Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products.

Near-infrared spectroscopy (NIRS) has emerged as a key technique for rapid quality detection owing to its fast, non-destructive, and eco-friendly characteristics. However, its practical implementation within the formulation industry is challenging owing to insufficient data, which renders model fitting difficult. The complexity of acquiring spectra and spectral reference values results in limited spectral data, aggravating the problem of low generalization, which diminishes model performance. To address this problem, we introduce what we believe to be a novel approach combining NIRS with Wasserstein generative adversarial networks (WGANs). Specifically, spectral data are collected from representative samples of raw material provided by a formula enterprise. Then, the WGAN augments the database by generating synthetic data resembling the raw spectral data. Finally, we establish various prediction models using the PLSR, SVR, LightGBM, and XGBoost algorithms. Experimental results show the NIRS-WGAN method significantly improves the performance of prediction models, with R2 and RMSE of 0.949 and 1.415 for the chemical components of sugar, respectively, and 0.922 and 0.243 for nicotine. The proposed framework effectively enhances the predictive capabilities of various models, addressing the issue caused by limited training data in NIRS prediction tasks.

Intelligent large-scale flue-cured tobacco grading based on deep densely convolutional network.

Flue-cured tobacco grading plays a crucial role in tobacco leaf purchase and the formulation of tobacco leaf groups. However, the traditional flue-cured tobacco grading mode is usually manual, which is time-consuming, laborious, and subjective. Hence, it is essential to research more efficient and intelligent flue-cured tobacco grading methods. Most existing methods suffer from the more classes less accuracy problem. Meanwhile, limited by different industry applications, the flue-cured tobacco datasets are hard to be obtained publicly. The existing methods employ relatively small and lower resolution tobacco data that are hard to apply in practice. Therefore, aiming at the insufficiency of feature extraction ability and the inadaptability to multiple flue-cured tobacco grades, we collected the largest and highest resolution dataset and proposed an efficient flue-cured tobacco grading method based on deep densely convolutional network (DenseNet). Diverging from other approaches, our method has a unique connectivity pattern of convolutional neural network that concatenates preceding tobacco feature data. This mode connects all previous layers to the subsequent layer directly for tobacco feature transmission. This idea can better extract depth tobacco image information features and transmit each layer's data, thereby reducing the information loss and encouraging tobacco feature reuse. Then, we designed the whole data pre-processing process and experimented with traditional and deep learning algorithms to verify our dataset usability. The experimental results showed that DenseNet could be easily adapted by changing the output of the fully connected layers. With an accuracy of 0.997, significantly higher than the other intelligent tobacco grading methods, DenseNet came to the best model for solving our flue-cured tobacco grading problem.

Spatiotemporal heterogeneity of land subsidence in Beijing.

Land subsidence induced by groundwater level decline has spatiotemporal variations. Taking the Interferometric Synthetic Aperture Radar (InSAR) results and the groundwater subsidence data acquired by the monitoring stations as the source material, this paper aims to reveal the spatiotemporal heterogeneity of groundwater-land subsidence in Beijing plain by using the Wind Rose Map (WRM) method and the Change Point Analysis (CPA) method. The WRM results show that the amount and variation in subsidence differs in different directions. This method detected the formation of new subsidence centers and the slowdown of land subsidence in 2008. The CPA results show that obvious changes are detected in subsidence development at the Wangsiying (WSY), Tianzhu (TZ) and Wangjing (WJ) stations. However, there is a relatively stable trend of groundwater decline and land subsidence at the Tianzhu (TZ) station. The stages of land subsidence development show a significant response to groundwater. Moreover, changes in land subsidence also show delayed response behind the changes in groundwater level. The time-lag could be affected by the variation in amplitude of the groundwater level.

Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR.

The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS.

Analysis of the influence of groundwater on land subsidence in Beijing based on the geographical weighted regression (GWR) model.

