Combination Of Response Surface Methodology Research Articles

Evaluation of Statistical and Machine Learning Analysis on CRDI Engine using Ternary Fuel doped with Cerium Oxide Nanoadditives

At present, diesel engines are confronted with the formidable task of reconciling aggressive emission regulations with the preservation of intended engine performance. In order to tackle this concern, India can implement a comprehensive strategy that includes waste reduction, recycling programs, and the innovative use of waste-to-energy technologies. The potential synergistic effects of combining fuel mixtures with cerium oxide nanoparticles in CRDI diesel engines remain unexplored. This study employs response surface methodology and MLP regression to investigate these effects. This study explores the experimental design includes variation in blend ratios of karanja biodiesel (upto 20 %), compression ratios (18–22), and injection pressure (400–1200 bar), as well as CeO2 nanoparticle concentrations (50–150 ppm) using 10 % biobutanol. A combination of response surface methodology (RSM) and artificial neural network (ANN) techniques is used to assess and optimise the influence of diverse process parameters on engine responses. The machine learning processor (MLP) regressor model is more capable of forecasting engine responses than RSM modelling, as it has lower MAE, RMSE, and higher R2 values. According to the study, the best blend ratio, CR, Injection Pressure, and CeO2 concentration for the best performance and lowest emissions are 9.97 % biodiesel blend, CR of 20, an injection pressure of 797.4 bar, and CeO2 nanoparticle concentration of 100.1 ppm. The MAE value of the MLP regressor-based model ranged from 0.0011 to 0.0119. In terms of RMSE, MLP regressor-based model outputs are within reasonable boundaries. This study highlights the optimization of engine operating parameters and the application of nanotechnology in biodiesel blends. These findings can lead to the development of durable and eco-friendly fuel alternatives for internal combustion engines, promoting sustainable transportation.

A novel univariate interpolation and bivariate regression hybrid method application to biodegradation of bisphenol A diglycidyl ether using laccases from Geobacillus stearothermophilus and Geobacillus thermoparafinivorans strains.

Bisphenol A diglycidyl ether (BADGE), a derivative of the well-known endocrine disruptor Bisphenol A (BPA), is a potential threat to long-term environmental health due to its prevalence as a micropollutant. This study addresses the previously unexplored area of BADGE toxicity and removal. We investigated, for the first time, the biodegradation potential of laccase isolated from Geobacillus thermophilic bacteria against BADGE. The laccase-mediated degradation process was optimized using a combination of response surface methodology (RSM) and machine learning models. Degradation of BADGE was analyzed by various techniques, including UV-Vis spectrophotometry, high-performance liquid chromatography (HPLC), Fourier transform infrared (FTIR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS). Laccase from Geobacillus stearothermophilus strain MB600 achieved a degradation rate of 93.28% within 30 min, while laccase from Geobacillus thermoparafinivorans strain MB606 reached 94% degradation within 90 min. RSM analysis predicted the optimal degradation conditions to be 60 min reaction time, 80°C temperature, and pH 4.5. Furthermore, CB-Dock simulations revealed good binding interactions between laccase enzymes and BADGE, with an initial binding mode selected for a cavity size of 263 and a Vina score of -5.5, which confirmed the observed biodegradation potential of laccase. These findings highlight the biocatalytic potential of laccases derived from thermophilic Geobacillus strains, notably MB600, for enzymatic decontamination of BADGE-contaminated environments.

Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies.

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soils contaminated with heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving an improvement of nearly 2.45 times the initial yield under specific conditions (Bennet's medium supplemented with glycerol [5g/L] and casamino-acid [3g/L] at 30°C for 72h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using energy-dispersive X-ray spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0.2 W/cm2), with temperatures increasing to 41.7°C, after 30min. In addition, treatment with light irradiation of E. coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5µg/mL, thereby enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12.

