Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning.

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

Quality study of animal-derived traditional Chinese medicinal materials based on spectral technology: Calculus bovis as a case.

INTRODUCTION: Calculus bovis (C. bovis) is a typical traditional Chinese medicine (TCM) derived from animals, which has a remarkable curative effect and high price. OBJECTIVES: Rapid identification of C. bovis from different types was realized based on spectral technology, and a rapid quantitative analysis method for the main quality control indicator bilirubin was established. METHODS: We conducted a supervised and unsupervised pattern recognition study on 44 batches of different types of C. bovis by five spectral pretreatment methods. Three variable selection methods were used to extract the essential information, and the partial least squares regression (PLSR) quantitative model of bilirubin by near-infrared (NIR) spectroscopy was constructed. RESULTS: The partial least squares discriminant analysis (PLS-DA) model could achieve 100% accuracy in identifying different types of C. bovis. The R2 of the NIR quantitative model was 0.979, which is close to 1, and the root mean square error of calibration (RMSEC) was 2.3515, indicating the good prediction ability of the model. CONCLUSION: The study was carried out to further improve the basic data of quality control of C. bovis and help the high-quality development of TCM derived from animals.

Implementation of near-infrared spectroscopy and convolutional neural networks for predicting particle size distribution in fluidized bed granulation.

Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation.

In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy.

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.

Nondestructive discrimination of Potentilla anserina L. from different production areas based on near-infrared spectroscopy.

INTRODUCTION: The traditional Chinese medicine (TCM) Potentilla anserina L. can use both as food and medicine. At present, the market mainly depends on experience to identify the species and determine the production areas of P. anserina. To ensure the quality of P. anserina, it is essential to improve the level of quality control. OBJECTIVE: We aimed to establish a rapid and nondestructive discrimination model to identify P. anserina from different production areas by near-infrared spectroscopy. METHODS: The spectra of complete P. anserina medicinal materials and their powder of the same variety from four production areas were collected, and principal component analysis discriminant analysis and partial least squares discriminant analysis (PLS-DA) were conducted based on different pretreatment methods and band selection methods. Then, the spectra of complete medicinal materials were converted into the spectra of medicinal powder for nondestructive identification. RESULTS: The correct recognition rate (CRR) of the PLS-DA discriminant model was the best after spectral preprocessing using autoscaling and competitive adaptive reweighted sampling for band selection. The CRRs of the calibration set and validation set were 100%, the CRRs of the external test set were 95%, 90%, 82%, and 88%, respectively, and the CRRs of the transfer external test set were 84%, 80%, 82%, and 86%, respectively. CONCLUSION: We realized the nondestructive and effective identification of P. anserina from different origins and laid a foundation for the industrialization and upgrading of TCM.

Detection and analysis of hyaluronic acid raw materials from different sources by NIR and aquaphotomics.

Hyaluronic acid (HA), a polysaccharide, is widely used for its essential physiological functions. Although the structures of low molecular weight HA produced by both acid and enzyme degradation methods are extremely similar, there are still differences due to the different degradation principles. There is currently no clear way to distinguish between HA prepared by acidolysis and enzymatic hydrolysis. Based on near-infrared (NIR) spectroscopy and aquaphotomics technology, a method for distinguishing HA raw materials and their mixtures from different sources was proposed, and HA with different mixed ratios was accurately quantified. First, NIR spectra of the HA samples were collected. The spectra were then preprocessed to improve the spectral resolution. Spectral information was extracted based on wavelet transform and principal component analysis, resulting in a final selection of 12 characteristic wavelengths containing classification information. The discriminative and quantitative models were then constructed using the 12 wavelengths. The discriminative model achieved a 100% identification rate for HA from different sources. The correlation coefficient of calibration (Rc), validation (Rp), external test (Rt), root mean square error of cross validation (RMSECV), calibration (RMSEC), validation (RMSEP), and external test (RMSET) of the mixed proportion quantitative model were 0.9876, 0.9876, 0.9898, 0.0546, 0.0433, 0.0440, and 0.0347, respectively. In this study, the problem of structural similarity and non-identifiability of HA produced by acidolysis and enzymatic hydrolysis was addressed, and quality monitoring of HA feedstock in HA circulating links was achieved. This is the first time to achieve accurate quantification of solid mixtures using the aquaphotomics method.