A global geological phenomenon caused by natural or human activities is described as land subsidence. Groundwater extraction plays a significant part in causing land subsidence. Due to economic development, urban expansion, and rapid population expansion, the unscientific exploitation of groundwater in Beijing has been accelerated, which makes it the region with the fastest land subsidence rate in China. To study the spatial heterogeneity of land subsidence caused by groundwater aquifers level changes, the monitoring results of land subsidence in 2003-2010 years were analyzed by using PS-InSAR, based on ENVISAT ASAR in Beijing plain area. The maximum value of accumulated land subsidence in the study area is 707 mm, and in this study area multiple subsidence center areas have been formed. A GWR model based on a regular grid has been established by exploring the effects of unconfined aquifer (UA), first confined aquifer (FCA), second confined aquifer (SCA), third confined aquifer (TCA) on land subsidence and their spatial non-stationarity. The change of subsidence in all subsidence areas is positively related to the change of SCA water level. Except the fact that the main control factors of Liyuan and Songzhuang are the change of UA layer, the change of SCA is the main control factor of land subsidence in most subsidence areas. Though the contribution rate of SCA to land subsidence is the highest, the contribution rate of TCA has been increasing. It is predicted that the impact on land subsidence will increase year by year. The results of this will not only help to understand the spatial impact patterns of aquifers on land subsidence zones, but also to formulate optimal groundwater regulation and recharge policies. There is a scarcity of the consideration of the compressible layer in the study and it will become more comprehensive if further datasets are obtained.

Land subsidence and its relation with groundwater aquifers in Beijing Plain of China.

Beijing is a major metropolis with significant land subsidence because of long-term overexploitation of groundwater. While the South-to-North Water Diversion Project (SWDP) has provided new water sources Beijing; it has changed the pattern of land subsidence evolution in Beijing since 2015. Here we address how land subsidence evolution before and after SWDP, and we quantify also the impact of groundwater level changes in different aquifers on land subsidence at spatial scale. Subsidence evolution before and after SWDP were compared by adopting Persistent Scatterer Inteferomotry (PSI) with Radarsat-2 and Sentinel-1 data. Spatial correlation between Interferometric Synthetic Aperture Radar (InSAR) derived subsidence and groundwater levels in four aquifers was investigated using the Random Forest (RF) machine learning algorithm and Geographical Detectors (GD) technique. Extensometer deformation data and corresponding variation in groundwater level observations at three monitoring stations were used for validations. The study reveals that: firstly, both InSAR-derived subsidence area and maximum annual deformation rate decreased from 79.2% and 141 mm/yr before SWDP, to 60.1% and 135 mm/yr after SWDP. A reduction of time series deformation at four subsidence centers started about two years after the commence of SWDP in 2015. Secondly, the variation of groundwater level in the second confined aquifer has the strongest spatial correlation with subsidence in all the aquifers, but its impact on this aquifer has decreased after SWDP. These findings have an important scientific significance for the rational allocation of water resources and management strategy for mitigating hazards associated with subsidence against the background of SWDP.

Monitoring Differential Subsidence along the Beijing-Tianjin Intercity Railway with Multiband SAR Data.

High-speed railways have strict standards of infrastructure deformation and post-construction settlement. The interferometric synthetic aperture radar (InSAR) has the ability to detect ground deformation with a high accuracy and wide coverage and is becoming a useful tool for monitoring railway health. In this study, we analyzed the Beijing-Tianjin Intercity Railway (BTIR) track using InSAR time-series analysis with different data sets. First, by using RADARSAT-2 images, we examined the areas along the BTIR with significant subsidence. Then, we characterized these areas by means of X-band TerraSAR-X data. We adopted the expectation (Ex) and entropy (En) method, combined with GIS spatial analysis, to analyze the ground settlement differences on both sides of the railway. The results show that the area with the most severe differential settlement occurs between 12 and 20 km along the railway and within 120 to 20 m on both sides of the Chaoyang-Tongzhou section (CTS). Thereafter, we analyzed the reasons for the large difference in this area by considering different factors, e.g., regional land subsidence, groundwater level changes, and the dynamic load. In addition, we studied the impact of regional subsidence on the safe operation of the BTIR. The results show that the maximum different settlement along the BTIR is within the safe range, according to the high-speed railway design standard between 2010 and 2015. This study aims to provide technical support for assessing the impact of subsidence on the safety of railway operations.

