Ultrasound-Assisted Nanoemulsion Loaded with Optimized Antibacterial Essential Oil Blend: A New Approach against Escherichia coli, Staphylococcus aureus, and Salmonella Enteritidis in Trout (Oncorhynchus mykiss) Fillets.

This study aimed to obtain and characterize an oil-in-water nanoemulsion (NE) loaded with an in vitro optimized bactericidal essential oil blend of 50% oregano, 40% thyme, and 10% lemongrass and to evaluate its potential at three different concentrations (0.5%, 1%, and 2%) in the inactivation of Escherichia coli, Staphylococcus aureus, and Salmonella enterica serotype Enteritidis inoculated in rainbow trout fillets stored at 4 °C for 9 days. Regarding the NE, the nanometric size (<100 nm) with low polydispersion (0.17 ± 0.02) was successfully obtained through ultrasound at 2.09 W/cm2. Considering the three concentrations used, S. Enteritidis was the most susceptible. On the other hand, comparing the concentrations used, the NE at 2% showed better activity, reducing S. Enteritidis, E. coli, and S. aureus by 0.33, 0.20, and 0.73 log CFU/g, respectively, in the trout fillets. Thus, this data indicates that this is a promising eco-friendly alternative to produce safe fish for consumption and reduce public health risks.

Design of three-component essential oil extract mixture from Cymbopogon flexuosus, Carum carvi, and Acorus calamus with enhanced antioxidant activity.

The development of novel antioxidant compounds with high efficacy and low toxicity is of utmost importance in the medicine and food industries. Moreover, with increasing concerns about the safety of synthetic components, scientists are beginning to search for natural sources of antioxidants, especially essential oils (EOs). The combination of EOs may produce a higher scavenging profile than a single oil due to better chemical diversity in the mixture. Therefore, this exploratory study aims to assess the antioxidant activity of three EOs extracted from Cymbopogon flexuosus, Carum carvi, and Acorus calamus in individual and combined forms using the augmented-simplex design methodology. The in vitro antioxidant assays were performed using DPPH and ABTS radical scavenging approaches. The results of the Chromatography Gas-Mass spectrometry (CG-MS) characterization showed that citral (29.62%) and niral (27.32%) are the main components for C. flexuosus, while D-carvone (62.09%) and D-limonene (29.58%) are the most dominant substances in C. carvi. By contrast, ß-asarone (69.11%) was identified as the principal component of A. calamus (30.2%). The individual EO exhibits variable scavenging activities against ABTS and DPPH radicals. These effects were enhanced through the mixture of the three EOs. The optimal antioxidant formulation consisted of 20% C. flexuosus, 53% C. carvi, and 27% A. calamus for DPPHIC50. Whereas 17% C. flexuosus, 43% C. carvi, and 40% A. calamus is the best combination leading to the highest scavenging activity against ABTS radical. These findings suggest a new research avenue for EOs combinations to be developed as novel natural formulations useful in food and biopharmaceutical products.

Oil-in-water emulsion loaded with optimized antioxidant blend improved the shelf-life of trout (Oncorhynchus mykiss) fillets: a study with simplex-centroid design.

This study aimed to obtain optimized mixture with three essential oils (EOs) for maximum antioxidant activity through the augmented simplex-centroid mixture design and evaluate the effect of this optimized blend on total aerobic psychrotrophic count (TAPC), lipid and protein oxidation, instrumental color parameters and texture profile in rainbow trout fillets at refrigerated storage for nine days. Considering the DPPH and FRAP assays, the ideal EO blend was 66% lemongrass and 34% oregano. During refrigerated storage, this blend at 2000 ppm was equally effective as BHT (100 ppm) (p > 0.05), mitigating the discoloration (a* and b*), lipid, and protein oxidation in 38.83%, 12.95%, 76.13%, and 35.13%, respectively, besides shows greater effectiveness for preserving texture changes (p < 0.05) and extending the shelf life in 13 h. The lemongrass + oregano EO blend reveals a promising natural alternative to enhance the quality of rainbow trout fillets under refrigerated storage. Furthermore, the multiresponse optimization showed to be a strong ally in enabling the use of these EOs by food industries.

Evaluation of the effects of canning variables on the mineral composition of canned cowpeas (Vigna unguiculata l. Walp) using multi-response analysis.

