Optimization of kidney bean antioxidants using RSM & ANN and characterization of antioxidant profile by UPLC-QTOF-MS

Screening and characterization of cosmetic efficacy components of Terminalia chebula based on biological activity-guided methodology

Terminalia chebula exhibits a high level of antioxidant capacity and is highly valued in medicine and cosmetics. However, its main efficacy and active ingredients related to antioxidant, whitening, and anti-aging are still unclear. In this study, the active site responsible for its cosmetic efficacy was specified by the biological activity-guided method and further characterized by using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS). T. chebula was ultrasonically extracted by five solvents, and 30% ethanol extract was screened out for subsequent purification by 1,1-D-iphenyl-2-picrylhydrazyl radical (DPPH), 2,2'-Azinobis-(3-ethylbenzothiazoline-6-sulphonate) (ABTS), hydroxyl, and superoxide anion free radical scavenging assays. Five elution fractions were obtained by column chromatography on D101 macroporous adsorbent resin eluted by an increased proportion of ethanol. The 30% ethanol elution fraction was specified as the enrichment site of active ingredients showing good antioxidant capacity and potent inhibitory activity against tyrosinase and elastase. A total of 30 compounds were identified by UHPLC-QTOF-MS/MS in the 30% ethanol elution fraction, including 11 gallotannins, 14 ellagitannins, and 5 other compounds, and these compounds may be the key ingredients in cosmetics beneficial for the skin. Such a biological activity-guided method has provided a simple and rapid venue for specifying the components of medicinal herbs responsible for cosmetic efficacy.

A green and efficient approach for the simultaneous extraction and mechanisms of essential oil and lignin from Cinnamomum camphora: Process optimization based on deep learning

The utilization and economic benefits of biomass resources can be maximized through rational design and process optimization. In this study, an innovative approach for the simultaneous extraction of essential oil and lignin from Cinnamomum camphora leaves by deep eutectic solvent (DES) and optimization of the process parameters was achieved using deep learning tools. With the water content of 40 %, liquid-solid ratio of 9.00 mL/g, and distillation time of 51.81 min, the yields of the essential oil and lignin reached 3.15 ± 0.02 % and 9.75 ± 0.15 %, respectively. Notably, the efficiency of simultaneous extraction of essential oil improved by 23 % compared to that of traditional steam distillation. Moreover, the extraction mechanism of the process was clarified. The connection between lignin with cellulose and hemicellulose was disintegrated by the DES, resulting in lignin shedding and hence accelerating the dissolution of essential oil. Moreover, the compositions of lignin and essential oil were also identified.

Effect of dielectric barrier discharge nonthermal plasma treatment on physicochemical, nutritional, and phytochemical quality attributes of pineapple [Ananas comosus (L.)] juice

Forward feed multilayered perception and central composite rotatable design were used to model the nonthermal plasma (NTP) experimental data in artificial neural network (ANN) and response surface methodology, respectively. The ANN was found to be more accurate in modeling the experimental dataset. The NTP process parameters (voltage and time) were optimized for pineapple juice within the range of 25-45 kV and 120-900 s using an ANN coupled with the genetic algorithm (ANN-GA). After 176 generations of GA, the ANN-GA approach produced the optimal condition, 38 kV and 631 s, and caused the inactivation of peroxidase (POD) and bromelain by 87.24% and 51.04%, respectively. However, 100.32% of the overall antioxidant capacity and 89.96% of the ascorbic acid were maintained in the optimized sample with a total color change (ΔE) of less than 1.97 at all plasma treatment conditions. Based on optimal conditions, NTP provides a sufficient level of POD inactivation combined with excellent phenolic component extractability and high antioxidant retention. Furthermore, plasma treatment had an insignificant effect (p > 0.05) on the physicochemical attributes (pH, total soluble solid, and titratable acidity) of juice samples. From the intensity peak of the Fourier-transform infrared spectroscopy analysis, it was found that the sugar components and phenolic compounds of plasma-treated juice were effectively preserved compared to the thermal-treated juice.

Enhancing phytochemical parameters in broccoli through vacuum impregnation and their prediction with comparative ANN and RSM models