Molecular Adsorption Mechanism of Elemental Carbon Particles on Leaf Surface.

Plant leaves can effectively capture and retain particulate matter (PM), improving air quality and human health. However, little is known about the adsorption mechanism of PM on leaf surface. Black carbon (BC) has great adverse impact on climate and environment. Four types of elemental carbon (EC) particles, carbon black as a simple model for BC, graphite, reduced graphene oxide, and graphene oxide, and C36H74/C44H88O2 as model compounds for epicuticular wax were chosen to study their interaction and its impact at the molecular level using powder X-ray diffraction and vibrational spectroscopy (infrared and Raman). The results indicate that EC particles and wax can form C-H···π type hydrogen bonding with charge transfer from carbon to wax; therefore, strong attraction is expected between them due to the cooperativity of hydrogen bonding and London dispersion from instantaneous dipoles. In reality, once settled on the leaf surface, especially without wax ultrastructures, BC with extremely large surface-to-volume ratio will likely stick and stay. On the other hand, BC particles can lead to phase transition of epicuticular wax from crystalline to amorphous structures by creating packing disorder and end- gauche defects of wax molecular chain, potentially causing water loss and thereby damage of plants.

Decreased expression of perforin in CD8+ T lymphocytes in patients with Mycobacterium tuberculosis infection and its potential value as a marker for efficacy of treatment.

BACKGROUND: Cytolytic activity against mycobacteria tuberculosis (MTB) within the infected macrophage is a crucial step in the immunity against TB infection, as MTB is an intracellular bacterium. Cytotoxic molecules such as perforin and granzymes produced by cytolytic T cells directly participate in this process. In this study, we evaluated the cytotoxicity function employing flow cytometry analysis of the level of expression of interferon-γ (IFN-γ), perforin and granzyme B in CD8+ T cells from patients with active pulmonary TB (PTB), stable PTB and healthy controls, and explored whether MTB antigen (MTB Ag)-stimulated cytotoxic molecules would be useful for monitoring responses to anti-TB treatment. METHODS: Intracellular IFN-γ, perforin, and granzyme B were measured by flow cytometry in CD8+ T lymphocyte populations from peripheral blood mononuclear cells before and after stimulation with ESAT-6 and CFP-10 peptides for 72 hours. A total of 38 healthy controls, 52 PTB patients after treatment for 2 months and 58 patients with active PTB were enrolled. RESULTS: The positive rate of IFN-γ+ CD8+ T cells was expressed higher in active PTB patients and stable PTB compared to healthy controls. Expression of perforin in CD8+ T lymphocytes was lower in the active PTB than the stable PTB. Positive downregulation of perforin and granzyme B after stimulation with ESAT-6 and CFP-10 peptides in active PTB and stable PTB was seen. IFN-γ was upregulated after stimulation. ROC curve analysis showed that the area under the curve (AUC) of perforin and perforin + IFN-γ after stimulation were 0.766 (P=0.000), 0.802 (P=0.000), respectively. CONCLUSIONS: Our results show that expression of perforin in CD8+ T lymphocytes is downregulated in PTB infection and ESAT-6 and CFP-10 peptides might participate in the downregulation process. This finding cautiously suggests that MTB Ag-stimulated perforin downregulation and IFN-γ upregulation might be a potential index for monitoring therapy response in active PTB patients.

Highly Sensitive and Selective Turn-on Fluorescent Probe for Hg2+ Based on Rhodamine 6G-Thiourea Conjugate.

An easily available novel fluorescent probe based on rhodamine 6G-thiourea conjugate (RGTU) has been synthesized. Upon mixed with Hg2+ in CH3CN-H2O (1:9, v/v), the dramatic enhancement of fluorescence intensity as well as the color change of the solution were observed. The presence of common coexisting alkali, alkaline earth, and transition metal ions showed small or no interference with the detection of Hg2+. The Job's plot and 1H NMR spectra indicated that Hg2+ induced spirolactam open to form 1:1 complex between RGTU and Hg2+.

Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data.

Hydraulic conductivity is a major parameter affecting the output accuracy of groundwater flow and transport models. The most commonly used semi-empirical formula for estimating conductivity is Kozeny-Carman equation. However, this method alone does not work well with heterogeneous strata. Two important parameters, grain size and porosity, often show spatial variations at different scales. This study proposes a method for estimating conductivity distributions by combining a stochastic hydrofacies model with geophysical methods. The Markov chain model with transition probability matrix was adopted to re-construct structures of hydrofacies for deriving spatial deposit information. The geophysical and hydro-chemical data were used to estimate the porosity distribution through the Archie's law. Results show that the stochastic simulated hydrofacies model reflects the sedimentary features with an average model accuracy of 78% in comparison with borehole log data in the Chaobai alluvial fan. The estimated conductivity is reasonable and of the same order of magnitude of the outcomes of the pumping tests. The conductivity distribution is consistent with the sedimentary distributions. This study provides more reliable spatial distributions of the hydraulic parameters for further numerical modeling.

The spatial-temporal variations of water quality in controlling points of the main rivers flowing into the Miyun Reservoir from 1991 to 2011.

Cluster analysis (CA), discriminant analysis (DA), and principal component analysis/factor analysis (PCA/FA) were used to analyze the interannual, seasonal, and spatial variations of water quality from 1991 to 2011 in controlling points (Xinzhuang Bridge, Daguan Bridge) of the main rivers (Chaohe River, Baihe River) flowing into the Miyun Reservoir. The results demonstrated that total nitrogen (TN) and total phosphorus (TP) exceeded China National Standard II for surface water separately 5.08 times and 1 time. CA showed that the water quality could be divided into three interannual (IA) groups: IAI (1991-1995, 1998), IAII (1996-1997, 1999-2000, 2002-2006), and IAIII (2001, 2007-2011) and two seasonal clusters: dry season 1 (December), dry season 2 (January-February), and non-dry season (March-November). At interannual scale, the higher concentration of SO4 (2-) from industrial activities, atmospheric sedimentation, and fertilizer use in IAIII accelerated dissolution of carbonate, which increased Ca(2+), Mg(2+), total hardness (T-Hard), and total alkalinity (T-Alk). The decreasing trend of CODMn contributed to the establishment of sewage treatment plants and water and soil conservation in the Miyun upstream. The changing trend of NO3 (-)-N indicated increasing non-point pollution load of IAII and effective non-point pollution controlling of IAIII. Only one parameter T in the seasonal scale verified improved non-point pollution controlling. The major pollution in two controlling points was NO3 (-)-N, T-Hard, TN, and other ion pollution (SO4 (2-), F(-), Ca(2+), Mg(2+), T-Hard, T-Alk). Higher concentration of NO3 (-)-N in Xinzhuang and CODMn in Daguan indicated different controlling measures, especially controlling agriculture intensification in Chaohe River to decrease N pollution and decreasing water and soil loss and cage culture in Baihe River to weaken organic pollution. Controlling SO4 (2-) from industrial activity, atmospheric sedimentation and fertilizer use in watershed can effectively control Ca(2+), Mg(2+), T-Hard, and T-Alk.

A Sensitive and Selective Fluorescent Coumarin-Based Probe for Detection of Hypochlorite ion and its Application to Cellular Imaging.

Selective, sensitive fluorescent probes for ClO(-) are desirable due to the importance of ClO(-) in biological processes. Herein, a readily available turn-off fluorescent probe for ClO(-) is reported, which displays highly selectivity and sensitivity over other common anions and reactive oxygen/nitrogen. Moreover, it is able to detect ClO(-) in Ramos cells via cellular imaging.

Accumulation of particles on the surface of leaves during leaf expansion.

