Optimiranje ekstrakcije polifenola iz okare

The objective of the present investigation is to examine okara, a suitable substrate for polyphenol extraction, and to develop a feasible eco-friendly process to maximize the yield of antioxidant phenolics. Box-Behnken design (BBD) based on response surface methodology (RSM) was employed to investigate the effect of temperature (°C), solvent fraction (%) and incubation time (min) on polyphenol extraction by using MINITAB 15 software. Acetone was used as solvent to extract the phenolic compounds possessing the antioxidant properties (DPPH radical scavenging activity, reducing power, and metal chelating activity). Extraction under the optimum conditions yielded total polyphenolic content of 1.16 mg/mL, DPPH radical scavenging activity of 61.07 %, metal chelating activity of 61.20 % and better reducing power. The effective model developed for antioxidant mining from okara under mild operational conditions can be a valuable technique for soybean-based food industry.Svrha je rada bila ispitati svojstva okare, supstrata za dobivanje polifenola, te razviti održivi, ekološki postupak izdvajanja maksimalne količine polifenola. Da bi se ispitao utjecaj temperature, udjela otapala i vremena inkubacije na ekstrakciju polifenola, upotrijebljen je Box-Behnken dizajn metodologije odzivnih površina uz pomoć softvera MINITAB 15. Upotrijebljen je aceton kao otapalo za ekstrakciju fenolnih spojeva, te su ispitana svojstva polifenola, i to: sposobnost uklanjanja DPPH radikala, reducirajuća snaga i aktivnost keliranja metala. Pri optimalnim je uvjetima dobiveno 1,16 mg/mL ukupnih polifenola, s aktivnošću uklanjanja DPPH radikala od 61,07 %; keliranja metala od 61,20 % i dobrom reducirajućom snagom. Razvijen je učinkoviti model izdvajanja antioksidativnih spojeva iz okare u umjerenim uvjetima procesa, što je važno u proizvodnji sojinih proizvoda

Optimiranje ekstrakcije polifenola iz okare

The objective of the present investigation is to examine okara, a suitable substrate for polyphenol extraction, and to develop a feasible eco-friendly process to maximize the yield of antioxidant phenolics. Box-Behnken design (BBD) based on response surface methodology (RSM) was employed to investigate the effect of temperature (°C), solvent fraction (%) and incubation time (min) on polyphenol extraction by using MINITAB 15 software. Acetone was used as solvent to extract the phenolic compounds possessing the antioxidant properties (DPPH radical scavenging activity, reducing power, and metal chelating activity). Extraction under the optimum conditions yielded total polyphenolic content of 1.16 mg/mL, DPPH radical scavenging activity of 61.07 %, metal chelating activity of 61.20 % and better reducing power. The effective model developed for antioxidant mining from okara under mild operational conditions can be a valuable technique for soybean-based food industry.Svrha je rada bila ispitati svojstva okare, supstrata za dobivanje polifenola, te razviti održivi, ekološki postupak izdvajanja maksimalne količine polifenola. Da bi se ispitao utjecaj temperature, udjela otapala i vremena inkubacije na ekstrakciju polifenola, upotrijebljen je Box-Behnken dizajn metodologije odzivnih površina uz pomoć softvera MINITAB 15. Upotrijebljen je aceton kao otapalo za ekstrakciju fenolnih spojeva, te su ispitana svojstva polifenola, i to: sposobnost uklanjanja DPPH radikala, reducirajuća snaga i aktivnost keliranja metala. Pri optimalnim je uvjetima dobiveno 1,16 mg/mL ukupnih polifenola, s aktivnošću uklanjanja DPPH radikala od 61,07 %; keliranja metala od 61,20 % i dobrom reducirajućom snagom. Razvijen je učinkoviti model izdvajanja antioksidativnih spojeva iz okare u umjerenim uvjetima procesa, što je važno u proizvodnji sojinih proizvoda

