessment of the model’s focus across different severity grades of blackheart condition, highlighting the importance of different wavelengths in the analysis. In these visualizations, the most significant features were predominantly found in the 650–750 nm range, with a notable peak near 700 nm. This peak was speculated to be associated with the vibrational activities of the C-H bond, specifically the fourth overtone of the C-H functional group, within the molecular structure of the potato components. This research demonstrated that the modified ResNet model combined with Vis/NIR could assist in the detection of different degrees of black in potatoes. [ABSTRACT FROM AUTHOR]

A rapid and online microvolume flow-through dialysis probe designed for sample preparation in the analysis of veterinary drug residues is introduced. This study addresses the need for efficient and green sample preparation methods that reduce chemical waste and reagent use. The dialysis probe integrates with liquid chromatography and mass spectrometry (LC-MS) systems, facilitating automated, high-throughput analysis. The dialysis method utilizes minimal reagent volumes per sample, significantly reducing the generation of solvent waste compared to traditional sample preparation techniques. Several veterinary drugs were spiked into tissue homogenates and analyzed to validate the probe's efficacy. A diagnostic sensitivity of >97% and specificity of >95% were obtained for this performance evaluation. The results demonstrated the effective removal of cellular debris and particulates, ensuring sample integrity and preventing instrument clogging. The automated dialysis probe yielded recovery rates between 27 and 77% for multiple analytes, confirming its potential to streamline veterinary drug residue analysis, while adhering to green chemistry principles. The approach highlights substantial improvements in both environmental impact and operational efficiency, presenting a viable alternative to conventional sample preparation methods in regulatory and research applications. [ABSTRACT FROM AUTHOR]

We investigated the applicability of proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) for quantitative analysis of mixtures comprising glycerin, acetol, glycidol, acetaldehyde, acetone, and propylene glycol. While PTR-TOF-MS offers real-time simultaneous determination, the method selectivity is limited when analyzing compounds with identical elemental compositions or when labile compounds present in the mixture produce fragments that generate overlapping ions with other matrix components. In this study, we observed significant fragmentation of glycerin, acetol, glycidol, and propylene glycol during protonation via hydronium ions (H3O+). Nevertheless, specific ions generated by glycerin (m/z 93.055) and propylene glycol (m/z 77.060) enabled their selective detection. To thoroughly investigate the selectivity of the method, various mixtures containing both isotope-labeled and unlabeled compounds were utilized. The experimental findings demonstrated that when samples contained high levels of glycerin, it was not feasible to perform time-resolved analysis in H3O+ mode for acetaldehyde, acetol, and glycidol. To overcome the observed selectivity limitations associated with the H3O+ reagent ions, alternative ionization modes were investigated. The ammonium ion mode proved appropriate for analyzing propylene glycol (m/z 94.086) and acetone (m/z 76.076) mixtures. Concerning the nitric oxide mode, specific m/z were identified for acetaldehyde (m/z 43.018), acetone (m/z 88.039), glycidol (m/z 73.028), and propylene glycol (m/z 75.044). It was concluded that considering the presence of multiple product ions and the potential influence of other compounds, it is crucial to conduct a thorough selectivity assessment when employing PTR-TOF-MS as the sole method for analyzing compounds in complex matrices of unknown composition. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are key compounds in atmospheric chemistries, but difficult to measure directly. In this study, a pre-concentration unit combined with gas chromatography-mass spectrometry (GC-MS) was developed for the quantitative analysis of 18 BVOCs in ambient air. The analytes are trapped on an empty silonite-coated tube, which is cooled by a thermoacoustic cooler to cryotrap at −150 °C, and then desorbed by rapid heating to 200 °C. The set-up involves neither the exchange of solid adsorbents nor any further condensation or refocusing steps. Reliable operation is ensured by the thermoacoustic cooler, which neither contains a liquid refrigerant nor requires refilling a cryogen. The pre-concentration unit parameters such as water removal temperature, desorption temperature and desorption time were optimized. All compounds had correlation coefficients that were better than 0.95, and the detection limits were 0.005–0.009 ppbv when the injection volume is 400 mL. The repeatability ranges were 0.9–5.8%. The recoveries were ranged from 81.8% to 93.2%. This new method was applied for the first time to measure ambient BVOCs in suburb Guangzhou in summer 2022. Isoprene concentrations ranged from 0.375 ppbv to 2.98 ppbv. In addition, several extremely low-level monoterpenes (e.g., α-pinene, β-pinene, and D-limonene) were also detected by the method. [ABSTRACT FROM AUTHOR]

