Response surface methodology and artificial neural network approach for the optimization of ultrasound-assisted extraction of polyphenols from garlic

Polyphenols From Mallotus peltatus (Geiseler) Mull. Arg. Ameliorates FFA-Induced Hepatic Steatosis in L02 Cells and Reduces Lipid Accumulation in Caenorhabditis elegans

Mallotus peltatus (Geiseler) Mull. Arg. (MPMA) is a specialty plant used to make tea in Hainan Province, China. However, its hypolipidemic activity has been rarely studied. In this study, three polyphenol fractions (MPMAP-1, MPMAP-2, and MPMAP-3) were purified from a 60% ethanol extract of MPMA, and the hypolipidemic activities were evaluated by establishing an FFA-induced L02 cell model to determine lipid accumulation, antioxidant enzyme activities, and gene levels related to the Nrf2/ARE pathway and lipid metabolism. In addition, noninduced and high glucose-induced models were established using Caenorhabditis elegans (C. elegans) to evaluate the lipid-lowering activity of MPMAP-1. The results showed that all three polyphenols could significantly inhibit lipid accumulation, reduce intracellular MDA content, and enhance the activities of CAT, SOD, and GPx in FFA-induced L02 cells. The qRT-PCR results indicated that the amount of fat accumulation in L02 cells could be regulated by modulating the relative expression of mRNA in the Nrf2/ARE signaling pathway and lipid metabolism pathway. The noninduced model and high glucose-induced model demonstrated that MPMAP-1 was able to reduce lipid accumulation and ROS levels and increase the activities of antioxidant enzymes in C. elegans. In summary, our results suggested that polyphenol compounds of MPMA may be a promising natural product for lipid-lowering.

Tyrosinase inhibitory activity of Sargassum fusiforme and characterisation of bioactive compounds

INTRODUCTION

Sargassum fusiforme (Harvey) Setchell, also known as Tot (in Korean) and Hijiki (in Japanese), is widely consumed in Korea, Japan, and China due to its health promoting properties. However, the bioactive component behind the biological activity is still unknown.

OBJECTIVES

We aimed to optimise the extraction conditions for achieving maximum tyrosinase inhibition activity by using two sophisticated statistical tools, that is, response surface methodology (RSM) and artificial neural network (ANN). Moreover, high-resolution mass spectrometry (HRMS) was used to tentatively identify the components, which are then further studied for molecular docking study using 2Y9X protein.

METHODOLOGY

RSM central composite design was used to conduct extraction using microwave equipment, which was then compared to ANN. Electrospray ionisation tandem mass spectrometry (ESI-MS/MS) was used to tentatively identify bioactive components, which were then docked to the 2Y9X protein using AutoDock Vina and MolDock software.

RESULTS

Maximum tyrosinase inhibition activity of 79.530% was achieved under optimised conditions of time: 3.27 min, temperature: 128.885°C, ethanol concentration: 42.13%, and microwave intensity: 577.84 W. Furthermore, 48 bioactive compounds were tentatively identified in optimised Sargassum fusiforme (OSF) extract, and among them, seven phenolics, five flavonoids, five lignans, six terpenes, and five sulfolipids and phospholipids were putatively reported for the first time in Sargassum fusiforme. Among 48 bioactive components, trifuhalol-A, diphlorethohydroxycarmalol, glycyrrhizin, and arctigenin exhibited higher binding energies for 2Y9X.

CONCLUSION

Taken together, these findings suggest that OSF extract can be used as an effective skin-whitening source on a commercial level and could be used in topical formulations by replacing conventional drugs.

Ultrasonic collaborative pulse extraction of sugarcane polyphenol with good antiaging and α-glucosidase inhibitory activity

Sugarcane, as one important and heavily planted industrial crop, is meaningful to develop its byproducts. In this paper, the ultrasonic collaborative pulse was beneficial for the yield improvement and good bioactivity protection. The sugarcane polyphenol extract (SPE) yield reached 2.42 ± 0.08 mg/g DW at the optimized conditions: pulse time of 60 s, pulse intensity of 2 kV/cm, ultrasonic time of 90 min, and ultrasonic power of 120 W. The SPE contained the total phenolic content of 6.01 ± 0.12 mg GAE/g extract and total flavonoids content of 7.15 ± 0.24 mg RE/g extract. The SPE was mainly composed of chlorogenic acid, schaftoside, hyperoside, quercitrin, and trans-3-hydroxycinnamic acid with 10.24 %, 14.92 %, 4.22 %, 12.05 %, 25.54 %, respectively. The SPE showed good radical scavenging activity with ORAC value of 134.57 μmol/g. The SPE could reduce the oxidative stress and extend the mean lifespan of nematodes by 7.19 % in vivo through increasing the activity of SOD and CAT to decrease the ROS level and MDA content. In addition, the SPE showed strong α-glucosidase inhibitory activity with IC50 of 0.53 mg/mL in a mixed inhibition type, which suggested that the SPE had good hypoglycemic potential.

Optimization of the Ultrasound-Assisted Extraction of Phenolic Antioxidants from Cistus Salvifolius L. Using Response Surface Methodology

The current work aims to optimize the ultrasound-assisted extraction (UAE) of Cistus salviifolius L. (aerial parts) antioxidative phenolic compounds using response surface methodology. A Box-Behnken design has been conducted to investigate the effect of four factors, namely: (i) percentage of ethanol (50-90 %, v/v), (ii) temperature (40-80 °C), (iii) solvent-solid ratio (10-50 mL g-1) and (iv) extraction time (5-25 min) on four responses, namely: total phenolic content (TPC), total flavonoid content (TFC) 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical inhibition, and ferric reducing antioxidant power (FRAP). Based on the desirability index, UAE with 50 % (v/v) ethanol, at 80 °C, using a solvent-solid ratio of 32.24 mL g-1, for 21 min resulted in the maximum recovery of phenolic antioxidants. Under optimum conditions, the experimental values of TPC, TFC, % DPPH radical scavenging activity, and FRAP were 171.67±4.69 mg GAE g-1, 26.87±0.78 mg CE g-1, 81.31±0.16 %, and 1038.22±7.69 μmol TE g-1, respectively. Results shows a reasonable agreement of experimental values with the predicted ones; the absolute error values being in all cases lower than 2.90 %. The present work provide a developed eco-friendly extarction method that is appropriate for the improved recovery of phenolic antioxidants from C. salviifolius L.

Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology

Plant products and its by-products are rich source of bioactive compounds like antioxidants, flavonoids, phenolics, pigments and phytochemicals. Bioactive compound's health-promoting properties are well studied. However, optimal extraction of bioactive compounds is a complex, labour- and time-intensive process. It is also highly sensitive to experimental variables. Predicting output variables can reduce the experimental work and has positive environmental impact. Various tools such as Response Surface Methodology (RSM), Mathematical modelling have been commonly used for optimization and predictive modelling of the extraction process. Although mathematical modelling and RSM are efficient, recent studies have used Artificial Neural Network (ANN) which is more efficient and accurate and can perform extensive predictions with high accuracy. The manuscript focuses on current trends of ANN application in optimizing the extraction of bioactive compounds. In this study, ANN and RSM have been compared in terms of their performances in optimizing and modelling the extraction of bioactive compounds from herbs, medicinal plants, fruit, vegetables, and their by-products. The findings from the literature indicate that efficiency of ANN was superior to RSM. Future researches can focus on use of ANN in industrial optimization experiments.

Potassium chloride-assisted heat treatment enhances the de-glycosylation efficiency and xanthine oxidase inhibitory activity of Sophora japonica L. flavonoids

Salt-assisted heat treatment is considered an effective way to enhance the bioactivities of flavonoids in Flos Sophorae Immaturus tea (FSIt). Herein, sodium chloride (NaCl)- and potassium chloride (KCl)-assisted heat treatment was employed to process FSIt, the components, xanthine oxidase (XO) inhibitory activity, and degradation or conversion kinetics of FSIt flavonoids were recorded. Results showed that KCl-assisted heat treatment significantly increased the XO inhibition rate of FSIt from 28.05 % to 69.50 %. The de-glycosylation of flavonoids was the crucial reason for enhancing XO inhibitory activity. Notably, KCl exhibited a better catalytic effect on the de-glycosylation reaction than NaCl. Meanwhile, conversion kinetics showed that the generation rate of quercetin, kaempferol, and isorhamnetin reached the maximum at 180, 160, 160 °C, respectively. Furthermore, the established artificial neural network model could accurately predict the changes of FSIt flavonoids during salt-assisted heat treatment. Thus, KCl can be used as a valuable food processing adjuvant to enhance the bioactivities of food materials.

Optimizing Recovery of High-Added-Value Compounds from Complex Food Matrices Using Multivariate Methods

In today's food industry, optimizing the recovery of high-value compounds is crucial for enhancing quality and yield. Multivariate methods like Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) play key roles in achieving this. This review compares their technical strengths and examines their sustainability impacts, highlighting how these methods support greener food processing by optimizing resources and reducing waste. RSM is valued for its structured approach to modeling complex processes, while ANNs excel in handling nonlinear relationships and large datasets. Combining RSM and ANNs offers a powerful, synergistic approach to improving predictive models, helping to preserve nutrients and extend shelf life. The review emphasizes the potential of RSM and ANNs to drive innovation and sustainability in the food industry, with further exploration needed for scalability and integration with emerging technologies.

Optimizing ultrasonication-assisted comprehensive extraction of bioactive flavonoids from Pterocarpus santalinus leaves using response surface methodology

The leaves of Pterocarpus santalinus have been identified as a good source of health-beneficial flavonoids through the amalgamation of untargeted metabolomics using UHPLC-ESI-MSn leading to the identification of flavone-glycosides bearing isorhamnetin and quercetin skeletons. To unveil the optimum ultrasonication extraction conditions required for the comprehensive extraction of major flavone-glycosides, isorhamnetin-3-O-β-d-(2-O-α-L-rhamnopyranosyl)glucopyranoside and isorhamnetin-3-O-β-d-glucopyranoside, the response surface methodology based on Box-Behnken design was adopted. The influence of input extraction parameters extraction time (X1): 15-45 min, temperature (X2): 40-60 °C and biomass-solvent ratio (X3): 60-100 on the extractive yield and comprehensive flavonoid content resulted in the optimal conditions as 19.09 min, 48.65 oC, and 72.15, respectively. The investigation provides a sustainable approach for recovering health-beneficial flavone-glycosides for utilization in various industries.

Decoding the bitter taste of Idesia polycarpa var. vestita Diels fruit: Bitterness contribution and mechanisms

To comprehensively explore the contribution and mechanisms of identified low-threshold bitter substances in Idesia polycarpa var. vestita Diels (I. vestita) fruit, we performed quantification and elucidated their interactions with main bitter taste receptors through molecular docking. The established method for quantifying bitter compounds in I. vestita fruit was validated, yielding satisfactory parameters for linearity, stability, and accuracy. Idescarpin (17.71-101.05 mg/g) and idesin (7.88-77.14 mg/g) were the predominant bitter compounds in terms of content. Taste activity values (TAVs) exceeded 10 for the bitter substances, affirming their pivotal role as major contributors to overall bitterness of I. vestita fruit. Notably, idescarpin with the highest TAV, played a crucial role in generating the bitterness of I. vestita fruit. Hydrogen bonds and hydrophobic interactions were the main driving forces. This study holds potential implications for industrial development of I. vestita fruit by providing novel insights into the mechanism underlying its bitterness formation.

Physical field-assisted deep eutectic solvent processing: A green and water-saving extraction and separation technology

Extraction of organic and bioactive compounds from plant materials with the traditional organic solvents aided by water or oil bath heating is not sustainable, because it consumes a lot of energy, time, water/oil, solvents, and results in lower yield. This review discusses deep eutectic solvent (DES) as a green solvent, physical field technology (PFT) as a water-saving and green technology, and how the coupling of PFT (ultrasound [US], microwave [MW], infrared [IR]) to DES will improve the yield and quality of protein, polysaccharides, polyphenols, pectin, and terpenoids extracted from plant materials. Ultrasonication increases DES extraction efficiency via cavitation dislodgement and pores creation. IR coupling to DES enhances the extraction yield of polyphenols and the antioxidant and antiradical activity. MW improves DES extraction yield, reduces energy consumption, operational cost, and compound degradation, and is inferred to be the greenest technology.

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.