Identification and determination of different processed products and their extracts of Crataegi Fructus by infrared spectroscopy combined with two-dimensional correlation analysis.

The fruit of Crataegus sp. is known as "Shanzha (SZ)" in China and is widely used in the food, beverage, and traditional Chinese medicine (TCM) industries. SZ usually requires thermal processing to reduce the irritation of its acidity to the gastric mucosa. Different processed products of SZ resulting from thermal processing have different or even opposite functions in clinical applications. In addition, 5-hydroxymethylfurfural (5-HMF) intermediates produced during thermal processing are carcinogenic to humans. Therefore, the aim of this study was to explore a rapid and accurate method by Fourier transform infrared spectroscopy (FT-IR) for the identification of different processed products and the determination of 5-HMF in extracts. In qualitative identification, a three-stage infrared spectroscopy identification method (raw spectra, the second derivative spectra, and two-dimensional correlation (2DCOS) spectra) was developed to distinguish different processed products of SZ step by step. In quantitative determination, partial least squares regression combined with different variable selection methods, especially the 2DCOS method, was applied to determine the 5-HMF content. The results show that temperature-induced 2DCOS synchronous spectra can effectively identify different processed products of SZ by shape, intensity, and position of auto-peaks or cross-peaks, and the variables selected by power spectra from concentration-induced 2DCOS synchronous spectra have better prediction ability for 5-HMF compared to full variables. The above results demonstrate that 2D-COS analysis is a potential tool in qualitative and quantitative analysis, which can improve sample identification accuracy and determination capabilities. This study not only establishes a rapid and accurate method for the identification of different processed products but also provides a practical reference for food safety and the efficient use of TCM.

Three Stages of Dynamic Assembly Process of Dipeptide-Based Supramolecular Gel Revealed by In Situ Infrared Spectroscopy.

The exploration of short peptide-based assembly is vital for understanding protein-misfolding-associated diseases and seeking strategies to attenuate aggregate formation. While, the molecular mechanism of their structural evolution remains poorly studied in view of the dynamic and unpredictable assembly process. Herein, infrared (IR) spectroscopy, which serves as an in situ and real-time analytical technique, was intelligently employed to investigate the mechanism of phase transition and aggregate formation during the dynamic assembly process of diphenylalanine. Combined with other spectroscopy and electron microscopy technologies, three stages of gel formation and the main driving forces in different stages were revealed. A variety of stoichiometric methods such as continuous wavelet transform, principal component analysis, and two-dimensional correlation spectroscopy techniques were conducted to analyze the original time-dependent IR spectra to obtain detailed information on the changes in the amide bands and hydration layer. The microenvironment of hydrogen bonding among amide bands was significantly changed with the addition of pyridine derivatives, resulting in great differences in the properties of co-assembled gels. This work not only provides a universal analytical way to reveal the dynamic assembly process of dipeptide-based supramolecular gel but also expands their applications in supramolecular regulation and high-throughput screens in situ.

Study on quality analysis of different species of Coptidis rhizome based on fingerprint-effect relationship.

INTRODUCTION: The quality evaluation of Coptidis rhizome (CR) is attributed to the origin and processing method, and this strategy of ignoring the bioactive components usually leads to biased quality analysis, which is difficult to indicate the clinical efficacy. OBJECTIVES: In order to evaluate the quality level of different species of CR, we collected 20 batches of CR and investigated the fingerprint-effect relationship. METHODS: High-performance liquid chromatography (HPLC) fingerprints of CR were established, and the fingerprint-effect relationship was explored using cluster analysis, principal component analysis, Pearson correlation analysis, grey relation analysis, and partial least squares regression. RESULTS: We have identified a total of 10 common peaks (1-10) with similarity scores above 0.96. The study on the relationship between spectra and potency further showed that the contents of peaks 8, 9, and 10 are potential key components. And based on a previous study, a method of one measurement and multiple evaluations of CR was established to achieve the goal of simplifying the analytical process and reducing costs. CONCLUSION: Through a combination of fingerprint analysis, antioxidant activity evaluation, fingerprint-efficacy relationship analysis, and simultaneous quantification of multiple components, a CR quality control index and method have been selected and established, which can also provide a more comprehensive quality evaluation for traditional Chinese medicine.

