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The objective of this study was to determine the effects on quality of incorporating raspberry and cranberry pomaces into American-style muffins prepared under various baking conditions. The different baking conditions did not affect the texture or microstructure of the control muffins. The enhanced samples baked at 140 °C for 30 min were characterized by a harder texture than the control muffins and by a distributed protein matrix and distorted starch granules, while those baked at 240 °C for 15 min had a moist texture and showed incomplete starch gelatinization. The mean percent recovery of ellagic acid, flavonols, tocopherols, tocotrienols, and anthocyanins after baking were 156, 53, 48, 43, and 22 %, respectively. Lower baking temperature was better for ellagic acid and tocotrienols, but worse for flavonols, tocopherols, and anthocyanins. It seems that, for the enhanced samples, the intermediate baking conditions (180 °C for 20 min) guarantee the best microstructure and texture and the appropriate retention of phytochemicals in muffins.Electronic supplementary materialThe online version of this article (doi:10.1007/s11130-016-0539-4) contains supplementary material, which is available to authorized users.

Abstract:The results of anaerobic digestion (AD) of buttermilk (BM) and cheese whey (CW) with a digested sewage sludge as inoculum is described. The substrate/inoculum mixtures were prepared using 10% buttermilk and 15% cheese whey. The essential parameters of the materials were described, including: total solids (TS), volatile solids (VS), pH, conductivity, C/N ratio (the quantitative ratio of organic carbon (C) to nitrogen (N)), alkalinity, chemical oxygen demand (COD). The potential directions of biodegradation of the organic waste types, as used in this study, are also presented. Appropriate chemical reactions illustrate the substrates and products in each phase of anaerobic decomposition of the compounds that are present in buttermilk and cheese whey: lactic acid, lactose, fat, and casein. Moreover, the biogas and biomethane production rates are compared for the substrates used in the experiment. The results have shown that buttermilk in AD generates more biogas

Yield forecasting is a rational and scientific way of predicting future occurrences in agriculture—the level of production effects. Its main purpose is reducing the risk in the decision-making process affecting the yield in terms of quantity and quality. The aim of the following study was to generate a linear and non-linear model to forecast the tuber yield of three very early potato cultivars: Arielle, Riviera, and Viviana. In order to achieve the set goal of the study, data from the period 2010–2017 were collected, coming from official varietal experiments carried out in northern and northwestern Poland. The linear model has been created based on multiple linear regression analysis (MLR), while the non-linear model has been built using artificial neural networks (ANN). The created models can predict the yield of very early potato varieties on 20th June. Agronomic, phytophenological, and meteorological data were used to prepare the models, and the correctness of their operation was verified on the basis of separate sets of data not participating in the construction of the models. For the proper validation of the model, six forecast error metrics were used: i.e., global relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). As a result of the conducted analyses, the forecast error results for most models did not exceed 15% of MAPE. The predictive neural model NY1 was characterized by better values of quality measures and ex post forecast errors than the regression model RY1.