scholarly journals Chemometric Analyses for the Characterization of Raw and Stir-Frying Processed Drynariae Rhizoma Based on HPLC Fingerprints

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jing Hu ◽  
Jiali Wang ◽  
Beibei Qin ◽  
Lizhi Wang ◽  
Xia Li
Keyword(s):  
Chinese Medicine ◽  
Hierarchical Cluster ◽  
Chemical Markers ◽  
Chemometric Methods ◽  
Hplc Fingerprint ◽  
Principle Components Analysis ◽  
Components Analysis ◽  
Chinese Medicine Theory

The processing of traditional Chinese medicine (TCM) is a necessary practice and usually occurs before most herbs are prescribed. According to Chinese medicine theory, raw (RDR) and stir-frying processed (PDR) Drynariae Rhizoma have different clinical applications. The purpose of this study was to establish HPLC fingerprints coupled with chemometric methods to evaluate the differences between RDR and PDR. Multivariate chemometric methods were applied to analyze the obtained HPLC fingerprints, including hierarchical cluster analysis (HCA), principle components analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results indicated that RDR and PDR samples showed a clear classification of the two groups, and seven chemical markers having great contributions to the differentiation were screened out. The findings suggested that 5-hydroxymethyl-2-furaldehyde (5-HMF) with a variable importance in the project (VIP > 1) can be used to differentiate between RDR and PDR. Moreover, 5-HMF, naringin, and neoeriocitrin were determined to evaluate their contents in RDR and PDR. The chemometrics combined with the quantitative analysis based on HPLC fingerprint results indicated that stir-frying processing may change the contents and types of components in Drynariae Rhizoma. These changes are probably responsible for the various pharmacological effects of RDR and PDR.

scholarly journals Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Ramzi Idoughi ◽  
Thomas H. G. Vidal ◽  
Pierre-Yves Foucher ◽  
Marc-André Gagnon ◽  
Xavier Briottet
Keyword(s):  
Synthetic Data ◽  
Hyperspectral Data ◽  
Trace Gas ◽  
Gas Concentration ◽  
Components Analysis ◽  
The Difference ◽  
Long Wave Infrared ◽  
Radiance Estimation

Hyperspectral imaging in the long-wave infrared (LWIR) is a mean that is proving its worth in the characterization of gaseous effluent. Indeed the spectral and spatial resolution of acquisition instruments is steadily decreasing, making the gases characterization increasingly easy in the LWIR domain. The majority of literature algorithms exploit the plume contribution to the radiance corresponding to the difference of radiance between the plume-present and plume-absent pixels. Nevertheless, the off-plume radiance is unobservable using a single image. In this paper, we propose a new method to retrieve trace gas concentration from airborne infrared hyperspectral data. More particularly the outlined method improves the existing background radiance estimation approach to deal with heterogeneous scenes corresponding to industrial scenes. It consists in performing a classification of the scene and then applying a principal components analysis based method to estimate the background radiance on each cluster stemming from the classification. In order to determine the contribution of the classification to the background radiance estimation, we compared the two approaches on synthetic data and Telops Fourier Transform Spectrometer (FTS) Imaging Hyper-Cam LW airborne acquisition above ethylene release. We finally show ethylene retrieved concentration map and estimate flow rate of the ethylene release.

scholarly journals Characterization of Musts, Wines, and Sparkling Wines Based on Their Elemental Composition Determined by ICP-OES and ICP-MS

Beverages ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Biel Granell ◽  
Anaïs Izquierdo-Llopart ◽  
Àngels Sahuquillo ◽  
José F. López-Sánchez ◽  
Javier Saurina
Keyword(s):  
Elemental Composition ◽  
Inductively Coupled Plasma ◽  
Pinot Noir ◽  
Sparkling Wine ◽  
Chemical Markers ◽  
Inductively Coupled ◽  
Icp Ms ◽  
Components Analysis ◽  
Grape Varieties