Usporedno ispitivanje metode odzivnih površina, umjetne neuralne mreže i genetskog algoritma radi optimiranja hidratacije zrna soje

The present investigation deals with the modelling and optimization of soybean hydration for facilitating soybean processing and it focuses on maximization of mass gain, water uptake and protein retention in the bean. Process variables considered for optimization were: soybean to water ratio (1:2.48 obtained with response surface methodology, RSM, and 1.19 obtained with artificial neural network and genetic algorithm, ANN/GA), time (2.0 h using RSM and 8.0 h using ANN/GA) and temperature (40.0 °C using RSM and 45.1 °C using ANN/GA). The findings in this first report on optimization of soaking conditions for soybean hydration employing response surface methodology, hybrid artificial neural network and genetic algorithms reveal a substantially better alternative to the time-consuming soaking process, extensively practiced in industries, in terms of process time economy. Reasonably accurate neural network model (regression coefficient of 0.9443) was obtained based on the experimental data. The optimized set of process conditions was predicted through genetic algorithm, and the effectiveness of the ANN/GA model, validated through experiments, was indicated by significant correlations (R2 and mean squared error (MSE) being 0.9380 and 5.9299, respectively). RSM also resulted in accurate models for predicting percentage mass gain, percentage water uptake and percentage protein retention (R2 and MSE in the range of 0.889–0.9297 and 0.80–4.94, respectively).U radu je modelirana i optimirana hidratacija zrna radi ubrzavanja prerade soje, pri čemu se pokušao ostvariti maksimalni prinos mase, usvajanje vode i retencija proteina. Metodom odzivnih površina te umjetnom neuralnom mrežom i genetskim algoritmom optimirane su sljedeće varijable procesa: omjer zrna soje i vode (optimalni omjer od 1:2,48 i 1:1,19), vrijeme (2 odnosno 8 sati) i temperatura (40 i 45, 1 °C). Tako je pronađena bolja alternativa klasičnom postupku namakanja zrna soje koji se učestalo koristi u industriji, a zahtijeva veliki utrošak vremena. Na osnovi rezultata razvijen je vrlo precizan model neuralne mreže (koeficijent regresije od 0,9443). Genetskim su algoritmom predviđeni optimalni uvjeti prerade, a učinkovitost je modela umjetne neuralne mreže i genetskog algoritma potvrđena ispitivanjem (koeficijent determinacije R2=0,938 i srednja kvadratna pogreška MSE=5,9299). Metodom odzivnih površina također je razvijen točan model procjene prinosa mase, usvajanja vode i retencije proteina (R2=0,8890–0,9297 i MSE=0,80–4,94)

Strengthening the policy, implementation, and accountability environment for quality care: experiences from quality of care network countries

Despite global commitment to universal health coverage with quality, poor quality of care (QOC) continues to impact health outcomes for mothers and newborns, especially in low-and-middle income countries. Although there is much experience from small-scale projects, without a long-term perspective it is unclear how to implement quality of care effectively and consistently for impact. In 2017, ten countries together with the WHO and a coalition of partners established the Network for Improving Quality of Care for Maternal, Newborn and Child Health (the Network). The Network agreed to pursue four strategic objectives—Leadership, Action, Learning and Accountability (LALA) for QOC. This paper describes, analyses and reflects on what has worked and some of the challenges faced in implementation of the LALA framework. The implementation of the LALA framework has served as a catalyst to develop an enabling environment for QOC in the Network countries through strengthening the policy, implementation, accountability and community engagement for quality care. Developing an enabling health system environment takes time, but it is possible and shows results. The implementation shows that health systems continue to face persistent challenges such as capacities to quickly scale up changes across subnational levels, limited workforce capability to implement quality improvement consistently and gaps in quality of relevant data. The implementation has also highlighted the need to develop new mechanisms for community engagement and learning systems that inform scaling up of good QOC practices across programmes and levels of care. Moving forward, the Network countries will build on the experiences and lessons learned and continue to strengthen the implementation of LALA strategic objectives for impact. We hope the Network experience will encourage other countries and partners to adopt the Network implementation model to enable delivery of quality care for everyone, everywhere, and actively collaborate and contribute to the QOC global learning network

Networks of Care: An Approach to Improving Maternal and Newborn Health.