Tailored transition metal-doped TiO2@Fe3O4 nanohybrids for efficient photocatalytic dye removal: Optimization via neural networks and response surface approaches

The study focused on predicting the degradation of Remazol Turquoise Blue (RTB) dye using a photocatalyst comprised of transition metal-doped TiO2@Fe3O4. Out of the transition metal ions considered, namely Cu, Cr, Co, and Ni, Cr emerges as the most effective dopant for TiO2@Fe3O4 nanohybrid photocatalysts. This optimal performance was achieved with a catalyst loading of 50 mg and a dye concentration of 10 ppm within a 1-h reaction time, resulting in a photocatalytic efficiency of 96.70%. The optimization strategy employed a combination of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). To conduct experiments systematically, a Box-Behnken Design (BBD) rooted in RSM principles was utilized to create experimental setups for the photocatalytic degradation of the dye with the doped photocatalyst. The variables such as reaction time, catalyst dosage and dye concentration were designed to optimize the RTB dye degradation. The Regression coefficient (R2) for the developed RSM and ANN models were found to be 0.9299 and 0.9925 respectively, and revealed that the ANN has higher prediction capability and accuracy than other model. The outcomes underlined that the dosage of the catalyst prominently emerged as the principal determinant governing the efficacy of the photocatalytic activity. Remarkably, the Cr doped TiO2@Fe3O4 photocatalyst maintained its activity through six degradation cycles, with efficiencies of 99.7%, 99.4%, 99.1%, 88.8%, 87.3%, and 86.3%, respectively.

A fast and automatic establishment method of the calculation formula for the tunnel face safety factor based on analytical solution

Assessing rock tunnel face stability when constructing mountain tunnels is of utmost importance because it is influenced by various factors, including the tunnel diameter, depth, excavation dimension, geological strength index, uniaxial compressive strength, and Hoek-Brown constant. This study established a tunnel face stability database based on analytical solutions that considered the correlation between the influencing parameters. Finally, a safety factor prediction equation was constructed by combining response surface methodology and the multivariate adaptive regression spline algorithm. The results of this study find applications at construction sites and provide a reference for the rapid evaluation of rock tunnel faces during construction.

Parametric optimization of the machining performance of Al-SiCp composite using combination of response surface methodology and desirability function

In this study, machining parameters such as speed, feed, and depth of cut were optimized for maximum material removal rate (MMR) of Al-SiCp composite turned with a tungsten carbide tool using response surface methodology based central composite design while keeping tool wear and surface roughness within specified limits. The influence of turning factors on flank wear and surface roughness height was investigated using regression models. ANOVA analysis is used to assess the appropriateness of the projected models and the significant factors for response optimization using the desirability method. The percentage contribution of the significant machining parameters and their interactions for optimization of the response is determined using ANOVA technique. Cutting speed is found to be the most significant factor for flank wear, average roughness height Ra and maximum roughness height Rt with contribution of 65.38%, 58.7% and 37.9% respectively for optimization of the response. Confirmation tests were carried out and the percentage error between observed and predicted value of the responses is found to be within acceptable limits, demonstrating that the created models correlates well with the experimental data. Under optimal conditions, utilizing a tungsten carbide tool can prove to be a cost-effective alternative for turning Al-SiCp composite.

Applications of integrated response surface methodology statistic techniques and artificial neural network‐based machine learning to optimize residual chlorine production and energy consumption

AbstractA multifactor interaction study was performed using the combined response surface methodology and an artificial neural network on the operational parameters and their influence on residual chlorine production. The operating variables, sodium chloride concentration, electrical potential, electrolysis time, and electrode gap, were evaluated over the response, residual chlorine and energy consumption. The results indicated that the optimum value for residual chlorine was 2450 mg/L achieved at an electrical potential of 8.8 V for 25 min in the presence of 25 g/L of sodium chloride and an electrode distance of 1 cm, and the optimum corresponding energy consumption was measured at 21.76 kWh/L. The study reveals that electric potential, sodium chloride concentration, and electrolysis time positively influence residual chlorine production. ANN models showed superior prediction ability compared with RSM models. This suggests electrolysis can be used for active chlorine production from saline solutions, potentially for industrial applications and water disinfection.

