Using vibrational infrared biomolecular spectroscopy to detect heat-induced changes of molecular structure in relation to nutrient availability of prairie whole oat grains on a molecular basis

Background： To our knowledge, there is little study on the interaction between nutrient availability and molecular structure changes induced by different processing methods in dairy cattle. The objective of this study was to investigate the effect of heat processing methods on interaction between nutrient availability and molecular structure in terms of functional groups that are related to protein and starch inherent structure of oat grains with two continued years and three replication of each year.Method： The oat grains were kept as raw（control） or heated in an air-draft oven（dry roasting： DO） at 120 °C for 60 min and under microwave irradiation（MIO） for 6 min. The molecular structure features were revealed by vibrational infrared molecular spectroscopy.Results： The results showed that rumen degradability of dry matter, protein and starch was significantly lower（P 〈0.05） for MIO compared to control and DO treatments. A higher protein α-helix to β-sheet and a lower amide I to starch area ratio were observed for MIO compared to DO and/or raw treatment. A negative correlation（-0.99, P 〈 0.01）was observed between α-helix or amide I to starch area ratio and dry matter. A positive correlation（0.99, P 〈 0.01） was found between protein β-sheet and crude protein.Conclusion： The results reveal that oat grains are more sensitive to microwave irradiation than dry heating in terms of protein and starch molecular profile and nutrient availability in ruminants.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.