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Quantification of curcuminoids in turmeric using visible reflectance spectra and a decision-tree based chemometric approach

Suresh, H and Behera, AR and Selvaraja, SK and Pratap, R (2020) Quantification of curcuminoids in turmeric using visible reflectance spectra and a decision-tree based chemometric approach. In: Journal of the Electrochemical Society, 167 (16).

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Official URL: https://dx.doi.org/10.1149/1945-7111/abd603


For quantification of curcumin content in turmeric, a low-cost multivariate-analysis-based sensing system is desired. It can be realized by exploiting the spectra in the visible region, which enables the use of off-the-shelf, relatively inexpensive light sources and detectors. To address this, we propose a novel decision-tree method for improved prediction accuracy. Two sets of models with PLSR algorithm are developed with the measured reflectance spectra from 66 turmeric samples in the range of 360-750 nm, and their respective curcuminoids content are quantified by HPLC. A suite of a coarse-model for initial prediction of turmeric samples in the broad range of 1-4, and five finer-models for subsequent prediction (in the ranges 1-2, 2-3, 3-4, 1.5-2.5, and 2.5-3.5) constitute the proposed decision-tree approach. The method's efficacy is substantiated from an improved coefficient of determination (R2) for the finer models (0.90-0.96) as compared to the coarse-model's 0.92. This is further corroborated with lower RMSECV of 0.06-0.13 and an RMSEP of 0.15-0.25 for finer models, as compared to 0.219 and 0.45 for the coarse model, respectively. Testing reveals that the method results in 46 reduction in prediction error. Realization of a robust prediction approach in the visible range sets the stage for the development of cost-effective field-deployable devices for on-site measurement of curcumin. © 2021 The Author(s).

Item Type: Journal Article
Publication: Journal of the Electrochemical Society
Publisher: IOP Publishing Ltd
Additional Information: cited By 0; IOP Publishing Ltd
Keywords: Cost benefit analysis; Cost effectiveness; Decision trees; Forecasting; Light sources; Multivariant analysis; Reflection, Chemometric approach; Coefficient of determination; Decision tree method; Multi variate analysis; On-site measurement; Prediction accuracy; Reflectance spectrum; Visible reflectance, Food products
Department/Centre: Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering
Date Deposited: 03 Feb 2021 10:05
Last Modified: 03 Feb 2021 10:05
URI: http://eprints.iisc.ac.in/id/eprint/67834

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