Plants can effectively remove airborne particles from ambient air and consequently improve air quality and human health. The accumulation of particles on the leaf surfaces of three plant species with different epicuticular wax ultrastructures, such as thin films, platelets and tubules, was investigated during leaf expansion in Beijing under extremely high particulate matter (PM) concentration. The accumulation of particles on the leaf surfaces after bud break rapidly reached a high amount within 4-7 days. Rainfall occasionally resulted in a considerable increase in the accumulation of particles on the leaf surfaces at a high PM concentration, which resulted from the wet deposition of PM, and balanced the amount of PM on the leaf surfaces over a longer period. The equilibrium value of the particle cover area on the adaxial leaf surface of the three test species in this study was 10%-50% compared with 3%-35% on the abaxial leaf surface. The epicuticular wax ultrastructures contributed significantly to the PM adsorption of the leaves. The capability of these ultrastructures to capture PM decreased in the following order: thin films, platelets and tubules. The ridges (at a scale of 1-2 µm) on the leaf surfaces were more efficient at accumulating PM, particularly PM2.5, compared with the roughness (P-V distance) at a 5-20-µm scale.

Receding water line and interspecific competition determines plant community composition and diversity in wetlands in Beijing.

Climate and human-induced wetland degradation has accelerated in recent years, not only resulting in reduced ecosystem services but also greatly affecting the composition and diversity of wetland plant communities. To date, the knowledge of the differences in community parameters and their successional trends in degraded wetlands remains scarce. Here based on remote sensing images, geographic information system technology, and statistical methods, we produced a successional gradient map of the Yeyahu Wetland Nature Reserve in Beijing, which has experienced a steady decline in water level in recent decades. In addition, we analyzed community composition and diversity along with each identified gradient. The results showed that community diversity decreases while dominance increases with the progress of succession, with the highest diversity occurring during the early stage of succession. Moreover, the community demonstrates greater similarity among subareas during later successional stages, and the similarity coefficients calculated from the important value (IV) of each species are more accurate. Correlation analysis showed that the impact of soil factors on diversity was not significant at a subarea scale, although these nutrients showed an increasing trend with the community succession. Furthermore, the IVs of the dominant species had a particularly significant impact on diversity, showing a significantly negative correlation with diversity indices and a significantly positive correlation with dominance indices. Further analysis showed that the retreat of water level resulted from sustained drought and local human activities was a major extrinsic driving force resulting in observed differences in the community successional stages, which resulted in differences in community composition and diversity. On the other hand, interspecific competition was the main intrinsic mechanism, which significantly influenced the IVs of the dominant species and community diversity. The results of this study could aid in improving the understanding of community composition, diversity, and its successional trends in degraded wetlands.

[Study on an Algorithm for Near Infrared Singular Sample Identification Based on Strong Influence Degree].

Correcting sample selection and elimination of singular sample is very important for the quantitative and qualitative modeling of near infrared spectroscopy. However, methods for identification of singular sample available are generally based on data center estimates which require an experience decision threshold, this largely limit its recognition accuracy and practicability. Aiming at the low accuracy of the existing methods of singular sample recognition problem, this paper improves the existing metric-Leverage value and presents a new algorithm for near infrared singular sample identification based on strong influence degree. This metric reduces the dependence on the data center to a certain extent, so that the normal samples become more aggregation, and the distance between the singular samples and the normal samples is opened; at the same time, in order to avoid artificial setting threshold unreasonably according to experience, this paper introduces the concept of the jump degree in the field of statistics, and proposes an automatic threshold setting method to distinguish singular samples. In order to verify the validity of our algorithm, abnormal samples of 200 representative samples were eliminated in the calibration set with using Mahalanobis distance, Leverage-Spectral residual method and the algorithm presented in this paper respectively; then through partial least squares (PLS), the rest of the calibration samples were made quantitative modelings (took Nicotine as index), and the results of quantitative modelings were made a comparative analysis; besides, 60 representative testing samples were made a prediction through the modelings; at last, all the algorithms above were made a comparison with took Root Mean Square Error of Cross Validation (RMSECV), Correlation Coefficient (r) and Root Mean Square Error of Prediction (RMSEP) as evaluation Index. The experimental results demonstrate that the algorithm for near infrared singular sample identification based on strong influence degree significantly improves the accuracy of singular sample identificition over existing methods. With lower RMSECV (0.104), RMSEP (0.112) and higher r (0.983), it also contribute to boost the stability and prediction ability of the model.

