Doehlert design in the optimization of procedures aiming food analysis - A review

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

BACKGROUND

Salsola laricifolia is a typical C3-C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies.

RESULTS

Among the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L-1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts.

CONCLUSION

The current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.

Response Surface Optimization for Water-Assisted Extraction of Two Saponins from Paris polyphylla var. yunnanensis Leaves

The process of extracting polyphyllin II and polyphyllin VII by water-assisted extraction was established and optimized in this study. Response surface methodology was used to establish a prediction model to optimize the extraction conditions. Based on the one-way test, the Box-Behnken design with three factors and three levels was used for the experimental program, and the composition analysis was carried out by high-performance liquid chromatography (HPLC). The optimal extraction conditions for polyphyllin II and polyphyllin VII were as follows: extraction time of 57 and 21 min, extraction temperature of 36 and 32 °C, solid-to-liquid ratio of 1:10 and 1:5 g/mL, respectively, and the yields of polyphyllin II and polyphyllin VII were 1.895 and 5.010%, which was similar to the predicted value of 1.835 and 4.979%. The results of the ANOVA showed that the model fit was good, and the Box-Behnken response surface method could optimize the water-assisted extraction of saponins from the leaves of Paris polyphylla var. yunnanensis. This study provides a theoretical basis for the application of polyphyllin II and polyphyllin VII in pharmaceutical production.

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.

Use of Doehlert Matrix as a Tool for High-Throughput Screening of Organic Acids and Essential Oils on Miniaturized Pork Loins, Followed by Lab-Scale Validation That Confirmed Tested Compounds Do Not Show Synergistic Effects against Salmonella Typhimurium

The many possible treatments and continuously changing consumer trends present a challenge when selecting antimicrobial interventions during pork processing. Thirty-five potential antimicrobials were screened at commercial working concentrations by individually adding them to miniaturized (69 cm3) disks of pork loin ends, followed by inoculation with Salmonella Typhimurium ATCC 19585. Two organic acids and nine essential oils significantly inhibited Salmonella counts on pork (p < 0.05). However, six compounds that represent different levels of significance (p < 0.05-p < 0.0001) were selected as independent variables to build a Response Surface Methodology model based on a Doehlert matrix (Doehlert Matrix-RSM): lactic acid 1.25%, formic acid 0.25%, cumin 0.25%, clove 0.25%, peppermint 0.5%, and spearmint 0.5%. The goal of the Doehlert Matrix-RSM was to study single and paired effects of these antimicrobials on the change in Salmonella over 24 h. The Doehlert Matrix-RSM model predicted that lactic acid, formic acid, cumin, peppermint, and spearmint significantly reduced Salmonella when added alone, while no significant interactions between these antimicrobials were found. A laboratory-scale validation was carried out on pork loin end slices, which confirmed the results predicted by the model. While this screening did not identify novel synergistic combinations, our approach to screening a variety of chemical compounds by implementing a miniaturized pork loin disk model allowed us to identify the most promising antimicrobial candidates to then formally design experiments to study potential interactions with other antimicrobials.

Synthesis and Application of a New Polymer with Imprinted Ions for the Preconcentration of Uranium in Natural Water Samples and Determination by Digital Imaging

This work proposes the synthesis of a new polymer with imprinted ions (IIP) for the pre-concentration of uranium in natural waters using digital imaging as a detection technique. The polymer was synthesized using 2-(5-bromo-2-pyridylazo)-5-diethylaminophenol (Br-PADAP) for complex formation, ethylene glycol dimethacrylate (EGDMA) as a crosslinking reagent, methacrylic acid (AMA) as functional monomer, and 2,2'-azobisisobutyronitrile as a radical initiator. The IIP was characterized by Fourier transform infrared spectroscopy and scanning electron microscopy (FTIR). Uranium determination was performed using digital imaging (ID), and some experimental conditions (sample pH, eluent concentration, and sampling flow rate) were optimized using a two-level full factorial design and Doelhert response surface methodology. Thus, using the optimized conditions, the system allowed the determination of uranium with detection and quantification limits of 2.55 and 8.51 µg L^-1, respectively, and a pre-concentration factor of 8.2. All parameters were determined using a 25 mL sample volume. The precision expressed as relative deviation (RSD%) was 3.5% for a solution with a concentration of 50 µg L^-1. Given this, the proposed method was used for the determination of uranium in four samples of natural waters collected in the city of Caetité, Bahia, Brazil. The concentrations obtained ranged from 35 to 75.4 μg L^-1. The accuracy was evaluated by the addition/recovery test, and the values found ranged between 91 and 109%.

