Effect of sequential fermentations and grape cultivars on volatile compounds and sensory profiles of Danish wines.

BACKGROUND: There has been an increasing interest in the use of selected non-Saccharomyces yeasts in co-culture with Saccharomyces cerevisiae. In this work, three non-Saccharomyces yeast strains (Metschnikowia viticola, Metschnikowia fructicola and Hanseniaspora uvarum) indigenously isolated in Denmark were used in sequential fermentations with S. cerevisiae on three cool-climate grape cultivars, Bolero, Rondo and Regent. During the fermentations, the yeast growth was determined as well as key oenological parameters, volatile compounds and sensory properties of finished rosé wines. RESULTS: The different non-Saccharomyces strains and cool-climate grape cultivars produced wines with a distinctive aromatic profile. A total of 67 volatile compounds were identified, including 43 esters, 14 alcohols, five acids, two ketones, a C13-norisoprenoid, a lactone and a sulfur compound. The use of M. viticola in sequential fermentation with S. cerevisiae resulted in richer berry and fruity flavours in wines. The sensory plot showed a more clear separation among wine samples by grape cultivars compared with yeast strains. CONCLUSION: Knowledge on the influence of indigenous non-Saccharomyces strains and grape cultivars on the flavour generation contributed to producing diverse wines in cool-climate wine regions. © 2017 Society of Chemical Industry.

A sampling approach for predicting the eating quality of apples using visible-near infrared spectroscopy.

BACKGROUND: Visible-near infrared spectroscopy remains a method of increasing interest as a fast alternative for the evaluation of fruit quality. The success of the method is assumed to be achieved by using large sets of samples to produce robust calibration models. In this study we used representative samples of an early and a late season apple cultivar to evaluate model robustness (in terms of prediction ability and error) on the soluble solids content (SSC) and acidity prediction, in the wavelength range 400-1100 nm. RESULTS: A total of 196 middle-early season and 219 late season apples (Malus domestica Borkh.) cvs 'Aroma' and 'Holsteiner Cox' samples were used to construct spectral models for SSC and acidity. Partial least squares (PLS), ridge regression (RR) and elastic net (EN) models were used to build prediction models. Furthermore, we compared three sub-sample arrangements for forming training and test sets ('smooth fractionator', by date of measurement after harvest and random). Using the 'smooth fractionator' sampling method, fewer spectral bands (26) and elastic net resulted in improved performance for SSC models of 'Aroma' apples, with a coefficient of variation CVSSC = 13%. The model showed consistently low errors and bias (PLS/EN: R(2) cal = 0.60/0.60; SEC = 0.88/0.88°Brix; Biascal = 0.00/0.00; R(2) val = 0.33/0.44; SEP = 1.14/1.03; Biasval = 0.04/0.03). However, the prediction acidity and for SSC (CV = 5%) of the late cultivar 'Holsteiner Cox' produced inferior results as compared with 'Aroma'. CONCLUSION: It was possible to construct local SSC and acidity calibration models for early season apple cultivars with CVs of SSC and acidity around 10%. The overall model performance of these data sets also depend on the proper selection of training and test sets. The 'smooth fractionator' protocol provided an objective method for obtaining training and test sets that capture the existing variability of the fruit samples for construction of visible-NIR prediction models. The implication is that by using such 'efficient' sampling methods for obtaining an initial sample of fruit that represents the variability of the population and for sub-sampling to form training and test sets it should be possible to use relatively small sample sizes to develop spectral predictions of fruit quality. Using feature selection and elastic net appears to improve the SSC model performance in terms of R(2), RMSECV and RMSEP for 'Aroma' apples.

Comprehensive analysis of chromatographic data by using PARAFAC2 and principal components analysis.

The most straightforward method to analyze an obtained GC-MS dataset is to integrate those peaks that can be identified by their MS profile and to perform a Principal Component Analysis (PCA). This procedure has some important drawbacks, like baseline drifts being scarcely considered or the fact that integration boundaries are not always well defined (long tails, co-eluted peaks, etc.). To improve the methodology, and therefore, the chromatographic data analysis, this work proposes the modeling of the raw dataset by using PARAFAC2 algorithm in selected areas of the GC profile and using the obtained well-resolved chromatographic profiles to develop a further PCA model. With this working method, not only the problems arising from instrumental artifacts are overcome, but also the detection of new analytes is achieved as well as better understanding of the studied dataset is obtained. As a positive consequence of using the proposed working method human time and work are saved. To exemplify this methodology the aroma profile of 36 apples being ripened were studied. The benefits of the proposed methodology (PARAFAC2+PCA) are shown in a practitioner perspective, being able to extrapolate the conclusions obtained here to other hyphenated chromatographic datasets.