Multi-instrument spectroscopic study for authentication of curcumin content in commercial turmeric powders using machine learning algorithms.

Rani, Amsaraj and Rishi, Ranjan and Bhanu Prakash, Rachaiah and Sarma, Mutturi (2024) Multi-instrument spectroscopic study for authentication of curcumin content in commercial turmeric powders using machine learning algorithms. Journal of Food Composition and Analysis, 134. p. 106543.

PDF 1-s2.0-S0889157524005775-main.pdf - Published Version Restricted to Registered users only Download (3MB) | Request a copy

Abstract

Adulteration during processing of turmeric powder not only causes health risks for the consumers but also affects its quality. There is a need for rapid and non-invasive analysis of its active ingredient, curcumin, during the supply-chain. In the present study a total six IR instruments ranging from hand-held (NIR), portable (NIR) and standalone (FTNIR and FTIR) were used to obtain spectral data of 160 different turmeric samples. The curcumin content quantified using HPLC procedure was used as the response variable for analytical model using machine learning tools. Real coded genetic algorithm (RCGA) as the variable selection procedure provided most critical variables in the sets of 10, 20, 30 and 40 variables. Sensitivity analysis has revealed the most critical fingerprint (s) in authenticating curcumin across all the instruments. The hand-held (NIR) device with only 20 spectral variables resulted in 93 % accuracy using SVM classifier, and RP (regression co-efficient of prediction) values of 0.970 and 0.997 using RF and XGBoost, respectively. In case of FTNIR and FTIR instruments 100 % classification accuracy was achieved using SVM, whereas RF and XGBoost resulted in RP values greater than 0.93. This study enables classification and quantification of curcumin content in commercial turmeric powders using nondestructive methodology.

Item Type:	Article
Uncontrolled Keywords:	Curcumin Turmeric powder IR spectroscopy Variable selection Machine learning
Subjects:	000 Computer science, information and general works > 02 Computer Science 500 Natural Sciences and Mathematics > 04 Chemistry and Allied Sciences > 10 Spectroscopic and Spectrometric analysis 600 Technology > 08 Food technology > 30 Spices/Condiments > 07 Turmeric
Divisions:	Food Microbiology
Depositing User:	Food Sci. & Technol. Information Services
Date Deposited:	07 Nov 2024 07:12
Last Modified:	07 Nov 2024 07:12
URI:	http://ir.cftri.res.in/id/eprint/18417

Actions (login required)

View Item