Nondestructive rapid identification of wild Cordyceps sinensis with portable instrument.

INTRODUCTION: Cordyceps sinensis (CS) is a precious medicinal fungus. Wild CS (WCS) and artificial CS (ACS) are destroyed for their identification using traditional methods, which are time consuming and labor-intensive. Therefore, it is crucial to establish a nondestructive identification method to rapidly screen WCS. OBJECTIVE: The aim of this study was to provide technical support for rapid screening of CS and evaluation of its quality. The applicability of the model was improved through model transfer. METHODS: In this study, continuous wavelet transform was used to analyze the differences in moisture content and active components between WCS and ACS from the perspective of characteristic molecular groups. A portable instrument and a laboratory benchtop instrument were used to determine CS spectra. Partial least squares discrimination analysis was conducted for the identification of WCS and ACS while preserving the original shape of CS. Moreover, improved principal component analysis was utilized to transfer the model between the two types of near-infrared spectroscopy (NIRS) instruments. RESULTS: The results demonstrated that three peaks, at 1443, 1941, and 2183 nm, were characteristic absorption peaks. The model based on NIRS could initially provide rapid differentiation between WCS and ACS. At the same time, the accuracy of the external test set was further improved to over 95% through forward transfer. CONCLUSION: Therefore, this method could be used for rapid screening of WCS and provides technical support for the nondestructive identification of CS and initial assessment of CS quality.

Therapeutic effect and transcriptome-methylome characteristics of METTL3 inhibition in liver hepatocellular carcinoma.

Methyltransferase-like 3 (METTL3) is the key subunit of methyltransferase complex responsible for catalyzing N6-methyladenosine (m6A) modification on mRNA, which is the most prevalent post-transcriptional modification in eukaryotes. In this study, we utilized online databases to analyze the association between METTL3 expression and various aspects of tumorigenesis, including gene methylation, immunity, and prognosis. Our investigation revealed that METTL3 serves as a prognostic marker and therapeutic target for liver hepatocellular carcinoma (LIHC). Through experimental studies, we observed frequent upregulation of METTL3 in LIHC tumor tissue and cells. Subsequent inhibition of METTL3 using a novel small molecule inhibitor, STM2457, significantly impeded tumor growth in LIHC cell lines, spheroids, and xenograft tumor model. Further, transcriptome and m6A sequencing of xenograft bodies unveiled that inhibition of METTL3-m6A altered genes enriched in SMAD and MAPK signaling pathways that are critical for tumorigenesis. These findings suggest that targeting METTL3 represents a promising therapeutic strategy for LIHC.

Tunable Chirality of Self-Assembled Dipeptides Mediated by Bipyridine Derivative.

Supramolecular peptide assemblies have been widely used for the development of biomedical, catalytical, and optical materials with chiral nanostructures in view of the intrinsic chirality of peptides. However, the assembly pathway and chiral transformation behavior of various peptides remain largely elusive especially for the transient assemblies under out-of-equilibrium conditions. Herein, the N-fluorenylmethoxycarbonyl-protected phenylalanine-tyrosine dipeptide (Fmoc-FY) was used as a peptide assembly platform, which showed that the assembly proceeds multistep evolution. The original spheres caused by liquid-liquid phase separation (LLPS) can nucleate and elongate into the formation of right-handed helices which were metastable and easily converted into microribbons. Interestingly, a bipyridine derivative can be introduced to effectively control the assembly pathway and induce the formation of thermodynamically stable right-handed or left-handed helices at different stoichiometric ratios. In addition, the chiral assembly can also be regulated by ultrasound or enzyme catalysis. This minimalistic system not only broadens the nucleation-elongation mechanisms of protein aggregates but also promotes the controllable design and development of chiral biomaterials.

A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration.

Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. "Data binning" was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. "Normalized mutual information" was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.

Real-time in-line prediction of drug loading and release rate in the coating process of diclofenac sodium spheres based on near infrared spectroscopy.

