Optimization of a natural antimicrobial formulation against potential meat spoilage bacteria and food-borne pathogens: Mixture design methodology and predictive modeling

Combination of sweet orange, lentisk and lemon eucalyptus essential oils: Optimization of a new complete antimicrobial formulation using a mixture design methodology

“…This mixture of EOs are mainly ascribed to the presence of oxygenated monoterpenes such as 1,8-cineole, β -citronellol, citronellal; and monoterpene hydrocarbons, including d -limonene, careen-3, β -myrcene. Each of the mentioned molecules have multiple sites of action on microbial cells [ 49 , 80 ].…”

“…The oxygenated terpenoids, such as monoterpenes and sesquiterpenes, are the major antimicrobial components compared to the terpene hydrocarbons, which do not contain hydroxyl groups (-OH) in their chemical structure [ 49 ]. Additionally, the interaction between major and minor compounds may act synergistically to have a significant effect on microorganisms [ 81 ].…”

“…High F LOF/PE values indicate poor model fit [ 48 ]. Furthermore, the coefficient of determination R 2 and R 2 Adj were used to assess the accuracy of the postulated models [ 49 ]. Student's t -test was utilized to assess the significance of the estimated coefficients.…”

Combination of Chemically-Characterized Essential Oils from Eucalyptus polybractea, Ormenis mixta, and Lavandula burnatii: Optimization of a New Complete Antibacterial Formulation Using Simplex-Centroid Mixture Design

“…Factors (E 1 , E 2 , and E 3 ) are used to explain response variation in a mixture, and they represent a portion of each experimental component in the mixture, which has a value range between 0 and 1 without constraints [38], with E 1 : the proportion of EOEO E 2 : the proportion of OMEO E 3 : the proportion of LBEO Te responses assessed in this investigation were the antibacterial action against two bacteria, including S. aureus and E. coli. Next, the data were ftted to a special cubic model employing least-squares regression to reveal the unidentifed coefcients in the following equation [39]:…”

Real-Time Quantification of Meat Paste Constituents Displaying Nonlinear Blending Behavior Including Salt Using an In-Line NIR MEMS Sensor

“…In this work, the complexity of differently composed meat pastes is modeled using model meat mixtures that were systematically varied according to an experimental design. Design of Experiments (DoE) in combination with Response Surface Methodology (RSM) is a powerful statistical tool, − which enables the identification and quantification of statistically significant recipe components as well as synergistic nonlinear blending behavior of mixture components, while requiring only a limited number of experiments making this approach much more effective and informative than conventional one-factor-at-a-time approaches. ,,, The methodology has thus been used in a wide variety of contexts including food technology. − Using a mixture design wherein salt was included as a component in the reference mixtures, the effect of compositional changes in the meat paste on the spectral characteristics in the NIR region was investigated. For this purpose, various meat pastes were produced based on a balanced mixture design with varying amounts of fat, protein, water, and salt and subsequently were analyzed with a novel process analyzer prototype using a MEMS-based NIR sensor.…”