A New Image Analysis Based Method for Measuring Electrospun Nanofiber Diameter

In this paper, a new image analysis based method for electrospun nanofiber diameter measurement has been presented. The method was tested by a simulated image with known characteristics and a real web. Mean (M) and standard deviation (STD) of fiber diameter obtained using this method for the simulated image were 15.02 and 4.80 pixels respectively, compared to the true values of 15.35 and 4.47 pixels. For the real web, applying the method resulted in M and STD of 324 and 50.4 nm which are extremely close to the values of 319 and 42 nm obtained using manual method. The results show that this approach is successful in making fast, accurate automated measurements of electrospun fiber diameters

Predicting methionine and lysine contents in soybean meal and fish meal using a group method of data handling-type neural network

8 páginas, 2 figuras, 2 tablas.Artificial neural network models offer an alternative to linear regression analysis for predicting the amino acid content of feeds from their chemical composition. A group method of data handling-type neural network (GMDH-type NN), with an evolutionary method of genetic algorithm, was used to predict methionine (Met) and lysine (Lys) contents of soybean meal (SBM) and fish meal (FM) from their proximate analyses (i.e. crude protein, crude fat, crude fibre, ash and moisture). A data set with 119 data lines for Met and 116 lines for Lys was used to develop GMDH-type NN models with two hidden layers. The data lines were divided into two groups to produce training and validation sets. The data sets were imported into the GEvoM software for training the networks. The predictive capability of the constructed models was evaluated by their abilities to estimate the validation data sets accurately. A quantitative examination of goodness of fit for the predictive models was made using a number of precision, concordance and bias statistics. The statistical performance of the models developed revealed close agreement between observed and predicted Met and Lys contents for SBM and FM. The results of this study clearly illustrate the validity of GMDH-type NN models to estimate accurately the amino acid content of poultry feed ingredients from their chemical composition. © 2015 INIA.Peer Reviewe

Predicting the metabolizable energy content of corn for ducks: A comparison of support vector regression with other methods

Support vector regression (SVR) is used in this study to develop models to estimate apparent metabolizable energy (AME), AME corrected for nitrogen (AMEn), true metabolizable energy (TME), and TME corrected for nitrogen (TMEn) contents of corn fed to ducks based on its chemical composition. Performance of the SVR models was assessed by comparing their results with those of artificial neural network (ANN) and multiple linear regression (MLR) models. The input variables to estimate metabolizable energy content (MJ kg-1) of corn were crude protein, ether extract, crude fibre, and ash (g kg-1). Goodness of fit of the models was examined using R2, mean square error, and bias. Based on these indices, the predictive performance of the SVR, ANN, and MLR models was acceptable. Comparison of models indicated that performance of SVR (in terms of R2) on the full data set (0.937 for AME, 0.954 for AMEn, 0.860 for TME, and 0.937 for TMEn) was better than that of ANN (0.907 for AME, 0.922 for AMEn, 0.744 for TME, and 0.920 for TMEn) and MLR (0.887 for AME, 0.903 for AMEn, 0.704 for TME, and 0.902 for TMEn). Similar findings were observed with the calibration and testing data sets. These results suggest SVR models are a promising tool for modelling the relationship between chemical composition and metabolizable energy of feedstuffs for poultry. Although from the present results the application of SVR models seems encouraging, the use of such models in other areas of animal nutrition needs to be evaluated.Funding, in part, was provided by the Canada Research Chairs program.Peer Reviewe

Garlic Powder as Blood Serum and Egg Yolk Cholesterol Lowering Agent

To assess the effect of supplying garlic powder (GAR) in the diet on blood serum and egg yolk cholesterol, maize-soybean diets supplemented with 0 (NC), 0.5 (GAR0.5), 1 (GAR1),1.5% (GAR1.5) as well as Tylosin (PC), were given to 200 hens as a completely randomized experiment throughout a 10 weeks production period. Blood samples for serum cholesterol determination were collected from wing vein of individual hen with two weeks intervals, and egg were collected once a week and subjected to cholesterol assay. Supplementation of diets with garlic powder and Tylosin had significant (p<0.01) effects both on serum and egg yolk cholesterol. The lowest serum cholesterl was obtained with GAR1, while the NC gave the highest level of cholesterol. A similar trend was observed with yolk cholesterol, in which GAR1 and NC were the treatments with the lowest and highest cholesterol. The results showed that inclusion of garlic powder significantly (p<0.01) decreased yolk weight, though there were no differences between the different level of the garlic powder. The correlation between yolk and serum cholesterol was negative and low (r=-0.09, p<0.05). In conclusion the results of this study clearly demonstrated that, there is, considerable advantages in using garlic powder rather than chemical reagents in hen rations and there would be even greater advantage both in results and in cost for either poultry industry and consumers, if garlic powder fed to the hens

Electrospun nanofibers and image analysis

In the first part of this chapter electrospinning process of nanofiber is introduced. In the second part, a new image analysis technique for measuring the diameters of electrospun nanofibers is developed