The preparation of diclofenac sodium spheres by fluidized bed is a common production mode for the pharmaceutical preparations at present, but the critical material attributes in the production process is mostly analyzed off-line, which is time-consuming and laborious, and the analysis results lag behind. In this paper, the real-time in-line prediction of drug loading of diclofenac sodium and the release rate during the coating process was realized by using near infrared spectroscopy. For the best near infrared spectroscopy (NIRS) model of drug loading, R2cv, R2p, RMSECV, RMSEP were 0.9874, 0.9973, 0.002549 mg/g, 0.001515 mg/g respectively. For the best NIRS model of three release time points, the R2cv, R2p, RMSECV and RMSEP were 0.9755, 0.9823, 3.233%, 4.500%; 0.9358, 0.9965, 2.598%, 0.7939% and 0.9867, 0.9927, 0.4085%, 0.4726% respectively. And the analytical ability of these model was verified. The organic combination of these two parts of work constituted an important basis for ensuring the safety and effectiveness of diclofenac sodium spheres from the perspective of production process.

Relaxin-Loaded Inhaled Porous Microspheres Inhibit Idiopathic Pulmonary Fibrosis and Improve Pulmonary Function Post-Bleomycin Challenges.

Idiopathic pulmonary fibrosis (IPF) causes worsening pulmonary function, and no effective treatment for the disease etiology is available now. Recombinant Human Relaxin-2 (RLX), a peptide agent with anti-remodeling and anti-fibrotic effects, is a promising biotherapeutic candidate for musculoskeletal fibrosis. However, due to its short circulating half-life, optimal efficacy requires continuous infusion or repeated injections. Here, we developed the porous microspheres loading RLX (RLX@PMs) and evaluated their therapeutic potential on IPF by aerosol inhalation. RLX@PMs have a large geometric diameter as RLX reservoirs for a long-term drug release, but smaller aerodynamic diameter due to their porous structures, which were beneficial for higher deposition in the deeper lungs. The results showed a prolonged release over 24 days, and the released drug maintained its peptide structure and activity. RLX@PMs protected mice from excessive collagen deposition, architectural distortion, and decreased compliance after a single inhalation administration in the bleomycin-induced pulmonary fibrosis model. Moreover, RLX@PMs showed better safety than frequent gavage administration of pirfenidone. We also found RLX-ameliorated human myofibroblast-induced collagen gel contraction and suppressed macrophage polarization to the M2 type, which may be the reason for reversing fibrosis. Hence, RLX@PMs represent a novel strategy for the treatment of IPF and suggest clinical translational potential.

Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion.

BACKGROUND: For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species. OBJECTIVE: This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR). METHOD: Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials. RESULTS: For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model. CONCLUSIONS: Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods. HIGHLIGHTS: This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.

Review of the Application of PAT in the Pharmaceutical Continuous Crystallization Process.

As an important pharmaceutical process, crystallization greatly impacts the final product. In recent years, the continuous crystallization process has attracted more attention from researchers, with the promotion of continuous manufacturing (CM) by the Food and Drug Administration (FDA). The continuous crystallization process has the advantages of high economic benefit, stable and uniform quality, a short production cycle, and personalization. To carry out continuous crystallization, some related process analytical technology (PAT) tools have become the focus of breakthroughs. Infrared (IR) spectroscopy, Raman spectroscopy, and focused beam reflection measurement (FBRM) tools have gradually become research hotspots due to their fast, non-destructive, and real-time monitoring characteristics. This review compared the advantages and disadvantages of the three technologies. Their applications in the upstream mixed continuous crystallization process, the middle reaches of crystal nucleation and growth, and the process of the downstream refining were discussed to provide corresponding guidance for the practice and further development of these three technologies in the continuous crystallization process and promote the development of CM in the pharmaceutical industry.

Review on the Discovery of Water Absorbance Spectral Pattern in Aquaphotomics based on Chemometrics Analytical Tools.

