Advances in the co-production of biosurfactant and other biomolecules: statistical approaches for process optimization.

An efficient microbial conversion for simultaneous synthesis of multiple high-value compounds, such as biosurfactants and enzymes, is one of the most promising aspects for an economical bioprocess leading to a marked reduction in production cost. Although biosurfactant and enzyme production separately have been much explored, there are limited reports on the predictions and optimization studies on simultaneous production of biosurfactants and other industrially important enzymes, including lipase, protease, and amylase. Enzymes are suited for an integrated production process with biosurfactants as multiple common industrial processes and applications are catalysed by these molecules. However, the complexity in microbial metabolism complicates the production process. This study details the work done on biosurfactant and enzyme co-production and explores the application and scope of various statistical tools and methodologies in this area of research. The use of advanced computational tools is yet to be explored for the optimization of downstream strategies in the co-production process. Given the complexity of the co-production process and with various new methodologies based on artificial intelligence (AI) being invented, the scope of AI in shaping the biosurfactant-enzyme co-production process is immense and would lead to not only efficient and rapid optimization, but economical extraction of multiple biomolecules as well.

Water quality index and sanitary and socioeconomic indicators in Minas Gerais, Brazil.

Minas Gerais is one of the 27 federative units of Brazil; it is the fourth state with the largest territorial area and the second in number of inhabitants. Since 1997, the monitoring of the surface water quality of the State of Minas Gerais has been carried out. In this study, generalized regression models were constructed to determine the correlation between the Water Quality Index (WQI) and the sanitary and socioeconomic variables: Municipal Population, Human Development Index (HDI), Gini Index, Percentage of Vulnerables to Poverty (Poverty), Monthly Per Capita Income, Percentage of Inadequate or Poor Sanitation. In addition to the sanitary and socioeconomic variables listed, it also used year of water quality monitoring, altitude of the monitoring point, and distance from the monitoring point to the urban center of the municipality. The results from the generalized models showed that the variables year, altitude, Gini Index, monthly per capita income, and poor sanitation variables were positively associated with WQI. In other words, high values of each variable increased WQI, while population variables HDI and poverty were negatively related to WQI, that is, a high population value, HDI, or poverty implies a low WQI value. Socioeconomic variables such as HDI, Gini Index, poorness, or poor sanitation percentage present the coefficients with the largest modulus. Thus, among the socioeconomic variables studied, these are the ones that most contribute to the variability of WQI. The year and altitude variables have positive regression coefficients, indicating that when these variables increase, WQI also increases. The positive correlation with the year shows that the surface water quality of Minas Gerais improved during the monitoring years.

Artificial Intelligence for the Evaluation of Operational Parameters Influencing Nitrification and Nitrifiers in an Activated Sludge Process.

Nitrification at a full-scale activated sludge plant treating municipal wastewater was monitored over a period of 237 days. A combination of fluorescent in situ hybridization (FISH) and quantitative real-time polymerase chain reaction (qPCR) were used for identifying and quantifying the dominant nitrifiers in the plant. Adaptive neuro-fuzzy inference system (ANFIS), Pearson's correlation coefficient, and quadratic models were employed in evaluating the plant operational conditions that influence the nitrification performance. The ammonia-oxidizing bacteria (AOB) abundance was within the range of 1.55 × 10(8)-1.65 × 10(10) copies L(-1), while Nitrobacter spp. and Nitrospira spp. were 9.32 × 10(9)-1.40 × 10(11) copies L(-1) and 2.39 × 10(9)-3.76 × 10(10) copies L(-1), respectively. Specific nitrification rate (qN) was significantly affected by temperature (r 0.726, p 0.002), hydraulic retention time (HRT) (r -0.651, p 0.009), and ammonia loading rate (ALR) (r 0.571, p 0.026). Additionally, AOB was considerably influenced by HRT (r -0.741, p 0.002) and temperature (r 0.517, p 0.048), while HRT negatively impacted Nitrospira spp. (r -0.627, p 0.012). A quadratic combination of HRT and food-to-microorganism (F/M) ratio also impacted qN (r (2) 0.50), AOB (r (2) 0.61), and Nitrospira spp. (r (2) 0.72), while Nitrobacter spp. was considerably influenced by a polynomial function of F/M ratio and temperature (r (2) 0.49). The study demonstrated that ANFIS could be used as a tool to describe the factors influencing nitrification process at full-scale wastewater treatment plants.

Comprehensive investigation of coffee acidity on eight different brewing methods through chemical analyses, sensory evaluation and statistical elaboration.

