scholarly journals Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin

2019 ◽  
Vol 412 (2) ◽  
pp. 463-472 ◽  
Author(s):  
Yiannis Fiamegos ◽  
Catalina Dumitrascu ◽  
Michele Ghidotti ◽  
Maria Beatriz de la Calle Guntiñas
Keyword(s):  
Geographical Origin ◽  
External Validation ◽  
Principal Component ◽  
Energy Dispersive ◽  
Sample Treatment ◽  
Classification Models ◽  
Botanical Variety ◽  
X Ray ◽  
Food Commodities

AbstractHoney is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties—orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka—and seven different geographical origins—Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples.

Multi-Variate Analysis of Trace Elements from XRF Studies for Classification According to Origin

1992 ◽  
Vol 46 (5) ◽  
pp. 843-847 ◽  
Author(s):  
C. T. Yap
Keyword(s):  
Principal Component Analysis ◽  
Trace Elements ◽  
Geographical Origin ◽  
Principal Component ◽  
Energy Dispersive ◽  
Fluorescence Technique ◽  
X Ray ◽  
Elegant Method ◽  
Variate Analysis ◽  
Made In

The concentrations of twelve trace elements (Mn, Fe, Co, Ni, Cu, Zn, As, Rb, Sr, Y, Zr, and Nb) in 143 pieces of Chinese porcelain made in Jingdezhen, China and elsewhere were obtained with the use of the energy-dispersive x-ray fluorescence technique. An elegant method of multi-variate analysis, known as principal component analysis, was successfully employed in fingerprinting the geographical origin of the porcelain samples.

An energy dispersive X-ray fluorescence spectrometry approach for the identification of geographical origin of wheat flour

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Zaiyong Si ◽  
Qianwei Cheng ◽  
Hui Chen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Principal Component ◽  
Component Analysis ◽  
Mineral Elements ◽  
Energy Dispersive ◽  
Fluorescence Spectrometry ◽  
Energy Channel ◽  
X Ray ◽  
Different Origins

Abstract In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

2013 ◽  
pp. 322-427
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Classification Models ◽  
Training Set ◽  
Linear Discriminant ◽  
Oecd Principles ◽  
Qsar Models ◽  
Validation Set ◽  
Global Accuracy

In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

Classification of mineral particles by non-linear mapping of electron microprobe energy dispersive X-ray spectra

The Analyst ◽  
1994 ◽  
Vol 119 (5) ◽  
pp. 971 ◽  
Author(s):  
Boris Treiger ◽  
Igor Bondarenko ◽  
Piet Van Espen ◽  
Ren� Van Grieken ◽  
Fred Adams
Keyword(s):  
Electron Microprobe ◽  
Linear Mapping ◽  
Energy Dispersive ◽  
X Ray ◽  
Mineral Particles ◽  
Non Linear

scholarly journals Metabolomics-based authentication of wines according to grape variety

2019 ◽  
Vol 37 (No. 4) ◽  
pp. 239-245 ◽  
Author(s):  
Leos Uttl ◽  
Kamila Hurkova ◽  
Vladimir Kocourek ◽  
Jana Pulkrabova ◽  
Monika Tomaniova ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Models ◽  
High Performance ◽  
Geographical Origin ◽  
Principal Component ◽  
White Wine ◽  
Tandem Mass ◽  
Grape Variety ◽  
Varietal Identification

In 2008, the European Commission highlighted the risk of wine mislabelling regarding the geographical origin and varietal identification. While analytical methods for the identification of wine by geographical origin exist, a reliable strategy for authentication of wine variety is still missing. Here, we investigate the suitability of the metabolomic fingerprinting of ethyl acetate wine extracts, using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. In total, 43 white wine samples (three varieties) were analysed within our study. The generated data were processed by principal component analysis and then by partial least squares discriminant analysis. The resulting statistical models were validated and assessed according to their R2 (cum) and Q2 (cum) parameters. The most promising models were based on positive ionisation data, enabling successful classification of 92% of wine samples.

Sign in / Sign up

Export Citation Format

Share Document