Determination and Visualization of Peimine and Peiminine Content in Fritillaria thunbergii Bulbi Treated by Sulfur Fumigation Using Hyperspectral Imaging with Chemometrics

Improving the geographical origin classification of Radix glycyrrhizae (licorice) through hyperspectral imaging assisted by U-Net fine structure recognition

Radix glycyrrhizae (licorice) is extensively employed in traditional Chinese medicine, and serves as a crucial raw material in industries such as food and cosmetics. The quality of licorice from different origins varies greatly, so classification of its geographical origin is particularly important. This study proposes a technique for fine structure recognition and segmentation of hyperspectral images of licorice using deep learning U-Net neural networks to segment the tissue structure patterns (phloem, xylem, and pith). Firstly, the three partitions were separately labeled using the Labelme tool, which was utilized to train the U-Net model. Secondly, the obtained optimal U-Net model was applied to predict three partitions of all samples. Lastly, various machine learning models (LDA, SVM, and PLS-DA) were trained based on segmented hyperspectral data. In addition, a threshold method and a circumcircle method were applied to segment licorice hyperspectral images for comparison. The results revealed that compared with the threshold segmentation method (which yielded SVM classifier accuracies of 99.17%, 91.15%, and 92.50% on the training set, validation set, and test set, respectively), the U-Net segmentation method significantly enhanced the accuracy of origin classification (99.06%, 94.72% and 96.07%). Conversely, the circumcircle segmentation method did not effectively improve the accuracy of origin classification (99.65%, 91.16% and 92.13%). By integrating Raman imaging of licorice, it can be inferred that the U-Net model, designed for region segmentation based on the inherent tissue structure of licorice, can effectively improve the accuracy origin classification, which has positive significance in the development of intelligence and information technology of Chinese medicine quality control.

Hyperspectral discrimination of ginseng variety and age from Changbai Mountain area

BACKGROUND

The efficacy and market value of Panax ginseng Meyer are significantly influenced by its diversity and age. Traditional identification methods are prone to subjective biases and necessitate the use of destructive sample processing, leading to the loss and wastage of ginseng. Consequently, non-destructive in-situ identification has emerged as a crucial subject of interest for both researchers and the ginseng industry. The advancement of technology and the expansion of research have introduced spectral technology and image processing technology as novel approaches and concepts for non-destructive in-situ identification.

METHODS

Hyperspectral imaging (HSI) is a methodology that combines conventional spectroscopy and imaging to acquire comprehensive spectral and spatial data from various samples. In this study, we investigated the use of Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) classifier algorithms, in conjunction with HSI classification technology, for quasi-Artificial Intelligence (quasi-AI) ginseng identification. To enhance the hyperspectral images prior to SVM classification, we compared the efficacy of Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA).

RESULTS

The classification of ginseng based on age was accomplished through the utilization of Radial Basis Function (RBF) kernel SVM and SAM algorithm, which was trained on feature enhanced images. The classification of WMG, MCG, and GG is primarily based on age, with the endmember spectrum serving as the foundation for SAM and SVM.

CONCLUSION

The "endmember spectrum set" derived from the classification outcomes can serve as the "mutation point" for identifying ginseng of different ages.

Identification of Turtle-Shell Growth Year Using Hyperspectral Imaging Combined with an Enhanced Spatial-Spectral Attention 3DCNN and a Transformer

Turtle shell (Chinemys reecesii) is a prized traditional Chinese dietary therapy, and the growth year of turtle shell has a significant impact on its quality attributes. In this study, a hyperspectral imaging (HSI) technique combined with a proposed deep learning (DL) network algorithm was investigated for the objective determination of the growth year of turtle shells. The acquisition of hyperspectral images was carried out in the near-infrared range (948.72-2512.97 nm) from samples spanning five different growth years. To fully exploit the spatial and spectral information while reducing redundancy in hyperspectral data simultaneously, three modules were developed. First, the spectral-spatial attention (SSA) module was developed to better protect the spectral correlation among spectral bands and capture fine-grained spatial information of hyperspectral images. Second, the 3D convolutional neural network (CNN), more suitable for the extracted 3D feature map, was employed to facilitate the joint spatial-spectral feature representation. Thirdly, to overcome the constraints of convolution kernels as well as better capture long-range correlation between spectral bands, the transformer encoder (TE) module was further designed. These modules were harmoniously orchestrated, driven by the need to effectively leverage both spatial and spectral information within hyperspectral data. They collectively enhance the model's capacity to extract joint spatial and spectral features to discern growth years accurately. Experimental studies demonstrated that the proposed model (named SSA-3DTE) achieved superior classification accuracy, with 98.94% on average for five-category classification, outperforming traditional machine learning methods using only spectral information and representative deep learning methods. Also, ablation experiments confirmed the effectiveness of each module to improve performance. The encouraging results of this study revealed the potentiality of HSI combined with the DL algorithm as an efficient and non-destructive method for the quality control of turtle shells.

