Chemometric Evaluation of Discrimination of Aromatic Plants by Using NIRS, LIBS

Ercioglu E., VELİOĞLU H. M., BOYACI İ. H.

FOOD ANALYTICAL METHODS, vol.11, no.6, pp.1656-1667, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 6
  • Publication Date: 2018
  • Doi Number: 10.1007/s12161-018-1145-x
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1656-1667
  • Hacettepe University Affiliated: Yes


Aromatic plants have different chemical compositions that give them specific properties such as colour, aroma and taste and can be classified based on differentiation of various chemical constituents such as protein, vitamins, minerals, volatile and non-volatile oil, carbohydrates and the presence of adulterants. The aim of the present study was to develop a fast, simple and non-destructive method for discrimination of aromatic plants, juniper (Juniperus communis), rosemary (Rosmarinus officinalis), laurel (Laurus nobilis), sweet basil (Ocimum basilicum), black pepper (Piper nigrum), thyme (Origanum majorana), lavender (Lavandula latifolia), spearmint (Mentha spicata) and ginger (Zingiber officinale), commonly used. In order to discriminate aromatic plants, chemometric methods, namely principal component analysis (PCA), were used together with spectroscopic methods. Analysis of plant samples was carried out using Raman spectroscopy (RS), near-infrared spectroscopy (NIRS) and laser-induced breakdown spectroscopy (LIBS). Although Raman spectra of aromatic plant samples could not be obtained due to problems with sample degradation and fluorescence effect, satisfying classification of aromatic plant samples was accomplished by LIBS and NIRS. PCA models developed using NIRS data showed that the first two principal components explained 82.56% of the total variance. Elemental composition of the aromatic plant samples was investigated using LIBS, and the first two principal components explained 77.97% of the total variance in the PCA model generated by using the LIBS data. The ability to rapidly discriminate various culinary herbs makes these spectroscopic methods available to use by the aromatic plant industry in order to perform a fast quality control of incoming raw materials.