Development of an FTIR based chemometric model for the qualitative and quantitative evaluation of cane sugar as an added sugar adulterant in apple fruit juices. Issue 4 (2nd April 2020)

• Record Type: Journal Article
• Authors: Dhaulaniya, Amit S.; Balan, Biji; Yadav, Amit; Jamwal, Rahul; Kelly, Simon; Cannavan, Andrew; Singh, Dileep K.
• Is Part Of: Food additives & contaminants. Volume 37:Issue 4(2020)
• Page Start: 539
• Page End: 551
• Publication Date: 2020-04-02
• Subjects: Cane sugar -- apple Juice -- FTIR -- chemometric -- prediction -- regression modelling; Food additives -- Periodicals; Food contamination -- Periodicals; 664.06
• DOI: 10.1080/19440049.2020.1718774
• Languages: English
• ISSNs: 1944-0049

Abstract

ABSTRACT: A Fourier Transform Infrared Spectroscopy based chemometric model was evaluated for the rapid identification and estimation of cane sugar as an added sugar adulterant in apple fruit juices. For all the ninety samples, spectra were acquired in the mid-infrared range (4000 cm −1 –400 cm −1 ). The spectral analysis provided information regarding the distinctive variable region, which lies in the range of 1200cm −1 to 900cm −1, designated as fingerprint region for the carbohydrates. A specific peak in the fingerprint region was observed at 997cm −1 in all the adulterated samples and was undetectable in pure samples. Based on different levels of cane sugar adulteration (5, 10, 15, and 20%), principal component analysis showed the clustering of samples and further helped us in compression of data by selecting wavenumbers with maximum variability based on the loading line plot. Supervised classification methods (SIMCA and LDA) were evaluated based on their classification efficiencies for a test set. Though SIMCA showed 100% classification efficiency (Raw data set), LDA was able to classify the test set with an accuracy of only 96.67% (Raw as well as Transformed data set) between pure and 5% adulterated samples. For the quantitative estimation, calibration models were developed using partial least square regression (PLS-R) and principal component regression method (PCR) methods. PLS-1 st derivative showed a maximum coefficient of determination (R 2 ) with a value of 0.991 for calibration and 0.992 for prediction. The RMSECV, RMSEP, LOD and LOQ observed for PLS-1 st derivative model were 0.75% w/v, 0.61% w/v, 1.28%w/v and 3.88%w/v, respectively. The coefficient of variation as a measure of precision (repeatability) was also determined for all models, and it ranged from 0.23% to 1.83% (interday), and 0.25% to 1.43% (intraday).