Insights into the Adsorption of Cr(VI) on Activated Carbon Prepared from Walnut Shells: Combining Response Surface Methodology with Computational Calculation

Walnut shells were used to produce highly microporous activated carbon. The prepared activated walnut shells were found to be an efficient adsorbent for removing Cr(VI). The study used the response surface methodology to investigate four independent variables effect: Cr(VI) concentration, pH, AC-Ws dose, and temperature on the Cr(VI) removal efficiency, which was studied in the concentration range of 0.1 to 0.3 g/L, 4 to 10, 15 to 35 °C and 1 to 5 mg/L, respectively. Through experiments designed, the optimum conditions were determined to be 4, 0.23 g/L, 298 k, and 2 g/L, respectively. At these conditions, the efficiency of removal was found to be 93%. The thermodynamic study of the adsorption process showed a spontaneous and exothermic nature. The kinetic model that explains the experimental data is the pseudo-second-order model. Furthermore, the Langmuir isotherm model was estimated to be an excellent representation of the equilibrium data. Quantum calculations and NCI analyses were also performed to get more light on the adsorption mechanism of the Cr(VI) atom and its complex form on the prepared AC-Ws surface.

Using VIKOR and RSM-DA in the optimization of dry turning of 9XC steel

Dry turning is an effective method for reducing the production costs and environmental impact of machining processes. In this study, the effect of cutting speed (Vc), feed rate (fz), and depth of cut (ap) on the surface roughness (Ra) and material removal rate (MRR) of 9XC steel during dry turning was investigated. A Box-Behnken experimental design was employed to analyze the main effects and interaction effects of these cutting parameters. In this research, the combining Response Surface Methodology - Desirability Approach (RSM-DA) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method were both employed for solving multiple objective optimization problems, and their performance was compared. The results from both methods can be assessed based on their ability to identify the optimal set of parameters that simultaneously optimize surface quality and production rate, as well as their computational efficiency and ease of implementation. Both RSM-DA and VIKOR have been found effective for solving multi-objective optimization problems, such as optimizing cutting parameters in dry turning. While RSM-DA is a statistical tool that combines multiple objectives into a single function, VIKOR is a decision-making method that ranks alternatives based on multiple criteria. The choice of which method to use depends on the specific requirements of the problem and the availability of resources for implementation. The research results show that both VIKOR and RSM-DA are suitable for solving the multi-objective optimization problem of the turning process. According to the VIKOR method, the optimum cutting conditions were found to be a cutting speed Vc of 120 m/min, a depth of cut ap of 0.1 mm a feed rate fz of 0.06 mm/rev, and, which resulted in a surface roughness of 0.209 um and a material removal rate of 0.72 cm3/min. Meanwhile, RSM-DA predicts the optimal Ra and MRR values of 0.254 um and 3.36 cm3/min, respectively; corresponding to the Vc of 180m/min, fz of 0.06mm/rev and ap of 0.3mm; That means, the increasing the value of surface roughness by 21.5% will increase the MRR by 366.7%. The findings of this study can provide guidance for selecting the appropriate cutting parameters for dry turning of 9XC steel to achieve desired surface roughness Ra and material removal rate MRR in the specific case.

Integration of genetic engineering and multi-factor fermentation optimization for co-production of carotenoid and DHA in Schizochytrium sp

Schizochytrium sp., a microalga with high lipid content, holds the potential for co-producing docosahexaenoic acid (DHA) and carotenoids. In this study, the ability of Schizochytrium sp. to naturally produce carotenoids was systematically explored. Further, by enhancing the precursor supply of geranylgeranyl diphosphate, regulating carbon source through sugar limitation fermentation and employing a combination of response surface methodology and artificial neural networks to precisely optimize nitrogen sources, a new record of 43-fold increase in β-carotene titer was achieved in the 5L bioreactor (653.2 mg/L). Meanwhile, a high DHA content was maintained (13.4 g/L). Furthermore, the use of corn stover hydrolysate has effectively lowered the production costs of carotenoid and DHA while sustaining elevated production levels (with total carotenoid titer and DHA titer reached 502.0 mg/L and 13.2 g/L, respectively). This study offers an efficient and cost-effective method for the co-production of carotenoid and DHA in Schizochytrium sp..