A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials

Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.

Application of Analytical Technologies in the Discrimination and Authentication of Herbs from Fritillaria: A Review

Medicinal plants of Fritillaria are widely distributed in numerous countries around the world and possess excellent antitussive and expectorant effects. In particular, Fritillariae Bulbus (FB) as a precious traditional medicine has thousands of years of medical history in China. Herbs of Fritillaria have a high market value and demand while limited by harsh growing circumstances and scarce wild resources. As a consequence, fraudulent behaviors are regularly engaged by the unscrupulous merchants in an attempt to reap greater profits. It is of an urgent need to evaluate the quality of Fritillaria herbs and their products using various analytical instruments and techniques. This review has scrutinized approximately 160 articles from 1995 to 2022 published on the investigation of Fritillaria herbs and related herbal products. The botanical classification of genus Fritillaria, types of counterfeits, technologies applied for differentiating Fritillaria species were comprehensively summarized and discussed in the current review. Molecular and chromatographic identification were the dominant technologies in the authentication of Fritillaria herbs. Additionally, we brought some potential and promising technologies and analytical strategies into attention, which are worthy attempting in the future researches. This review could conduce to excellent reference value for further investigations of the authenticity assessment of Fritillaria species.

Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii

Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.

Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.

Peimine-induced apoptosis and inhibition of migration by regulating reactive oxygen species-mediated MAPK/STAT3/NF-κB and Wnt/β-catenin signaling pathways in gastric cancer MKN-45 cells

Peimine (PM), a natural product extracted from Fritillaria, has anti-inflammatory, drug resistance reversal, and other pharmacological effects. The purpose of this study was to investigate the antitumor effects and the molecular mechanisms of PM using gastric cancer MKN-45 cells. Cell counting kit-8 assays were used to evaluate the viability of gastric cancer cells after treatment with PM. The results showed that PM significantly reduced the activity of gastric cancer cells, and the effect was most obvious in MKN-45 cells. Annexin V-FITC/propidium iodide staining and flow cytometry were used to assess apoptosis of MKN-45 cells after PM treatment. Our results showed that PM-induced apoptosis of MKN-45 cells. Flow cytometry was also used to determine the mitochondrial membrane potential and reactive oxygen species (ROS) levels, and to assess PM-induced cell-cycle arrest. Additionally, Western blot was used to analyze the expression of signaling pathway proteins and the relationship between apoptosis and ROS accumulation. Our findings showed that PM destroyed the mitochondria by diminishing the mitochondrial membrane potential. In addition, PM regulated the mitogen-activated protein kinase (MAPK), signal transducer and activator of transcription 3, and nuclear factor kappa-B signaling pathways by promoting the accumulation of ROS in MKN-45 cells. PM also caused cell-cycle arrest in the G2/M phase by increasing ROS accumulation. Furthermore, PM inhibited cell migration by regulating the Wnt/β-catenin pathway. In conclusion, PM plays an anticancer role through endogenous apoptosis pathways and by inhibiting cell migration, and it has the potential to be a useful treatment for gastric cancers.

Effects of chloroform fraction of Fritillariae Thunbergii Bulbus on atopic symptoms in a DNCB-induced atopic dermatitis-like skin lesion model and in vitro models

ETHNOPHARMACOLOGICAL RELEVANCE

Fritillariae thunbergii Bulbus (FT), knowns as "Jeolpaemo ()" in Korean traditional medicine, is a perennial plant belonging to the Liliaceae family and has been used to treat symptoms such as cough, sputum formation, and purulent pneumonia. Owing to its effects of lowering heat, removing sputum, and reducing swelling, the plant has also been used as an external prescription medicine to treat inflammation.

AIM OF THE STUDY

To analyze the anti-inflammatory effects of FT-ethanol extract (FT-Et) and FT-chloroform fraction extract (FT-Cl) on 1-chloro-2,4-dinitrobenzene (DNCB)-induced atopic dermatitis (AD) in vivo and in vitro.

