Main content area

Quality assessment of fresh tea leaves by estimating total polyphenols using near infrared spectroscopy

Hazarika, Ajanto Kumar, Chanda, Somdeb, Sabhapondit, Santanu, Sanyal, Sandip, Tamuly, Pradip, Tasrin, Sahnaz, Sing, Dilip, Tudu, Bipan, Bandyopadhyay, Rajib
Journal of food science and technology 2018 v.55 no.12 pp. 4867-4876
algorithms, grinding, heating systems, least squares, leaves, models, near-infrared spectroscopy, polyphenols, prediction, reflectance, sieving, tea, wavelengths
This paper reports on the development of an integrated leaf quality inspecting system using near infrared reflectance (NIR) spectroscopy for quick and in situ estimation of total polyphenol (TP) content of fresh tea leaves, which is the most important quality indicator of tea. The integrated system consists of a heating system to dry the fresh tea leaves to the level of 3–4% moisture, a grinding and sieving system fitted with a 250 micron mesh sieve to make fine powder from the dried leaf. Samples thus prepared are transferred to the NIR beam and TP is measured instantaneously. The wavelength region, the number of partial least squares (PLS) component and the choice of preprocessing methods are optimized simultaneously by leave-one-sample out cross-validation during the model calibration. In order to measure polyphenol percentage in situ, the regression model is developed using PLS regression algorithm on NIR spectra of fifty-five samples. The efficacy of the model developed is evaluated by the root mean square error of cross-validation, root mean square error of prediction and correlation coefficient (R²) which are obtained as 0.1722, 0.5162 and 0.95, respectively.