A rapid qualitative and quantitative evaluation of grape berries at various stages of development using Fourier-transform infrared spectroscopy and multivariate data analysis.

Fourier transform (FT) near-infrared (NIR) and attenuated total reflection (ATR) FT mid-infrared (MIR) spectroscopy were used to qualitatively and quantitatively analyse Vitis vinifera L. cv Sauvignon blanc grape berries. FT-NIR and ATR FT-MIR spectroscopy, coupled with spectral preprocessing and multivariate data analysis (MVDA), provided reliable methods to qualitatively assess berry samples at five distinct developmental stages: green, pre-véraison, véraison, post-véraison and ripe (harvest), without any prior metabolite extraction. Compared to NIR spectra, MIR spectra provided more reliable discrimination between the berry samples from the different developmental stages. Interestingly, ATR FT-MIR spectra from fresh homogenized berry samples proved more discriminatory than spectra from frozen homogenized berry samples. Different developmental stages were discriminated by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). In order to generate partial least squares (PLS) models from the MIR/NIR spectral datasets; the major sugars (glucose and fructose) and organic acids (malic acid, succinic acid and tartaric acid) were separated and quantified by high performance liquid chromatography (HPLC) and the data used as a reference dataset. PLS regression was used to develop calibration models to predict the concentration of the major sugars and organic acids in the berry samples from different developmental stages. Our data show that infrared (IR) spectroscopy could provide a rapid, reproducible and cost-effective alternative to the chromatographic analysis of the sugar and organic acid composition of grape berries at various developmental stages, using small sample volumes and requiring limited sample preparation. This provides scope and support for the possible development of hand-held devices to assess quality parameters in field-settings in real-time and non-destructively using IR technologies.