Amidst increasing demand for nutritious foods, the quest for effective methods to enhance health-promoting attributes has intensified. Vacuum impregnation (VI) is a promising technique to augment produce properties while minimizing impacts on biochemical attributes. In light of broccoli's growing popularity driven by its nutritional benefits, this study explores the impact of VI using ascorbic acid and calcium chloride as impregnation agents on enhancing its phytochemical properties. Response surface methodology (RSM) was used for optimization of the vacuum impregnation process with Vacuum pressure (0.6, 0.4, 0.2 bar), vacuum time (3, 7, 11 min), restoration time (5, 10, 15 min), and concentrations (0.5, 1.0, 1.5%) as independent parameters. The influence of these process parameters on six targeted responses viz. total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid content (AAC), total chlorophyll content (TCC), free radical scavenging activity (FRSA), and carotenoid content (CC) were analysed. Levenberg-Marquardt back propagated neural network (LMB-ANN) was used to model the impregnation process. Multiple response optimization of the vacuum impregnation process indicated an optimum condition of 0.2 bar vacuum pressure, 11 min of vacuum time, 12 min of restoration time, and 1.5% concentration of solution for vacuum impregnation of broccoli. The values of TPC, TFC, AAC, TCC, FRSA, and CC obtained at optimized conditions were 291.20 mg GAE/100 g, 11.29 mg QE/100 g, 350.81 mg/100 g, 1.21 mg/100 g, 79.77 mg, and 8.51 mg, respectively. The prediction models obtained through ANN was found suitable for predicting the responses with less standard errors and higher R2 value as compared to RSM models. Instrumental characterization (FTIR, XRD and SEM analysis) of untreated and treated samples were done to see the effect of impregnation on microstructural and morphological changes in broccoli. The results showed enhancement in the TPC, TFC, AAC, TCC, FRSA, and CC values of broccoli florets with impregnation. The FTIR and XRD analysis also supported the results.

Optimization of a polyvinyl butyral synthesis process based on response surface methodology and artificial neural network

High quality polyvinyl butyral (PVB) can be used as the intermediate film of automobile and building glass and the packaging film of photovoltaic cells. Therefore, it is necessary to optimize its synthesis process to obtain suitable products with a high acetalization degree (AD) and small particle size (d _p). In this work, a deep eutectic solvent (DES) was selected as the catalyst, and response surface methodology (RSM) and artificial neural network (ANN) were utilized to optimize the synthesis process of PVB. The concentration of polyvinyl alcohol (A), the dosage of DES (B) and n-butanal (C), and the aging temperature (D) were selected as process variables, and the comprehensive score (AD, d _p and material and energy consumption) was introduced as the response. The results showed that single-factors B, C, D, and the interactions AB, BC and CD had significant effects on the comprehensive score, and the qualified PVB products (AD > 81%, d _p = 3-3.5 μm) were obtained under the optimal conditions obtained by RSM and ANN models. ANN is a better and more precise optimization tool than RSM. Also, DES played a dual role in catalysis and dispersion in the synthesis of PVB and showed good reusability, so it has great application potential in PVB industrial production.

Optimization of the processing technology of schizonepetae herba carbonisata using response surface methodology and artificial neural network and comparing the chemical profiles between raw and charred schizonepetae herba by UPLC-Q-TOF-MS

In this study, response surface methodology (RSM) and artificial neural network (ANN) were used to predict and validate the optimal processing method of Schizonepetae Herba Carbonisata (SHC). The highest overall desirability (OD) value of the total flavonoids content (TFC), total tannin content (TTC), and adsorption capacity (AC) were used as response values. The optimal processing technology processing time lasted 10 min at a processing temperature of 178 °C and the herbs/machine had a volume of 77 g/5 L. The Ultra Performance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-TOF-MS), combined with chemometrics, was used to investigate the changes of compounds in Schizonepetae Herba (SH) before and after being charred. A total of 104 compounds were tentatively identified in SH and 83 in SHC. Fifteen differential compounds were found between by chemometrics SH and SHC. Altogether, our findings can provide a practical approach to the processing technology of carbonizing by stir-frying SH.

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.

Identification and Optimization of a Novel Taxanes Extraction Process from Taxus cuspidata Needles by High-Intensity Pulsed Electric Field

Taxanes are a series of natural compounds with great application potential in antitumor therapy, whereas the lack of efficient taxanes extraction methods significantly hinders the development of taxanes. The high-intensity pulsed electric field (PEF) is a novel technology used to extract bioactive ingredients from food and other natural products. However, the prospect of using PEF for taxanes extraction remains to be elucidated. Herein, we extracted taxanes from Taxus cuspidata via PEF and explored the effects of seven extraction conditions on the yields of target compounds. The Placket–Burman design (PBD) assay revealed that electric field strength, pulse number, and particle size are key factors for taxanes extraction. The response surface methodology (RSM) and back-propagation neural network conjugated with genetic algorithm (GA-BP) were further used to model and predict the optimal extraction conditions, and GA-BP exerted higher reliability, leading to a maximum extraction yield of 672.13 μg/g under electric field strength of 16 kV/cm, pulse number of 8, particle size of 160 meshes, solid–liquid ratio of 1:60, a single extraction, centrifugal speed of 8000 r/min, and flow rate of 7 mL/min, which was 1.07–1.84 folds that of control, solid–liquid extraction (SL), and ultrasonic extraction (US) groups. Additionally, the scanning electron microscopy (SEM) results indicated that the sample particles extracted by PEF method exhibited a coarser surface morphology. Thus, we present for the first time that PEF is feasible for the extraction of taxanes from Taxus cuspidata and highlight the application value of the PBD, RSM, and GA-BP models in parameters optimization during extraction process.