The Networks of Care approach has the potential to harmonize existing strategies and optimize health systems functions for maternal and newborn health, thereby strengthening the quality of care and ultimately improving outcomes

Galvanizing collective action to accelerate reductions in maternal and newborn mortality and prevention of stillbirths

Every day, there are an estimated 810 maternal and 7,000 newborn deaths, and more than 5,000 stillbirths, most of which are preventable.1-3 While progress has been made in reducing maternal and neonatal morbidity and mortality and preventing stillbirths worldwide, inequities and gaps in quality of care persist4 and are disproportionately most dire in countries affected by conflict.5 In 2020, the coronavirus disease (COVID19) pandemic and response exposed multiple system vulnerabilities, exacerbated inequities to accessing care, and caused widespread disruption in reproductive, maternal, newborn, and child health services.6,7 Emerging evidence and modeling estimates of the indirect effects of the COVID-19 pandemic on maternal and newborn mortality in low- and middle-income countries (LMICs) reflect a sobering picture of what could lay ahead, with additional deaths estimated to be in the tens of thousands for mothers and hundreds of thousands for stillbirths and children aged under 5 years.3, 8-10 A dedicated, focused effort must be made to ensure maternal and newborn health (MNH) and prevention of stillbirths remain a priority

Integrating maternal, newborn, child health and non-communicable disease care in the sustainable development goal era

Noncommunicable diseases (NCDs) and maternal newborn and child health (MNCH) are two deeply intertwined health areas that have been artificially separated by global health policies, resource allocations and programming. Optimal MNCH care can provide a unique opportunity to screen for, prevent and manage early signs of NCDs developing in both the woman and the neonate. This paper considers how NCDs, NCD modifiable risk factors, and NCD metabolic risk factors impact MNCH. We argue that integrated management is essential, but this faces challenges that manifest across all levels of domestic health systems. Progress toward Sustainable Development targets requires joined-up action

A call for standardised age-disaggregated health data.

The 2030 Sustainable Development Goals agenda calls for health data to be disaggregated by age. However, age groupings used to record and report health data vary greatly, hindering the harmonisation, comparability, and usefulness of these data, within and across countries. This variability has become especially evident during the COVID-19 pandemic, when there was an urgent need for rapid cross-country analyses of epidemiological patterns by age to direct public health action, but such analyses were limited by the lack of standard age categories. In this Personal View, we propose a recommended set of age groupings to address this issue. These groupings are informed by age-specific patterns of morbidity, mortality, and health risks, and by opportunities for prevention and disease intervention. We recommend age groupings of 5 years for all health data, except for those younger than 5 years, during which time there are rapid biological and physiological changes that justify a finer disaggregation. Although the focus of this Personal View is on the standardisation of the analysis and display of age groups, we also outline the challenges faced in collecting data on exact age, especially for health facilities and surveillance data. The proposed age disaggregation should facilitate targeted, age-specific policies and actions for health care and disease management

Value addition to soybean whey through microbial and enzymatic intervention

397-401The wide range of functional properties of soy protein and its high nutritive value makes it base of a novel food platform. Various products are in the market like soy protein isolate, soy protein concentrate, tofu, soymilk which are well accepted by the consumers. Nevertheless, soybean processing operations generate a large proportion of liquid effluent termed as soybean whey. This yellowish liquid waste can be functionally and nutritionally valuable because of their nutrient composition. Discarded whey is not only accountable for pollution problem, but also represents an economic and nutritional penalty in this era. Till now, there are only few reports on effective use of soybean whey, for this reason the present article emphasis to summarize all the extensive research developed for its utilization, so that soybean whey can be well recognized as a potential feedstock both for the microbial and enzymatic intervention