In recent years, spectral analysis methods have developed rapidly. A key feature is the use of chemometric methods to process spectral data for performing qualitative and quantitative analysis of complex mixtures. The coupling of chemometric methods to spectroscopic techniques led to distinct advantages in speed, cost, efficiency, automation, and portability compared to the traditional methods in agriculture, food, pharmaceutical, petroleum, chemical, environmental, and medical fields. This paper comments on the review papers published during the past three years (2020–2022) on the topic of the combination of spectral and chemometric methods. The development status, existing challenges, and the future direction of this field is discussed. [ABSTRACT FROM AUTHOR]

Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and cellular biology to pharmacology. However, because enzyme kinetics involves concepts rarely employed in other areas of biology, it can be challenging for students and researchers. Traditional graphical analysis was replaced by computational analysis, requiring another skill not core to many life sciences curricula. Computational analysis can be time‐consuming and difficult in free software (e.g., R) or require costly software (e.g., GraphPad Prism). We present Enzyme Kinetics Analysis (EKA), a web‐tool to augment teaching and learning and streamline EKA. EKA is an interactive and free tool for analyzing enzyme kinetic data and improving student learning through simulation, built using R and RStudio's ShinyApps. EKA provides kinetic models (Michaelis–Menten, Hill, simple reversible inhibition models, ternary‐complex, and ping‐pong) for users to fit experimental data, providing graphical results and statistics. Additionally, EKA enables users to input parameters and create data and graphs, to visualize changes to parameters (e.g., KM or number of measurements). This function is designed for students learning kinetics but also for researchers to design experiments. EKA (enzyme-kinetics.shinyapps.io/enzkinet_webpage/) provides a simple, interactive interface for teachers, students, and researchers to explore enzyme kinetics. It gives researchers the ability to design experiments and analyze data without specific software requirements. [ABSTRACT FROM AUTHOR]

The classification of gender from handwriting is a challenging issue that have great attention recently. Most of the exiting works were conducted on gender classification using face image and offline handwritten texts. This study explored an automated system for gender classification from online handwritten patterns. The handwritten samples were collected from 79 (Male: 32 and Female: 47) using pen tablet device. Each subject was asked to perform four tasks such as Zigzag trace (ZigZ-T), Zigzag predict (ZigZ-P), periodic line trace (PL-T), and periodic line predict (PL-P) and repeated its into three times. Thirty-six statistical features were derived from the six raw features, obtained from the Pen tablet device. Following that, we selected the best subset of features by employing Sequential Forward Floating Selection (SFFS)-based algorithm. At the same time, four machine learning (ML)-based algorithms like support vector machine (SVM), random forest, AdaBoost, and Gradient Boosting (GB) were employed for gender classification. We trained these four ML-based algorithms with leave-one-out method and optimized their hyperparameters using Optuna. The experimental results showed that SVM achieved a recognition accuracy of 88.10% for adult ZigZ-T tasks and 90.09% recognition accuracy was obtained by GB-based algorithm for children whose drawing ZigZ-P tasks. our proposed system demonstrates promise in automating gender classification based on handwriting patterns, offering insights into the significant differences between adult and child handwriting. The ability to identify gender accurately from handwriting has broad applications, including security enhancements and personalized service provision.

