Online monitoring for extraction of Tibetan medicine Meconopsis quintuplinervia regel. Based on near infrared spectroscopy coupled with chemometrics.

The extraction process plays a crucial role in the production of Tibetan medicines. This study focused on assembling a set of online near-infrared (NIR) spectroscopy detection devices for the extraction of medicinal herbs. The original infrared device was transformed into an online detection system. After evaluating the stability of the system, we applied online NIR spectroscopy monitoring to the flavonoid contents (total flavonoids, quercetin-3-O-sophoroside, and luteolin) of Meconopsis quintuplinervia Regel. during the ultrasonic extraction process and determined the extraction endpoint. Nine batches of samples were employed to construct quantitative and discriminant models, half of the remaining two batches of samples are used for external verification. Our research shows that the residual predictive deviation (RPD) values of total flavonoids, quercetin-3-O-sophoroside and luteolin models exceeded 2.5. The R values for external verification of the three ingredients were above 0.9, with RPD values generally exceeding 2 and RSEP values within 10 %, demonstrating the model's strong predictive performance. Most of the extraction endpoints of the flavonoid components in M. quintuplinervia ranged from 18 to 58 min, with high consistency between the predicted extraction endpoints of the external validation, suggesting accurate determination of extraction endpoints based on predicted values. This study can provide a reference for the online NIR spectroscopy quality monitoring of the extraction process of Chinese and Tibetan herbs.

Differential photoacoustic spectroscopy for flow gas detection based on single microphone.

Differential photoacoustic spectroscopy (PAS) for flow gas detection based on single microphone is innovatively proposed and experimentally demonstrated. Unlike the traditional systems, only one microphone is used to suppress flowing gas noise. Wavelength modulation spectroscopy and second harmonic detection technique are applied in this PAS system with Q-point demodulation for acetylene (C2H2) gas detection. The experiment is conducted at 1 atm and 300 K. Different concentrations and flow rates of C2H2 from 0 sccm to 225 sccm are detected by using nitrogen (N2) as the carrier gas, which indicates that the system can respond well to flowing gases while maintaining the noise at the same level. The system response time decreases to 3.58 s while the gas velocity is 225 sccm. The detection limit of 43.97 ppb with 1 s integration time and normalized noise equivalent absorption (NNEA) coefficient of 4.0 × 10-9 cm-1 W Hz-1/2 is achieved at the flow rate of 225 sccm. The firstly proposed differential PAS based on single microphone greatly simplifies the system structure for flow gas detection, which provides a novel route for development of PAS with significant practical implementation prospects.

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion.

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization-extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.

The Design and Experimentation of a Corn Moisture Detection Device Based on Double Capacitors.

Detecting the moisture content of grain accurately and rapidly has important significance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low detection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capacitor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the moisture content. Finally, the support vector machine (SVM) regressions between the capacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dynamic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains.

Moisture content online detection system based on multi-sensor fusion and convolutional neural network.

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model's predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.

The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature.

GOAL: Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB count rates is critical. We propose an online detection system with a neural network-based model to estimate MB count rate using five parameters: suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature. METHODS: Perfusion experiments were performed using an actual CPB circuit, and MB count rates were measured using the five varying parameters. RESULTS: Bland-Altman analysis indicated a high estimation accuracy (R2 > 0.95, p < 0.001) with no significant systematic error. In clinical practice, although the inclusion of clinical procedures slightly decreased the estimation accuracy, a high coefficient of determination for 30 clinical cases (R2 = 0.8576) was achieved between measured and estimated MB count rates. CONCLUSIONS: Our results highlight the potential of this system to improve patient outcomes and reduce MB-associated complication risk.

Online water vapor removal membrane inlet mass spectrometer for high-sensitivity detection of dissolved methane.

Underwater mass spectrometry is characterized by excellent consistency, strong specificity, and the ability to simultaneously detect multiple substances, making it a valuable tool in research fields such as aquatic ecosystems, hydrothermal vents, and the global carbon cycle. Nevertheless, current underwater mass spectrometry encounters challenges stemming from the high-water vapor content, constituting proportions of nearly 90%. This results in issues such as peak overlap, interference with peak height, decreased ionization efficiency and, consequently, make it difficult to achieve low detection limits for extremely low concentrations of gases, such as methane, and impede the detection of background CH4 levels. In this study, we optimized the design of the sampling gas path and developed a high gas-tightness, high pressure-resistant membrane inlet system, coupled with a small-volume, low-power online water vapor removal system. This innovation efficiently eliminates water vapor while maintaining a high permeation flux of the target gases. By elevating the vacuum level to the order of 1E-6 Torr, the ionization efficiency and detection performance were improved. Based on this, we created an online water vapor removal membrane inlet mass spectrometer and conducted experimental research. Results indicated that the water removal efficiency approached 100%, and the vacuum level was elevated by more than 2 orders of magnitude. The detection limit for CH4 increased from over 600 nmol/L to 0.03 nmol/L, representing an improvement of over 4 orders of magnitude, and reaching the level of detecting background CH4 signals in deep-sea and lakes. Furthermore, the instrument exhibited excellent responsiveness and tracking capability to concentration changes on the second scale, enabling in situ analysis of rapidly changing concentration scenarios.