Determination of Drying Characteristics and Physicochemical Properties of Mint (Mentha spicata L.) Leaves Dried in Refractance Window

Drying is one of the most common and effective techniques for preserving the quantitative and qualitative characteristics of medicinal plants in the post-harvest phase. Therefore, in this research, the effect of the new refractance window (RW) technology on the kinetics, thermodynamics, greenhouse gasses, color indices, bioactive properties, and percentage of mint leaf essential oil was investigated in five different water temperatures in the form of a completely randomized design. This process was modeled by the methods of mathematical models and artificial neural networks (ANNs) with inputs (drying time and water temperature) and an output (moisture ratio). The results showed that with the increase in temperature, the rate of moisture removal from the samples increased and as a result, the drying time, specific energy consumption, CO2, NOx, enthalpy, and entropy decreased significantly (p < 0.05). In addition, the drying water temperature had a significant effect on the rehydration ratio, color indices, bioactive properties, and essential oil percentage of the samples (p < 0.05). The highest value of rehydration ratio was obtained at 80 °C. By increasing temperature, the main color indices such as b*, a*, L*, and Chroma decreased significantly compared to the control (p < 0.05). However, with the increase in temperature, the overall color changes (ΔE) and L* first had a decreasing trend and then an increasing trend, and this trend was the opposite for the rest of the indicators. The application of drying water temperature from 50 to 70 °C increased antioxidant, phenol content, and flavonoid content, and higher drying temperatures led to a significant decrease in these parameters (p < 0.05). On the other hand, the efficiency of the essential oil of the samples was in the range of 0.82 to 2.01%, and the highest value was obtained at the water temperature of 80 °C. Based on the analysis performed on the modeled data, a perceptron artificial neural network with 2-15-14-1 structure with explanation coefficient (0.9999) and mean square error (8.77 × 10-7) performs better than the mathematical methods for predicting the moisture ratio of mint leaves.

Optimization of accelerated solvent extraction of zeaxanthin from orange paprika using response surface methodology and an artificial neural network coupled with a genetic algorithm

This study aimed to optimize the accelerated solvent extraction (ASE) condition of zeaxanthin from orange paprika using a response surface methodology (RSM) or an artificial neural network (ANN) with a genetic algorithm (GA). Input variables were ethanol concentration, extraction time, and extraction temperature, while output variable was zeaxanthin. The mean squared error and regression correlation coefficient of the developed ANN model were 0.3038 and 0.9983, respectively. Predicted optimal extraction conditions from ANN-GA for maximum zeaxanthin were 100% ethanol, 3.4 min, and 99.2 °C. The relative errors under the optimal extraction conditions were RSM for 10.46% and ANN-GA for 2.18%. We showed that the recovery of hydrophobic zeaxanthin could be performed using ethanol, an eco-friendly solvent, via ASE, and the extraction efficiency could be improved by ANN-GA modeling than RSM. Therefore, combining ASE and ANN-GA might be desirable for the efficient and eco-friendly extraction of hydrophobic functional materials from food resources.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-023-01514-8.

Extraction Optimization of Phenolic Compounds from Triadica sebifera Leaves: Identification, Characterization and Antioxidant Activity

Triadica sebifera (T. sebifera) has attracted much attention because of the high oil content in its seeds, but there are few systematic studies on the phenolic compounds of T. sebifera leaves (TSP). In this study, the extraction process of TSP was optimized by response surface methodology. The phenolic components of these extracts were analyzed by high-performance liquid chromatography (HPLC). Moreover, the effects of hot air drying (HD), vacuum drying (VD) and freeze drying (FD) on the antioxidant activity and characterization of T. sebifera leaf extract (TSLE) were evaluated. Under the conditions of ethanol concentration 39.8%, liquid-solid ratio (LSR) 52.1, extraction time 20.2 min and extraction temperature 50.6 °C, the maximum TSP yield was 111.46 mg GAE/g dw. The quantitative analysis and correlation analysis of eight compounds in TSP showed that the type and content of phenolic compounds had significant correlations with antioxidant activity, indicating that tannic acid, isoquercitrin and ellagic acid were the main components of antioxidant activities. In addition, through DPPH and ABTS determination, VD-TSLE and FD-TSLE showed strong scavenging ability, with IC50 values of 138.2 μg/mL and 135.5 μg/mL and 73.5 μg/mL and 74.3 μg/mL, respectively. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) infrared spectroscopy revealed small differences in the extracts of the three drying methods. This study lays a foundation for the effective extraction process and drying methods of phenolic antioxidants from T. sebifera leaves, and is of great significance for the utilization of T. sebifera leaves.

Optimization, Metabolomic Analysis, Antioxidant Potential andDepigmenting Activity of Polyphenolic Compounds fromUnmature Ajwa Date Seeds (Phoenix dactylifera L.) Using Ultrasonic-Assisted Extraction

This study sought to optimize the ultrasonic-assisted extraction of polyphenolic compounds from unmature Ajwa date seeds (UMS), conduct untargeted metabolite identification and assess antioxidant and depigmenting activities. Response surface methodology (RSM) utilizing the Box-Behnken design (BBD) and artificial neural network (ANN) modeling was applied to optimize extraction conditions, including the ethanol concentration, extraction temperature and time. The determined optimal conditions comprised the ethanol concentration (62.00%), extraction time (29.00 min), and extraction temperature (50 °C). Under these conditions, UMS exhibited total phenolic content (TPC) and total flavonoid content (TFC) values of 77.52 ± 1.55 mgGAE/g and 58.85 ± 1.12 mgCE/g, respectively, with low relative standard deviation (RSD%) and relative standard error (RSE%). High-resolution mass spectrometry analysis unveiled the presence of 104 secondary metabolites in UMS, encompassing phenols, flavonoids, sesquiterpenoids, lignans and fatty acids. Furthermore, UMS demonstrated robust antioxidant activities in various cell-free antioxidant assays, implicating engagement in both hydrogen atom transfer and single electron transfer mechanisms. Additionally, UMS effectively mitigated tert-butyl hydroperoxide (t-BHP)-induced cellular reactive oxygen species (ROS) generation in a concentration-dependent manner. Crucially, UMS showcased the ability to activate mitogen-activated protein kinases (MAPKs) and suppress key proteins including tyrosinase (Tyr), tyrosinase-related protein-1 and -2 (Trp-1 and -2) and microphthalmia-associated transcription factor (MITF), which associated melanin production in MNT-1 cell. In summary, this study not only optimized the extraction process for polyphenolic compounds from UMS but also elucidated its diverse secondary metabolite profile. The observed antioxidant and depigmenting activities underscore the promising applications of UMS in skincare formulations and pharmaceutical developments.