Biomass gasification to produce syngas is promising, but the limited hydrogen content of the syngas restricts its potential applications. This study proposed an efficient thermochemical approach that used cost-effective fly ash pretreated with washing and calcination (WCFA) as a catalyst to enhance hydrogen-rich syngas production from biomass steam gasification. Catalyst characterizations indicated that washing removed most chloride salts in raw fly ash by dissolution, and subsequent calcination decomposed Ca-containing compounds into CaO, resulting in smaller catalyst grains, more developed pore structure, and greater capability and reaction rate of CO 2 sorption. With WCFA, the H 2 release during gasification was prolonged by ∼10 min and its release peak increased by over 1.5 times, compared to no catalyst. Consequently, the cold gas efficiency, syngas yield, and H 2 /CO ratio increased from 53%, 792 mL/g biomass , and 0.39 for no catalyst to 72%, 1132 mL/g biomass , and 1.05 for WCFA. The WCFA catalyst calcined at 800 °C for 60 min was optimal for H 2 formation, due to its enriched CaO composition and improved porosity, which effectively catalyzed volatiles cracking/reforming and absorbed CO 2 to lead the chemical equilibrium of water gas shift reaction move foward. [Display omitted] • Fly ash pretreated by coupling of washing and calcination (WCFA) showed enriched CaO and improved porosity. • WCFA enhanced the H 2 release. • WCFA increased cold gas efficiency, syngas yield and H 2 /CO ratio by 1.36, 1.43 and 2.69 folds. • WCFA catalyzed volatile cracking/reforming and promoted water gas shift reaction by CO 2 sorption. [ABSTRACT FROM AUTHOR]

In order to improve the extraction ability of carbon fibers (CFs) for microextraction of polycyclic aromatic hydrocarbons (PAHs), biochar nanospheres derived from glucose were in-situ grown onto the surface of CFs via hydrothermal synthesis. The surface morphology and elemental composition of biochar nanospheres-CFs were investigated by scanning electron microscopy and X-ray photoelectron spectroscopy. Thereafter, the biochar nanosphere-CFs were pulled into the polyetheretherketone tube for solid-phase microextraction, and the tube was combined with high-performance liquid chromatography-diode array detector to online detect PAHs. With the help of π-stacking, hydrophobic, and hydrophilic effect of biochar nanospheres, the extraction efficiency of CFs was greatly enhanced (enrichment factor increased by 293% compared with the original). The conditions affecting the analytical performance (sampling volume, sampling rate, methanol content, and desorption time) were investigated. Under the optimal conditions, an online analytical method for microextraction and determination of several PAHs was developed, and satisfactory results were achieved. The limits of detection were 0.003-0.010 ng mL-1 owing to high enrichment effect (2973-3600), linearity ranged from 0.010-15.0 ng mL-1, and relative standard deviations were 0.4%-1.6% (intra-day) and 2.4%-4.4% (inter-day), respectively. The method was applied to analyze environmental water samples (rain water, snow water, and river water), and spiked recoveries within 80.0%-119% were obtained. [ABSTRACT FROM AUTHOR]

The processing of abrasive fibres in the carding process, in particular high-performance fibres such as glass, carbon or aramid fibres, can cause increased wear of the card clothing. In the FutureTex project 'HPFGarnitur', the wear of card clothing was investigated and an online wear measurement system has been developed. The aim of the project was both, to optimize the clothings to enable gentler processing of the fibres, and to develop a digital monitoring system to observe the degree of wear of the clothings, which offers a new possibility for maintenance prediction and production planning in the sense of Industry 4.0. [ABSTRACT FROM AUTHOR]

The automation of traditional Chinese medicine (TCM) pharmaceuticals has driven the development of process analysis from offline to online. Most of common online process analytical technologies are based on spectroscopy, making the identification and quantification of specific ingredients still a challenge. Herein, we developed a quality control (QC) system for monitoring TCM pharmaceuticals based on paper spray ionization miniature mass spectrometry (mini-MS). It enabled real-time online qualitative and quantitative detection of target ingredients in herbal extracts using mini-MS without chromatographic separation for the first time. Dynamic changes of alkaloids in Aconiti Lateralis Radix Praeparata (Fuzi) during decoction were used as examples, and the scientific principle of Fuzi compatibility was also investigated. Finally, the system was verified to work stably at the hourly level for pilot-scale extraction. This mini-MS based online analytical system is expected to be further developed for QC applications in a wider range of pharmaceutical processes.

