scholarly journals Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 3013 ◽  
Author(s):  
Jian Zhang ◽  
Ruidong Yang ◽  
Rong Chen ◽  
Yuncong Li ◽  
Yishu Peng ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Geographical Origin ◽  
Principal Component ◽  
Mineral Elements ◽  
Guizhou Province ◽  
Correct Classification ◽  
Tea Leaves ◽  
Linear Discriminant ◽  
Discriminant Models ◽  
Origin Identification

This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions (p < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01–1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.

scholarly journals Validation and analysis of the geographical origin of Angelica sinensis (Oliv.) Diels using multi-element and stable isotopes

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11928
Author(s):  
Shanjia Li ◽  
Hui Wang ◽  
Ling Jin ◽  
James F. White ◽  
Kathryn L. Kingsley ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Stable Isotopes ◽  
Discriminant Analysis ◽  
Industrial Development ◽  
Cross Validation ◽  
Geographical Origin ◽  
Mineral Elements ◽  
Partial Least Square ◽  
Angelica Sinensis ◽  
Linear Discriminant

Background Place of origin is an important factor when determining the quality and authenticity of Angelica sinensis for medicinal use. It is important to trace the origin and confirm the regional characteristics of medicinal products for sustainable industrial development. Effectively tracing and confirming the material’s origin may be accomplished by detecting stable isotopes and mineral elements. Methods We studied 25 A. sinensis samples collected from three main producing areas (Linxia, Gannan, and Dingxi) in southeastern Gansu Province, China, to better identify its origin. We used inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) to determine eight mineral elements (K, Mg, Ca, Zn, Cu, Mn, Cr, Al) and three stable isotopes (δ13C, δ15N, δ18O). Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to verify the validity of its geographical origin. Results K, Ca/Al, δ13C, δ15N and δ18O are important elements to distinguish A. sinensis sampled from Linxia, Gannan and Dingxi. We used an unsupervised PCA model to determine the dimensionality reduction of mineral elements and stable isotopes, which could distinguish the A. sinensis from Linxia. However, it could not easily distinguish A. sinensis sampled from Gannan and Dingxi. The supervised PLS-DA and LDA models could effectively distinguish samples taken from all three regions and perform cross-validation. The cross-validation accuracy of PLS-DA using mineral elements and stable isotopes was 84%, which was higher than LDA using mineral elements and stable isotopes. Conclusions The PLS-DA and LDA models provide a theoretical basis for tracing the origin of A. sinensis in three regions (Linxia, Gannan and Dingxi). This is significant for protecting consumers’ health, rights and interests.

scholarly journals Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Jiaji Ding ◽  
Caimei Gu ◽  
Linfang Huang ◽  
Rui Tan
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Prediction Model ◽  
Linear Discriminant Analysis ◽  
Geographical Origin ◽  
Electronic Tongue ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Discriminant ◽  
Very High

Cynomorium songaricum Rupr. is a well-known and widespread plant in China. It has very high medicinal values in many aspects. The study aimed at discriminating and predicting C. songaricum from major growing areas in China. An electronic tongue was used to analyze C. songaricum based on flavor. Discrimination was achieved by principal component analysis and linear discriminant analysis. Moreover, a prediction model was established, and C. songaricum was classified by geographical origins with 100% degree of accuracy. Therefore, the identification method presented will be helpful for further study of C. songaricum.

scholarly journals Evaluation of Geographic Origin of Torrontés Wines by Chemometrics

2013 ◽  
Vol 2 (5) ◽  
pp. 48 ◽  
Author(s):  
Silvana Mariela Azcarate ◽  
Miguel Angel Cantarelli ◽  
Eduardo Jorge Marchevsky ◽  
José Manuel Camiña
Keyword(s):  
Discriminant Analysis ◽  
Geographical Origin ◽  
Uv Spectroscopy ◽  
Geographic Origin ◽  
Principal Component ◽  
San Juan ◽  
Electromagnetic Spectrum ◽  
Linear Discriminant ◽  
Rio Negro ◽  
Vis Spectroscopy

