ABSTRACT
The objective was to evaluate the technological processing (protection strategies and storage conditions) influence on viability, on probiotic properties and adsorbent aflatoxin B1 capacity of S. boulardii RC009. Also, the yeast biological safety was evaluated. Lyophilisation (DL) and encapsulation â+ âlyophilisation (EL) were conducted. Yeast protected with maltodextrin (M) or WPC stored at 4 â°C reduced 1 and 2 log the viability, respectively. Yeast protected with M stored at 25 â°C reduced 1 log after 70 âd; with WPC the viability significantly reduced 3 log after 30 âd. Technological processing improved the coaggregation's capacity with pathogens and DL process allowed the greatest AFB1 adsorption. S. boulardii 106 âcells/mL were no toxic to Vero cells (pË0.05). Saccharomyces boulardii RC009 protected with M or WPC maintained viability after technological processing. It possesses a great capacity for AFB1 adsorption and probiotic properties and could be considered a candidate with proven safety for functional food products development.
ABSTRACT
The spatial recognition feature of near infrared hyperspectral imaging (HSI-NIR) makes it potentially suitable for Fusarium and deoxynivalenol (DON) management in single kernels to break with heterogeneity of contamination in wheat batches to move towards individual kernel sorting and provide more quick, environmental-friendly and non-destructive analysis than wet-chemistry techniques. The aim of this study was to standardize HSI-NIR for individual kernel analysis of Fusarium damage and DON presence, to predict the level of contamination and classify grains according to the EU maximum limit (1250 µg/kg). Visual inspection on Fusarium infection symptoms and HPLC analysis for DON determination were used as reference methods. The kernels were scanned in both crease-up and crease-down position and for different image captures. The spectra were pretreated by Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), 1st and 2nd derivatives and normalisation, and they were evaluated also by removing spectral tails. The best fitted predictive model was on SNV pretreated data (R2 0.88 and RMSECV 4.8 mg/kg) in which 7 characteristic wavelengths were used. Linear Discriminant Analysis (LDA), Naïve Bayes and K-nearest Neighbours models classified with 100% of accuracy 1st derivative and SNV pretreated spectra according to symptomatology and with 98.9 and 98.4% of correctness 1st derivative and SNV spectra, respectively. The starting point results are encouraging for future investigations on HSI-NIR technique application to Fusarium and DON management in single wheat kernels to overcome their contamination heterogeneity.