A rapid and online microvolume flow-through dialysis probe designed for sample preparation in the analysis of veterinary drug residues is introduced. This study addresses the need for efficient and green sample preparation methods that reduce chemical waste and reagent use. The dialysis probe integrates with liquid chromatography and mass spectrometry (LC-MS) systems, facilitating automated, high-throughput analysis. The dialysis method utilizes minimal reagent volumes per sample, significantly reducing the generation of solvent waste compared to traditional sample preparation techniques. Several veterinary drugs were spiked into tissue homogenates and analyzed to validate the probe’s efficacy. A diagnostic sensitivity of >97% and specificity of >95% were obtained for this performance evaluation. The results demonstrated the effective removal of cellular debris and particulates, ensuring sample integrity and preventing instrument clogging. The automated dialysis probe yielded recovery rates between 27 and 77% for multiple analytes, confirming its potential to streamline veterinary drug residue analysis, while adhering to green chemistry principles. The approach highlights substantial improvements in both environmental impact and operational efficiency, presenting a viable alternative to conventional sample preparation methods in regulatory and research applications.

Biogenic volatile organic compounds (BVOCs) are key compounds in atmospheric chemistries, but difficult to measure directly. In this study, a pre-concentration unit combined with gas chromatography-mass spectrometry (GC-MS) was developed for the quantitative analysis of 18 BVOCs in ambient air. The analytes are trapped on an empty silonite-coated tube, which is cooled by a thermoacoustic cooler to cryotrap at −150 °C, and then desorbed by rapid heating to 200 °C. The set-up involves neither the exchange of solid adsorbents nor any further condensation or refocusing steps. Reliable operation is ensured by the thermoacoustic cooler, which neither contains a liquid refrigerant nor requires refilling a cryogen. The pre-concentration unit parameters such as water removal temperature, desorption temperature and desorption time were optimized. All compounds had correlation coefficients that were better than 0.95, and the detection limits were 0.005–0.009 ppbv when the injection volume is 400 mL. The repeatability ranges were 0.9–5.8%. The recoveries were ranged from 81.8% to 93.2%. This new method was applied for the first time to measure ambient BVOCs in suburb Guangzhou in summer 2022. Isoprene concentrations ranged from 0.375 ppbv to 2.98 ppbv. In addition, several extremely low-level monoterpenes (e.g., α-pinene, β-pinene, and D-limonene) were also detected by the method.

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Virtual reality and digital communication represent significant aspects of contemporary life, significantly affecting individual psychic development. The relationship between the real and the virtual is often viewed from the polarized perspective of an alternative opposition, where the real is what is authentic, while the virtual is perceived as a world of make-believe that hinders the possibilities of individuative development. The author of the article, while acknowledging that these risks are absolutely present in the pervasive use of technological tools that characterizes contemporaneity, nevertheless stresses that in order to understand the experiences of many of our patients, especially the youngest, "digital natives", it is appropriate to consider the virtual as an extension of reality, rather than an alternative to it. The author also addresses the topic of online analysis, trying, also with reference to some clinical cases, to reflect on some aspects related in particular to the fate of the body in online analysis and the experience of presence. [ABSTRACT FROM AUTHOR]

Monitoring critical process parameters of chemical and biotechnological processes is an essential tool at every stage of drug manufacturing technology. The aim of Process Analytical Technology (PAT) is to provide effective tools, such as multidimensional data analysis, modern analytical methods, and monitoring tools, for the continuous improvement of process understanding and knowledge. Among the methods of wide interest are optical and spectroscopic techniques that can be used in the control of chemical and biotechnological processes. The selection of the appropriate method is crucial and depends on many factors, including the nature of the process, the number of variables, and analytical limitations. This review focuses on a brief and precise characterization of spectroscopic and optical methods that can be applied to monitoring and control of chemical and biotechnological processes. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Refinery Engineering is the property of Petroleum Refinery Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biopolymers, polymers derived from renewable biomass sources, have gained increasing attention in recent years due to their potential to replace traditional petroleum-based polymers in a range of applications. Among the many advantages of biopolymers can be included their biocompatibility, excellent mechanical properties, and availability from renewable feedstock. However, the development of biopolymers has been limited by a lack of understanding of their properties and processing behaviours. Continuous analysis techniques have the potential to hasten progress in this area by providing real-time insights into the properties and processing of biopolymers. Significant research in polymer chemistry has focused on petroleum-derived polymers and has thus provided a wealth of synthetic and analytical methodologies which may be applied to the biopolymer field. Of particular note is the application of flow technology in polymer science and its implications for accelerating progress towards more sustainable and environmentally friendly alternatives to traditional petroleum-based polymers. In this mini review we have outlined several of the most prominent use cases for biopolymers along with the current state-of-the art in continuous analysis of polymers in flow, including defining and differentiating atline, inline, online and offline analysis. We have found several examples for continuous flow analysis which have direct application to the biopolymer field, and we demonstrate an atline continuous polymer analysis method using size exclusion chromatography. [ABSTRACT FROM AUTHOR]