Rhizosphere bacterial community of Typha angustifolia L. and water quality in a river wetland supplied with reclaimed water.

Wetland plant rhizosphere microorganisms play a significant role in the purification of ecological restoration of reclaimed water replenishment wetlands. In this study, water quality discriminant analysis indicated the wetland had a distinctive role in the purification of total nitrogen (TN), total phosphorus (TP), and nitrate (NO3 (-)) from reclaimed water, of which removal rates were 42.15, 47.34, and 28.56 % respectively. All the sequences of 16S ribosomal RNA (16S rRNA) gene clone library were affiliated with Proteobacteria (74.50 %), Bacteroidetes (6.54 %), Gemmatimonadetes (5.88 %), Chloroflexi (4.25 %), Chlorobi (2.94 %), Nitrospira (2.61 %), Acidobacteria (2.29 %), and Actinobacteria (0.98 %). Assessment of water quality purification and rhizosphere bacterial properties revealed that the major biogeochemical reactions were nitrogen, phosphorus, carbon, and sulfur cycles (33.70, 15.40, 14.40, and 4.90 %, respectively). In addition, approximately 5.90 and 4.60 % of the clones are closely related with the minor biogeochemical degradations of antibiotics and halogenated hydrocarbons, which were the typical characteristics of reclaimed water wetland different from freshwater wetlands. The finding of water quality discriminant is consistent with that of bacterial community, but the latter was a more powerful method than the former which reveals possible implications of wetland plant purification on the reclaimed water.

[Comparative analysis of trace elements in five marine-derived shell TCM using multivariate statistical analysis].

A comparable study were carried out by determination of trace elements on five marine-derived shell traditional Chinese medicine (TCM) (Ostreae Concha, Haliotidis Concha, Margaritifera Concha, Meretricis Concha, and Arcae Concha), which were recorded in the Chinese Pharmacopoeia (2010 version). Seven trace elements in 51 batches of this type of shell TCM were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS), combined with principal component analysis (PCA) methods. The content of element Se, which exhibited significant differences among different drugs, could be used as a key element to distinguish this type of drugs. Meanwhile, the contents of elements Co, Cu, Mo, and Ba in Haliotidis Concha, Co and As in Margaritifera Concha, Mo and As in Meretricis Concha, Mo, As, and Ba in Arcae Concha, and Zn in Meretricis Concha were relatively stable. In the PCA plot, Arcae Concha and Meretricis Concha could be efficiently distinguished from Ostreae Concha together with Haliotidis Concha, and Margaritifera Concha. The results also showed a correlation with their medicinal function. In conclusion, trace elements in marine-derived shell TCM could not be neglected for their quality control.

[Identification of marine-derived shell TCM by near infrared spectroscopy].

The identification of five marine-derived shell traditional Chinese medicine (TCM) recorded in the Chinese Pharmacopoeia were studied. Using near infrared technology (NIR) combined with principal component analysis (PCA) methods, Ostreae Concha, Haliotidis Concha, and Margaritifera Concha could be efficiently distinguished from Meretricis Concha together with Arcae Concha. In the first principal components, Ostreae Concha exhibited obvious differences with high loadings in 4 236, 5 263, 7 142 cm(-1) concerning to the contents of CaCO3 and H2O in the samples. Arcae Concha and Meretricis Concha displayed significant differences with others in the second principal components, which can be illustrated by high loadings in 5 000 -4 430 cm(-1) areas. It is indicated that the second principal components might be related to organics which contained NH and CH groups, for example proteins. Meanwhile, our data showed a correlation between the function of these shell TCM and their distribution in the PCA plot. These results suggested that organic components in marine-derived shell TCM could not be neglected for their quality control.