Samples from the different processing stages in the elaboration of sparkling wine (cava)—including must, base wine, and sparkling wine—of Pinot Noir and Xarel·lo grape varieties from different vineyard qualities (A, B, C, D) have been analyzed by inductively coupled plasma (ICP) techniques to determine their elemental composition. The resulting data has been used to characterize these products according to oenological features and product qualities. For this purpose, box plot diagrams, bar charts, and principal components analysis (PCA) have been used. The study of the behavior of each given species has pointed out the relevance of some elements as markers or descriptors of winemaking processes. Among others, Cu and K are abundant in musts and their concentrations progressively decrease through the cava production process. S levels suddenly increase at the base wine step (and further decay) due to the addition of sulfites as preserving agents. Finally, concentrations of Na, Ca, Fe, and Mg increase from the first fermentation due to the addition of clarifying agents such as bentonite. PCA has been applied to try to extract solid and global conclusions on trends and chemical markers within the groups of samples more easily and efficiently than more conventional approaches.

scholarly journals Recognition of a Single Dynamic Gesture with the Segmentation Technique HS-ab and Principle Components Analysis (PCA)

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1114 ◽  
Author(s):  
Diana Alejandra Contreras Alejo ◽  
Francisco Javier Gallegos Funes
Keyword(s):  
Color Space ◽  
Principle Components Analysis ◽  
Training Images ◽  
Segmentation Technique ◽  
Detection And Tracking ◽  
Surrounding Environment ◽  
Dynamic Gestures ◽  
Components Analysis ◽  
Hand Recognition

A continuous path performed by the hand in a period of time is considered for the purpose of gesture recognition. Dynamic gestures recognition is a complex topic since it spans from the conventional method of separating the hand from surrounding environment to searching for the fingers and palm. This paper proposes a strategy of hand recognition using a PC webcam, a segmentation technique (HS-ab which means HSV and CIELab color space), pre-processing of images to reduce noise and a classifier such as Principle Components Analysis (PCA) for the detection and tracking of the hand of the user. The results show that the segmentation technique HS-ab and the method PCA are robust in the execution of the system, although there are various conditions such as illumination, speed and precision of the movements. It is for this reason that a suitable extraction and classification of features allows the location of the gesture. The system was tested with the database of the training images and has a 94.74% accuracy.

scholarly journals Characterization of Genetic Diversity in Dhofari Wild Gazelles

2021 ◽  
Vol 26 (2) ◽  
pp. 35-41
Author(s):  
Mohammed Al-Abri
Keyword(s):  
Genetic Diversity ◽  
Nuclear Markers ◽  
Principle Components Analysis ◽  
Gradual Loss ◽  
The Status ◽  
The Mean ◽  
Preliminary Study ◽  
Components Analysis ◽  
Human Stool

Wild gazelles are scattered in most of the arid and semi-arid areas of the Sultanate of Oman particularly in valleys, mountains and sandy zones of Rub' al Khali desert. Recently, gazelles’ populations have been facing reduction in the numbers. Consequently, gradual loss of genetic diversity is inevitable. This loss is considered one of the main threats that attribute to a loss of habitat and may lead to gazelle extinction. Till now, little is known about the status of the genetic diversity of the wild Dhofari gazelle. This project aimed to determine the extent of inbreeding, population structure and genetic diversity in the Dhofari gazelles’ populations.  DNA was extracted from gazelles’ fecal samples using the human stool DNA extraction protocol. Following extraction, four microsatellite nuclear markers were used to calculate the level of inbreeding, population differentiation and genetic diversity. PCR inhibitors were significantly removed by the addition of Bovine Serum Albumin (BSA) and dimethyl sulfoxide (DMSO). The mean inbreeding for the population was 0.228 for all loci with a standard error of 0.09. It is therefore postulated that Dhofari gazelles are generally undergoing gradual inbreeding which could lead to lack of fitness in future generations. The genetic differentiation (Fst) ranged between 0.071 (between Gara and Stom) and 0.231 (between Gara and Ayon). On the other hand, the Fst estimate between Solot (most distant) versus other Dhofari gazelles populations (pooled together) was 3.7%. Principle Components Analysis clustered Ayon and Gara populations apart from one another and closer to Stom while placing Solot further than all other populations which is in agreement with the Fst results and the geographical distribution. In conclusion, the results of this preliminary study will provide insight towards the conservation of wild gazelles in Dhofar. This is the first study to report the genetic diversity and status of wild Gazelles and provides a reference point for future studies assessing their genetic diversity and variability.

