Improved 1,3-propanediol production with maintained physical conditions and optimized media composition: Validation with statistical and neural approach

Biochemical Engineering Journal

https://doi.org/10.1016/j.bej.2017.07.003

This work is aimed at assessing the use of response surface methodology (RSM) and artificial neural networks (ANNs) for modelling, and predicting, the optimum parameters for 1,3-Propanediol production by Lactobacillus brevis N1E9.3.3 from glycerol and glucose co-fermentation. A preliminary study of physical parameters was conducted using Plackett-Burman design to reduce the number of input variables up to seven; i) beef extract, ii) yeast extract, iii) MgSO4·7H2O, iv) MnSO4·H2O, v) vitamin B12, vi) glycerol and vii) glucose. The traditional RSM models were improved by ANN models between a 54.08% and 12.19% in terms of root mean square error (RMSE). This study suggested that RSM and ANN can be considered as effective tools to model and predict optimum parameters for 1,3-Propanediol production by L. brevis N1E9.3.3.

Dietary exposure and neurotoxicity of the environmental free and bound toxin β-N-methylamino-l-alanine

Food Research International, 100, 2017, Pages 1-13

https://doi.org/10.1016/j.foodres.2017.07.033

The growing evidence supporting a link between exposure to the naturally occurring toxin β-N-methylamino-l-alanine (BMAA) and progressive neurodegenerative diseases, has recently arisen the interest of the scientific community. Latest investigations suggest that dietary exposure to this algal toxin may have been largely underestimated. This paper reviews the state of the art regarding BMAA, with special attention paid to its neurotoxicity, its concentration levels in food, and human exposure. As for other environmental toxins, dietary intake is most likely the main route of exposure to BMAA for the general population. However, data concerning BMAA levels in foodstuffs are still scarce. It is concluded that further investigations on dietary intake and potential human health effects are clearly necessary to assess the risks to public health associated with BMAA exposure. Some critical remarks and recommendations on future research in this area are provided, which may help to identify approaches to reduce dietary BMAA exposure.

Chromatographic Fingerprinting with Multivariate Data Analysis for Detection and Quantification of Apricot Kernel in Almond Powder

Food Analytical Methods

https://doi.org/10.1007/s12161-017-0903-5

Adulteration of almond powder samples with apricot kernel was solved by gas chromatographic fatty acid fingerprinting combined with multivariate data analysis methods (principal component analysis (PCA), PCA-linear discriminant analysis (PCA-LDA), partial least squares (PLS), and LS support vector machine (LS-SVM). Different almond and apricot kernel samples were mixed at concentrations ranging from 10 to 90% w/w. PCA and PCA-LDA methods were applied for the classification of almonds, apricot kernels, and mixtures. PLS and LS-SVM were used for the quantification of adulteration ratios of almond. Models were developed using a training data set and evaluated using a validation data set. The root mean square error of prediction (RMSEP) and coefficient of determination (R2) of validation data set obtained for PLS and LS-SVM were 5.01, 0.964 and 2.29, 0.995, respectively. The results showed that the methods can be applied as an effective and feasible method for testing almond adulteration.

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