Process optimization and quality prediction of laser aided additive manufacturing SS 420 based on RSM and WOA-Bi-LSTM

Laser aided additive manufacturing (LAAM) is a promising method for surface modification, repair, and direct fabrication 3D components. Nevertheless, the high cost and time-consuming nature of trial-and-error based process development hinder the progress of this technology. Consequently, machine learning has garnered substantial attention from both the academia and industrial sectors due to its ability to expedite process study for both research and industrial applications. This paper presents a study on the LAAM of 420 stainless steel on the surface of Q235 steel using the combined Response Surface Methodology (RSM) and Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-Bi-LSTM) model. The results show that the process parameters can be effectively optimised to improve the prediction accuracy using the multi-objective whale optimisation algorithm for the fitted equations obtained in response surface analysis. At the same time, with the support of a large number of data, the WOA-Bi-LSTM model can accurately predict the quality of the LAAMed material, which is better than the response surface analysis method.

A novel multi-objective optimization model of solar-driven methanol steam reforming system combining response surface methodology and three-dimensional numerical simulation

Solar-driven methanol steam reforming (MSR) to produce hydrogen is a pivotal thermochemical process for enhancing green energy utilization with low-carbon emissions. While this process has been explored in the thermochemical reforming literature, with valuable studies on fuel to hydrogen, this study marks the first comprehensive examination and multi-objective optimization of the MSR process within a solar-driven catalyst-filled reactor. The study begins by modeling and validating the MSR’s reaction kinetic. Subsequently, the effects of the reactor surface energy flux density and key operating characteristics, such as steam-to-methanol ratio (SMR) and space velocity, on the gas composition and conversion efficiency as well as H2 yield are comprehensively analyzed. In order to effectively optimize the operating parameters, the interactions between the various factors were elucidated and an optimization procedure was established to scrutinize the impact of these factors on hydrogen production. The model accuracy is assessed using Analysis of Variance (ANOVA), which also identifies optimal operating parameters under varying conditions. Predictions from the Response surface methodology (RSM) indicate optimal conditions with a SMR of 1.13, a gas hourly space velocity of 45.24, and an inlet temperature of 507.4 K. Numerical calculations confirm the practicality of these conditions, yielding an actual methanol conversion of 0.9998, deviating only by 0.17 % from the predicted value. This work underscores the feasibility and efficacy of studying MSR performance based on response surface method. The analysis of factor significance and interactions provides valuable guidance for practical operations.

A Numerical Simulation and Multi-objective Optimization for the Plastic Injection Molding of a Centrifugal Pump Casing

Plastic pumps are used to efficiently move liquids within aquaculture systems and seafood processing facilities. Commonly, the centrifugal pump casing is fabricated by plastic injection molding technology. In the plastic injection molding process, the control parameters are essential to the success of plastic molding product fabrication. This study aims to optimize process parameters for the quality of centrifugal pump casing during plastic injection molding using a combination of response surface methodology (RSM) and the NSGA-II method. Firstly, the L27 orthogonal array design of the experiment method is used, which has four input variables, including plastic temperature, mold temperature, cooling time, and injection time (filling time and packing time). The output responses of the injection molding process to be evaluated are the warpage and shrinkage deformation of the molded product. After obtaining data, the RSM model is applied to describe the relationship between the input and output variables. Finally, multi-objective optimization uses the NSGA-II method for the best product quality. The results show that the optimized parameters for injection molding of the centrifugal pump casing are a plastic temperature of 200°C, mold temperature of 65°C, cooling time of 25s, and injection time of 10s with warpage of 0.868 mm, and shrinkage of 10.104mm.