scholarly journals A Preferable Approach for the Quality Control of Xiaoer Chiqiao Qingre Granules Based on the Combination of Chromatographic Fingerprints and Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tong Xu ◽  
Xiaoqi Li ◽  
Mengmeng Huang ◽  
Qi Wang ◽  
Chao Li ◽  
...  
Keyword(s):  
Quality Control ◽  
Hierarchical Cluster ◽  
Integrated Analysis ◽  
Chemical Markers ◽  
Active Ingredients ◽  
Hplc Fingerprint ◽  
Comprehensive Method ◽  
Chromatographic Fingerprints ◽  
In The Beginning

A preferable approach of a combination of a multiwavelength fusion HPLC fingerprint and chemometrics for the quality control of Xiaoer Chiqiao Qingre granules (XCQG) was established in this study. A single-wavelength HPLC fingerprint was performed to identify 18 peaks as common peaks in the beginning, and 12 of them were recognized by HPLC-Q/TOF-MS. To overcome the limitation of the single-wavelength HPLC fingerprint, a three-wavelength (230 nm, 250 nm, and 330 nm) fusion fingerprint was established for a more thorough quality assessment. Six main active ingredients (geniposide, paeoniflorin, forsythin, forsythoside A, baicalin, and wogonoside) were selected as chemical markers for simultaneous quantitative analysis, while the results indicated that the content of other five ingredients except forsythoside A presented comparatively stable. Chemometrics including hierarchical cluster analysis (HCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to evaluate the homogeneity and heterogeneity of sixteen batches of XCQG. The results of the multiwavelength fingerprint were clearly classified into two clusters by HCA, whereas the single-wavelength fingerprint showed no distinct difference between them. OPLS-DA was further employed to prove that the above six main active ingredients made great contributions to clustering. In summary, this integrated analysis provided a better promoted and more comprehensive method to control the quality of XCQG.

scholarly journals Field Spectroscopy Applied to the Kaolinite Polytypes Identification

2021 ◽  
Vol 6 (1) ◽  
pp. 16
Author(s):  
Andrea García-Vicente ◽  
Adrián Lorenzo ◽  
Juan Morales ◽  
Emilia García-Romero ◽  
Mercedes Suárez
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Second Derivative ◽  
X Ray Diffraction ◽  
Components Analysis ◽  
Xrd Patterns ◽  
Good Agreement

A detailed characterization of a group of kaolin samples rich in some minerals of the kaolinite group was done. The mineralogical and structural characterization was conducted by X-ray diffraction (XRD) together with the study of the spectroscopy response in visible-near infrared and short wave (VNIR–SWIR), and the main objective was the determination of kaolinite polytypes. The XRD patterns group the samples according to the kaolinite polytype into five kaolinites, two dickites and six halloysites. Diagnostic peaks for kaolinite, dickite polytypes and halloysite were identified in the spectra and in the second derivative of the SWIR region. The position and intensity of the peaks in the second derivative were statistically treated with the aim of classifying the spectra according to the polytype. In good agreement with the XRD results, the statistical analysis of the spectroscopic data, both by cluster analysis and by principal components analysis, allows an unequivocal classification of the samples according to the polytype from their VNIR–SWIR spectra.

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Alifia Puspaningrum ◽  
Nahya Nur ◽  
Ozzy Secio Riza ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Hierarchical Clustering ◽  
Gabor Filter ◽  
Hierarchical Cluster ◽  
Experimental Result ◽  
Main Process ◽  
Percentile Method ◽  
Cluster Analysis Method ◽  
2D Gabor Filter

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

Sign in / Sign up

Export Citation Format

Share Document