This study aims to investigate the effects of canning variables (cooking time, storage time, volume of vinegar, salt and sugar) on the mineral composition of canned cowpea (Vigna unguiculata (L.) Walp) and which conditions provide optimised preservation of the mineral content of the grains. Different formulations of canned cowpeas were produced following two levels factorial experimental design using five variables. A set of 11 different formulations were evaluated using the desirability function with essential minerals (Ca, Cu, Fe, Mn, Mg, P and Zn) as the response. The optimal multi-response conditions for higher mineral retention were: 360 days of storage at 30 ± 5 °C (ST2), 30 ml of vinegar, 9.0 g of NaCl, 18 min of cooking time, and 9.0 g, 19.5 g or 30 g of sugar (the effect of the sugar content at the evaluated range was not significant at 95% confidence level).

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis.

Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi-objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi-objective optimization study of the Nomex Honeycomb core previously. A Taguchi-based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

Design of Experiments for Optimizing Ultrasound-Assisted Extraction of Bioactive Compounds from Plant-Based Sources.

Plant-based materials are an important source of bioactive compounds (BC) with interesting industrial applications. Therefore, adequate experimental strategies for maximizing their recovery yield are required. Among all procedures for extracting BC (maceration, Soxhlet, hydro-distillation, pulsed-electric field, enzyme, microwave, high hydrostatic pressure, and supercritical fluids), the ultrasound-assisted extraction (UAE) highlighted as an advanced, cost-efficient, eco-friendly, and sustainable alternative for recovering BC (polyphenols, flavonoids, anthocyanins, and carotenoids) from plant sources with higher yields. However, the UAE efficiency is influenced by several factors, including operational variables and extraction process (frequency, amplitude, ultrasonic power, pulse cycle, type of solvent, extraction time, solvent-to-solid ratio, pH, particle size, and temperature) that exert an impact on the molecular structures of targeted molecules, leading to variations in their biological properties. In this context, a diverse design of experiments (DOEs), including full or fractional factorial, Plackett-Burman, Box-Behnken, Central composite, Taguchi, Mixture, D-optimal, and Doehlert have been investigated alone and in combination to optimize the UAE of BC from plant-based materials, using the response surface methodology and mathematical models in a simple or multi-factorial/multi-response approach. The present review summarizes the advantages and limitations of the most common DOEs investigated to optimize the UAE of bioactive compounds from plant-based materials.

Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass.

The presence of dye pollutants in industrial wastewater poses significant environmental and health risks, necessitating effective treatment methods. The optimal adsorption treatment of methylene blue (MB) and crystal violet (CV) dye-simulated wastewater utilising Saccharum officinarum L presents a key challenge in the selection of appropriate modelling approaches. While RSM and ANN models are frequently used, there is a noticeable knowledge gap when it comes to evaluating their relative strengths and weaknesses in this context. The study compared the predictive abilities of response surface methodology (RSM) and artificial neural network (ANN) for the adsorption treatment of MB and CV dye-simulated wastewater using Saccharum officinarum L. The process experimental variables were modelled and predicted using a three-layer artificial neural network trained using the Levenberg-Marquard backpropagation algorithm and 30 central composite designs (CCD). The adsorption study used a specific mechanism, which led to noteworthy maximum removals of 98.3% and 98.2% for dyes (MB and CV), respectively. The RSM model achieved an impressive R2 of 0.9417, while the ANN model achieved 0.9236 in MB. Adsorption is commonly used to remove colour from many different materials. Saccharum officinarum L., a byproduct of sugarcane processing, has shown potential as an efficient and ecological adsorbent in this environment. The purpose of this study is to evaluate sugarcane bagasse's potential as an adsorbent for the removal of dyes MB and CV from industrial wastewater, providing a long-term strategy for reducing dye pollution. Due to its beneficial economic and environmental characteristics, the Saccharum officinarum L. adsorbent has prompted research into sustainable resources with low pollutant indices.

Evolutionary Optimizing Process Parameters in the Induction Hardening of Rack Bar by Response Surface Methodology and Desirability Function Approach under Industrial Conditions.