Polyphenols Recovery from Thymus serpyllum Industrial Waste Using Microwave-Assisted Extraction-Comparative RSM and ANN Approach for Process Optimization

The aim of this study was to valorize Thymus serpyllum L. herbal dust, the particular fraction distinguished as industrial waste from filter-tea production. This work demonstrated comparable analysis considering model fitting, influence analysis and optimization of microwave-assisted extraction (MAE) of bioactive compounds from the aforementioned herbal dust using face-centered central composite experimental design within the response surface methodology (RSM), as well as artificial neural networks (ANN). In order to increase yield and amount of compounds of interest and minimize solvent, time and energy consumption, the ethanol concentration (45, 60 and 75%), extraction time (5, 12.5 and 20 min), liquid–solid ratio (10, 20 and 30 mL/g) and irradiation power (400, 600 and 800 W) were used as independent variables. Total extraction yield (Y), total phenols yield (TP), as well as antioxidant activity parameters obtained by DPPH and ABTS assays, were selected as responses. It could be concluded that the MAE technique is an efficient approach for the extraction of biologically active compounds from T. serpyllum herbal dust, which represents a high-value source of natural antioxidants with great potential for further use in various forms within different branches of industry.

Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression

The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12–30 °C and a_w = 0.75–0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted a_w, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R² = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.

A hybrid RSM-ANN-GA approach on optimisation of extraction conditions for bioactive component-rich laver (Porphyra dentata) extract

This research established the optimal conditions for infusion extraction (IE) and ultrasound-assisted extraction (UAE) of bioactive components from laver (Porphyra dentata) using response surface methodology (RSM) and artificial neural network coupled with genetic algorithm (RSM-ANN-GA). The variables, temperatures (60, 80, and 100 ℃) and times (10, 15, and 20 min) were designed to optimise total phenolic, total flavonoid, total amino acid, a* value, and R-phycoerythrin content of laver extract. The optimised condition for IE and UAE was achieved at 60 ℃ for 18.08 min and 80.66℃ for 14.76 min in RSM while showing 60 ℃ for 19 min and 80℃ for 15 min in the RSM-ANN-GA mode, respectively. Results revealed that RSM-ANN-GA provided better predictability and greater accuracy than the RSM model and laver extract from UAE gave the higher values of responses compared to those from IE. These findings highlight the high-efficient extraction method along with better statistical approach.

Ultrasound Processing of Vinegar: Modelling the Impact on Bioactives and Other Quality Factors

In recent years, non-thermal technology has been used for the enrichment of ultrasound bioactive components. For this purpose, it was applied to tomato vinegar and modeled with response surface methodology (RSM) and artificial neural network (ANN). At the end of the RSM, cupric reducing antioxidant capacity (68.64%), 1,1-diphenyl-2-picrylhydrazyl (62.47%), total flavonoid content (2.44 mg CE/mL), total phenolic content (12.22 mg GAE/mL), total ascorbic acid content (2.53 mg/100 mL) and total lycopene (5.44 μg/mL) were determined. The ANN model has higher prediction accuracy than RSM. The microstructure, microbiological properties, sensory analysis, ACE (angiotensin-converting-enzyme) inhibitor and antidiabetic effects of the ultrasound-treated tomato vinegar (UTV) (8.9 min and 74.5 amplitude), traditional tomato vinegar (TTV) and pasteurized tomato vinegar (PTV) samples were then evaluated. UTV was generally appreciated by the panelists. It was determined that the microbiological properties were affected by the ultrasound treatment. UTV was found to have more effective ACE inhibitor and antidiabetic properties than other vinegar samples. As a result, the bioactive components of tomato vinegar were enriched with ultrasound treatment and positive effects on health were determined.

Optimization, modeling, and characterization study for the physicochemical properties of raw banana and defatted soy composite extrudates

Novel raw banana and defatted soy composite extrudates were formulated to combat celiac enteropathy. The influence of four independent parameters viz. barrel temperature (BT: 60-80 °C), screw speed (SS: 200-300 rpm), moisture content (MC: 10-20%, wb), and defatted soy flour content (SFC: 0-32%) on physicochemical properties of extrudates were evaluated. Response surface methodology (RSM) identified the optimum extrusion conditions (80 °C BT, 200 rpm SS, 10% MC, and 32% SFC). Compared to RSM (R²:0.379-0.918), artificial neural network (R²:0.909-0.991) proved as a better tool for response prediction. Scanning electron microscopy confirmed the antagonistic effect of SFC addition on extrudate porosity. The addition of SFC decreased the crystallinity of the starch granules (71 to 49%), whereas, storage time had a positive effect (49 to 53%) on crystallinity of the composite extrudates. Fourier transform infrared spectroscopy analysis elucidated the secondary structures of protein in the composite flour which were degraded during the extrusion cooking.