One-step online analysis of antibiotics in highly saline seawater by nano-based slug-flow microextraction.

The transition to mass spectrometry (MS) in the analysis of antibiotics in the marine environment is highly desirable, particularly in the enhancement of sensitivity for high-salinity (3.5 wt%) seawater samples. However, the persistence of complex operational procedures poses substantial challenges to this transition. In this study, a rapid method for the online analysis of antibiotics in seawater samples via nano-electrospray ionization (nESI) MS based on slug-flow microextraction (SFME) has been proposed. Comparisons with other methods, complex laboratory setups for sample processing are now seamlessly integrated into a single online step, completing the entire process, including desalination and detection, SFME-nESI-MS provides faster results in less than 2 min while maintaining sensitivity comparable to that of other detection methods. Using SFME-nESI, six antibiotics in high-salinity (3.5 wt%) seawater samples have been determined in both positive and negative ion modes. The proposed method successfully detected clarithromycin, ofloxacin, and sulfadimidine in seawater within a linear range of 1-1000 ng mL-1 and limit of detection (LOD) of 0.23, 0.06, and 0.28 ng mL-1, respectively. The method recovery was from 92.8% to 107.3%, and the relative standard deviation was less than 7.5%. In addition, the response intensity of SFME-nESI-treated high-salinity (3.5 wt%) samples surpassed that of untreated medium-salinity (0.35 wt%) samples by two to five orders of magnitude. This advancement provides an exceptionally simplified protocol for the online rapid, highly sensitive, and quantitative determination of antibiotics in high-salinity (3.5 wt%) seawater.

Raman and Surface-Enhanced Raman Scattering Detection in Flowing Solutions for Complex Mixture Analysis.

Raman scattering provides a chemical-specific and label-free method for identifying and quantifying molecules in flowing solutions. This review provides a comprehensive examination of the application of Raman spectroscopy and surface-enhanced Raman scattering (SERS) to flowing liquid samples. We summarize developments in online and at-line detection using Raman and SERS analysis, including the design of microfluidic devices, the development of unique SERS substrates, novel sampling interfaces, and coupling these approaches to fluid-based chemical separations (e.g., chromatography and electrophoresis). The article highlights the challenges and limitations associated with these techniques and provides examples of their applications in a variety of fields, including chemistry, biology, and environmental science. Overall, this review demonstrates the utility of Raman and SERS for analysis of complex mixtures and highlights the potential for further development and optimization of these techniques.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy.

Introduction: Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. Methods: To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. Results: The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). Discussion: This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

High-resolution nucleic acid detection using online polyacrylamide gel electrophoresis platform.

Polyacrylamide gel electrophoresis (PAGE) is one of the most popular techniques for the separation and detection of nucleic acids. However, it requires a complicated detection procedure and offline detection format, which inevitably leads to band broadening and thus compromises the separation resolution. To overcome this problem, we developed an online PAGE (OPAGE) platform by integrating the gel electrophoresis apparatus with the gel imaging system, so as to obviate the need for the complicated detection procedure. Notably, OPAGE enabled the real-time monitoring of the separation process and the immediate imaging of the separation results once the electrophoresis ended. Using a series of synthetic DNAs with different lengths as samples, we demonstrated that the OPAGE platform enhanced 32-64 % of the number of theoretical plates, showed a robust dynamic range of 0.1-12.5 ng/µL, and realized a limit of detection as low as 0.08 ng/µL DNA. Based on our results, we anticipate that the OPAGE platform is a promising alternative to traditional nucleic acid gel electrophoresis for simple and high-resolution detection and quantification and nucleic acid.

Plasmonic cellulose microfilament assisted SERS detection in microfluidics.