Conditions of industrial production introduce additional complexities while attempting to solve optimization problems of material technology processes. The complexity of the physics of such processes and the uncertainties arising from the natural variability of material parameters and the occurrence of disturbances make modeling based on first principles and modern computational methods difficult and even impossible. In particular, this applies to designing material processes considering their quality criteria. This paper shows the optimization of the rack bar induction hardening operation using the response surface methodology approach and the desirability function. The industrial conditions impose additional constraints on time, cost and implementation of experimental plans, so constructing empirical models is more complicated than in laboratory conditions. The empirical models of nine system responses were identified and used to construct a desirability function using expert knowledge to describe the quality requirements of the hardening operation. An analysis of the hypersurface of the desirability function is presented, and the impossibility of using classical gradient algorithms during optimization is empirically established. An evolutionary strategy in the form of a floating-point encoded genetic algorithm was used, which exhibits a non-zero probability of obtaining a global extremum and is a gradient-free method. Confirmation experiments show the improvement of the process quality using introduced measures.

Analyzing Surface Roughness Variations in Material Extrusion Additive Manufacturing of Nylon Carbon Fiber Composites.

In recent years, fused deposition modeling (FDM) based on material extrusion additive manufacturing technology has become widely accepted as a cost-effective method for fabricating engineering components with net-shapes. However, the limited exploration of the influence of FDM process parameters on surface roughness parameters, i.e., Ra (average surface roughness), Rq (root mean square surface roughness), and Rz (maximum height of the profile) across different sides (bottom, top, and walls) poses a challenge for the fabrication of functional parts. This research aims to bridge the knowledge gap by analyzing surface roughness under various process parameters and optimizing it for nylon carbon fiber printed parts. A definitive screening design (DSD) was employed for experimental runs. The Pareto chart highlighted the significant effects of layer height, part orientation, and infill density on all surface roughness parameters and respective sides. The surface morphology was analyzed through optical microscopy. Multi-response optimization was performed using an integrated approach of composited desirability function and entropy. The findings of the present study hold significant industrial applications, enhancing the quality and performance of 3D printed parts. From intricate prototypes to durable automotive components, the optimized surfaces contribute to production of functional and visually appealing products across various sectors.

Optimisation of the machining time required by insole orthotic shoes for patients with clubfoot using the Taguchi and response surface methodology approach.

In this study, the application of the computer-aided reverse engineering system (CARE) to the novel design and manufacture of a comfortable insole for a clubfoot patient is presented. The Taguchi method (TM) and response surface methodology (RMS) were used to predict the machining time of the orthotic boot insole during both computer-aided manufacturing (CAM) simulation and computer numerical control (CNC) machining. Taguchi's experimental design, presented as a matrix orthogonal array L2736, was acquired for controlling parameters, namely tool path strategy (A), spindle speed (B), step-down (C), step-over of the cutter (D), cutter diameter (E), and dimensional tolerance (F) of the insole size. In this method, the model generated by the RMS method evaluates the six parameters influencing the machining time. The objective of this study is to develop a regression model that demonstrates the relationship between the cutting parameters and insole machining time. The optimal parameters are A1B1C3D2E1F2, where A1 denotes raster finishing, B1 denotes a spindle speed of 10,000 rpm, C3 denotes a step-down of 850 mm, D2 denotes a step-over of 0.25 mm, E1 denotes a cutter diameter of 20-35 mm, and F2 deontes a tolerance of 0.75 mm. The experimental and calculated machining time (tm) results were 236 and 125.4 min, respectively. However, the real machining results were 334 and 152.25 min with error values of 46.86% and 54.42%, respectively. Meanwhile, with the tm RMS method, the simulated and calculated machining time results were 189.22 and 236.35 min, whereas the real tm values were 236.52 and 334.86 min with error values of 19.94% and 29.37%, respectively. This research obtains improvements of 19.82% (simulation time) and 29.19% (real-time).

Influence of Ultrasonic and Chemical Pretreatments on Quality Attributes of Dried Pepper (Capsicum annuum).

This study investigates the effects of ultrasound, in combination with chemical pretreatments, on the quality attributes (total phenolic and carotenoid content, antioxidant activity (2,2-Diphenyl-1-picrylhydrazyl assay (DPPH)), ferric-reducing ability (FRAP), CIE L* a* b* color, non-enzymatic browning, rehydration ratio, textural and morphological properties) of red pepper subjected to drying (hot air drying or freeze drying). The fractional factorial design was used to assess the impact of factors. The global Derringer desirability function was used to determine the optimal conditions for the best quality attributes of dried pepper. The drying method influenced total phenolic content, a* (redness), and initial rehydration ratio; pretreatment time significantly affected FRAP antiradical activity, a*, chroma and non-browning index, while pH-value had a significant effect on the texture of dried pepper. Non-enzymatic browning was reduced to 72.6%, while the DPPH antioxidant capacity of freeze-dried peppers was enhanced from 4.2% to 71.9%. Ultrasonic pretreatment led to changes in the pepper morphology, while potassium metabisulfite (KMS) was a more effective additive than citric acid.