Even if the acids composition and their role in coffee still need to be clarified, acidity is one of the main sought-after features in coffee and it is becoming one of the main quality markers. Hence, the aim of this paper was to evaluate the main parameters influencing coffee acidity with a focus on carboxylic acids. To the best of our knowledge, this is the first study regarding filter coffee prepared from specialty and mainstream coffee, differently roasted and through eight diverse extraction methods. Coffee cup chemical composition in terms of organic and chlorogenic acids, caffein and physicochemical parameters were correlated with perceived sourness and mouthfeel to better understand the influence of extracted compounds on the final beverage acidity. Statistical tools revealed that a major impact of chlorogenic acids emerged in pH and titratable acidity, while the sensorial sourness appeared more correlated with organic acids concentration. Thus, these findings suggests that organic acids could be potential predictors of beverage perceived acidity.

Technical analysis of wind energy potentials using a modified Weibull and Raleigh distribution model parameters approach in the Gambia.

Wind energy has gained prominence over the past few decades because of its environment-friendly nature and abundant availability. However, the exploration of wind energy requires adequate knowledge of the wind distribution parameters before installing the wind turbine. This study assessed the potential of harvesting wind energy at two Gambian locations by fitting the best model comparing Weibull and Raleigh Distributions. A novel approach combining the energy pattern factor and standard deviation methods in estimating the distribution parameters of the characteristics of wind in the Gambian locations of Yundum and Basse has been presented and statistically analyzed using the Weibull and Raleigh distribution functions. The results showed that the shape parameters ranged from 4.88 to 6.98 and 3.87-6.15 for Yundum and Basse locations, the Weibull scale parameter ranged from 6.60 to 10.58 m/s and 4.51-8.69 m/s for Yundum and Basse while the calculated wind power densities ranged from 139 to 718 W/m2 and 46-390 W/m2 for Yundum and Basse respectively. These results clearly show a high potential for generating electricity with wind in the study areas. The statistical analysis revealed that the Weibull models perform better at Yundum in terms of RMSD = 0.33, NSCOE=0.45,MAE=0.29 and χ2= 1.57 while the Raleigh distribution gives a better fit for Basse in terms of R2=0.88, and MAE=0.39 only making it more suitable for calculating the wind power potentials.

Olive Oil Sensory Analysis as a Tool to Preserve and Valorize the Heritage of Centenarian Olive Trees.

In inland areas of Portugal and some regions of the Mediterranean basin, olive production is based on traditional olive groves, with low intensification, local cultivars, aged plants, and centenarian trees. These plants play a key role in the ecosystem, contributing to carbon sequestration and possessing a high genetic diversity, particularly important for selecting cultivars more resistant to climatic changes. Appreciation of the value of this genetic diversity implies genetic, morphological, and physicochemical characterization of centenarian trees, which is expensive and time-consuming. Sensory evaluation is also of utmost importance. Thus, in this study, centenarian olive trees were selected in the Côa Valley region, a UNESCO World Heritage site. The descriptive sensory profile of their extracted olive oils was established and used to cluster the oils, using hierarchical clustering analysis, and consequently the olive trees, into five groups with similar intensities of perceived olfactory-gustatory attributes. Each cluster revealed olive oils with unique sensory patterns, presumably due to similarities of the olive trees, confirming the potential of the proposed screening approach. The identification of sensorially homogeneous oil-tree groups would reduce the number of specimens needed for subsequent morphological, genetic, and chemical characterization, allowing a cost-effective and robust future evaluation procedure.

Comparative analysis of statistical tools for oil palm phytochemical research.

Recent advances in phytochemical analysis have allowed the accumulation of data for crop researchers due to its capacity to footprint and distinguish metabolites that are present within an organisms, tissues or cells. Apart from genotypic traits, slight changes either by biotic or abiotic stimuli will have significant impact on the metabolite abundances and will eventually be observed through physicochemical characteristics. Apposite data mining to interpret the mounds of phytochemical information from such a dynamic system is thus incumbent. In this investigation, several statistical software platforms ranging from exploratory and confirmatory technique of multivariate data analysis from four different statistical tools of COVAIN, SIMCA-P+, MetaboAnalyst and RIKEN Excel Macro were appraised using an oil palm phytochemical data set. As different software tool encompasses its own advantages and limitations, the insights gained from this assessment were documented to enlighten several aspects of functions and suitability for the adaptation of the tools into the oil palm phytochemistry pipeline. This comparative analysis will certainly provide scientists with salient notes on data assessment and data mining that will later allow the depiction of the overall oil palm status in-situ and ex-situ.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods. Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings. Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included. Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

COVID-19 prevalence estimation: Four most affected African countries.

The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.

