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 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 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.

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Nadège Dossat ◽  
Alain Mangé ◽  
Jérôme Solassol ◽  
William Jacot ◽  
Ludovic Lhermitte ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Supervised Classification ◽  
Protein Profiling ◽  
Clinical Proteomics ◽  
High Dimensional ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Supervised Classification Methods

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

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.

Authentication of geographical origin of palm oil by chromatographic fingerprinting of triacylglycerols and partial least square-discriminant analysis

Talanta ◽  
2013 ◽  
Vol 116 ◽  
pp. 788-793 ◽  
Author(s):  
Cristina Ruiz-Samblás ◽  
Cristina Arrebola-Pascual ◽  
Alba Tres ◽  
Saskia van Ruth ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Discriminant Analysis ◽  
Palm Oil ◽  
Geographical Origin ◽  
Partial Least Square ◽  
Least Square ◽  
Chromatographic Fingerprinting

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

2021 ◽  
Author(s):  
Silvana Nisgoski ◽  
Thaís A P Gonçalves ◽  
Júlia Sonsin-Oliveira ◽  
Adriano W Ballarin ◽  
Graciela I B Muñiz
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
New Technologies ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Eucalyptus Species ◽  
Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

scholarly journals Changes in Amino Acid and Acylcarnitine Plasma Profiles for Distinguishing Patients with Multiple Sclerosis from Healthy Controls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Marat F. Kasakin ◽  
Artem D. Rogachev ◽  
Elena V. Predtechenskaya ◽  
Vladimir J. Zaigraev ◽  
Vladimir V. Koval ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Amino Acid ◽  
Discriminant Analysis ◽  
Clinical Decision ◽  
Partial Least Square ◽  
Least Square ◽  
Control Group ◽  
Healthy Controls ◽  
Linear Discriminant ◽  
Tandem Mass Spectrometric

McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n=16) and control (n=12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.

Coupling Proton Transfer Reaction−Mass Spectrometry with Linear Discriminant Analysis:  a Case Study

2003 ◽  
Vol 51 (25) ◽  
pp. 7227-7233 ◽  
Author(s):  
Franco Biasioli ◽  
Flavia Gasperi ◽  
Eugenio Aprea ◽  
Daniela Mott ◽  
Elena Boscaini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Proton Transfer ◽  
Linear Discriminant Analysis ◽  
Transfer Reaction ◽  
Proton Transfer Reaction ◽  
Reaction Mass ◽  
Linear Discriminant
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