Elsevier

Carbohydrate Research

Volume 336, Issue 1, November 2001, Pages 63-74
Carbohydrate Research

Rapid analysis of sugars in fruit juices by FT-NIR spectroscopy

https://doi.org/10.1016/S0008-6215(01)00244-0Get rights and content

Abstract

A simple analytical procedure using FT-NIR and multivariate techniques for the rapid determination of individual sugars in fruit juices was evaluated. Different NIR detection devices and sample preparation methods were tested by using model solutions to determine their analytical performance. Aqueous solutions of sugar mixtures (glucose, fructose, and sucrose; 0–8% w/v) were used to develop a calibration model. Direct measurements were made by transflection using a reflectance accessory, by transmittance using a 0.5-mm cell, and by reflectance using a fiberglass paper filter. FT-NIR spectral data were transformed to the second derivative. Partial least-squares regression (PLSR) was used to create calibration models that were cross-validated (leave-one-out approach). The prediction ability of the models was evaluated on fruit juices and compared with HPLC and standard enzymatic techniques. The PLSR loading spectra showed characteristic absorption bands for the different sugars. Models generated from transmittance spectra gave the best performance with standard error of prediction (SEP) <0.10% and R2 of 99.9% that accurately and precisely predicted the sugar levels in juices, whereas lower precision was obtained with models generated from reflectance spectra. FT-NIR spectroscopy allowed for the rapid (∼3 min analysis time), accurate and non-destructive analysis of sugars in juices and could be applied in quality control of beverages or to monitor for adulteration or contamination.

Simple partial least-squares regression FT-NIR models generated from transmittance spectra reproducibly and precisely predicted the individual sugar content in different juice matrices using an external calibration prepared with sugar standard solutions. The technique allowed for the rapid, accurate, non-destructive and simultaneous analysis of sugars in juices and could be applied in quality control of beverages or to monitor for adulteration and contamination.

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Introduction

The determination of individual sugar content in fresh fruits and vegetables and their juices is an important chemical analysis carried out to evaluate quality and to detect adulteration or contamination. Analytical techniques such as liquid chromatography using different separation techniques (reverse-phase, ion-exclusion, ion chromatography) and detectors (refractive index, UV absorption, amperometric), thin-layer chromatography, and gas chromatography have been commonly used for qualitative and quantitative analyses of fruit juices.1 While chromatographic techniques are very accurate, they are time-consuming and require extensive sample preparation. Sugar analyses carried out by enzymatic methods are specific, rapid and reproducible,2 however the analyses require single determinations for each compound, which results in time-consuming procedures and high cost of analysis.3

Near-infrared spectroscopy (NIR) is a non-destructive, fast and accurate technique for measurement of chemical components based on overtone and combination bands of specific functional groups.4., 5. The NIR bands are 10–100 times less intense than the corresponding mid-infrared fundamental bands. This enables direct analysis of samples that are highly absorbing and strongly light scattering without dilution or extensive sample preparation.5., 6. Nevertheless, NIR measurement of aqueous systems has been difficult because of interference from broad vibrational bands of water.7 The application of NIR transmittance analysis of raw products such as sugarcane and beet juice/syrups for pol (sucrose) and °Brix is a conventional analytical technique for quality monitoring in the sugar industry.8 Lanza and Li9 reported the application of NIR spectroscopy for the direct analysis of total sugar content in fruit juices. However, they concluded that it was not possible to determine individual sugars with acceptable accuracy or precision by using the transmission mode with a quartz cell pathlength of 2.2 mm. Giangiacomo and Dull10 developed NIR models based on transmittance measurements that predicted individual sugars (sucrose, glucose, and fructose) in aqueous mixtures with a standard error of prediction of 0.35–0.69. Improved sensitivity and accuracy for the quantitative analysis of individual sugars in juices have been accomplished by placing the liquid sample on a fiberglass support, eliminating the water and measuring the dry extract by diffuse reflectance spectroscopy.11., 12.

Advances in Fourier transform NIR (FT-NIR) spectroscopic instrumentation and multivariate data analysis techniques have had significant impact in the determination of changes in food composition. FT-NIR improves spectra reproducibility and wavenumber precision,13 which can minimize the effects of solvent interference. Multivariate statistics have provided chemometric tools such as principal components analysis (PCA) and partial least-squares regression (PLSR) methods that are able to model relationships between large numbers of dependant variables having extremely complex variations (such as NIR spectra) and independent variables (such as chemical concentrations).14 PLSR has been particularly successful in developing multivariate calibration models for NIR spectroscopy because it uses the concentration information (Y-variable) actively in determining how the regression factors are computed from the spectral data matrix (X), reducing the impact of irrelevant X-variations in the calibration model.15., 16. This capability provides a more information-rich data set of reduced dimensionality and eliminates data noise that results in more accurate and reproducible calibration models.17

The objective of this study was to develop methodology for the rapid identification and quantification of individual sugars in fruit juices using FT-NIR spectroscopy and multivariate methods, based on a calibration set of aqueous standard solutions. Different NIR sampling techniques that included reflectance, transflection and transmittance were tested by using model solutions to determine their analytical performance.

Section snippets

Sample preparation

Analytical grade d-glucose, d-fructose and sucrose (Sigma, St. Louis, MO) were used to prepare 20 g/100 mL stock solutions. The calibration set (n=60) was composed of solutions containing the tertiary mixtures of sugars at concentration levels of 0, 2, 4, and 8 g/100 mL. The ranges were chosen to evaluate the adequacy of the method for application to fruit juice samples.18 The prediction capabilities of the PLSR-NIR model were evaluated on commercial apple (four brands) and orange (two brands)

Development of the PLS calibration model for sugars in aqueous systems

The FT-NIR spectra of sugar solutions obtained by transmittance, transflectance and reflectance are presented in Fig. 1. The strong water absorption peaks centered at 6900 cm−1 (first OH overtone) and 5200 cm−1 (OH combination) overlapped the analyte spectral signal for samples measured by transmittance and transflectance techniques. Elimination of the solvent and analysis of the dried sugar extract on glass microfibre paper by diffuse reflectance allowed the extraction of several spectral

Conclusions

The application of FT-NIR spectroscopy and PLSR multivariate techniques allowed for the simultaneous quantitation of individual sugars in juices. The PLSR-FT-NIR models generated from transmittance spectra reproducibly and precisely predicted the individual sugar content in different juice matrices, including clarified apple juice (scatter-free) and orange juice (which gives some scattering properties) using a simple external calibration prepared with sugar standard solutions. FT-NIR

Acknowledgements

This project was funded by the Technical Support Working Group through the Department of the Army with BFRC number DAAD05-98-R0548. We express our appreciation to the Technical Support Working Group for sponsoring this research effort.

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