MATERIALS AND METHODS

The effect of FT-Et and FT-Cl on AD was observed using an AD-like skin lesion model induced by DNCB in vivo. HaCaT and RBL2H3 cells were used to determine the effects of FT-Et and FT-Cl in vitro. After inducing AD-like skin lesions in vivo, FT was topically applied to the skin lesion for 35 days. Epidermal thickness, dermal thickness, scratching behavior, infiltration of inflammatory cells, and expression of skin barrier proteins were measured. TARC, MDC, and IL-4 levels were analyzed using ELISA in HaCaT cells. Beta-hexosaminidase and IL-4 levels were measured in RBL2H3 cells. The expression of filaggrin (FLG), loricrin (LOR), involucrin (INV), and aquaporin-3(AQP-3) was measured by PCR. Phosphorylation of MAPKs was analyzed using Western blot technique.

RESULTS

FT-Cl significantly reduced ear swelling, scratching behavior, SCORAD index, epidermal thickness, infiltration of inflammatory cells, and loss of skin barrier proteins. FT-Et inhibited the infiltration of mast cells and CD8⁺ cells and decreased the loss of skin barrier proteins. In TNF-α/IFN-γ-stimulated HaCaT cells, FT-Cl inhibited TRAC, MDC, and IL-4 expression and upregulated the expression of FLG, INV, and AQP-3, whereas FT-Et inhibited the expression of TRAC and MDC and increased the expression of FLG, INV, and AQP-3 at high concentrations. In RBL2H3, FT-Cl downregulated β-hexosaminidase and IL-4 expression. In addition, FT-Cl inhibited the phosphorylation of ERK and p-38 in HaCaT and RBL2H3 cells.

CONCLUSIONS

Collectively, FT-Cl showed better effect than FT-Et in vivo and in vitro. These results suggest that a specific component present in FT-Cl acted against AD. Future research should focus on the analysis of components contained in FT-Cl and the anti-inflammatory effects of the active ingredient.

Pharmacokinetic Study of Zhebeirine in Mouse Blood by Ultra- Performance Liquid Chromatography/tandem Mass Spectrometry

Introduction:

In this study, a precise, rapid and accurate ultra-performance liquid chromatography- tandem mass spectrometry (UPLC-MS/MS) method for the quantification of zhebeirine in mouse blood was developed, and pharmacokinetics of zhebeirine was studied for the first time after intravenous and oral administration.

Methods:

The mobile phase consisted of acetonitrile-0.1% formic acid, with a flow rate at 0.4 mL/m during 4 min run time. MRM modes of m/z 414.5→81.0 for zhebeirine and m/z 430.2→412.2 for 3- dehydroverticine (internal standard) were utilized to perform quantitative analysis. Protein in mouse blood was directly precipitated with acetonitrile for sample preparation.

Results:

The linear range was 1-3000 ng/mL with r>0.995, and LLOQ was 1 ng/mL. The intra-and inter-day precision of zhebeirine in mouse blood was less than 13%. The accuracy ranged from 91.2% to 112.5%, while the matrix effects were between 84.8% and 106.4%.

Conclusion:

The UPLC-MS/MS was successfully applied to the pharmacokinetic study on zhebeirine after intravenous and oral administration, and the bioavailability was determined to be 22.8%.

Anti-inflammatory effects of Fritillaria thunbergii Miquel extracts in LPS-stimulated murine macrophage RAW 264.7 cells

The aim of the present study was to demonstrate that Fritillaria thunbergii Miquel extract exerts anti-inflammatory and antioxidant effects on lipopolysaccharide-stimulated RAW 264.7 cells. To confirm the inhibitory effect of ethyl acetate fraction of FTM (EAFM) on inflammation, the expression of nitric oxide (NO) and inflammatory cytokines was assessed by performing ELISA. Expression of intracellular mRNA and protein was confirmed by reverse transcription PCR and western blotting. In addition, the anti-inflammatory and anti-oxidant mechanisms of NF-κB, MAPK and heme oxygenase-1 (HO-1) were also investigated. EAFM significantly inhibited the expression of inflammatory factors including NO, IL-6 and TNF-α at non-toxic concentrations. EAFM also inhibited the mRNA and protein expression of inducible nitric oxide synthase in a concentration-dependent manner, but did not alter the expression of cyclooxygenase-2. Pre-treatment with EAFM inhibited the nuclear translocation of NF-κB, and suppressed the phosphorylation of ERK and JNK. In addition, EAFM induced 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical scavenging activity and an increase in the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and HO-1. The results indicated that EAFM inhibited the expression of pro-inflammatory cytokines by inhibiting ERK/JNK phosphorylation and NF-κB translocation. EAFM also exerted antioxidant effects via Nrf2/HO-1 stimulation. Collectively, the results of the present study indicated that EAFM may be a valuable alternative for the treatment of a variety of inflammatory diseases.