The automation of traditional Chinese medicine (TCM) pharmaceuticals has driven the development of process analysis from offline to online. Most of common online process analytical technologies are based on spectroscopy, making the identification and quantification of specific ingredients still a challenge. Herein, we developed a quality control (QC) system for monitoring TCM pharmaceuticals based on paper spray ionization miniature mass spectrometry (mini-MS). It enabled real-time online qualitative and quantitative detection of target ingredients in herbal extracts using mini-MS without chromatographic separation for the first time. Dynamic changes of alkaloids in Aconiti Lateralis Radix Praeparata (Fuzi) during decoction were used as examples, and the scientific principle of Fuzi compatibility was also investigated. Finally, the system was verified to work stably at the hourly level for pilot-scale extraction. This mini-MS based online analytical system is expected to be further developed for QC applications in a wider range of pharmaceutical processes. [Display omitted] • The PSI-mini-MS has been developed as a PAT for online quality monitoring of TCM pharmaceuticals. • PSI-mini-MS allows real-time monitoring of TCM pharmaceutical processes without LC separation. • PSI-mini-MS enables identification and quantification of target ingredients in TCM extracts. • Changes of Fuzi alkaloids during decoction monitored by PSI-mini-MS were used as examples. • The scientific principle of Fuzi compatibility was demonstrated by PSI-mini-MS online analysis. [ABSTRACT FROM AUTHOR]

In this paper the author explores some of the issues raised by the practice of online analysis. In particular, he emphasizes the difference between the setting and the frame and proposes a theoretical approach of the latter as emerging within the transferential dynamic. [ABSTRACT FROM AUTHOR]

Copyright of Mining & Metallurgy (10057854) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Analytical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring critical process parameters of chemical and biotechnological processes is an essential tool at every stage of drug manufacturing technology. The aim of Process Analytical Technology (PAT) is to provide effective tools, such as multidimensional data analysis, modern analytical methods, and monitoring tools, for the continuous improvement of process understanding and knowledge. Among the methods of wide interest are optical and spectroscopic techniques that can be used in the control of chemical and biotechnological processes. The selection of the appropriate method is crucial and depends on many factors, including the nature of the process, the number of variables, and analytical limitations. This review focuses on a brief and precise characterization of spectroscopic and optical methods that can be applied to monitoring and control of chemical and biotechnological processes.

Three-dimensional (3D) cell cultures, including organ-on-a-chip (OOC) devices, offer the possibility to mimic human physiology conditions better than 2D models. The organ-on-a-chip devices have a wide range of applications, including mechanical studies, functional validation, and toxicology investigations. Despite many advances in this field, the major challenge with the use of organ-on-a-chips relies on the lack of online analysis methods preventing the real-time observation of cultured cells. Mass spectrometry is a promising analytical technique for real-time analysis of cell excretes from organ-on-a-chip models. This is due to its high sensitivity, selectivity, and ability to tentatively identify a large variety of unknown compounds, ranging from metabolites, lipids, and peptides to proteins. However, the hyphenation of organ-on-a-chip with MS is largely hampered by the nature of the media used, and the presence of nonvolatile buffers. This in turn stalls the straightforward and online connection of organ-on-a-chip outlet to MS. To overcome this challenge, multiple advances have been made to pre-treat samples right after organ-on-a-chip and just before MS. In this review, we summarised these technological advances and exhaustively evaluated their benefits and shortcomings for successful hyphenation of organ-on-a-chip with MS.