Optimization of polydopamine coating process for poly lactic acid‐based 3D printed bone plates using machine learning approaches

AbstractThe three‐dimensional (3D) printed poly lactic acid (PLA) bone plates lack mechanical strength, resulting in premature failure. Coating these plates with polydopamine (PDM) forms covalent bonds with the PLA molecular structure, enhancing their mechanical properties. The mechanical strength of the coated bone plates is influenced by infill density, submersion time, shaker speed, and coating solution concentration. However, conducting experiments for each parameter value to achieve maximum biomechanical tensile strength (BTS) and biomechanical flexural strength (BFS) is time‐consuming and costly. Overall, the combination of response surface methodology (RSM) and machine learning (ML) enables determination of the best printing parameters, leading to reduced material waste, personalized bone plates tailored to individual anatomy, improved implant fit, and functionality. Moreover, this approach has the potential to reduce the need for additional surgeries and overall costs. To optimize coating parameters, this study employs RSM and ML techniques, including genetic algorithm (GA), particle swarm optimization (PSO), random search optimization (RSO), and differential evolution (DE). Experimental validation of the optimized process parameters and their corresponding fitness values is carried out using both RSM and ML approaches. The results demonstrate that GA has the closest relationship between experimental and fitness values, followed by DE, RSM, PSO, and RSO.Highlights Direct immersion coating of polydopamine on 3D printed PLA bone plates. Evaluating mechanical strength for bone plates coated at varying parameters. Statistical modeling and optimization of mechanical strength using RSM. Mechanical strength optimization and convergence properties for ML models. Experimental validation of RSM and ML‐based optimization algorithms.

A multi-objective optimization design approach of large mining planetary gear reducer

A two-stage computational framework is proposed to optimize the radiated noise and weight of a large mining planetary gear reducer under the rated conditions, based on a combination of response surface methodology and multi-objective optimization. The well-established transient dynamic analysis model of a large mining planetary gear reducer, which is used to analyze the mechanical strength and acoustic characteristics of the gear reducer. A unified experimental design is developed to obtain the response surface of the gearbox radiated noise and the mass of the gearbox housing. After obtaining the multi-objective optimization function, the multi-objective optimization problem for a lightweight and low-noise gearbox is performed using non-dominated sorting from the Genetic Algorithm II (NSGA-II). The research results demonstrates the effectiveness of the proposed optimization method in reducing vibrating amplitude and weight of the gearbox. This is crucial for minimizing energy consumption and enhancing the overall performance of the system. Additionally, the optimized gearbox design not only saves energy but also contributes to the reduction of carbon emissions, making it environmentally friendly.

Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R2 values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R2 of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.

Biodiesel production from mahua oil: characterisation, optimisation, and modelling with a hybrid statistical approach

Present study investigates the feasibility of utilising mahua oil as a feedstock for the production of high-quality biodiesel via the transesterification process. Mahua fruit kernels offer an excellent biodiesel source, with an oil content of approximately 35–40%. This study employs a hybrid statistical approach, combining Response Surface Methodology (RSM) and Genetic Algorithm (GA), to optimise the transesterification process. Through the execution of L31 pre-designed experiments, this work analyzes the significance of four key process parameters: methanol-to-oil ratio, catalyst concentration, reaction temperature, and reaction time. The collective influence of these parameters on biodiesel production is comprehensively examined. The GA-derived optimal conditions for biodiesel production are determined as a molar ratio of 7.28 with a 0.93 wt.% catalyst concentration, a reaction temperature of 59.43°C, and a reaction time of 113.62 min. Experimental results validate the model predictions, as the actual yield of Mahua biodiesel reaches 97.54%, with a deviation of only 2.82% from the predicted value. Furthermore, the fuel characteristics of Mahua methyl ester were assessed and compared to ASTM standards. In conclusion, this study highlights the potential of Mahua oil as a valuable and cost-effective feedstock for biodiesel production, thereby opening avenues for its future application as an alternative fuel source.