A study of volatiles of young citrus fruits from four areas based on GC-MS and flash GC e-nose combined with multivariate algorithms

The present study has successfully established a scientific and precise approach for distinguishing the geographical origins of young citrus fruits (Qingpi) from four primary production regions in China, using gas chromatography-mass spectrometry (GC-MS) and flash gas chromatography electronic nose (flash GC e-nose) to analyze the volatile composition and odor characteristics. Through the application of chemometric analysis, a clear differentiation among Qingpi samples was established using GC-MS. Additionally, the application of flash GC e-nose facilitated the extraction of flavor information, which enabled the discrimination of geographical origins. Several flavor components were identified as significant factors for origin certification. Furthermore, two pattern recognition algorithms were employed to achieve high accuracy in regional identification. The results of this investigation demonstrate that the amalgamation of multivariate chemometrics and algorithms can proficiently discern the sources of those young citrus fruits. The findings of this research can provide a reference for the assessment of quality control in food and other agricultural commodities in the times ahead.

Ultrasound assisted phytochemical extraction of red cabbage by using deep eutectic solvent: Modelling using ANFIS and optimization by genetic algorithms

The present investigation studied the effect of process parameters on the extraction of phytochemicals from red cabbage by the application of ultrasonication and temperature. The solvent selected for the study was deep eutectic solvent (DES) prepared by choline chloride and citric acid. The ultrasound assisted extraction process was modeled using adaptive neuro-fuzzy inference system (ANFIS) algorithm and integrated with the genetic algorithm for optimization purposes. The independent variables that influenced the responses (total phenolic content, antioxidant activity, total anthocyanin activity, and total flavonoid content) were ultrasonication power, temperature, molar ratio of DES, and water content of DES. Each ANFIS model was formed by the training of three Gaussian-type membership functions (MF) for each input, trained by a hybrid algorithm with 500 epochs and linear type MF for output MF. The ANFIS model predicted each response close to the experimental data which is evident by the statistical parameters (R2>0.953 and RMSE <1.165). The integrated hybrid ANFIS-GA algorithm predicted the optimized condition for the process parameters of ultrasound assisted extraction of phytochemicals from red cabbage was found to be 252.114 W for ultrasonication power, 52.715 °C of temperature, 2.0677:1 of molar ratio of DES and 25.947 % of water content in DES solvent with maximum extraction content of responses, with fitness value 3.352. The relative deviation between the experimental and ANFIS predicted values for total phenolic content, antioxidant activity, total anthocyanin activity, and total flavonoid content was found to be 1.849 %, 3.495 %, 2.801 %, and 4.661 % respectively.

Natural deep eutectic solvents for the extraction of lentinan from shiitake mushroom: COSMO-RS screening and ANN-GA optimizing conditions

Using natural deep eutectic solvents (NDES) for green extraction of lentinan from shiitake mushroom is a high-efficiency method. However, empirical and trial-and-error methods commonly used to select suitable NDES are unconvincing and time-consuming. Conductor-like screening model for realistic solvation (COSMO-RS) is helpful for the priori design of NDES by predicting the solubility of biomolecules. In this study, 372 NDES were used to evaluate lentinan dissolution capability via COSMO-RS. The results showed that the solvent formed by carnitine (15 wt%), urea (40.8 wt%), and water (44.2 wt%) exhibited the best performance for the extraction of lentinan. In the extraction stage, an artificial neural network coupled with genetic algorithm (ANN-GA) was developed to optimize the extraction conditions and to analyze their interaction effects on lentinan content. Therefore, COSMO-RS and ANN-GA can be used as powerful tools for solvent screening and extraction process optimization, which can be extended to various bioactive substance extraction.

The Optimisation of Bitter Gourd-Grape Beverage Fermentation Using a Consolidated Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Approach

The present study adopted a response surface methodology (RSM) approach validated by artificial neural network (ANN) models to optimise the production of a bitter gourd-grape beverage. Aset of statistically pre-designed experiments were conducted, and the RSM optimisation model fitted to the obtained data, yielding adequately fit models for the monitored control variables R2 values for alcohol (0.79), pH (0.89), and total soluble solids (TSS) (0.89). Further validation of the RSM model fit using ANN showed relatively high accuracies of 0.98, 0.88, and 0.82 for alcohol, pH, and TSS, respectively, suggesting satisfactory predictability and adequacy of the models. A clear effect of the optimised conditions, namely fermentation time at (72 h), fermentation temperature (32.50 and 45.11 °C), and starter culture concentration (3.00 v/v) on the total titratable acidity (TTA), was observed with an R2 value of (0.40) and RSM model fit using ANN overall accuracy of (0.56). However, higher TTA values were observed for samples fermented for 72 h at starter culture concentrations above 3 mL. The level of 35% bitter gourd juice was optimised in this study and was considered desirable because the goal was to make a low-alcohol beverage.

Impact of thermal pasteurization and thermosonication treatments on black grape juice (Vitis vinifera L): ICP-OES, GC-MS/MS and HPLC analyses

Grape juice is a widely consumed fruit due to its bioactive compounds, minerals, and aroma components. Our objective was to investigate ultrasound treatment of black grape juice affects its bioactive components due to using response surface methodology (RSM) and artificial neural network (ANN) optimization. At the same time, mineral components, sugar components, organic acids, and volatile aroma profiles were compared in black grape juice treated with thermal and ultrasound pasteurization. ANN showed superior predictive values (>99%) to RSM. Optimal combinations were obtained at 40 °C, 12 min, and 65% amplitude for thermosonication. Under these conditions, phenolic, flavonoid, antioxidant activity, and anthocyanin values were 822.80 mg GAE/L, 97.50 mg CE/L, 24.51 mmol Trolox/L, and 368, 81 mg of mv-3-glu/L, respectively. Thermosonicated grape juice (TT-BGJ) was tested against black grape juice (P-BGJ) produced with conventional thermal methods. This study investigated the effects of thermal pasteurization and thermosonication on black grape juice bioactive compounds and minerals, aroma profile, and sensory evaluation. Thermosonication affected the aroma profile less, 329.98 μg/kg (P-BGJ) and 495.31 μg/kg (TT-BGJ). TT-BGJ was detected to contain seven different mineral elements (Mn, K, Fe, Mg, Cu, Zn, and Na). Thermosonication caused an increase in Fe, Zn, Mn, and K minerals. Panelists generally liked the TT-BGJ sample. These results suggest that the thermosonication process may potentially replace the traditional black grape juice processing thermal process.