Several commonly used detection methods in the pry-Olympics study of energetic materialism with their advantages and disadvantages were bile-fly introduced. The "soft" ionization characteristics of routinizations mass spec--Tome-qty (PI MS) and As advantages in the analysis of pry-colitis produces were described. "Soft" ionization can make the hp-duces ionize without genera-ion of ionization fragments, which is convenient of- the sapid analysis of complex systems. It is conducive to online detection of the rapidly evolving inter-mediates generated from the pry-Olympics hp-chess of energetic materialism. Recent application hp-ogress of PI MS in study of pyrolysis of energetic materialism, such as NOT, FOX-7, FOX-12 and JCL-20, we-e summarized emphatically. The PI MS methods used in these studies include Jingle photon ionization mass spec--Tome-qty and atmospheric pressure pontoon-anion high -resolution mass spec--Tome-qty based on vacuum UI-ravioli (VU) bight generated from disc ha-GE lamp O- synch-O---on -radiation sources. The new pry-colitis hp-duchies and inter-mediates detected by online PI MS were ISO summarized. Finally, he future-e opportunities of PI MS in study of gene-genic materialism we-e hp-inspected. Of- characterization of fast -reactions, by Hopi-mazing he mass spec--mete- and developing high-lightness routinizations sources, it is expected to achieve Jaime-resolved measurement at millisecond O- sub-millisecond. It will help to study the pry-colitis and combustion hp-chess of gene-genic materialism undue- high temperature and high hp-assure. Of- quantitative/e measurement, it is necessary to establish and imp-rove he pontoon-action ac-doss section database of energetic materialism and main hp-duchies. 61 Refed-fences were attached. [ABSTRACT FROM AUTHOR]

Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears. [ABSTRACT FROM AUTHOR]

As the efficient sample preparation technique, in-tube solid-phase microextraction (SPME) was often combined with some analytical means containing chromatography or mass spectrometry for sensitive detection of various analytes. Especially, in-tube SPME-liquid chromatographic detection not only reduced the analytical time but also improved the accuracy of result. Moreover, this technique was environmentally friendly and employed minimal amount of organic solvents. Various sorbent materials and different extraction devices have been developed to improve enrichment efficiency. Four main types of extraction devices for in-tube SPME, including fiber-filled tube, monolithic column, sorbent-coated and particle-packed tubes, were reviewed from 2020 to 2024. Except for the preparation methods and new sorbents of these extraction devices, their applications in food analysis, environmental pollution and biomedical detection fields were also described. Additionally, the current challenges and future trends of in-tube SPME were discussed. • The recent development of in-tube SPME devices is summarized. • The recent applications of in-tube SPME in different fields are overviewed. • The extraction mechanism of sorbents with various targets for in-tube SPME is described. • The online analytical methods mainly by coupling in-tube SPME with liquid chromatography are reviewed. • Challenges and prospects of in-tube SPME are discussed. [ABSTRACT FROM AUTHOR]

In order to improve the selectivity and stability of melamine–formaldehyde (MF) aerogel, it was composited with TiO2 aerogel. A TiO2-MF hybrid aerogel was in situ prepared on the surface of carbon fibers for in-tube solid-phase microextraction (SPME). The extraction performance of TiO2-MF aerogel was regulated by changing the ratio of TiO2 sol and MF sol during the material preparation. Coupled with high-performance liquid chromatography-diode array detection (HPLC–DAD), the extraction tube filled by TiO2-MF aerogel-coated carbon fibers was evaluated with several types of environmental pollutants including polycyclic aromatic hydrocarbons (PAHs), estrogens, and ultraviolet filters. Because of favourable extraction performance of PAHs they were selected as model analytes, and some important influence factors were optimized for satisfactory sensitivity. The detection limits were in the range 0.05–0.10 μg L−1, owing to high enrichment factors (653–1007). The online in-tube SPME-HPLC–DAD method was verified for the determination of trace PAHs in environmental water samples, and acceptable recovery (70–118%) was achieved. The analytical methods also displayed some advantages in comparison with other reports. Moreover, the extraction tube exhibited satisfactory chemical stability. [ABSTRACT FROM AUTHOR]