Response surface methodology-based characterization and optimization of fibre reinforced cemented tailings backfill with Slag

ABSTRACT With the increasing depths of underground mines due to the scarcity of near-surface ores, the introduction of fibre-reinforced cemented paste backfill (F-CPB) has emerged as a novel solution to address the demanding geomechanical conditions in deep mining operations. However, the widespread adoption of F-CPB in mining and industrial backfill operations necessitates a comprehensive understanding of its key engineering properties, including strength, yield stress, modulus of elasticity, and cost. Moreover, it is crucial to investigate the influence of the constituent materials (water, fibres, binders, tailings) and their interactions on these properties. This research paper presents the application of response surface methodology (RSM) to model the effects of binder content (Portland cement/Slag), water content, fibre content, tailings and their interactions on the mechanical and rheological properties, as well as the cost of F-CPB. Central Composite Design (CCD) experiments were conducted, and a high degree of agreement was observed between the experimental and predicted responses. The RSM approach proves suitable for accurately estimating the responses and assessing the interactions between the model parameters and the properties of F-CPB. Furthermore, a combination of RSM and the desirability approach enables the development of an optimisation tool for F-CPB, facilitating the formulation of optimal backfill mixtures. The results obtained from this study highlight the effectiveness of the combined RSM and desirability approach in F-CPB mix proportioning, offering an advanced engineering approach to F-CPB mix design. The proposed design method has the potential to reduce the laboratory testing protocol required for determining the optimal mix composition.

Response surface methodology and desirability approach to investigate and optimize the performance of a CO2 geothermal thermosyphon

Geothermal thermosyphons (GTs) are increasingly used in many heating and cooling geothermal applications, owing to their high heat transfer performance. In this paper a combined response surface methodology and multi-objective desirability method is proposed for the investigation and optimization of the thermal performance of a geothermal thermosyphon. The filling ratio, temperature, and flow rate of the heat transfer fluid are selected as the designing parameters, and heat transfer rate and effectiveness are adopted as response parameters (objective functions). First, a dedicated experimental GT test bench filled with carbon dioxide (CO2) was built and subjected to different test conditions defined by RSM. A multi-level design of experiment-based response surface methodology (RSM) was used to establish corresponding models between the input parameters and responses. Various diagnostic tests were used to evaluate the quality and validity of the best-fit models, which explain respectively 98.9 and 99.2% of the output result's variability. Further, a surface response analysis of the effect of each input parameter on the responses was performed and a desirability function-based model was employed to determine the optimal combination of factors that would maximize the desired responses. The results revealed that the parameter-setting obtained using the proposed procedure satisfies the requirements of each response, with a desirability of 0.98.

Determination of seventeen sugars and sugar alcohols in fruit juice samples using hydrophilic interaction liquid chromatography-tandem mass spectrometry combining response surface methodology design

Carbohydrates are the main source of energy for all organisms to sustain their life activities and probably the most common organic substances in nature, whose main source is grains, starchy vegetables and fruits. To clarify the content and distribution of sugars and sugar alcohols in different fruit species samples accurately, a rapid and sensitive hydrophilic interaction liquid chromatography-electrospray ionization-tandem mass spectrometry (HILIC-ESI-MS/MS) method combining ultrasonic-assisted extraction (UAE) and solid phase extraction (SPE) purification was developed for quantitative determination seventeen sugars and sugar alcohols in fruit juice. In this method, the targets were firstly extracted from 1 g of fruit juice sample using 40 mL double-distilled water by ultrasonic-assisted extraction (UAE) for 10 min according to the optimization of response surface methodology (RSM) design. Then, 1.0 mL of UAE extracted solution was cleaned using an amino (NH2) SPE cartridge in the following SPE procedure. Seventeen compounds were separated in 25 min using the HILIC column by a gradient with 0.1% of ammonium hydroxide and acetonitrile as mobile phase. The HILIC-MS/MS method was validated, with the following studies including limit of detection, linearity range, stability and recovery. The limits of detection (LOD) were between 1.2 and 228 ng/g and linear correlation coefficients (R2) were more than 0.994. The average recoveries of 17 sugars and sugar alcohols ranging from 81.8 to 114.9%. The results showed that inter-day and intra-day precision were found to be within acceptable range. The strategy was successfully used for profiling of sugars and sugar alcohols in various fruit juice matrices.