Advances in Natural Product Extraction Techniques, Electrospun Fiber Fabrication, and the Integration of Experimental Design: A Comprehensive Review

The present review explores the growing interest in the techniques employed for extracting natural products. It emphasizes the limitations of conventional extraction methods and introduces superior non-conventional alternatives, particularly ultrasound-assisted extraction. Characterization and quantification of bioactive constituents through chromatography coupled with spectroscopy are recommended, while the importance of method development and validation for biomarker quantification is underscored. At present, electrospun fibers provide a versatile platform for incorporating bioactive extracts and have extensive potential in diverse fields due to their unique structural and functional characteristics. Thus, the review also highlights the fabrication of electrospun fibers containing bioactive extracts. The preparation of biologically active extracts under optimal conditions, including the selection of safe solvents and cost-effective equipment, holds promising potential in the pharmaceutical, food, and cosmetic industries. Integration of experimental design into extraction procedures and formulation development is essential for the efficient production of health products. The review explores potential applications of encapsulating natural product extracts in electrospun fibers, such as wound healing, antibacterial activity, and antioxidant properties, while acknowledging the need for further exploration and optimization in this field. The findings discussed in this review are anticipated to serve as a valuable resource for the processing industry, enabling the utilization of affordable and environmentally friendly, natural, and raw materials.

Modeling and Optimization of Ultrasound-Assisted Extraction of Bioactive Compounds from Allium sativum Leaves Using Response Surface Methodology and Artificial Neural Network Coupled with Genetic Algorithm

This study explains the effect of ultrasound on the extraction of the bioactive compounds from garlic (Allium sativum L.) leaf powder. The experiment was carried out by varying the ultrasound amplitude (30-60%), treatment time (5-15 min), and ethanol concentration (40-60%) required to obtain the maximum extraction yield of total phenol content (TPC), total flavonoid content (TFC), and antioxidant activity. Rotatable central composite design (RCCD) provided experimental parameter combinations in the ultrasound-assisted extraction (UAE) of garlic leaf powder. The values of extraction yield, TPC, TFC, and antioxidant activity for the optimized condition of RSM were obtained at 53% amplitude, 13 min of treatment time, and 50% ethanol concentration. The values of the target compounds predicted at this optimized condition from RSM were 32.2% extraction yield, 9.9 mg GAE/g TPC, 6.8 mg QE/g TFC, and 58% antioxidant activity. The ANN-GA optimized condition for the leaf extracts was obtained at 60% amplitude, 13 min treatment time, and 53% ethanol concentration. The predicted values of optimized condition obtained by ANN-GA were recorded as 32.1738% extraction yield and 9.8661 mg GAE/g, 6.8398 mg QE/g, and 58.5527% for TPC, TFC, and antioxidant activity, respectively. The matured leaves of garlic, if not harvested during its cultivation, often go waste despite being rich in antioxidants and phenolic compounds. With the increased demand for the production of value-added products, the extraction of the bioactive compounds from garlic leaves can resolve waste management and potential health issues without affecting the crop yield through the process for high-end use in value addition.

Microwave-Assisted Extraction Optimization and Effect of Drying Temperature on Catechins, Procyanidins and Theobromine in Cocoa Beans

Cocoa beans (Theobroma cacao L.) are an important source of polyphenols. Nevertheless, the content of these compounds is influenced by post-harvest processes. In this sense, the concentration of polyphenols can decrease by more than 50% during drying. In this study, the process of procyanidins extraction was optimized and the stability of catechins, procyanidins, and theobromine to different drying temperatures was evaluated. First, the effectiveness of methanol, ethanol, acetone, and water as extract solvents was determined. A Box-Behnken design and response surface methodology were used to optimize the Microwave-Assisted Extraction (MAE) process. The ratios of methanol-water, time, and temperature of extraction were selected as independent variables, whereas the concentration of procyanidins was used as a response variable. Concerning the drying, the samples were dried using five temperatures, and a sample freeze-dried was used as a control. The quantitative analyses were carried out by HPLC-DAD-ESI-IT-MS. The optimal MAE conditions were 67 °C, 56 min, and 73% methanol. Regarding the drying, the maximum contents of procyanidins were obtained at 40 °C. To our knowledge, this is the first time that the stability of dimers, trimers, and tetramers of procyanidins on drying temperature was evaluated. In conclusion, drying at 40 °C presented better results than the freeze-drying method.

Optimization of Ultrasonic-Assisted Extraction Conditions for Bioactive Components and Antioxidant Activity of Poria cocos (Schw.) Wolf by an RSM-ANN-GA Hybrid Approach

In this study, a response surface methodology and an artificial neural network coupled with a genetic algorithm (RSM-ANN-GA) was used to predict and estimate the optimized ultrasonic-assisted extraction conditions of Poria cocos. The ingredient yield and antioxidant potential were determined with different independent variables of ethanol concentration (X₁; 25-75%), extraction time (X₂; 30-50 min), and extraction solution volume (mL) (X₃; 20-60 mL). The optimal conditions were predicted by the RSM-ANN-GA model to be 55.53% ethanol concentration for 48.64 min in 60.00 mL solvent for four triterpenoid acids, and 40.49% ethanol concentration for 30.25 min in 20.00 mL solvent for antioxidant activity and total polysaccharide and phenolic contents. The evaluation of the two modeling strategies showed that RSM-ANN-GA provided better predictability and greater accuracy than the response surface methodology for ultrasonic-assisted extraction of P. cocos. These findings provided guidance on efficient extraction of P. cocos and a feasible analysis/modeling optimization process for the extraction of natural products.

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.

Response surface methodology as a tool to optimize the extraction of bioactive compounds from plant sources

Response surface methodology (RSM) is a widely used mathematical and statistical technique for modeling and optimizing the process for the extraction of bioactive compounds. This review explains the optimization approach through the use of experimental design and empirical models for response prediction and the utilization of the desirability function for multiple response optimization. This paper also reviews recent studies on the application of RSM to optimize bioactive compound extraction processes such as conventional solvent extraction, microwave-assisted extraction, supercritical fluid extraction, and ultrasound-assisted extraction. Finally, the challenges associated with the use of RSM and the efforts made to improve RSM in the extraction process are also highlighted. Overall, this review informs many aspects of RSM that are occasionally ignored or insufficiently discussed with regard to the optimization of bioactive compound extraction processes, and it summarizes significant applications where RSM proved suitable. © 2022 Society of Chemical Industry.