Current Advances in Meat Nutritional, Sensory and Physical Quality Improvement.

Meat is an important source of proteins, vitamins, minerals and fat, and these nutrients are important for their beneficial effects on human health. In recent years, meat quality has become a more relevant topic for consumers with regard to health and sensory characteristics, and for beef industry stakeholders because it affects their profitability. Therefore, the control of meat quality, including technological, sensory and nutritional quality traits, constitutes an important target for any farm animal production. What those qualities are and how we best evaluate them at the different levels of the continuum from the farm to fork are critical to understanding meat production and consumption patterns. However, despite the efforts of the industrials to control the eating and nutritional quality traits of meat and meat products, there remains a high level of variability, which is one reason for consumer dissatisfaction. This Special Issue focuses on the study of continuum aspects from farm to fork, which would have an impact on the control of the nutritional, sensory and technological aspects of carcass, muscle, meat and meat-product qualities. It groups fourteen original studies and one comprehensive review within five main topics that are (i) production systems and rearing practices, (ii) prediction of meat qualities, (iii) statistical approaches for meat quality prediction/management, (iv) muscle biochemistry and proteomics techniques and (v) consumer acceptability, development and characterisation of meat products.

Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools.

The process parameters for xylanase biobleaching of mixed hardwood pulp like, reaction time (6-35 h), pulp consistency (2.5-15%) and xylanase dose (5-35 U) were optimized using OFAT approach and hybrid statistical tools viz. GA-ANN and GA-ANFIS. The biobleaching ability of xylanase in terms of reducing sugar yield increased up to 28.16 mg g-1 (28.05%) than OFAT optimization (21.99 mg g-1 of pulp) after employing hybrid statistical tools. After TCF bleaching of xylanase treated pulp, we observed that lignin content reduced to 0.29% whereas it was still 0.41% in the untreated pulp. Moreover, the brightness level achieved up to 70.4% in xylanase treated pulp while it was only 53.60% in the chemically treated pulp. The kappa number for xylanase treated, chemically treated, and xylanase-chemical treated pulp was recorded 9.90, 7.10 and 4.70, respectively. The present study reports an improved eco-friendly biobleaching method using novel GA-ANN and GA-ANFIS hybrid statistical tools.

Basic statistical tools in research and data analysis.

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

Characterization of Argentine honeys on the basis of their mineral content and some typical quality parameters.

BACKGROUND: The levels of 19 elements (As, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Se, Tl, U, V, Zn) from sixteen different Argentine production sites of unifloral [eucalyptus (Eucaliptus rostrata), chilca (Baccharis salicifolia), Algarrobo (Prosopis sp.), mistol (Ziziphus mistol) and citric] and multifloral honeys were measured with the aim to test the quality of the selected samples. Typical quality parameters of honeys were also determined (pH, sugar content, moisture). Mineral elements were determined by using inductively coupled plasma mass spectrometer (ICP-MS DRC). We also evaluated the suitability of honey as a possible biomonitor of environmental pollution. Thus, the sites were classified through cluster analysis (CA) and then pattern recognition methods such as Principal Component Analysis (PCA) and discriminant analysis (DA) were applied. RESULTS: MEAN VALUES FOR QUALITY PARAMETERS WERE: pH, 4.12 and 3.81; sugar 82.1 and 82.0 °brix; moisture, 16.90 and 17.00% for unifloral and multifloral honeys respectively. The water content showed good maturity. Likewise, the other parameters confirmed the good quality of the honeys analysed. Potassium was quantitatively the most abundant metal, accounting for 92,5% of the total metal contents with an average concentration of 832.0 and 816.2 µg g(-1) for unifloral and multifloral honeys respectively. Sodium was the second most abundant major metal in honeys with a mean value of 32.16 and 33.19 µg g(-1) for unifloral and multifloral honeys respectively. Mg, Ca, Fe, Mn, Zn and Cu were present at low-intermediate concentrations. For the other 11 trace elements determined in this study (As, Be, Cd, Co, Cr, Ni, Pb, Se, Tl, U and V), the mean concentrations were very low or below of the LODs. The sites were classified through CA by using elements' and physicochemical parameters data, then DA on the PCA factors was applied. Dendrograms identified three main groups. PCA explained 52.03% of the total variability with the first two factors. CONCLUSIONS: In general, there are no evidences of pollution for the analysed honeys. The analytical results obtained for the Argentine honeys indicate the products' high quality. In fact, most of the toxic elements were below LODs. The chemometric analysis combining CA, DA and PCA showed their aptness as useful tools for honey's classification. Eventually, this study confirms that the use of honey as biomonitor of environmental contamination is not reliable for sites with low levels of contamination.

