Prediction of Colour and pH in Grapes Using a Diode Array Spectrophotometer (400–1100 nm)

Abstract

One hundred and twenty thawed samples of homogenised and whole frozen grape berries were analysed using a diode array spectrophotometer (400-1100 nm) (CORONA 45VISNIR, Carl Zeiss, Germany). The spectra and the analytical data were used to develop partial least squares calibrations to predict colour and pH in both presentation modes to the instrument. The visible (vis: 400-700 nm), near infrared (NIR: 700-1100 nm) and vis + NIR (400-1100 nm) regions were used to perform the calibrations. Cross validation models for colour and pH on homogenised samples gave a coeffi cient of determination in validation (R 2 val ) and the root mean square error of cross-validation (RMSECV) of 0.92 and 0.07 mg g -1 for colour, and 0.90 and 0.04 for pH, respectively, using the vis region. Presentation of intact whole grape berries gave R 2 val and RMSECV values of 0.50 and 0.14 mg g -1 for colour, 0.60 and 0.08 for pH using the NIR region. It was concluded that homogenised samples gave the best calibration statistics. More research needs to be done to improve calibration on whole samples if the technology is to be used for rapid analysis for either on-farm or on-harvester applications.

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

“…However, nowadays, this large-scale analysis is very difficult to do, or even impossible, because the conventional methods for chemical analysis of grapes are invasive, costly and time-consuming since these methods require sample preparation. The present study describes a solution to this problem: a recent methodology (Cozzolino et al, 2004(Cozzolino et al, , 2006 which combines portable spectroscopy and machine learning algorithms to determine, without sample preparation, some grape enological parameters for estimating grape maturity, such as pH, sugars and anthocyanin content.…”

Evaluation of Least Squares Support Vector Machine Regression and other Multivariate Calibrations in Determination of Internal Attributes of Tea Beverages

“…Therefore, it is common for the evaluation of quality of beverages by measuring the SSC and pH content in the beverage industry. The lack of a simple, reliable, and nondestructive method for the determination of chemical composition in beverage has been one of the main obstacles for the development of fast quality control in both the food industry and commercial grade (Cozzolino et al 2004;Behrendt et al 2002). In recent years, the spectroscopic technique has been frequently adapted for the determination of the chemical composition of food (Osborne and Fearn 1986).…”

Effect of microwave heating on the near infrared spectra and on the prediction accuracy of chemical parameters in red grape homogenates

“…No separation or clustering was observed between samples according to the time of heating. The highest eigenvectors were observed in the VIS region around 576 nm related to anthocyanins and at 670 nm and 680 nm related to chlorophylls [9,[13][14][15][16]. This indicates that microwaving might have an effect on the absorbance of some of the grape pigments.…”

“…Previous studies have reported the effect of homogenisation and storage on both chemical composition and near infrared (NIR) spectra of red grapes [9,[12][13][14][15][16]. However, no reports have been found on the effect of microwaving on chemical composition of red grapes.…”