Determination of metal contents in aromatic herbs and spices from Algeria: Chemometric approach

Determination of metal contents in aromatic herbs and spices from Algeria: Chemometric approach

Trace metals (As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr, and Zn) in acid digests of some aromatic herbs and spices (basil, fennel, laurel, mint, oregano, rosemary, thyme, black pepper, cinnamon, coriander, and cumin) were determined quantitatively using ICP‐AES method. The highest concentrations (μg g−...

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Bibliographic Details
Published in:Journal of chemometrics Vol. 36; no. 9
Main Authors: Kachbi, Abdelmalek, Arezoug, Djoumad, Kara‐Abdelfettah, Dalila, Benamor, Mohamed, Senhadji‐Kebiche, Ounissa
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 01-09-2022
Subjects:
aromatic herbs and spices
Cadmium
chemometric methods
Chemometrics
Chromium
Cluster analysis
Clustering
Copper
Discriminant analysis
Dry matter
Herbs
ICP‐AES
Iron
Least squares
Magnesium
Manganese
Metal concentrations
metal contents
Nickel
Oregano
Pattern recognition
Principal components analysis
Rosemary
Spices
Strontium
Trace metals
Zinc
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Summary:Trace metals (As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr, and Zn) in acid digests of some aromatic herbs and spices (basil, fennel, laurel, mint, oregano, rosemary, thyme, black pepper, cinnamon, coriander, and cumin) were determined quantitatively using ICP‐AES method. The highest concentrations (μg g−1 dry matter) of each of the 11 investigated metals were found as follows: As (0.42) and Pb (1.6) in rosemary samples; Cd (0.1) in basil; Co (0.62), Cu (12.13), and Zn (52.26) in oregano; Cr (2.95) and Mg (3110) in fennel, Fe (494) and Ni (7.61) in cumin; Mn (192) in black pepper; and finally Sr (60.68) in mint. Some chemometric techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least square discriminant analysis (PLS‐DA) were used on metallic concentrations data in an attempt to classify these aromatic herbs and spices. The unsupervised pattern recognition PCA and HCA models gave the same result about similarities and differences between the studied plant samples, and five clusters of similar aromatic herbs and spices samples were formed. In order to verify the results of this clustering, we used a supervised pattern recognition method called partial least square discriminant analysis (PLS‐DA). Classes were groups or clusters of similar plants obtained previously. A hierarchical model builder (HMB) based on four PLS‐DA models was used to simultaneous determination of the class of each sample. It was found that all samples were correctly classified by PLS‐DA in their original groups as determined by PCA and HCA. The aim of the present study is to evaluate whether or not there are similarities and/or differences between several Algerian aromatic herbs and spices according to their metals contents. Trace metals in plant samples were determined quantitatively using ICP‐AES. The chemometric study was performed using Pearson's correlation analysis, principal component analysis, hierarchical cluster analysis, and a hierarchical model builder with the partial least square discriminant analysis. The results obtained demonstrate that there is a relationship between plant samples, which can then be classified into five groups of similar plant samples based on their metals contents.
Bibliography:Funding information
Algerian MESRS and DGRSDT, Grant/Award Number: A16N01UN060120140005
ISSN:0886-9383
1099-128X
DOI:10.1002/cem.3437