Extraction of Ursolic Acid from Apple Peel with Hydrophobic Deep Eutectic Solvents: Comparison between Response Surface Methodology and Artificial Neural Networks

Extracting ursolic acid (UA) from plant resources using organic solvents is incompatible with food applications. To address this, in this study, 15 edible hydrophobic deep eutectic solvents (HDESs) were prepared to extract UA from apple peel, the extraction conditions were optimized, and the optimization strategies were compared. It was found that the solubility of UA in the HDESs can be 9 times higher than the traditional solvent such as ethanol. The response surface optimization concluded that temperature had the greatest effect on the extraction and the optimized test conditions obtained as follows: temperature of 49 °C, time of 32 min, solid-liquid ratio of 1:16.5 g/mL, respectively. Comparing the response surface methodology (RSM) and artificial neural networks (ANN), it was concluded that ANN has more accurate prediction ability than RSM. Overall, the HDESs are more effective and environmentally friendly than conventional organic solvents to extract UA. The results of this study will facilitate the further exploration of HDES in various food and pharmaceutical applications.

Isolation of Garlic Bioactives by Pressurized Liquid and Subcritical Water Extraction

Garlic (Allium sativum L.) is widely used in various food products and traditional medicine. Besides unique taste and flavour, it is well known for its chemical profile and bioactive potential. The aim of this study was to apply subcritical water extraction (SWE) and pressurized liquid extraction (PLE) for the extraction of bioactive compounds from the Ranco genotype of garlic. Moreover, PLE process was optimized using response surface methodology (RSM) in order to determine effects and optimize ethanol concentration (45-75%), number of cycles (1-3), extraction time (1-3 min) and temperature (70-110 °C) for maximized total phenols content (TP) and antioxidant activity evaluated by various in vitro assays. Furthermore, temperature effect in SWE process on all responses was evaluated, while allicin content (AC), as a major organosulphur compound, was determined in all samples. Results indicated that PLE provided tremendous advantage over SWE in terms of improved yield and antioxidant activity of garlic extracts. Therefore, high-pressure processes could be used as clean and green procedures for the isolation of garlic bioactives.

Response surface methodology optimization and HPLC-ESI-QTOF-MS/MS analysis on ultrasonic-assisted extraction of phenolic compounds from okra (Abelmoschus esculentus) and their antioxidant activity

Okra (Abelmoschus esculentus) has attracted a growing attention for its nutritional and medicinal values, while few studies focused on systemic study of okra polyphenols (OP). In order to obtain the maximum extracted efficiency, response surface methodology was used to optimize ultrasonic-assisted extraction conditions. The maximum TPC was 7.02 mg GAE/g dw under the condition of solid-liquid ratio 1:25, ethanol concentration 70 %, 40 min, and 142 W at 46 °C. Then 27 compounds in OP were identified by HPLC-ESI-QTOF-MS/MS, among which 7-hydroxycoumarin, scopoletin, luteolin and et al were firstly identified from okra. Furthermore, OP exhibited antioxidant activity in reducing power (FRAP, 9.77 mM Fe²⁺/g OP) and radical scavenging (DPPH, IC₅₀ 19.31 µg/mL; SARC, IC₅₀ 210.81 µg/ml). Moreover, OP significantly inhibited cell apoptosis and ROS generation, and alleviated oxidative damage in t-BHP induced HUVECs. Overall, our findings could provide perspective for further potential employments of okra as functional food.

Comparison of Alliin Recovery from Allium sativum L. Using Soxhlet Extraction and Subcritical Water Extraction

Garlic (Allium sativum L.) is an herbaceous plant and is recognised for its numerous medicinal and culinary properties, and it is used in diverse food preparations for its characteristic flavour and aroma. High alliin content increases the formation of allicin, a bioactive compound of garlic. Therefore, this research aimed to compare different extraction methods for garlic (Allium sativum L.) between subcritical water extraction (SWE) and Soxhlet extraction to obtain a high extraction yield and alliin content. The SWE conditions were 120 °C and 180 °C temperatures and 2 mL/min and 6 mL/min flow rates at a constant pressure of 15 MPa for a 10 min extraction time, respectively. In the meanwhile, the extraction time for Soxhlet extraction with various solvents, namely, distilled water, ethanol–water (1:1), and 100% ethanol, was two hours. High-performance liquid chromatography (HPLC) was used to analyse alliin. Soxhlet extraction had the best yield (1.96 g) using ethanol–water (1:1) as the solvent in comparison to SWE extraction (1.28 g) at 180 °C and 6 mL/min. In contrast, SWE yielded a greater concentration of alliin (136.82 mg/g) at 120 °C and 2 mL/min than the Soxhlet method when using distilled water as the solvent (65.18 mg/g). Therefore, SWE may replace Soxhlet extraction as the conventional method for extracting alliin from garlic at a high concentration, and SWE has advantages that favour garlic extracts.

Phenolics from Defatted Black Cumin Seeds (Nigella sativa L.): Ultrasound-Assisted Extraction Optimization, Comparison, and Antioxidant Activity

An ultrasound-assisted method was used for the extraction of phenolics from defatted black cumin seeds (Nigella sativa L.), and the effects of several extraction factors on the total phenolic content and DPPH radical scavenging activity were investigated. To improve the extraction efficiency of phenolics from black cumin seed by ultrasonic-assisted extraction, the optimal extraction conditions were determined as follows: ethanol concentration of 59.1%, extraction temperature of 44.6 °C and extraction time of 32.5 min. Under these conditions, the total phenolic content and DPPH radical scavenging activity increased by about 70% and 38%, respectively, compared with conventional extraction. Furthermore, a complementary quantitative analysis of individual phenolic compounds was carried out using the HPLC-UV technique. The phenolic composition revealed high amounts of epicatechin (1.88–2.37 mg/g) and rutin (0.96–1.21 mg/g) in the black cumin seed extracts. Ultrasonic-assisted extraction can be a useful extraction method for the recovery of polyphenols from defatted black cumin seeds.

