Traceability of the geographical origin of Siraitia grosvenorii based on multielement contents coupled with 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.

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Traceability of the Geographical Origin of Siraitia grosvenorii based on Multielement Contents coupled with Chemometric Techniques

10.21203/rs.3.rs-463128/v1 ◽

2021 ◽

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

Abstract Siraitia 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 (PDO) 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 (ICP-OES). 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 (PCA). 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.

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The effect of collecting area on the element content of Hungarian acacia honeys

Acta Agraria Debreceniensis ◽

10.34101/actaagrar/150/1709 ◽

2018 ◽

pp. 129-138

Author(s):

Nikolett Czipa ◽

Andrea Kántor ◽

Loránd Alexa ◽

Béla Kovács

Keyword(s):

Inductively Coupled Plasma ◽

Geographical Origin ◽

Optical Emission ◽

Emission Spectrometry ◽

Linear Discriminant ◽

Inductively Coupled ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

Plasma Optical Emission Spectrometry ◽

One Way Anova

Six macroelements and twelve microelements were identified in thirty-six Hungarian acacia honeys collected from ten counties by inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS). One-Way ANOVA (LSD and Dunnett T3 test) and linear discriminant analysis (LDA) were used to determine the statistically verified differences among the honey samples with different geographical origin. Significant differences were established among the samples from different counties in Na, P, S, Fe, Ni, Cu and Sr concentrations. Based on the macroelement content of honeys, the separation of samples with different geographical origin was not successful because the percent of correctly categorised cases was only 64.9%. However, examining the As, B, Ba, Cu, Fe Mn, Ni and Sr concentration, the separation of different groups was convincing since the percent of correctly classified cases was 97.2%. Thus, the examination of microelement concentration may be able to determine the geographical origin of acacia honeys.

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Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses

Molecules ◽

10.3390/molecules26226875 ◽

2021 ◽

Vol 26 (22) ◽

pp. 6875

Author(s):

Francesca Di Donato ◽

Martina Foschi ◽

Nadia Vlad ◽

Alessandra Biancolillo ◽

Leucio Rossi ◽

...

Keyword(s):

Elemental Composition ◽

Inductively Coupled Plasma ◽

Predictive Accuracy ◽

Principal Component ◽

Major Elements ◽

Partial Least Square ◽

Least Square ◽

Emission Spectrometry ◽

Icp Oes ◽

Protected Designation Of Origin

The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the accurate and precise determination of eight major elements (Ba, Ca, Fe, K, Mg, Na, P, and Zn). The ICP-OES data acquired from 17 PR, 20 PS, and 16 PF samples were processed by unsupervised (Principal Component Analysis, PCA) and supervised (Partial Least Square-Discriminant Analysis, PLS-DA) multivariate methods. PCA revealed a relatively high variability of the multi-elemental composition within the samples of a given variety, and a fairly good separation of the Pecorino cheeses according to the geographical origin. Concerning the supervised classification, PLS-DA has allowed obtaining excellent results, both in calibration (in cross-validation) and in validation (on the external test set). In fact, the model led to a cross-validated total accuracy of 93.3% and a predictive accuracy of 91.3%, corresponding to 2 (over 23) misclassified test samples, indicating the adequacy of the model in discriminating Pecorino cheese in accordance with its origin.

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A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Foods ◽

10.3390/foods10061411 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1411

Author(s):

José Luis P. Calle ◽

Marta Ferreiro-González ◽

Ana Ruiz-Rodríguez ◽

Gerardo F. Barbero ◽

José Á. Álvarez ◽

...

Keyword(s):

Random Forest ◽

Raw Materials ◽

Principal Component ◽

Hierarchical Cluster ◽

Raw Material ◽

Support Vector ◽

Protected Designation Of Origin ◽

Ft Ir

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

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Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽

10.3390/s19204523 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4523 ◽

Cited By ~ 1

Author(s):

Carlos Cabo ◽

Celestino Ordóñez ◽

Fernando Sáchez-Lasheras ◽

Javier Roca-Pardiñas ◽

and Javier de Cos-Juez

Keyword(s):

Random Forest ◽

Laser Scanning ◽

Supervised Classification ◽

Computing Time ◽

Principal Component ◽

Point Clouds ◽

Support Vector ◽

Linear Discriminant ◽

Vector Machines ◽

Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

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Assessment of essential elements and chemical contaminants in thirteen fish species from the Bay Aratu, Bahia, Brasil

Brazilian Journal of Biology ◽

10.1590/1519-6984.02415 ◽

2016 ◽

Vol 76 (4) ◽

pp. 871-877 ◽

Cited By ~ 4

Author(s):

E. Silva ◽

Z. C. V. Viana ◽

N. F. A. Souza ◽

M. G. A. Korn ◽

V. L. C. S. Santos

Keyword(s):