<p>This work discusses the determination of the provenance of commercial Torrontés wines from different Argentinean provinces (Mendoza, San Juan, Salta and Rio Negro) by the use of UV-vis spectroscopy and chemometric techniques. In order to find classification models, wines (n = 80) were analyzed using UV-Vis region of the electromagnetic spectrum. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) were used to classify Torrontés wines according to their geographical origin. Classification rates obtained were highly satisfactory. The PLS-DA and LDA calibration models showed that 100% of the Mendoza, San Juan, Salta and Rio Negro Torrontés wine samples had been correctly classified. These results demonstrate the potential use of UV spectroscopy with chemometric data analysis as a method to classify Torrontés wines according to their geographical origin, a procedure which requires low-cost equipment and short-time analysis in comparison with other techniques.</p>

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

scholarly journals Traceability of the geographical origin of Siraitia grosvenorii based on multielement contents coupled with chemometric techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao-Ping Huang ◽  
Lei Lei ◽  
Shun-Xin Lei ◽  
Wei-Wei Zhu ◽  
Jun Yan
Keyword(s):  
Inductively Coupled Plasma ◽  
Geographical Origin ◽  
Principal Component ◽  
Emission Spectrometry ◽  
Support Vector ◽  
Linear Discriminant ◽  
Protected Designation Of Origin ◽  
Siraitia Grosvenorii ◽  
Discrimination Rate ◽  
Chemometric Techniques

AbstractSiraitia grosvenorii (LHG) is widely used as a medicinal and edible material around the world. The objective of this study was to develop an effective method for the authentication of the geographical origin of LHG in its main producing area Guangxi, China, which is identified as Chinese Protected Designation of Origin product, against other producing regions in China. The content of 14 elements (K, Na, Ca, P, Mg, Al, B, Ba, Cu, Fe, Mn, Ni, Zn, and Sr) of 114 LHG samples was determined by inductively coupled plasma optical emission spectrometry. Multivariate analysis was then performed to classify the geographical origin of LHG samples. The contents of multielement display an obvious trend of clustering according to the geographical origin of LHG samples based on radar plot and principal component analysis. Finally, three supervised statistical techniques, including linear discriminant analysis (LDA), k-nearest neighbours (k-NN), and support vector machine (SVM), were applied to develop classification models. Finally, 40 unknown LHG samples were used to evaluate the predictive ability of model and discrimination rate of 100%, 97.5% and 100% were obtained for LDA, k-NN, and SVM, respectively. This study indicated that it is feasible to attribute unknown LHG samples to its geographical origin based on its multielement content coupled with chemometric techniques.

scholarly journals Linear discriminant analysis based on gas chromatographic measurements for geographical prediction of USA medical domestic cannabis

2020 ◽  
Author(s):  
Ramia Z. Al Bakain ◽  
Yahya S. Al-Degs ◽  
James V. Cizdziel ◽  
Mahmoud A. Elsohly
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Components ◽  
Supervised Classification ◽  
Geographical Origin ◽  
Active Components ◽  
Linear Discriminant ◽  
Sample Extraction ◽  
Analytical Range

AbstractFifty four domestically produced cannabis samples obtained from different USA states were quantitatively assayed by GC–FID to detect 22 active components: 15 terpenoids and 7 cannabinoids. The profiles of the selected compounds were used as inputs for samples grouping to their geographical origins and for building a geographical prediction model using Linear Discriminant Analysis. The proposed sample extraction and chromatographic separation was satisfactory to select 22 active ingredients with a wide analytical range between 5.0 and 1,000 µg/mL. Analysis of GC-profiles by Principle Component Analysis retained three significant variables for grouping job (Δ9-THC, CBN, and CBC) and the modest discrimination of samples based on their geographical origin was reported. PCA was able to separate many samples of Oregon and Vermont while a mixed classification was observed for the rest of samples. By using LDA as a supervised classification method, excellent separation of cannabis samples was attained leading to a classification of new samples not being included in the model. Using two principal components and LDA with GC–FID profiles correctly predict the geographical of 100% Washington cannabis, 86% of both Oregon and Vermont samples, and finally, 71% of Ohio samples.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

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