Abstract
The fingerprinting method is generally performed to determine specific molecules or the behavior of specific molecular bonds in the desired sample content. A novel, robust and simple method based on surface enhanced Raman spectroscopy (SERS) was developed to obtain the full spectrum of tea varieties for detection of the purity of the samples based on the type of processing and cultivation. For this purpose, the fingerprint of seven different varieties of tea samples (herbal tea (rose hip, chamomile, linden, green and sage tea), black tea and earl grey tea) combined with silver colloids was obtained by SERS in the range of 200–2000 cm−1 with an analysis time of 20 s. Each of the thirty-nine tea samples tested showed its own specific SERS spectra. Principal Component Analysis (PCA) was also applied to separate of each tea variety and different models developed for tea samples including three different models for the herbal teas and two different models for black and earl grey tea samples. Herbal tea samples were separated using mean centering, smoothing and median centering pre-processing steps while baselining and derivatisation pre-processing steps were applied to SERS data of black and earl grey tea. The novel spectroscopic fingerprinting technique combined with PCA is an accurate, rapid and simple methodology for the assessment of tea types based on the type of processing and cultivation differences. This method is proposed as an alternative tool in order to determine the characteristics of tea varieties.
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Highlights • Spectral measurements are performed in order to obtain tea fingerprints with SERS. Silver colloids are used to provide signal enhancement. • The PCA tool is used to discriminate seven tea kinds (black, earl grey, linden, chamomile, rose hip, sage, and green tea).
• Leading tea adulteration since fingerprints of types of tea are successfully separated from each other.
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Buyukgoz, G.G., Soforoglu, M., Basaran Akgul, N. et al. Spectroscopic fingerprint of tea varieties by surface enhanced Raman spectroscopy. J Food Sci Technol 53, 1709–1716 (2016). https://doi.org/10.1007/s13197-015-2088-5
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DOI: https://doi.org/10.1007/s13197-015-2088-5