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Radix Astragali is a prized traditional Chinese functional food that is used for both medicine and food purposes, with various benefits such as immunomodulation, anti-tumor, and anti-oxidation. The geographical origin of Radix Astragali has a significant impact on its quality attributes. Determining the geographical origins of Radix Astragali is essential for quality evaluation. Hyperspectral imaging covering the visible/short-wave near-infrared range (Vis-NIR, 380-1030 nm) and near-infrared range (NIR, 874-1734 nm) were applied to identify Radix Astragali from five different geographical origins. Principal component analysis (PCA) was utilized to form score images to achieve preliminary qualitative identification. PCA and convolutional neural network (CNN) were used for feature extraction. Measurement-level fusion and feature-level fusion were performed on the original spectra at different spectral ranges and the corresponding features. Support vector machine (SVM), logistic regression (LR), and CNN models based on full wavelengths, extracted features, and fusion datasets were established with excellent results; all the models obtained an accuracy of over 98% for different datasets. The results illustrate that hyperspectral imaging combined with CNN and fusion strategy could be an effective method for origin identification of Radix Astragali.

The effects of sulfur fumigation processing on Panacis Quinquefolii Radix in chemical profile, immunoregulation and liver and kidney injury

ETHNOPHARMACOLOGICAL RELEVANCE

The influence of sulfur fumigation processing on chemical profile, pharmacological activity and safety of Chinese herbs has attracted great attention. Panacis Quinquefolii Radix (PQR) was more widely used as edible and medicinal than Ginseng because of its tonifying effect and characteristic of not getting inflamed. The disadvantage of sulfur fumigated (SF) Ginseng has been reported, but the systematic study of SF-PQR is deficient and urgently needed.

AIM OF THE STUDY

To systematically describe the influence of sulfur fumigation on chemical profile, characteristic products, immunoregulation and liver and kidney injury of PQR.

MATERIALS AND METHODS

ICP-MS and HPLC-DAD were used to detect 11 inorganic elements and 3 ginsenosides, respectively. Principal component analysis (PCA) was used to distinguish SF-PQR from non-sulfur fumigated (NSF)-PQR by combining the content changes of inorganic elements and ginsenosides. UPLC/Orbitrap-MS was applied to screen the characteristic products (m/z) after sulfur fumigation. For the effectiveness and safety, male KM mice were used to compare the immunomodulatory effects of NSF-PQR or SF-PQR under both healty and cyclophosphamide induced immunosuppressive conditions by net growth rate of body weight, thymus and spleen indices, serum IL-6, SOD, BUN, AST levels, and HE staining of liver and kidney.

RESULTS

Sulfur fumigation processing significantly reduced the contents of ginsenosides Rb₁, Re and Rg₁ with the elevation of inorganic elements in 20 batches PQR. Based on the scatter distribution of PCA, SF-PQR and NSF-PQR can be distinguished. According to the Rt, Precursor ion (m/z) and Product ion (m/z) produced by UPLC/Orbit trap-MS, R₁-SO₃ (m/z, 1059.53), Re-SO₃ (m/z, 1025.55), Rg₁-SO₃ (m/z, 878.47), Ro-SO₃ (m/z, 1035.32), Rb₁-SO₃ (m/z, 1179.58), and Rk₃-SO₃ (m/z, 745.40) could be confirmed as important markers for identifying SF-PQR. The effect of SF-PQR on reversing immunosuppression induced by cyclophosphamide was significantly reduced (P < 0.05) evidenced by the inhibition of net growth rate of body weight, immune organ index, IL-6 level and SOD activity. For healthy mice, SF-PQR not only failed to maintain the normal indexes, but also reduced the indexes to lower levels. After 2 weeks of continuous gastric administration, the abnormal liver and kidney functions in healthy mice were damaged and manifested by the increasing of BUN and AST levels, which was consistent with hepatic lesion area and renal tubular injury observed by HE staining.

