Development of a quantification method for the analysis of sugars in apple fruit juice using attenuated total reflection‐Fourier transform infrared spectroscopy coupled with multivariate regression modeling

Abstract

AbstractBackgroundSugars are a major component of apple juices. Sugar content plays an important role in quality analysis of the apple juice. In this study, an attempt is made to develop a simple and reliable method for the direct estimation of sugar content in apple juice using attenuated total reflection‐Fourier transform infrared spectroscopy (ATR‐FTIR) coupled with chemometric technique. The spectral information obtained from the FTIR is utilized to develop predictive models based on partial least square regression (PLS‐R) and principal component regression (PCR) for sugar analysis.ResultsBased on the analysis of FTIR spectra, a fingerprint region (between 1200 and 900 cm−1) for carbohydrates in apple juice was identified. This region was utilized to develop PLS‐R and PCR models. Ultimately, PLS‐R models were selected for prediction because of their superiority in terms of root mean square error of calibration (RMSEC), root mean standard error for cross‐validation (RMSECV), and R2 over PCR models. For fructose and glucose content, the prediction model generated with raw spectra obtained the best optimized statistical parameters (R2 fructose; 0.9952, R2 glucose; 0.9961). However, for total sugar and sucrose (R2 total sugar; 0.9968, R2 sucrose; 0.9983) content, first‐derivative FTIR models were found best suitable for the prediction of test set.ConclusionsThis study offers a reliable, rapid, and nondestructive method with least sample preparation for the direct estimation of sugars in apple juices. It allows the determination of several sugars in a single measurement, which is worth emphasizing. The fundamental methodology of the proposed model can also be advantageous for simultaneous determination of major sugars in complex matrices other than fruit juices.