Box-Behnken design with desirability function for methylene blue dye adsorption by microporous activated carbon from pomegranate peel using microwave assisted K2CO3 activation.

This research aims to convert pomegranate peel (PP) into microporous activated carbon (PPAC) using a microwave assisted K2CO3 activation method. The optimum activation conditions were carried out with a 1:2 PP/K2CO3 impregnation ratio, radiation power 800 W, and 15 min irradiation time. The statistical Box-Behnken design (BBD) was employed as an effective tool for optimizing the factors that influence the adsorption performance and removal of methylene blue (MB) dye. The output data of BBD with a desirability function indicate a 94.8% removal of 100 mg/L MB at the following experimental conditions: PPAC dose of 0.08 g, solution pH of 7.45, process temperature of 32.1 °C, and a time of 30 min. The pseudo-second order (PSO) kinetic model accounted for the contact time for the adsorption of MB. At equilibrium conditions, the Freundlich adsorption isotherm describes the adsorption results, where the maximum adsorption capacity of PPAC for MB dye was 291.5 mg g-1. This study supports the utilization of biomass waste from pomegranate peels and conversion into renewable and sustainable adsorbent materials. As well, this work contributes to the management of waste biomass and water pollutant sequestration.

The novelty of this research work comes from the conversion of the biomass waste, namely; the conversion of pomegranate peel (PP) into microporous activated carbon (PPAC) via a microwave assisted K₂CO₃ activation process. The applicability of the PPAC toward the removal of methylene blue dye (MB) was statistically optimized using Box Behnken design in the response surface methodology (BBD-RSM).

Performance of Pristine versus Magnetized Orange Peels Biochar Adapted to Adsorptive Removal of Daunorubicin: Eco-Structuring, Kinetics and Equilibrium Studies.

Drugs and pharmaceuticals are an emergent class of aquatic contaminants. The existence of these pollutants in aquatic bodies is currently raising escalating concerns because of their negative impact on the ecosystem. This study investigated the efficacy of two sorbents derived from orange peels (OP) biochar (OPBC) for the removal of the antineoplastic drug daunorubicin (DNB) from pharmaceutical wastewater. The adsorbents included pristine (OPBC) and magnetite (Fe3O4)-impregnated (MAG-OPBC) biochars. Waste-derived materials offer a sustainable and cost-effective solution to wastewater bioremediation. The results showed that impregnation with Fe3O4 altered the crystallization degree and increased the surface area from 6.99 m2/g in OPBC to 60.76 m2/g in the case of MAG-OPBC. Placket-Burman Design (PBD) was employed to conduct batch adsorption experiments. The removal efficiency of MAG-OPBC (98.51%) was higher compared to OPBC (86.46%). DNB adsorption onto OPBC followed the D-R isotherm, compared to the Langmuir isotherm in the case of MAG-OPBC. The maximum adsorption capacity (qmax) was 172.43 mg/g for MAG-OPBC and 83.75 mg/g for OPBC. The adsorption kinetics for both sorbents fitted well with the pseudo-second-order (PSO) model. The results indicate that MAG-OPBC is a promising adsorbent for treating pharmaceutical wastewater.

Local Linear Regression and the problem of dimensionality: a remedial strategy via a new locally adaptive bandwidths selector.

Local Linear Regression (LLR) is a nonparametric regression model applied in the modeling phase of Response Surface Methodology (RSM). LLR does not make reference to any fixed parametric model. Hence, LLR is flexible and can capture local trends in the data that might be too complicated for the OLS. However, besides the small sample size and sparse data which characterizes RSM, the performance of the LLR model nosedives as the number of explanatory variables considered in the study increases. This phenomenon, popularly referred to as curse of dimensionality, results in the scanty application of LLR in RSM. In this paper, we propose a novel locally adaptive bandwidths selector, unlike the fixed bandwidths and existing locally adaptive bandwidths selectors, takes into account both the number of the explanatory variables in the study and their individual values at each data point. Single and multiple response problems from the literature and simulated data were used to compare the performance of the L L R P A B with those of the OLS, L L R F B and L L R A B . Neural network activation functions such ReLU, Leaky-ReLU, SELU and SPOCU was considered and give a remarkable improvement on the loss function (Mean Squared Error) over the regression models utilized in the three data.

