Effects of Supplemental Liquid DL-methionine Hydroxy Analog Free Acid in Diet on Growth Performance and Gastrointestinal Functions of Piglets

This study was conducted to determine the effect of dietary supplementation of liquid DL-methionine hydroxy analog free acid (DL-MHA) on growth performance and gastrointestinal conditions of piglets. One hundred and eighty crossbred barrow piglets (Large White×Landrace, body weight: 12.48±0.33 kg) were divided into three groups with ten replications of six piglets each. Piglets received DL-MHA in diet at a concentration of 0 (control group), 0.15%, or 0.24%. The results indicated that increasing the standardized ileal digestible (SID) of sulfur amino acids (SAA) to lysine (SID SAA:Lys) ratio by supplementation of DL-MHA tended to increase (quadratic; p<0.10) weight gain and ADG, and showed slightly greater (linear; p<0.10) gain:feed ratio. The pH in the diet and cecum linearly decreased (p<0.01), whereas pH in colon had a quadratic response (p<0.01) with increasing supplementation of DL-MHA. By greater supplementation of DL-MHA, the population of Lactobacillus spp. in rectum was likely to increase (quadratic; p<0.10), but Escherichia coli population in the diet was reduced (quadratic; p<0.05). Acetic acid concentration and total short-chain fatty acids in cecum linearly increased (p<0.05), whereas valeric acid in cecum quadratically increased (p<0.05) with increasing DL-MHA levels. Moreover, the villous height of the jejunum quadratically increased (p<0.01) as the supplementation of DL-MHA was increased. It is concluded that the addition of DL-MHA in diet improved the growth performance and the morphology of gastrointestinal tract of piglets

Comparison of linear multivariable, partial least square regression, and artificial neural network analyses to study the effect of different parameters on anode properties

Carbon anodes constitute a substantial part of the cost during the electrolytic production of aluminum. The industry tries to minimize the consumption of anodes by improving their quality. Therefore, a clear understanding of the impact of the quality of raw materials as well as process parameters on anode properties is important. The plants have a large collection of data, which is complex and difficult to analyze using conventional methods. In this article, linear multivariable (LMA), partial least square regression (PLS), and artificial neural network (ANN) analyses are presented and compared as tools to predict the influence of different parameters on anode properties. Published laboratory data have been processed using Matlab software to carry out the analyses. The results clearly show that ANN is the best tool for prediction purposes. Unlike other methods, ANN can handle nonlinear complex relations even if a well-defined relationship is not available