Limited by the narrow enhanced area of nanoscale on the metal surface, the sensitivity of surface-enhanced Raman spectroscopy (SERS) detection in solution is usually much lower than the detection in a solid substrate, which is dramatic in microfluidics for online detection. In this work, a cellulose microfilament embraced by Ag nanoparticles, called plasmonic cellulose microfilament, is located in a microchannel for SERS detection in microfluidics. Benefiting from the congestion caused by the plasmonic cellulose microfilament in a microchannel, the trace molecule in the solution is much easier to gather in Ag nanoparticles for Raman enhancement. To obtain high sensitivity, the structure of plasmonic cellulose microfilament is optimized. The SERS spectra collected in this novel microfluidics demonstrate that the plasmonic cellulose microfilament presents a high sensitivity at 10-13 M and good reproducibility in SERS detection. In addition, automatic identification of urea presence or absence was achieved based on deep learning (DL) here. The results show excellent diagnostic accuracy (99 %), which suggests that a fast, label-free urea screening tool can be developed. These results point out this SERS microfluidics with plasmonic cellulose microfilament has a great application prospective in online SERS detection with high sensitivity.

[Application progress on data-driven technologies in intelligent manufacturing of traditional Chinese medicine extraction].

The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.

Analysis of protein secretion in Bacillus subtilis by combining a secretion stress biosensor strain with an in vivo split GFP assay.

BACKGROUND: Bacillus subtilis is one of the workhorses in industrial biotechnology and well known for its secretion potential. Efficient secretion of recombinant proteins still requires extensive optimization campaigns and screening with activity-based methods. However, not every protein can be detected by activity-based screening. We therefore developed a combined online monitoring system, consisting of an in vivo split GFP assay for activity-independent target detection and an mCherry-based secretion stress biosensor. The split GFP assay is based on the fusion of a target protein to the eleventh ß-sheet of sfGFP, which can complement a truncated sfGFP that lacks this ß-sheet named GFP1-10. The secretion stress biosensor makes use of the CssRS two component quality control system, which upregulates expression of mCherry in the htrA locus thereby allowing a fluorescence readout of secretion stress. RESULTS: The biosensor strain B. subtilis PAL5 was successfully constructed by exchanging the protease encoding gene htrA with mCherry via CRISPR/Cas9. The Fusarium solani pisi cutinase Cut fused to the GFP11 tag (Cut11) was used as a model enzyme to determine the stress response upon secretion mediated by signal peptides SPPel, SPEpr and SPBsn obtained from naturally secreted proteins of B. subtilis. An in vivo split GFP assay was developed, where purified GFP1-10 is added to the culture broth. By combining both methods, an activity-independent high-throughput method was created, that allowed optimization of Cut11 secretion. Using the split GFP-based detection assay, we demonstrated a good correlation between the amount of secreted cutinase and the enzymatic activity. Additionally, we screened a signal peptide library and identified new signal peptide variants that led to improved secretion while maintaining low stress levels. CONCLUSION: Our results demonstrate that the combination of a split GFP-based detection assay for secreted proteins with a secretion stress biosensor strain enables both, online detection of extracellular target proteins and identification of bottlenecks during protein secretion in B. subtilis. In general, the system described here will also enable to monitor the secretion stress response provoked by using inducible promoters governing the expression of different enzymes.

Rapid online plant leaf area change detection with high-throughput plant image data.

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

Synthesis of CsPbBr3 in Micro Total Reaction System: Fast Operation Space Mapping and Subsecond Growth Process Monitoring.

Lead halide perovskite nanocrystals (LHP NCs) have the characteristics of fast reaction kinetics and crystal instability due to the intrinsically highly ionic bonding between the respective ions, which bring challenges for revealing the growth kinetics and practical applications. Compared with conventional batch synthesis methods, the single-function microreactor can achieve precise and stable control of the NCs synthesis process, but it still has the shortcoming of not being able to obtain information about the growth process. In this study, a micro Total Reaction System (µTRS) with remote control, online detection, and rapid data analysis functions is designed. µTRS can sample the photoluminescence information of CsPbBr3 NCs growth in ligand-assisted reprecipitation method. CsPbBr3 NCs with an emission range of 435-492 nm are successfully detected, which breaks the record of the smallest size of CsPbBr3 NCs synthesized directly from precursors. The real-time feature of µTRS enables the construction of an automated close-loop synthesis system. Besides, the rapid acquisition and timely processing of product information enable the rapid mapping of the operation space for CsPbBr3 NCs preparation, which provides a reliable and learnable data set for designing a fully autonomous microreaction system capable of synthesizing NCs.

Advances in Online Detection Technology for Laser Additive Manufacturing: A Review.

