Direct, simultaneous quantification of fructooligosaccharides by FT-MIR ATR spectroscopy and chemometrics for rapid identification of superior, engineered ß-fructofuranosidases.

Fructooligosaccharides (FOS) are popular components of functional foods produced by the enzymatic transfer of fructose units to sucrose. Improving ß-fructofuranosidase traits by protein engineering is restricted by the absence of a rapid, direct screening method for the fructooligosaccharide products produced by enzyme variants. The use of standard high-performance liquid chromatography (HPLC) methods involves time-consuming sample preparation and chromatographic and data analysis steps. To overcome these limitations, this work presents a rapid method for screening ß-fructofuranosidase variant libraries using Fourier transform mid-infrared attenuated total reflectance (FT-MIR ATR) spectroscopy and calibration using partial least squares (PLS) regression. The method offers notable improvements in terms of sample analysis times and cost, with the added benefit of the absence of toxic eluents. Wavenumber interval selection methods were tested to develop optimised PLS regression models that were successfully applied to quantify of glucose, fructose, sucrose, 1-kestose and nystose, the substrates and products of ß-fructofuranosidase activity. To the best of our knowledge, this is the first report on the use of infrared spectroscopy and PLS calibration for the quantification of 1-kestose and nystose. Independent test set-validated results indicated that optimal wavenumber selection by interval PLS (iPLS) served to provide the best models for all sugars, bar glucose. Application of this screening method will facilitate the engineering of ß-fructofuranosidases and other glycosyltransferase enzymes by random mutagenesis strategies, as it provides, for the first time, a rapid, direct assay for transferase products that may be adapted to a high-throughput set-up.

Comparative metabolic profiling to investigate the contribution of O. oeni MLF starter cultures to red wine composition.

In this research work we investigated changes in volatile aroma composition associated with four commercial Oenococcus oeni malolactic fermentation (MLF) starter cultures in South African Shiraz and Pinotage red wines. A control wine in which MLF was suppressed was included. The MLF progress was monitored by use of infrared spectroscopy. Gas chromatographic analysis and capillary electrophoresis were used to evaluate the volatile aroma composition and organic acid profiles, respectively. Significant strain-specific variations were observed in the degradation of citric acid and production of lactic acid during MLF. Subsequently, compounds directly and indirectly resulting from citric acid metabolism, namely diacetyl, acetic acid, acetoin, and ethyl lactate, were also affected depending on the bacterial strain used for MLF. Bacterial metabolic activity increased concentrations of the higher alcohols, fatty acids, and total esters, with a larger increase in ethyl esters than in acetate esters. Ethyl lactate, diethyl succinate, ethyl octanoate, ethyl 2-methylpropanoate, and ethyl propionate concentrations were increased by MLF. In contrast, levels of hexyl acetate, isoamyl acetate, 2-phenylethyl acetate, and ethyl acetate were reduced or remained unchanged, depending on the strain and cultivar evaluated. Formation of ethyl butyrate, ethyl propionate, ethyl 2-methylbutryate, and ethyl isovalerate was related to specific bacterial strains used, indicating possible differences in esterase activity. A strain-specific tendency to reduce total aldehyde concentrations was found at the completion of MLF, although further investigation is needed in this regard. This study provided insight into metabolism in O. oeni starter cultures during MLF in red wine.

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.

Instrumental measurement of bitter taste in red wine using an electronic tongue.

An electronic tongue (ET) based on potentiometric chemical sensors was assessed as a rapid tool for the quantification of bitterness in red wines. A set of 39 single cultivar Pinotage wines comprising 13 samples with medium to high bitterness was obtained from the producers in West Cape, South Africa. Samples were analysed with respect to a set of routine wine parameters and major phenolic compounds using Fourier transform infrared-multiple internal reflection spectroscopy (WineScan) and high-performance liquid chromatography, respectively. A trained sensory panel assessed the bitterness intensity of 15 wines, 13 of which had a bitter taste of medium to high intensity. Thirty-one wine samples including seven bitter-tasting ones were measured by the ET. Influence of the chemical composition of wine on the occurrence of the bitter taste was evaluated using one-way analysis of variance. It was found that bitter-tasting wines had higher concentrations of phenolic compounds (catechin, epicatechin, gallic and caffeic acids and quercetin) than non-bitter wines. Sensitivity of the sensors of the array to the phenolic compounds related to the bitterness was studied at different pH levels. Sensors displayed sensitivity to all studied compounds at pH 7, but only to quercetin at pH 3.5. Based on these findings, the pH of wine was adjusted to 7 prior to measurements. Calibration models for classification of wine samples according to the presence of the bitter taste and quantification of the bitterness intensity were calculated by partial least squares-discriminant analysis (PLS-DA) regression. Statistical significance of the classification results was confirmed by the permutation test. Both ET and chemical analysis data could discriminate between bitter and control wines with the correct classification rates of 94% and 91%, respectively. Prediction of the bitterness intensity with good accuracy (root mean square error of 2 and mean relative error of 6% in validation) was possible only using ET data.

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.

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.

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.

Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples.

Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content < 30 g/L, alcohol > 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.

The use of Vis/NIRS and chemometric analysis to predict fruit defects and postharvest behaviour of 'Nules Clementine' mandarin fruit.

The use of chemometrics to analyse Vis/NIRS signal collected from intact 'Nules Clementine' mandarin fruit at harvest, to predict the rind physico-chemical profile after eight weeks postharvest was explored. Vis/NIRS signals of 150 fruit were obtained immediately after harvest. Reference data on the rind were obtained after eight-week storage, including colour index (CI), rind dry matter (DM), and concentration of sugars. Partial least squares (PLS) regression was applied to develop models. Principal component analysis (PCA) followed by PLS-discriminant analysis (PLS-DA) were used to classify fruit according to canopy position. Optimal PLS model performances for DM, sucrose, glucose and fructose were obtained using multiple scatter correction pre-processing, showing respective residual predictive deviation (RPD) of 3.39, 1.75, 2.19 and 3.08. Clusters of sample distribution in the PCA and PLS-DA models based on canopy position were obtained. The results demonstrated the potential applications of Vis/NIRS to predict postharvest behaviour of mandarin fruit.