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 (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.

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.

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 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.

OBJECTIVE: 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.

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.

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.

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.

UNASSIGNED: Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.