In additive manufacturing (AM), the mechanical properties of manufactured parts are often insufficient due to complex defects and residual stresses, limiting their use in high-value or mission-critical applications. Therefore, the research and application of nondestructive testing (NDT) technologies to identify defects in AM are becoming increasingly urgent. This article reviews the recent progress in online detection technologies in AM, a special introduction to the high-speed synchrotron X-ray technology for real-time in situ observation, and analysis of defect formation processes in the past 5 years, and also discusses the latest research efforts involving process monitoring and feedback control algorithms. The formation mechanism of different defects and the influence of process parameters on defect formation, important parameters such as defect spatial resolution, detection speed, and scope of application of common NDT methods, and the defect types, advantages, and disadvantages associated with current online detection methods for monitoring three-dimensional printing processes are summarized. In response to the development requirements of AM technology, the most promising trends in online detection are also prospected. This review aims to serve as a reference and guidance for the work to identify/select the most suitable measurement methods and corresponding control strategy for online detection.

Real-time changepoint detection in a nonlinear expectile model.

An online changepoint detection procedure based on conditional expectiles is introduced. The key contribution is threefold: nonlinearity of the underlying model improves the overall flexibility while a parametric form of the unknown regression function preserves a simple and straightforward interpretation; The conditional expectiles, well-known in econometrics for being the only coherent and elicitable risk measure, introduce additional robustness-especially with respect to asymmetric error distributions common in various types of data; The proposed statistical test is proved to be consistent and the distribution under the null hypothesis does not depend on the functional form of the underlying model nor the unknown parameters. Empirical properties of the proposed real-time changepoint detection test are investigated in a simulation study and a practical applicability is illustrated using the Covid-19 prevalence data from Prague.

Research on Online Nondestructive Detection Technology of Duck Egg Origin Based on Visible/Near-Infrared Spectroscopy.

As living standards rise, people have higher requirements for the quality of duck eggs. The quality of duck eggs is related to their origin. Thus, the origin traceability and identification of duck eggs are crucial for protecting the rights and interests of consumers and preserving food safety. As the world's largest producer and consumer of duck eggs, China's duck egg market suffers from a severe lack of duck egg traceability and rapid origin identification technology. As a result, a large number of duck eggs from other regions are sold as products from well-known brands, which seriously undermines the rights and interests of consumers and is not conducive to the sound development of the duck egg industry. To address the above issues, this study collected visible/near-infrared spectral data online from duck eggs of three distinct origins. To reduce noise in the spectral data, various pre-processing algorithms, including MSC, SNV, and SG, were employed to process the spectral data of duck eggs in the range of 400-1100 nm. Meanwhile, CARS and SPA were used to select feature variables that reflect the origin of duck eggs. Finally, classification models of duck egg origin were developed based on RF, SVM, and CNN, achieving the highest accuracy of 97.47%, 98.73%, and 100.00%, respectively. To promote the technology's implementation in the duck egg industry, an online sorting device was built for duck eggs, which mainly consists of a mechanical drive device, spectral software, and a control system. The online detection performance of the machine was validated using 90 duck eggs, and the final detection accuracy of the RF, SVM, and CNN models was 90%, 91.11%, and 94.44%, with a detection speed of 0.1 s, 0.3 s, and 0.5 s, respectively. These results indicate that visible/near-infrared spectroscopy can be exploited to realize rapid online detection of the origin of duck eggs, and the methodologies used in this study can be immediately implemented in production practice.

Full-surface defect detection of navel orange based on hyperspectral online sorting technology.

The whole-surface hyperspectral image acquisition of navel orange is particularly important for surface defect detection and quality classification. Because the light intensity at the edge of the navel orange is lower than that in the middle, the defects on the surface of the navel orange cannot be effectively identified. In this paper, a hyperspectral online sorting device for the whole-surface defects of navel orange is proposed. First of all, the image data of navel orange is collected by online detection sorting equipment and the spectral image of the characteristic wave peak of 1655.72 nm was extracted. Then, the light intensity at the edge of the navel orange is enhanced by nonuniformity correction based on quadratic curve fitting, and the light intensity correction of the navel orange is realized. Finally, the corrected image is segmented by the threshold to obtain surface defects, and the number of surface defect pixels is improved effectively compared with that before light intensity correction. Ultimately, the online sorting test is carried out, and the detection accuracy is 100%. This indicates that this method effectively improves the sensitivity of defect detection. At the same time, the dimensionality reduction of hyperspectral data is also carried out, which is conducive to improving the efficiency of online detection.