Optimization of asymmetric bioreduction conditions of 1-(thiophen-2-yl)ethanone by Weissella cibaria N9 using a desirability function-embedded face-centered optimization model.

Prochiral ketones can be effectively bio-reduced to chiral secondary alcohols by whole-cell biocatalysts, which are possible useful precursors to synthesize physiologically active chemicals and natural products. When whole-cell biocatalysts strains are used, bioreduction process can be influenced by various cultural factors, and it is vital to optimize these factors that affect selectivity, conversion rate, and yield. In this study, Weissella cibaria N9 was used as whole-cell biocatalyst for bioreduction of 1-(thiophen-2-yl)ethanone, and cultural design factors were optimized using a desirability function-embedded face-centered optimization model. For this, effects of pH (4.5-5.5-6.5, x1), (2) temperature (25-30-35 °C, x2), (3) incubation period (24-48-72 h, x3), and (4) agitation speed (100-150-200 rpm, x4) on two response variables; (1) ee (%) and (2) cr (%) were tested. Next, desirability function-embedded face-centered optimization model revealed that a pH of 6.43, a temperature of 26.04 °C, an incubation period of 52.41 h, and an agitation speed of 150 rpm were the optimum levels and the estimated ee and cr responses were 99.31% and 98.16%, respectively. Importantly, the actual experimental ee and cr responses were similar to the estimated values indicating the capability of the offered desirability function-embedded face-centered optimization model when using the optimum cultural conditions.

Exploring the use of the desirability function to optimize the separation of oligonucleotide impurities by ion pair-RP LCMS.

The use of small alkyl amines as ion pair reagents permits enhanced separation of impurities of phosphate diester oligonucleotides, which can be beneficial to quality control applications, and aid elucidation of the mechanisms of impurity formation. In general, however, separation of the individual components that comprise the majority of oligonucleotide impurities requires development of several independent chromatographic methods. Ideally, a single method capable of separating the individual components of all impurity classes would be developed. The mathematical concept of the desirability function has been explored here for its ability to determine the combination of experimental factors that result in a single, globally optimized chromatographic method. The optimized mobile phase, consisting of 1 mM propylamine (PA), 30 mM ammonium bicarbonate (ABC), and 1 mM octanoic acid (C8A), produced excellent agreement between measured and predicted peak resolution values for a set of n - 1 impurities. The relative importance of the mobile phase constituents on the mechanism of separation has been discussed. The approach holds great promise for the improved separation of components in complex chromatographic systems.

Optimization and characterization of self-nanoemulsifying drug delivery system of iloperidone using box-behnken design and desirability function.

PURPOSE: Iloperidone (IP) is an antipsychotic drug which belongs to Biopharmaceutical Classification System (BCS) II exhibiting poor aqueous solubility. The current investigation explores the possibility of enhancement of solubility and dissolution characteristics of IP by formulation of liquid self-nano emulsifying drug delivery system (L-SNEDDS) utilizing Box-Behnken Design (BBD) and desirability function. METHODS: The oils, surfactants and co-surfactants used in the study were selected based on solubility of the drug and their emulsification ability. Optimization of the formulation was performed using BBD by employing four response variables such as globule size (nm), percentage transmittance (%), self-emulsification time (sec) and percent drug released in 15min. 2D contour plots and 3D response surface plots were constructed using Design Expert software. RESULTS: The developed optimal L-SNEDDS of IP through BBD approach resulted in improvement of solubility and dissolution rate as compared with the pure drug. Based on desirability function, optimized formulation was prepared and was assessed for response variables (globule size, percentage transmittance, self-emulsification time and percent drug dissolved in 15min). The characterization studies revealed droplet size to be 21.80±2.41nm, 99.584±0.65% transmittance, 24.43±2.12sec emulsification time and 95.31±1.57% cumulative drug release in 15min. CONCLUSION: The results conclude the potentiality of prepared L-SNEDDS in improving solubility and dissolution rate of IP.