Optimization of the Extraction Procedure for the Phenolic-Rich Glechoma hederacea L. Herb and Evaluation of Its Cytotoxic and Antioxidant Potential

The dried Glechoma hederacea L. herb has a long history of use in traditional medicine. Its therapeutic potential is related to the presence of phenolic compounds. To optimize extraction efficiency the effect of the use of different techniques (HRE—heat reflux extraction, I/ME—infusion combined with maceration, UE—sonication and SE—Soxhlet extraction), various solvents (water and ethanol) and processing time (15 min to 2 h) on phenolics content was investigated. The HPLC method was applied to determine and compare the content of phenolic acids (rosmarinic, chlorogenic, protocatechuic) and flavonoids (rutin, isoquercetin) in the extracts. Furthermore, the cytotoxic activity of the extracts was examined for the first time against human cancer and normal cells of skin origin (A375, HTB140, HaCaT) and gastrointestinal origin (Caco-2 and HT-29, HepG2). In addition, the antioxidant potential was evaluated using the DPPH and FRAP method. The I/ME-water and HRE/ethanol procedures turned out to be optimal for obtaining extracts of dried G. hederacea L. herb rich in bioactive phenolics. These extracts exhibited high antioxidant activity, correlated with the content of the compounds analyzed. Furthermore, the extracts of the dried Glechoma herb were not toxic to normal human cells, indicating its safe use both internally and externally.

Optimization of accelerated solvent extraction of ellagitannins in black raspberry seeds using artificial neural network coupled with genetic algorithm

This study aimed to identify ellagitannins in black raspberry seeds (BRS) and to optimize accelerated solvent extraction of ellagitannins using an artificial neural network (ANN) coupled with genetic algorithm. Fifteen monomeric and dimeric ellagitannins were identified in BRS. For ANN modeling, extraction time, extraction temperature, and solvent concentration were set as input variables, and total ellagitannin content was set as output variable. The trained ANN had a mean squared error value of 0.0102 and a regression correlation coefficient of 0.9988. The predicted optimal extraction conditions for maximum total ellagitannin content were 63.7% acetone, 4.21 min, and 43.9 °C. The actual total ellagitannin content under the optimal extraction conditions was 13.4 ± 0.0 mg/g dry weight, and the prediction error was 0.75 ± 0.27%. This study is the first attempt to analyze the composition of ellagitannins in BRS and to determine optimal extraction conditions for maximum total ellagitannin content from BRS.

Optimization of the extraction conditions of Nypa fruticans Wurmb. using response surface methodology and artificial neural network

In this study, we conducted response surface methodology (RSM) and artificial neural network (ANN) to predict and estimate the optimized extraction condition of Nypa fruticans Wurmb. (NF). The effect of ethanol concentration (X1; 0-100%), extraction time (X2; 6-24 h), and extraction temperature (X3; 40-60 °C) on the antioxidant potential was confirmed. The optimal conditions (57.6% ethanol, 19.0 h extraction time, and 51.3 °C extraction temperature) of 2,2-diphenyl-1-1picrylhydrazyl (DPPH) scavenging activity, cupric reducing antioxidant capacity (CUPRAC) and ferric reducing antioxidant power (FRAP), total phenolic content (TPC), and total flavonoid contents (TFC) resulted in a maximum value of 62.5%, 41.95 and 48.39 µM, 143.6 mg GAE/g, and 166.8 CAE/g, respectively. High-resolution mass spectroscopic technique was performed to profile phenolic and flavonoid compounds. Upon analyzing, total 48 compounds were identified in NF. Altogether, our findings can provide a practical approach for utilizing NF in various bioindustries.

Response surface methodology (RSM) and artificial neural network (ANN) approach to optimize the photocatalytic conversion of rice straw hydrolysis residue (RSHR) into vanillin and 4-hydroxybenzaldehyde

Effective use of waste lignin is always a challenging task, technologies have been applied in the past to get value-added compounds from waste lignin. However, the existing technologies are not economical and efficient to produce the value-added chemicals. Alkali soluble lignin from rice straw hydrolysis residue (RSHR) is subjected to photocatalytic conversion into value-added compounds. Photocatalysis is one of the multifarious advanced oxidation processes (AOPs), carried out with TiO2 nanoparticles under a 125 W UV bulb. Gas chromatography mass spectroscopy (GCMS) confirmed the formation of vanillin and 4-hydroxybenzaldehyde. RSM and ANN techniques are adopted to optimize the process conditions for the maximization of the products. The response one (Y 1) vanillin (24.61 mg) and second response (Y 2) 4-hydroxybenzaldehyde (19.51 mg) is obtained at the optimal conditions as 7.0 h irradiation time, 2.763 g/L catalyst dose, 15 g/L lignin concentration, and 14.26 g/L NaOH dose for alkali treatment, suggested by face-centered central composite design (CCD). RSM and ANN models are statistically analyzed in terms of RMSE, R 2 and AAD. For RSM the R 2 0.9864 and 0.9787 while for ANN 0.9875 and 0.9847, closer to one warrant the good fitting of the models. Therefore, in terms of higher precision and predictive ability of both models the ANN model showed excellence for both responses as compared to the RSM model.

Chemometric approach in environmental pollution analysis: A critical review

With the ever-increasing global population and industrialization, it has become a call of the hour to start taking care of the environment to balance the ecosystem. For this, effective monitoring and assessment are required, which involves collecting and measuring environmental details, temporal and spatial readings of environmental data, and parameters. However, assessment of the environment is very tedious as it includes monitoring target analytes, identifying their sources, and reporting, which invariably implies that detailed environmental monitoring would be an intricate and expensive process. The traditional protocols in environmental measures are often manual and time demanding, which makes it further difficult. Moreover, several changes also occur within the environment, which could be chemical, physical, or biological, and since these environmental impacts are often cumulative, it becomes difficult to measure an isolated system. Furthermore, the chances of skipping significant results and trends become high. Also, experimental data obtained from the environmental analysis are usually non-linear and multi-variant due to different associations among various contributing variables. Therefore, it is implied that accurate measurements and environment monitoring are not using traditional analytical protocols. Thus, the need for a chemometric approach in environmental pollution analysis becomes paramount due to the inherent limitations associated with the conventional approach of analyzing environmental datasets. Chemometrics has appeared as a potential technique, which enhances the particulars of the chemical datasets by using statistical and mathematical analysis methods to analyze chemical data beyond univariate analysis. Utilizing chemometrics to study the environmental data is a revolutionary idea as it helps identify the relationship between sources of contaminations, environmental drivers, and their impact on the environment. Hence, this review critically explores the concept of chemometrics and its application in environmental pollution analysis by briefly highlighting the idea of chemometrics, its types, applications, advantages, and limitations in the environmental domain. An attempt is also made to present future trends in applications of chemometrics in environmental pollution analysis.