Aquaphotomics, as a new discipline is a powerful tool for exploring the relationship between the structure of water and the function of matter by analyzing the interaction between water and light of various frequencies. However, chemometric tools, especially the Water Absorbance Spectral Pattern (WASP) determinations, are essential in this kind of data mining. In this review, different state-of-the-art chemometrics methods were introduced to determine the WASP of aqueous systems. We elucidate the methods used for identifying activated water bands in three aspects, namely: 1) improving spectral resolution; the complexity of water species in aqueous systems leads to a serious overlap of NIR spectral signals, therefore, we need to obtain reliable information hidden in spectra, 2) extracting spectral features; sometimes, certain spectral information cannot be revealed by simple data processing, it is necessary to extract deep data information, 3) overlapping peak separation; since the spectral signal is produced by multiple factors, overlapping peak separation can be used to facilitate the extraction of spectral components. The combined use of various methods can characterize the changes of different water species in the system with disturbance and can determine the WASP. WASPs of research systems vary from each other, and it is visually displayed in the form of the aquagram. As a new omics family member, aquaphotomics could be applied as a holistic marker in multidisciplinary fields.

Molecular and metabolic mechanisms of bufalin against lung adenocarcinoma: New and comprehensive evidences from network pharmacology, metabolomics and molecular biology experiment.

BACKGROUND: This study aims to evaluate the efficacy and therapeutic mechanism of bufalin on lung adenocarcinoma (LUAD) through a comprehensive strategy integrating network pharmacology, metabolomics and molecular biology verification. METHODS: The putative targets of bufalin were discerned from PharmMapper and Swiss Target Prediction database. LUAD-related targets were obtained by target filtering of GeneCard database and data mining of GEO database. PPI network was constructed to screen the core targets, and their clinical significance was assessed through several public databases. GO and KEGG pathway analyses were performed to identify possible enrichment of genes with specific biological themes. Molecular docking and molecular dynamics (MD) simulation were employed to determine the correlation and binding pattern between bufalin and core targets. The potential mechanisms of bufalin acting on LUAD, as predicted by network pharmacology analyses, were experimentally validated using in-vitro and in-vivo models. Finally, the effects of bufalin intervention on metabolite profile and metabolic pathway in LUAD nude mice were investigated by non-targeted metabolomics. RESULTS: 209 bufalin targets and 1082 LUAD-associated targets were harvested, of which 51 intersection targets were identified. 10 core targets including Akt1, STAT3, EGFR, CASP3 and SRC were picked out through network topology analysis, and they had a potent binding activity with bufalin as indicated by molecular docking and MD simulation. Hub module of PPI network was closely related to cell proliferation and apoptosis. GO and KEGG enrichment analyses suggested that bufalin exerted therapeutic effects on LUAD possibly by inhibiting proliferation and promoting apoptosis via PI3K/Akt, FoxO1 and MAPK/ERK pathways, which were confirmed by a series of in-vitro studies as well as HE, TUNEL and Ki-67 staining of tumor tissues. Further metabolomics analysis revealed that bufalin mainly regulated ABC transporter and remodeled AA metabolism, thereby contributing to the treatment of LUAD. CONCLUSION: From molecular and metabolic perspective, the present study not only provided a unique insight into the possible mechanisms of bufalin against LUAD after successfully filtering out associated key target genes, differential endogenous metabolites, and signaling pathways, but also proposed a novel promising therapeutic strategy for LUAD.

A rapid extraction process monitoring of Swertia mussotii Franch. With near infrared spectroscopy.

Swertia mussotii Franch. (SMF), a traditional Tibetan medicine, which has miraculous effect on treating hepatitis diseases. However, there is no research on its entire production process, and invisible production process has seriously hindered the SMF modern development. In this study, principal component analysis (PCA), subtractive spectroscopy, and two-dimensional correlation spectroscopy (2D-COS) were used to explain changes of characteristic groups in the extraction process. Four main characteristic peaks at 1884 nm, 1944 nm, 2246 nm and 2308 nm were identified to describe the changes of molecular structure information of total active components in SMF extraction process. In addition, multi critical quality attributes (CQAs) models were established by near-infrared spectroscopy (NIRS) combined with the total quantum statistical moment (TQSM). The coefficients of determination (R2eval and R2ival) were both greater than 0.99. The ratios of the standard deviation of validation to the standard error of the prediction (RPDe and RPDi) were greater than five. The quantitative model of AUCT could save time on primary data measurement by not requiring determination of indicator components compared with others. In conclusion, these results demonstrated that it was feasible to understand the SMF extraction process through AUCT and characteristic groups. These could realize the visual digital characterization and quality stability of the SMF extraction process.