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

Background

Sugars 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.

Results

Based 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.

Conclusions

This 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.