Optimization of Infrared Postharvest Treatment of Barhi Dates Using Response Surface Methodology (RSM)

Barhi dates are widely consumed at Khalal maturity stage and preserving the freshness quality of Barhi at this stage is a challenging task as this period is short and the fruits are more perishable. In this study, response surface methodology (RSM) was applied to optimize the infrared (IR) treatment and storage conditions for preserving the physicochemical, microbial, and bioactive attributes of fresh Barhi dates. The effect of four factors, IR temperature (50, 70, 90, and 110 °C), IR time (1, 2, 3, and 4 min), storage temperature (1, 5, 15, and 25 °C), and storage time (1, 6, 11, 16, and 21 days), on the responses of total soluble solids (TSS), hardness, total color change (ΔE), total viable count (TVC), total phenolic content (TPC), antioxidant activity (DPPH), and glucose content were evaluated following central composite design (CCD). IR temperature, IR time, storage temperature, and storage time significantly affected the physical, microbial, and bioactive attributes of Barhi dates. The optimal conditions for minimizing the physical changes and microbial load and maximizing the bioactive attributes were IR temperature of 50 °C, IR time of 1.2 min, storage temperature of 1 °C, and storage time of 20 days. At the optimum conditions, the values of TSS, hardness, ΔE, TVC, TPC, DPPH, and glucose were 37.22%, 70.17 N, 11.12, 2.9 log CFU/g, 36.1 mg GAE/g, 65.31%, and 25.38 mg/g, respectively and these values were similar to predicted values. In conclusion, this study identified the ideal IR treatment and storage conditions for maintaining the overall quality attributes of Barhi dates during prolonged storage.

Antibacterial Activity of Nanoparticles of Garlic (Allium sativum) Extract against Different Bacteria Such as Streptococcus mutans and Poryphormonas gingivalis

To combat the threat of antimicrobial resistance, it is important to discover innovative and effective alternative antibacterial agents. Garlic has been recommended as a medicinal plant with antibacterial qualities. Hence, we conducted this study to evaluate the antibacterial activity of ultrasonicated garlic extract against Escherichia coli, Staphylococcus aureus sub. aureus, Streptococcus mutans, and Poryphyromonas gingivalis. Aqueous ultrasonicated garlic extract was tested against these strains, and their antibacterial activity quantified using both agar disk diffusion and agar well diffusion methods; the plate count technique was used to estimate the total viable count. Moreover, Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), and microplate spectrophotometry were used to characterize garlic nanoparticles. The results confirmed that all tested bacteria were sensitive to both sonicated and non-sonicated garlic extracts. Streptococcus mutans was the most susceptible bacteria; on the other hand, Escherichia coli was the most resistant bacteria. Furthermore, characterization of the prepared garlic nanoparticles, showed the presence of organosulfur and phenolic compounds, carboxyl groups, and protein particles. Based on the obtained results, ultrasonicated garlic extract is a potent antibacterial agent. It can come in handy while developing novel antibiotics against bacteria that have developed resistance.

Prediction of the thermo-physical properties of deep-fat frying plantain chips (ipekere) using artificial neural network

This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m3 while thermal diffusivity and conductivity were least affected with 0.192 x 10−6 m2/s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips.

An Overview on the Use of Response Surface Methodology to Model and Optimize Extraction Processes in the Food Industry

Response surface methodology (RSM) is a widely used tool for modeling and optimization for food processes. The objective of this review is to evaluate recent findings on the use of RSM in the extraction of compounds from agri-food products. First, the steps for the application of RSM were briefly detailed. According to the analysis performed, RSM is suitable because it evaluates the effects of the independent variables and their interactions on the responses, which is ideal for the optimization of different techniques for the extraction of multiple bioactive compounds and therefore, in the various studies, has allowed to significantly increase the yield and even the biological activities of the extracts; however, RSM has limitations and considering the complexity and dynamics of foods, the challenge is much greater. In this sense, it was determined that simultaneous use with other techniques is necessary in order to optimally describe the process and obtain more accurate results.

Chemometric study on the biochemical marker of the manglicolous fungi to illustrate its potentiality as a bio indicator for heavy metal pollution in Indian Sundarbans

The study represents in vitro chemometric approach for assessing the heavy metal pollution in Indian Sundarbans. Physio-chemical and elemental characterisation of the sediment samples of Indian Sundarbans had shown high enrichments of toxic metal ions. It was characterised by elevated enrichment factors (2.16-10.12), geo-accumulation indices (0.03 -1.21), contamination factors (0.7-3.43) and pollution load indices (1.0-1.25) which showed progressive sediment quality deterioration and ecotoxicological risk due to metal ions contamination. The physio-chemical parameters of the sediments were replicated and computational chemometric modeling was utilized to assess fungal metabolic growth. All the fungi isolates had shown maximum metabolic activity in high temperature, alkaline pH, and high salinity. Further, the fungal metabolic activity was assessed in different gradient of heavy metal concentration. The significant deterioration of biochemical marker with increasing concentration of heavy metal indicates the status of the microbial health due to toxic metal pollution in the mangrove habitat.

Simultaneous process optimization of ultrasound-assisted extraction of polyphenols and ellagic acid from pomegranate (Punica granatum L.) flowers and its biological activities

This study was designed to optimize the extraction rate of total polyphenols and ellagic acid from pomegranate flowers. Single factors were investigated for liquid-to-material ratio (5-25), ethanol concentration (20%-60%), sonication time (5-60 min), and sonication power (150-500 W). The level range of the Box-Bokhen design was determined with respect to the single-factor results. The components of each index were normalized using the entropy weighting method for obtaining the comprehensive evaluation value. Under the actual conditions, the final optimization results were 17 for liquid-to-material ratio, 43% for ethanol concentration, 10 min for ultrasonic time, and 300 W for ultrasonic power. The extracts obtained under optimal conditions were tested for the inhibition of Streptococcus mutans and its biofilm, and results showed that pomegranate flowers exerted some inhibitory effects on the bacterium. Phosphomolybdenum and FRAP assays were used, and DPPH, ABTS, and O^2- radical scavenging tests were conducted, indicating that pomegranate flower extracts have good antioxidant capacity.