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

被引:2
|
作者
Wahid, Aseeya [1 ]
Giri, Saroj Kumar [1 ]
Kate, Adinath [1 ]
Tripathi, Manoj Kumar [1 ]
Kumar, Manoj [1 ]
机构
[1] ICAR Cent Inst Agr Engn, Bhopal 462038, India
来源
SCIENTIFIC REPORTS | 2023年 / 13卷 / 01期
关键词
RESPONSE-SURFACE METHODOLOGY; ANTIOXIDANT CAPACITY; OSMOTIC DEHYDRATION; ASCORBIC-ACID; QUALITY; EXTRACTION; OPTIMIZATION; FRUIT; ENHANCEMENT; KINETICS;
D O I
10.1038/s41598-023-41930-8
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Amidst increasing demand for nutritious foods, the quest for effective methods to enhance health-promoting attributes has intensified. Vacuum impregnation (VI) is a promising technique to augment produce properties while minimizing impacts on biochemical attributes. In light of broccoli's growing popularity driven by its nutritional benefits, this study explores the impact of VI using ascorbic acid and calcium chloride as impregnation agents on enhancing its phytochemical properties. Response surface methodology (RSM) was used for optimization of the vacuum impregnation process with Vacuum pressure (0.6, 0.4, 0.2 bar), vacuum time (3, 7, 11 min), restoration time (5, 10, 15 min), and concentrations (0.5, 1.0, 1.5%) as independent parameters. The influence of these process parameters on six targeted responses viz. total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid content (AAC), total chlorophyll content (TCC), free radical scavenging activity (FRSA), and carotenoid content (CC) were analysed. Levenberg-Marquardt back propagated neural network (LMB-ANN) was used to model the impregnation process. Multiple response optimization of the vacuum impregnation process indicated an optimum condition of 0.2 bar vacuum pressure, 11 min of vacuum time, 12 min of restoration time, and 1.5% concentration of solution for vacuum impregnation of broccoli. The values of TPC, TFC, AAC, TCC, FRSA, and CC obtained at optimized conditions were 291.20 mg GAE/100 g, 11.29 mg QE/100 g, 350.81 mg/100 g, 1.21 mg/100 g, 79.77 mg, and 8.51 mg, respectively. The prediction models obtained through ANN was found suitable for predicting the responses with less standard errors and higher R-2 value as compared to RSM models. Instrumental characterization (FTIR, XRD and SEM analysis) of untreated and treated samples were done to see the effect of impregnation on microstructural and morphological changes in broccoli. The results showed enhancement in the TPC, TFC, AAC, TCC, FRSA, and CC values of broccoli florets with impregnation. The FTIR and XRD analysis also supported the results.
引用
收藏
页数:14
相关论文
共 48 条
  • [31] Development of ternary models for prediction of biogas yield in a novel modular biodigester: a case of fuzzy Mamdani model (FMM), artificial neural network (ANN), and response surface methodology (RSM)
    Okwu, Modestus O.
    Samuel, Olusegun D.
    Otanocha, Omonigho B.
    Tartibu, Lagouge K.
    Omoregbee, Henry O.
    Mbachu, Victor M.
    BIOMASS CONVERSION AND BIOREFINERY, 2023, 13 (02) : 917 - 926
  • [32] Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters
    Meenal, R.
    Selvakumar, A. Immanuel
    RENEWABLE ENERGY, 2018, 121 : 324 - 343
  • [33] A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction
    Poul, Ahmad Khazaee
    Shourian, Mojtaba
    Ebrahimi, Hadi
    WATER RESOURCES MANAGEMENT, 2019, 33 (08) : 2907 - 2923
  • [34] Enhancing Vault Prediction and ICL Sizing Through Advanced Machine Learning Models
    Zhu, Jun
    Li, Fen -Fen
    Li, Gao-Xiang
    Jiang, Shang -Yang
    Cheng, Dan
    Bao, Fang -Jun
    Wu, Shuang-Qing
    Dai, Qi
    Ye, Yu-Feng
    JOURNAL OF REFRACTIVE SURGERY, 2024, 40 (03) : e126 - e132
  • [35] Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
    Adeoye, Babatunde Kazeem
    Ajala, Olajide Olukayode
    Oke, Emmanuel Olusola
    CHEMICAL PRODUCT AND PROCESS MODELING, 2023, 18 (02): : 313 - 324
  • [36] Prediction of specific cutting energy consumption in eco-benign lubricating environment for biomedical industry applications: Exploring efficacy of GEP, ANN, and RSM models
    Sen, Binayak
    Bhowmik, Abhijit
    Prakash, Chander
    Ammarullah, Muhammad Imam
    AIP ADVANCES, 2024, 14 (08)
  • [37] Comparative study of LSTM and ANN models for power consumption prediction of variable refrigerant flow (VRF) systems in buildings
    Hsu, Po-Ching
    Gao, Lei
    Hwang, Yunho
    INTERNATIONAL JOURNAL OF REFRIGERATION, 2025, 169 : 55 - 68
  • [38] Modeling surface quality, cost and energy consumption during milling of alloy 2017A: a comparative study integrating GA-ANN and RSM models
    Bousnina, Kamel
    Hamza, Anis
    Ben Yahia, Noureddine
    INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION, 2024,
  • [39] Comparative Analysis of Slope Stability Prediction for Earth Dams Using Response Surface Method, Statistical Models, and ANN
    Luis Santos
    Claudio Resende
    Karl Martins
    Roberto Quevedo
    Marko Lopez
    Geotechnical and Geological Engineering, 2025, 43 (5)
  • [40] Comparative study using RSM and ANN modelling for performance-emission prediction of CI engine fuelled with bio-diesohol blends: A fuzzy optimization approach
    Dey, Suman
    Reang, Narath Moni
    Das, Pankaj Kumar
    Deb, Madhujit
    FUEL, 2021, 292