Restricted-access materials (RAMs) allow biological samples to directly enter the chromatographic column for analysis owing to the steric exclusion function ability for biomolecules and extraction function for small-molecule analytes, which promoting the development of rapid, efficient, and automated in vivo drug analysis. Few reports on chiral RAMs that have been used to analyze enantiomers and positional isomers in serum by direct injection in currently. In this study, a chiral porous organic cage material RCC3 was innovatively introduced into the inner surface of silica gel and modified the outer surface with polyethylene glycol to prepare a novel type of chiral RAM-RCC3, and reported the use of chiral RAM-RCC3 as a stationary phase for the separation of chiral compounds and positional isomers in blank serum using high-performance liquid chromatography. The novel RAM-RCC3 column exhibited good performance in the online analysis of nine enantiomers and five positional isomers in serum samples. The effects of analyte mass, temperature, and composition of the mobile phase on the separation of o-, m-, and p-nitrophenol in serum samples using the RAM-RCC3 column were also investigated. Even after 300 injections, the RAM-RCC3 column exhibited good reproducibility and stability. These results indicate the potential of the chiral RAM-RCC3 column as a stationary phase for direct injection analysis of both chiral separation and positional isomers in biological samples, which also rendering it suitable to be further developed as a new type of RAM for online analysis of various small molecules in biological samples., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

A novel automated flow‐cell coupled dynamic solid‐phase microextraction (SPME) method was developed to study the emission of volatile fragrance compounds from fabric samples in a microchamber. Calibration curves were developed with the liquid‐inner tube diffusion‐film‐emission (LIFE) vial technique to quantify the gas‐phase concentration of nine significant volatile fragrance compounds. The linearity range for the various volatile fragrance compounds ranged from 0.001 to 37.9 mg/m3. The limit of quantitation (LOQ) for each fragrance compound was determined as ~3.6–12.1 μg/m3. Using this method, we obtained comprehensive emission curves of target volatiles to quantify the volatile emission profiles of fabric samples. Compared with traditional chamber test method procedures, this work provides a novel and automated method to allow the online monitoring of the emission of various volatiles from multiple samples in one experiment. The repeatability and sensitivity of the method are comparable to or better than existing offline techniques. [ABSTRACT FROM AUTHOR]

To improve the extraction performance of the silica aerogel, a melamine-terephthalaldehyde polymer was used to hybridize silica aerogel, and the hybridized aerogel was coated on the surface of stainless steel wire to prepare a fiber-filled extraction tube through placing four wires into a polyetheretherketone tube. The tube was combined with high-performance liquid chromatography, then the online extraction and detection were established. Several polycyclic aromatic hydrocarbons (PAHs) were selected as the target analytes. Under the optimum extraction and desorption conditions, the limit of detection was as low as 3.0 ng L−1, and the linear range was 0.01–20.0 μg L−1. The enrichment factors of PAHs were in the range of 1724–2393. Three environmental water samples of mineral water, tap water and river water were analyzed by this method, and the recoveries that spiked at 1.0–10.0 μg L−1 were between 80.5–126%. It showed many advantages compared with other methods, such as better sensitivity, faster detection and online analysis. [ABSTRACT FROM AUTHOR]

Covalent organic frameworks with tunable porous crystallinity and outstanding stability have exhibited fascinating pretreatment performance as ideal extraction media. Herein, the β‐ketoenamine‐linked TpPa‐1 synthesized by 1,3,5‐triformylphloroglucinol and paraphenylenediamine was employed as the absorbent for online micro‐solid phase extraction of trace bisphenols combined with high‐performance liquid chromatography detection. A series of characterizations indicated that the TpPa‐1 possessed large surface areas, high stability, and hydrophobicity. The main experimental parameters affecting the extraction efficiency were optimized in detail. Compared with four commercial sorbents, the TpPa‐1 exhibited superior enrichment capacity for extracting bisphenols. Under the optimum conditions, the established method demonstrated a wide linear range and high sensitivity with the limit of detection ranging from 0.05–0.06 μg/L. Furthermore, the developed method was successfully applied to determine bisphenols in plastic samples. Bisphenol A was actually detected in a transparent box with a concentration of 0.31 μg/g, and the recoveries of the four bisphenols in the plastic samples were 80.5–116% with the relative standard deviation less than 9.2%. Such performance was attributed to recognition affinity, including the π‐π affinity, hydrophobic effect, and hydrogen bond. These results demonstrated that TpPa‐1 possessed great potential to be an excellent pretreatment medium for online separation and analysis of trace analytes in complex samples. [ABSTRACT FROM AUTHOR]

This essay consists of eight letters exchanged between the authors over the course of April 2020, on the topic of online analysis. Resuming the methodological approach of theoretical production via correspondence, widely used by Freud and his interlocutors, the article weaves concepts-events regarding psychological care mediated by information and communication technologies, as well as shifts in the status of clinical listening, which reverberate especially on the unconscious, the presence of the analyst and issue of recording. The epistolary writing enabled an affectionate and deinstitutionalized elaboration, breaking with academic logic, without losing the conceptual rigor of the Freudian-Lacanian field in dialogue with other authors. The discussions presented reflect on which lives can be heard in analysis, online or otherwise, in varying unfoldings that circumscribe a contemporary clinic. [ABSTRACT FROM AUTHOR]

Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears.