CONCLUSION

Sulfur fumigation processing not only reduced the immunomodulatory effect of PQR, but also brought the hidden danger in liver and kidney injury. The sulfonated products provided in this paper can be applied for the identification of SF-PQR accurately.

The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals

Basing on the wavelengths with consistent and stable spectral signals between spectrometers, wavelength combinations were screened by different methods to obtain robust and simple near infrared spectra (NIR) calibration models that can be shared by slave spectrometers directly. Firstly, the wavelength set of Usc, at which the spectral signals between spectrometers are consistent and stable, was obtained by the method of screening the wavelengths with consistent and stable signals between spectrometers (SWCSS for short). Then, the wavelength set of Uscr whose spectral responses are correlated with dependent variables strongly was selected from Usc. Basing on Uscr, the methods of uninformative variable elimination (UVE), variable importance in projection (VIP) and selectivity ratio (SR) were applied to further screen optimal wavelength sets to obtain better NIR calibration models. These sets were recorded as Uscr^UVE, Uscr^VIP and Uscr^SR, respectively. The NIR partial least squares (PLS) models for predicting total alkaloids content of tobacco leaves were built on the three optimal wavelength sets, and named as Uscr^UVE-PLS, Uscr^VIP-PLS, Uscr^SR-PLS, respectively. Both Uscr^UVE-PLS and Uscr^VIP-PLS give satisfactory prediction errors for master and slave samples, and work better than the PLS model built on the whole wavelengths (WW-PLS) after piecewise direct standardization (PDS) calibration. The results show that further optimizing wavelength combinations based on consistent and stable spectral information cannot only simplify PLS models and improve the models' efficiency, but also ensure the models' accuracy when they are transferred to slave spectrometers. Wavelength selection based on the whole wavelengths without considering spectra consistency between spectrometers can improve the performance of the calibration models on the master spectrometer but cannot ensure the prediction accuracy of the slave samples.

Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification

In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.

A Single Standard to Determine Multi-Components Method Coupled with Chemometric Methods for the Quantification, Evaluation and Classification of Notopterygii Rhizoma et Radix from Different Regions

An ultra-high-performance liquid chromatography-quadrupole/time of flight mass spectrometry is used to identify 33 compounds in Notopterygii rhizoma and radix, after which a single standard to determine multi-components method is established for the simultaneous determination of 19 compounds in Notopterygii rhizoma and radix using chlorogenic acid and notopterol as the internal standard. To screen the potential chemical markers among Notopterygii rhizoma and radix planted in its natural germination area and in others, the quantitative data of 19 compounds are analyzed via partial least-squares discriminant analysis (PLS-DA). Depending on the variable importance parameters (VIP) value of PLS-DA, six compounds are selected to be the potential chemical markers for the discrimination of Notopterygii rhizoma and radix planted in the different regions. Furthermore, the Fisher's discriminant analysis is used to build the models that are used to classify Notopterygii rhizoma and radix from the different regions based on the six chemical markers. Experimental results indicate that Notopterygii rhizoma and radix planted in the Sichuan province are distinguished successfully from those in other regions, reaching a 96.0% accuracy rating. Therefore, a single standard to determine multi-components method combined with a chemometrics method, which contains the advantages such as simple, rapid, economical and accurate identification, offers a new perspective for the quantification, evaluation and classification of Notopterygii rhizoma and radix from the different regions.

Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis

The rapid and nondestructive determination of active compositions in Chrysanthemum morifolium (Hangbaiju) is of great value for producers and consumers. Hyperspectral imaging as a rapid and nondestructive technique was used to determine total polysaccharides and total flavonoids content in Chrysanthemum morifolium. Hyperspectral images of different sizes of Chrysanthemum morifolium flowers were acquired. Pixel-wise spectra within all samples were preprocessed by wavelet transform (WT) followed by standard normal variate (SNV). Partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to build prediction models using sample average spectra calculated by preprocessed pixel-wise spectra. The LS-SVM model performed better than the PLS models, with the determination of the coefficient of calibration (R²c) and prediction (R²p) being over 0.90 and the residual predictive deviation (RPD) being over 3 for total polysaccharides and total flavonoids content prediction. Prediction maps of total polysaccharides and total flavonoids content in Chrysanthemum morifolium flowers were successfully obtained by LS-SVM models, which exhibited the best performances. The overall results showed that hyperspectral imaging was a promising technique for the rapid and accurate determination of active ingredients in Chrysanthemum morifolium, indicating the great potential to develop an online system for the quality determination of Chrysanthemum morifolium.