Novel modeling and optimization framework for Navy Blue adsorption onto eco-friendly magnetic geopolymer composite.

The disproportionate potency of dyes in textile wastewater is a global concern that needs to be contended. The present study comprehensively investigates the adsorption of Navy-Blue dye (NB) onto bentonite clay based geopolymer/Fe3O4 nanocomposite (GFC) using novel statistical and machine learning frameworks in the following steps; (1) synthesis and characterization of GFC, (2) experimental testing and modelling of NB adsorption onto GFC following Box-Behnken design and three response surface prediction models namely stepwise regression analysis (SRA), Support vector regression (SVR) and Kriging (KR), (3) parametric, sensitivity, thermodynamic and kinetic analysis of pH, GFC dose and contact time on adsorption performance, and (4) finding global parametric solution of the process using Latin Hypercube, Sobol and Taguchi orthogonal array sampling and combining SRA-SVR-KR predictions with novel hybrid simulated annealing (SA)-desirability function (DF) approach. Under the given testing range, parametric/sensitivity analysis revealed the critical role of pH over others accounting ∼37% relative effect and primarily derived the NB adsorption. The statistical evaluation of models revealed that all models could be utilized for elucidating and predicting the NB removal using GFC, however, SVR accuracy was better among others for this particular work, as the overall computed root mean squared error was only 0.55 while the error frequency counts remained <1 for 90% predictions. GFC showed 86.29% NB removal for the given experimental matrix which can be elevated to 96.25% under optimum conditions. The NB adsorption was found to be physical, spontaneous, favorable and obeyed pseudo-2nd order kinetics. The results demonstrate the suitability of GFC as the promising cost-effective and efficient alternative for the decolourization of urban and drinking water streams and elucidate the potential of machine learning models for accurate prediction & elevation of adsorption processes with less experimentation in water purification applications.

Mechanical, optical, and physicochemical properties of HPMC-based doxazosin mesylate orodispersible films

Abstract In this study, orodispersible films formed from hydroxypropyl methylcellulose (HPMC) E6 (2, 2.5, and 3%) and plasticizers ((glycerin (Gly), propylene glycol (PP), or polyethylene glycol (PEG)), containing doxazosin mesylate, were prepared by the solvent casting method and characterized. Design of experiments (DoE) was used as a statistical tool to facilitate the interpretation of the experimental data and allow the identification of optimal levels of factors for maximum formulation performance. Differential scanning calorimetry (DSC) curves and X-ray powder diffraction (XRPD) diffractograms showed doxazosin mesylate amorphization, probably due to complexation with the polymer (HPMC E6), and the glass transition temperature of the polymer was reduced by adding a plasticizer. Fourier transformed infrared (FTIR) spectroscopy results showed that the chemical structure of doxazosin mesylate was preserved when introduced into the polymer matrix, and the plasticizers, glycerin and PEG, affected the polymer matrix with high intensity. The addition of plasticizers increased the elongation at break and adhesiveness (Gly > PEG > PP), confirming the greater plasticizer effect of Gly observed in DSC and FTIR studies. Greater transparency was observed for the orodispersible films prepared using PP. The addition of citric acid as a pH modifier was fundamental for the release of doxazosin mesylate, and the desirability formulation had a release profile similar to that of the reference product

Multi-Response Optimization of the Malting Process of an Italian Landrace of Rye (Secale cereale L.) Using Response Surface Methodology and Desirability Function Coupled with Genetic Algorithm.

Rye is used in some applications in the food and beverage industry and for the preparation of functional foods. It is an interesting raw material in malting and brewing due to its characteristic contribution to the beer's color, turbidity, foam and aroma. The aim of this work was to optimize the micro-malting process of a rye landrace. The response surface methodology (RSM) was applied to study the influence of three malting parameters (germination time, germination temperature and degree of steeping) on the quality traits of malted rye. Long germination times at high temperatures resulted in an increase in the extract and Kolbach index. The model for the apparent attenuation limit showed a particular pattern, whereby time and temperature inversely influenced the response. The lowest viscosities were determined in the worts produced from highly modified malts. Optimization of the variables under study was achieved by means of a desirability function and a genetic algorithm. The two methodologies provided similar results. The best combination of parameters to optimize the malting process on the rye landrace under study was achieved at 6 days, 12 °C and 44 g/100 g.