The use of biogas in the energy sector requires efficient removal of harmful trace levels of impurities. Reliable and robust online analytical equipment to measure these impurities is necessary and must be validated using offline techniques. In this work, total silicon and siloxanes were measured at an industrial solid oxide fuel cell fed by biogas. SOFC anodes are sensitive to silicon-based contaminants even at parts-per-billion levels. Online measurements of total silicon were carried out using a nondispersive infrared sensor (NDIR). Different sampling methods, including chemical adsorption, liquid quenching, and solid-phase adsorption were used at six biogas sampling points. Organic silicon compounds in the samples were determined by offline methods including GC-FID and GC-ICP-MS. The online analyzer's performance was satisfactory in both untreated biogas, where it agreed within the error margin of the most reliable offline technique used, and in clean biogas, where <0.1 mg Si /m3 siloxanes were detected. Several operational issues with different offline sampling and analytical methods are discussed, as is the performance of the sorbent-based biogas cleaning system used. • Round-robin, field comparison of gas-phase silicon measurement methods in biogas. • Six sampling points along a biogas cleaning process for a solid oxide fuel cell. • The biogas cleaning system was found to remove silicon to below 0.1 mgSi/m3. • Online NDIR analyzer agreed with offline methods in both raw and clean biogas. • Biogas matrix complexity affects certain sampling and analytical methods. [ABSTRACT FROM AUTHOR]

A continuous manufacturing platform was developed for the synthesis of aqueous colloidal 10–20 nm gold nanoparticles (Au NPs) in a flow reactor using chloroauric acid, sodium citrate and citric acid at 95 oC and 2.3 bar(a) pressure. The use of a two-phase flow system – using heptane as the continuous phase – prevented fouling on the reactor walls, while improving the residence time distribution. Continuous syntheses for up to 2 h demonstrated its potential application for continuous manufacturing, while live quality control was established using online UV-Vis photospectrometry that monitored the particle size and process yield. The synthesis was stable and reproducible over time for gold precursor concentration above 0.23 mM (after mixing), resulting in average particle size between 12 and 15 nm. A hydrophobic membrane separator provided successful separation of the aqueous and organic phases and collection of colloidal Au NPs in flow. Process yield increased at higher inlet flow rates (from 70 % to almost 100 %), due to lower residence time of the colloidal solution in the separator resulting in less fouling in the PTFE membrane. This study addresses the challenges for the translation of the synthesis from batch to flow and provides tools for the development of a continuous manufacturing platform for gold nanoparticles. Graphical abstract [ABSTRACT FROM AUTHOR]

In this paper we present SpikeOnChip, a custom embedded platform for neuronal activity recording and online analysis. The SpikeOnChip platform was developed in the context of automated drug testing and toxicology assessments on neural tissue made from human induced pluripotent stem cells. The system was developed with the following goals: to be small, autonomous and low power, to handle micro-electrode arrays with up to 256 electrodes, to reduce the amount of data generated from the recording, to be able to do computation during acquisition, and to be customizable. This led to the choice of a Field Programmable Gate Array System-On-Chip platform. This paper focuses on the embedded system for acquisition and processing with key features being the ability to record electrophysiological signals from multiple electrodes, detect biological activity on all channels online for recording, and do frequency domain spectral energy analysis online on all channels during acquisition. Development methodologies are also presented. The platform is finally illustrated in a concrete experiment with bicuculline being administered to grown human neural tissue through microfluidics, resulting in measurable effects in the spike recordings and activity. The presented platform provides a valuable new experimental instrument that can be further extended thanks to the programmable hardware and software. [ABSTRACT FROM AUTHOR]

Copyright of Tobacco Science & Technology is the property of Tobacco Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)