Updating the status quo on the extraction of bioactive compounds in agro-products using a two-pot multivariate design. A comprehensive review

Extraction is regarded as the most crucial stage in analyzing bioactive compounds. Nonetheless, due to the intricacy of the matrix, numerous aspects must be optimized during the extraction of bioactive components. Although one variable at a time (OVAT) is mainly used, this is time-consuming and laborious. As a result, using an experimental design in the optimization process is beneficial with few experiments and low costs. This article critically reviewed two-pot multivariate techniques employed in extracting bioactive compounds in food in the last decade. First, a comparison of the parametric screening methods (factorial design, Taguchi, and Plackett-Burman design) was delved into, and its advantages and limitations in helping to select the critical extraction parameters were discussed. This was followed by a discussion of the response surface methodologies (central composite (CCD), Doehlert (DD), orthogonal array (OAD), mixture, D-optimal, and Box-Behnken designs (BBD), etc.), which are used to optimize the most critical variables in the extraction of bioactive compounds in food, providing a sequential comprehension of the linear and complex interactions and multiple responses and robustness tests. Next, the benefits, drawbacks, and possibilities of various response surface methodologies (RSM) and some of their usages were discussed, with food chemistry, analysis, and processing from the literature. Finally, extraction of food bioactive compounds using RSM was compared to artificial neural network modeling with their drawbacks discussed. We recommended that future experiments could compare these designs (BBD vs. CCD vs. DD, etc.) in the extraction of food-bioactive compounds. Besides, more research should be done comparing response surface methodologies and artificial neural networks regarding their practicality and limitations in extracting food-bioactive compounds.

Study and optimization of oil-in-water emulsions formulated by low- and high-frequency ultrasounds

OBJECTIVE

A combined treatment using both low-frequency (20 kHz) and high-frequency ultrasounds (1.63 MHz) is a promising new process to stabilize emulsions with minimalist formulation. In order to optimize process parameters, a Doehlert experimental design was performed with oil-in-water emulsions, presently used for cosmetic products, composed of water, caprylic/capric triglycerides and oleic acid.

METHODS

Effects of treatment time, oil content and oleic acid content were studied on emulsion properties (droplet size, polydispersity index, ζ-potential and yield of oil incorporation) and on emulsion stability after a 28-day storage (creaming index, Turbiscan stability index (TSI) and oil release).

RESULTS

From experimental data, a model was established that allowed to study effects of each parameter and their interactions on emulsion formation and stability. Oleic acid content had a great impact on emulsion formation: It reduced droplet size, PDI and ζ-potential and increased yield of oil incorporation. However, a critical value could be highlighted, beyond which oleic acid effects reversed. Treatment time had an important beneficial effect on emulsion stability as it decreased creaming index, TSI and oil release after 28 days of storage. Oil content had a negative effect on emulsion formation and on emulsion stability. However, treatment time and oil content often had a beneficial synergistic effect.

CONCLUSION

The optimized conditions for emulsion processing were obtained through a desirability approach. They were experimentally validated.

A new and accessible instrumentation to determine urea in UHT milk using digital image analysis

This research demonstrates the development, optimization and application of a new low-cost detection system, based on digital image analysis, for the detection of urea in ultra-high-temperature (UHT) milk samples. The apparatus built in the laboratory, allows the capture of images through a simple system built by polyvinyl chloride (PVC) tubes, a digital microscope and a peristaltic mini-pump, after the colorimetric reaction between urea and diacetylmonoxime (butane-2,3-dionammonoxime). The red, green and blue (RGB) and hue, saturation and value (HSV) color systems were studied, with the saturation channel of the HSV color system selected as the analytical signal. Subsequently, the experimental chemical conditions were evaluated through multivariate experimental designs and the optimal conditions were defined. The proposed method was validated, and the detection and quantification limits presented by the method were 0.35 mg L^-1 and 0.52 mg L^-1, respectively; precision, ranged between 1.6  and 2.8 %. The results were compared with those obtained using the mid-infrared technique and no statistically significant differences were observed at a 95 % confidence level. The proposed method was applied to eight UHT milk samples that presented urea content ranging from 187 to 386 mg L^-1. The mean values obtained are in agreement with values presented in other studies for the determination of urea in milk. The results indicated that the system described and validated here is promising and can be applied to assess the authenticity and quality of milk.