Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks

Table grape browning is a complex physiological disorder that occurs during cold storage. There is a need to investigate novel and innovative ways to manage the problem that hampers the progressive and sustainable growth of table grape industries. Given the complex nature of the browning phenomenon, techniques such as near-infrared (NIR) spectroscopy can be utilized for the non-destructive classification of different browning phenotypes. In this study, NIR coupled with partial least squares discriminant analysis (PLS-DA) and artificial neural networks (ANN) were used to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact 'Regal Seedless' table grape bunches that were cold-stored over different periods. Friction browning appears as circular spots close to the pedicel area that are formed when table grape berries move against each other, and chocolate browning appears as discoloration, which originates mostly from the stylar-end of the berry, although the whole berry may appear brown in severe instances. The evaluation of the models constructed using PLS-DA was done using the classification error rate (CER), specificity, and sensitivity and for the models constructed using ANN, the kappa score was used. The CER for chocolate browning (25%) was better than that of friction browning (46%) for weeks 3 and 4 for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 for friction browning were not as good as that of chocolate browning. With ANN, the kappa score was tested to classify table grape bunches as clear or having chocolate browning or friction browning and showed that chocolate browning could be classified with a strong agreement during weeks 3 and 4 and weeks 5 and 6 and that friction browning could be classified with a moderate agreement during weeks 3 and 4. These results open up new possibilities for the development of quality checks of packed table grape bunches before export. This has a significant impact on the table grape industry for it will now be possible to evaluate bunches non-destructively during packaging to determine the possibility of these browning types being present when reaching the export market.

Measuring Internal Maturity Parameters Contactless on Intact Table Grape Bunches Using NIR Spectroscopy

The determination of internal maturity parameters of table grape is usually done destructively using manual methods that are time-consuming. The possibility was investigated to determine whether key fruit attributes, namely, total soluble solids (TSS); titratable acidity (TA), TSS/TA, pH, and BrimA (TSS - k x TA) could be determined on intact table grape bunches using Fourier transform near-infrared (FT-NIR) spectroscopy and a contactless measurement mode. Partial Least Squares (PLS) regression models were developed for the maturity and sensory quality parameters using grapes obtained from two consecutive harvest seasons. Statistical indicators used to evaluate the models were the number of latent variables (LVs) used to build the model, the prediction correlation coefficient (R²p) and root mean square error of prediction (RMSEP). For the respective parameters TSS, TA, TSS/TA, pH, and BrimA, the LVs were 21, 23, 5, 7, and 24, the R²p = 0.71, 0.33, 0.57, 0.28, and 0.77, and the RMSEP = 1.52, 1.09, 7.83, 0.14, and 1.80. TSS performed best when moving smoothing windows (MSW) + multiplicative scatter correction (MSC) was used as spectral pre-processing technique, TA with standard normal variate (SNV), TSS/TA with Savitzky-Golay first derivative (SG1d), pH with SG1d, and BrimA with MSC. This study provides the first steps towards a completely nondestructive and contactless determination of internal maturity parameters of intact table grape bunches.

Modelling the sensory space of varietal wines: Mining of large, unstructured text data and visualisation of style patterns

The increasingly large volumes of publicly available sensory descriptions of wine raises the question whether this source of data can be mined to extract meaningful domain-specific information about the sensory properties of wine. We introduce a novel application of formal concept lattices, in combination with traditional statistical tests, to visualise the sensory attributes of a big data set of some 7,000 Chenin blanc and Sauvignon blanc wines. Complexity was identified as an important driver of style in hereto uncharacterised Chenin blanc, and the sensory cues for specific styles were identified. This is the first study to apply these methods for the purpose of identifying styles within varietal wines. More generally, our interactive data visualisation and mining driven approach opens up new investigations towards better understanding of the complex field of sensory science.

Investigation of the volatile composition of pinotage wines fermented with different malolactic starter cultures using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF-MS)

Headspace solid phase microextraction in combination with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (HS-SPME-GC × GC-TOF-MS) was used for the detailed investigation of the impact of malolactic fermentation (MLF) using three commercial Oenococcus oeni strains on the volatile composition of Vitis vinifera cv. Pinotage wines. GC × GC allowed the identification of 115 volatile compounds, including both major constituents and trace-level compounds, in a single analysis. A number of compounds differing in mean concentration levels between the control wines and those fermented with different starter cultures were shown for the first time to be influenced by MLF and/or the bacterial strain. Principal component analysis (PCA) provided excellent separation between the wines fermented with different MLF starter cultures and the control wine. Significantly different levels for some volatile compounds in wines fermented with one of the LAB starter cultures could be indicative of metabolic differences of this strain.