Fish Species ◽

Inductively Coupled Plasma ◽

Certified Reference Materials ◽

Principal Component ◽

Optical Emission ◽

Hierarchical Cluster ◽

Essential Elements ◽

Emission Spectrometry ◽

Oyster Tissue ◽

National Agency

Abstract Concentrations of ten elements (Cd, Cr, Cu, Fe, Ni, Pb, Se, Sr, V and Zn) were determinate in muscle tissues of 13 fish species from Aratu Bay, Bahia, Brazil by inductively coupled plasma optical emission spectrometry. The accuracy and precision of our results were checked by using two certified reference materials: BCR-422 cod muscle and SRM 1566b oyster tissue. The average trace element concentrations in the fish species varied in the following ranges, in μg g–1: 0.03-0.8 for Cr; 2.0-33.7 for Cu, 2.4-135.1 for Fe, 1.6-25.6 for Se; 1.6-35.1 for Sr; and 2.8-40.5 for Zn. The Diaptereus rhombeus (carapeba) specie presented the highest concentrations of Se, Cu and Fe. Chromium and Se were present at levels above the limit of tolerance allowed by the National Agency of Sanitary Vigilance (ANVISA). The results were also evaluated using the multivariate analysis techniques: principal component analysis (PCA) and hierarchical cluster analysis (HCA).

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Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽

10.3390/s18092936 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2936 ◽

Cited By ~ 6

Author(s):

Xianghao Zhan ◽

Xiaoqing Guan ◽

Rumeng Wu ◽

Zhan Wang ◽

You Wang ◽

...

Keyword(s):

Herbal Medicine ◽

Electronic Nose ◽

Nearest Neighbor ◽

Herbal Medicines ◽

Principal Component ◽

Support Vector ◽

Linear Discriminant ◽

Convenient Means ◽

Electronic Nose System ◽

Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

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Discrimination of the Geographical Origin of the Lateral Roots of Aconitum carmichaelii Using the Fingerprint, Multicomponent Quantification, and Chemometric Methods

Molecules ◽

10.3390/molecules24224124 ◽

2019 ◽

Vol 24 (22) ◽

pp. 4124 ◽

Cited By ~ 3

Author(s):

Lu-Lin Miao ◽

Qin-Mei Zhou ◽

Cheng Peng ◽

Chun-Wang Meng ◽

Xiao-Ya Wang ◽

...

Keyword(s):

High Performance ◽

Geographical Origin ◽

Lateral Roots ◽

Principal Component ◽

Hierarchical Cluster ◽

Similarity Analysis ◽

Diterpenoid Alkaloids ◽

Linear Discriminant ◽

Hplc Fingerprint ◽

Aconitum Carmichaelii

Fuzi is a well-known traditional Chinese medicine developed from the lateral roots of Aconitum carmichaelii Debx. It is rich in alkaloids that display a wide variety of bioactivities, and it has a strong cardiotoxicity and neurotoxicity. In order to discriminate the geographical origin and evaluate the quality of this medicine, a method based on high-performance liquid chromatography (HPLC) was developed for multicomponent quantification and chemical fingerprint analysis. The measured results of 32 batches of Fuzi from three different regions were evaluated by chemometric analysis, including similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA), and linear discriminant analysis (LDA). The content of six representative alkaloids of Fuzi (benzoylmesaconine, benzoylhypaconine, benzoylaconine, mesaconitine, hypaconitine, and aconitine) were varied by geographical origin, and the content ratios of the benzoylmesaconine/mesaconitine and diester-type/monoester-type diterpenoid alkaloids may be potential traits for classifying the geographical origin of the medicine. In the HPLC fingerprint similarity analysis, the Fuzi from Jiangyou, Sichuan, was distinguished from the Fuzi from Butuo, Sichuan, and the Fuzi from Yunnan. Based on the HCA and PCA analyses of the content of the six representative alkaloids, all of the batches were classified into two categories, which were closely related to the plants’ geographical origins. The Fuzi samples from Jiangyou were placed into one category, while the Fuzi samples from Butuo and Yunnan were put into another category. The LDA analysis provided an efficient and satisfactory prediction model for differentiating the Fuzi samples from the above-mentioned three geographical origins. Thus, the content of the six representative alkaloids and the fingerprint similarity values were useful markers for differentiating the geographical origin of the Fuzi samples.

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Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Applied Sciences ◽

10.3390/app10031173 ◽

2020 ◽

Vol 10 (3) ◽

pp. 1173 ◽

Cited By ~ 2

Author(s):

Zhiqi Hong ◽

Yong He

Keyword(s):

Hyperspectral Imaging ◽

Nutritional Value ◽

Geographical Origin ◽

Principal Component ◽

Hyperspectral Images ◽

Imaging Systems ◽

Support Vector ◽

Spectral Ranges ◽

Tea Leaf ◽

Non Destructive

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

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Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Foods ◽

10.3390/foods8100450 ◽

2019 ◽

Vol 8 (10) ◽

pp. 450 ◽

Cited By ~ 4

Author(s):

Annalisa De Girolamo ◽

Marina Cortese ◽

Salvatore Cervellieri ◽

Vincenzo Lippolis ◽

Michelangelo Pascale ◽

...

Keyword(s):

Durum Wheat ◽

Near Infrared ◽

Southern Italy ◽

Geographical Origin ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Model ◽

North Central ◽

Linear Discriminant ◽

The World

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

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