Revisiting hydrocarbons source appraisal in sediments exposed to multiple inputs.

The aim of this work was to test the efficiency of statistical methods as compared to the traditional diagnostic ratios to improve hydrocarbon source identification in sediments subjected to multiple inputs. Hydrocarbon determination in Guanabara Bay sediments pointed out high degradation and ubiquitous petrogenic pollution through the presence of high unresolved complex mixture. Polycyclic aromatic hydrocarbon (PAHs) ratios suggested pervasive contamination derived from combustion in all sediments and failed discriminating samples despite the specificity of sources in different sampling sites. Principal component analysis (PCA) effectively distinguished the petrogenic imprint superimposed to the ubiquitous combustion contamination, since this technique reduces the influence of PAHs distribution which is common to all samples. PCA associated to multivariate linear regression (MLR) allowed a quantitative assessment of sources confirming predominance of the pervasive contaminant component superimposed to a generalized petrogenic imprint. The pervasive component derives from combustion contributions as well as from differential PAHs degradation.

Desenvolvimento e otimização de protetores solares empregando os conceitos de qualidade por design (QbD) e tecnologia analí­tica de processos (PAT) / Development and Optimization of sunscreen applying Quality by Design (QbD) and Process Analytical Technology (PAT), 2018. (Master Degree)

Os protetores solares (PS) são os grandes responsáveis pela proteção da pele quando exposta à radiação solar, por isso a importância sanitária de se otimizar o desenvolvimento deste cosmético tipo II e monitorar para que seja eficaz em seu propósito. O principal objetivo deste trabalho é aplicar os conceitos de Qualidade por Design (QbD), ferramentas estatísticas de desenho experimental (DoE - Design of Experiments) e o conceito de tecnologia analítica de processo (PAT - Process Analytical Technology) para desenvolver uma formulação e processo produtivo de um PS de modo a modernizar os processos da indústria cosmética, fazendo as análises durante o processo e eliminando o controle de qualidade final. Trata-se de um sistema de desenvolvimento sistematizado, onde se executa as ferramentas de QbD para avaliar os dados obtidos ao longo da fase experimental. Para a fase experimental, empregou-se o desenho fatorial e desenho do compósito central (CCD - Central Composite Design) como ferramenta estatística, para a execução do planejamento de experimentos (DoE - Design of Experiments). As respostas foram analisadas através da metodologia de superfície resposta (RSM - Response Surface Methodology). Tais ferramentas são fundamentais para a determinação do desenho de concepção (design space), para se obter o PS com as melhores características físico-químicas e de processo dentro do escopo delineado. Para o desenvolvimento da metodologia de análise in line, optou-se pela utilização da espectrometria UV, utilizando-se ferramentas como análise de regressão dos mínimos quadrados (PLS) devido a praticidade em transforma-la em uma ferramenta PAT, para isto, a quimiometria foi empregada para modelar sistemas que são desconhecidos e complexos, como um PS, e trazendo respostas diretas como a aprovação do produto antes de ser embalado, por exemplo. A abordagem apresentada baseia-se na construção da qualidade ao longo do desenvolvimento e otimização de PS e torna possível o monitoramento da qualidade em tempo real

The sunscreens are great responsible for the skin protection when it is exposed to direct sunlight, so it means a great importance of health to optimize the development of type II cosmetic and monitor for it to be effective in its purpose. The objective of this work is to apply the concepts of Quality by Design and statistical tools of experimental design (DoE - Design of experiments), as well as applying the process analytical technology (PAT - Process Analytical Technology) concept for formulation and manufacturing process development of a topical sunscreen being able to modernize the cosmetic industry processing, including real time analyses and eliminating quarantine step, which waits analysis approval performed by the quality assurance, and then release the product for sale. As it is a systematic development, where critical quality attributes and risk assessment were performed to evaluate over obtained data. During experimental phase, the factorial design was used as a statistical tool for design of experiments implementation, and the responses were analyzed by response surface methodology (RSM - Response Surface Methodology). This mapping is critical to determination of the product design (design space), i.e. get sunscreen with the best physical and chemical characteristics and processing within the outlined scope. For in line methodology development, UV spectrometry was opted to be used due to less effort in sample preparation and due to great easiness to turn it into a PAT tool. For this, chemometrics was used, which brings together chemical and statistical elements to obtain three main elements: empirical modeling, multivariate modeling and chemical data, making it able to model systems that are unknown and complex, as a sunscreen, getting direct answers as product release approval before being packed, for example. The presented approach was based on the construction of quality throughout the sunscreen development and optimization making possible the real time quality monitoring