Analysis of volatile profiles of fermenting grape must by headspace solid-phase dynamic extraction coupled with gas chromatography-mass spectrometry (HS-SPDE GC-MS): novel application to investigate problem fermentations

The occurrence of stuck and sluggish wine fermentations is a persisting problem in the wine industry worldwide. This study illustrates the suitability of headspace solid-phase dynamic extraction coupled with gas chromatography-mass spectrometry (HS-SPDE GC-MS) for wine analysis and the subsequent application to discriminate between control and problem fermentations using partial least-squares discriminant analysis (PLS-DA) models. The specific analytical technique is relatively new and has not yet to the authors' knowledge been evaluated for the analysis of wine within this context of problem fermentations. HS-SPDE GC-MS was used to determine 68 volatile compounds (higher alcohols, fatty acids, esters, and carbonyl compounds) in 94 monovarietal fermenting must samples consisting of 56 red and 38 white cultivars. PLS-DA models showed the potential to discriminate between control and problem fermentations using corrected peak area headspace data for the 68 analytes. This possibility to discriminate between problem and control fermentations with only the headspace data could possibly be applied for the prediction of problem fermentations in future studies and to better understand the chemical causes of problem fermentations.

Characterization of selected South African young cultivar wines using FTMIR spectroscopy, gas chromatography, and multivariate data analysis

The powerful combination of analytical chemistry and chemometrics and its application to wine analysis provide a way to gain knowledge and insight into the inherent chemical composition of wine and to objectively distinguish between wines. Extensive research programs are focused on the chemical characterization of wine to establish industry benchmarks and authentication systems. The aim of this study was to investigate the volatile composition and mid-infrared spectroscopic profiles of South African young cultivar wines with chemometrics to identify compositional trends and to distinguish between the different cultivars. Data were generated by gas chromatography and FTMIR spectroscopy and investigated by using analysis of variance (ANOVA), principal component analysis (PCA), and linear discriminant analysis (LDA). Significant differences were found in the volatile composition of the cultivar wines, with marked similarities in the composition of Pinotage wines and white wines, specifically for 2-phenylethanol, butyric acid, ethyl acetate, isoamyl acetate, isoamyl alcohol, and isobutyric acid. Of the 26 compounds that were analyzed, 14 had odor activity values of >1. The volatile composition and FTMIR spectra both contributed to the differentiation between the cultivar wines. The best discrimination model between the white wines was based on FTMIR spectra (98.3% correct classification), whereas a combination of spectra and volatile compounds (86.8% correct classification) was best to discriminate between the red wine cultivars.

Rapid screening of the fermentation profiles of wine yeasts by Fourier transform infrared spectroscopy

A rapid screening method for the evaluation of the major fermentation products of Saccharomyces wine yeasts was developed using Fourier transform infrared spectroscopy and principal component factor analysis. Calibration equations for the quantification of volatile acidity, glycerol, ethanol, reducing sugar and glucose concentrations in fermented Chenin blanc and synthetic musts were derived from the Fourier transform infrared spectra of small-scale fermentations. The accuracy of quantification of volatile acidity in both Chenin blanc and synthetic must was excellent, and the standard error of prediction was 0.07 g l(-1) and 0.08 g l(-1), respectively. The respective standard error of prediction in Chenin blanc and synthetic musts for ethanol was 0.32% v/v and 0.31% v/v, for glycerol was 0.38 g l(-1) and 0.32 g l(-1), for reducing sugar in Chenin blanc must was 0.56 g l(-1) and for glucose in synthetic must was 0.39 g l(-1). These values were in agreement with the accuracy obtained by the respective reference methods used for the quantification of the components. The screening method was applied to quantify the fermentation products of glycerol-overproducing hybrid yeasts and commercial wine yeasts. Principal component factor analysis of the fermentation data facilitated an overall comparison of the fermentation profiles (in terms of the components tested) of the strains. The potential of Fourier transform infrared spectroscopy as a tool to rapidly screen the fermentative properties of wine yeasts and to speed up the evaluation processes in the initial stages of yeast strain development programs is shown.