Empirijski kinetički model hidrolize proteina belanceta pretretiranih ultrazvučnim talasima visoke frekvencije

The subject of this paper was the examination of the influence of enzyme and substrate concentrations and temperature on the initial reaction rate of hydrolysis of the egg white catalyzed with Alcalase 2.4 L (Protease from Bacillus licheniformis). The main objective of this paper was investigating the effect of the ultrasound on the reaction rate of hydrolysis and modeling of enzymatic process of hydrolysis of the egg white protein in order to develop the process and design the enzyme reactor. The substrate in this reaction was 10 % w/w solution of egg white pretreated with ultrasound waves the frequency of 35 kHz during 30 min. Proper kinetic model with substrate inhibition and the enzyme inactivation were applied to the results and good congruence between model and experimental data was achieved. The calculated kinetic constants indicate that the ultrasonic pretreatment causes an increase in the degree of hydrolysis of the enzyme reaction.U ovom radu ispitivan je uticaj koncentacije enzima, supstrata i temperature na početnu brzinu reakcije hidrolize proteina belanceta katalizovane Alkalazom 2,4 L (proteaza iz Bacillus licheniformis). Glavni cilj ovog istraživanja bio je ispitivanje uticaja ultrazvučnih talasa na brzinu reakcije hidrolize, kao i modelovanje enzimskog procesa hidrolize proteina belanceta u cilju dobijanja projektnih jednačina neophodnih za projektovanje i dizajn enzimskog reaktora. Kao supstrat korišćen je 10 % w/w rastvor belanceta prethodno tretiran ultrazvučnim talasima frekvencije 35 kHz u toku 30 minuta. Dobijeni eksperimentalni rezultati modelovani su kinetičkim modelom koji uzima u obzir inhibiciju supstratom i deaktivaciju enzima. Predloženi kinetički model dao je dobro slaganje sa dobijenim eksperimentalnim rezultatima. Izračunate kinetičke konstante ukazuju da pretretman ultrazvučnim talasima dovodi do povećanja stepena hidrolize

Antioksidativna aktivnost hidrolizata belanceta i njegovih frakcija dobijenih membranskom ultrafiltraciom

Bioactive peptides with different biological properties can be obtained by egg white proteins hydrolysis. In this study we used the high intensity ultrasound pretreatment of the egg white proteins that were then hydrolyzed by different types of proteases in the one-step and two-step procedure. Membrane ultrafiltration into molecular size of 1 kDa, 10 kDa and 30 kDa was used to separated the obtained hydrolzyates and antioxidative activities of obtain fractions were studied. Between fractions less than 1 kDa, containing bioactive peptides, the ultrasound pretreated hydrolyzate obtained by using alcalaseflevorzyme in a two-stage procedure has shown the highest antioxidant activity.Hidrolizom proteina belanceta dobijaju se bioaktivni peptidi koji imaju različita biološka svojstva. U ovom radu korišćena je tehnologija ultrazvuka visokog intenziteta kao pretretman pripreme proteina belanceta koji su zatim hidrolizovani različitim vrstama proteaza u jednostepenom i dvostepenom postupku. Dobijeni hidrolizati su razdvojeni korišćenjem ultrafiltracionih membrana promera 1, 10 i 30 kDa i dobijenim frakcijama je ispitana antioksidativna aktivnost. Među frakcijama veličine manje od 1 kDa koje sadrže bioaktivne peptide, najveću antioksidativnu aktivnost je pokazao ultrazvučno pretretiran hidrolizat nastao delovanjem alkalaza-flevorzima u dvostepenom enzimskom postupku

Big Data for weed control and crop protection

Farmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide-resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established

Big Data for weed control and crop protection

Farmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide-resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established