ORIGINAL RESEARCH ARTICLE - Modelling and prediction of hydrolysis index of gluten-free cookies from cardaba banana starch vis-?-vis response surface methodology and support vector machine.
), Oyekemi Popoola(2), Adekunbi A. Malomo(3), Oladapo F. Fagbohun(4), Esther Eduzor(5),
(1) First Technical University, Ibadan
(2) First Technical University, Ibadan
(3) Obafemi Awolowo University
(4) First Technical University, Ibadan Dalhousie University, Truro, Canada
(5) Federal Polytechnic Bauchi
Corresponding Author
Abstract
The increase in the onset of celiac disease among the world populace had increased the demand for gluten-free products. Therefore, this study aimed at modelling and predicting the hydrolysis index of gluten-free cookies using response surface methodology (RSM) and support vector machine (SVM). The baking temperature (150 -180 ?) and baking time (15-25 min) were varied using a central composite design. The obtained result revealed that both modelling approaches (RSM and SVM) accurately predict the hydrolysis index of the gluten-free cookies owing to their higher coefficient of determinant (R2 > 0.9). The predictive capability assessment of response surface methodology and support vector machine revealed the superiority of support vector machine (0.9658, 0.9329, 0.059) in predicting the hydrolysis index of the gluten-free cookies over response surface model (0.9613, 0.9241, 0.063) owing to its high correlation coefficient (R), Coefficient of determinant (R2) and lower mean square of error as well as root mean square of error (RMSE).
Keywords
References
Ayo-Omogie HN & Ogunsakin R (2013). Assessment of chemical, rheological and sensory properties of fermented maize-cardaba banana complementary food. Food and Nutrition Sciences, 4(08), 844.
Giuberti G, Fortunati P, Cerioli C, & Gallo A. (2015). Gluten free Maize Cookies Prepared with High-amylose Starch: In Vitro Starch Digestibility and Sensory Characteristics. Journal of Nutrition & Food Sciences, 05(06). https://doi.org/10.4172/2155-9600.1000424
Giuberti G, Gallo A, Fortunati P, & Rossi F (2016). Influence of high-amylose maize starch addition on in vitro starch digestibility and sensory characteristics of cookies. Starch - Strke, 68(5-6), 469-475. https://doi.org/10.1002/star.201500228
Gutirrez TJ (2018). Plantain flours as potential raw materials for the development of gluten-free functional foods. Carbohydrate Polymers, 202, 265-279. https://doi.org/https://doi.org/10.1016/j.carbpol.2018.08.121
Klaushofer H, Berghofer E, & Steyrer W (1978). Die Neuentwicklung modifizierter strken am beispiel von citratstrke. Ernahrung/Nutrition, 2, 51-55.
Martnez-Villaluenga C, Peas E, & Hernndez-Ledesma B. (2020). Pseudocereal grains: Nutritional value, health benefits and current applications for the development of gluten-free foods. Food and Chemical Toxicology, 137, 111178. https://doi.org/https://doi.org/10.1016/j.fct.2020.111178
Morakinyo TA, Olawoye B, Taiwo KA, & Akanbi CT (2021). Modeling of the Cracking Efficiency of Tenera Cultivar Palm Nuts Using Comparative Analysis Between Artificial Neural Network and Response Surface Methodology. Agricultural Research. https://doi.org/10.1007/s40003-021-00555-x
Odejobi OJ, Olawoye B, & Ogundipe OR (2018). Modelling and Optimisation of Yoghurt Production from Tigernut (Cyperus esculentus L.) Using Response Surface Methodology (RSM). Asian Food Science Journal, 4(3), 1-12. https://doi.org/https://doi.org/10.9734/AFSJ/2018/44137
Olawoye B, Fagbohun OF, Gbadamosi SO, & Akanbi CT (2020a). Succinylation improves the slowly digestible starch fraction of cardaba banana. A process parameter optimization study. Artificial Intelligence in Agriculture. https://doi.org/https://doi.org/10.1016/j.aiia.2020.09.004
Olawoye B & Gbadamosi SO. (2020). Digestion kinetics of native and modified cardaba banana starch: A biphasic approach. International Journal of Biological Macromolecules, 154, 31-38. https://doi.org/https://doi.org/10.1016/j.ijbiomac.2020.03.089
Olawoye B, Gbadamosi SO, Otemuyiwa IO, & Akanbi CT. (2020b). Gluten-free cookies with low glycemic index and glycemic load: optimization of the process variables via response surface methodology and artificial neural network. Heliyon, 6(10), e05117. https://doi.org/https://doi.org/10.1016/j.heliyon.2020.e05117
Olawoye B, Gbadamosi SO, Otemuyiwa IO, & Akanbi CT (2020c). Improving the resistant starch in succinate anhydride-modified cardaba banana starch: A chemometrics approach. Journal of Food Processing and Preservation, 44(9), e14686. https://doi.org/10.1111/jfpp.14686
Olawoye B, & Kadiri O. (2016). Optimization and response surface modelling of antioxidant activities of Amaranthus virides seed flour extract. Annals Journal of Food Science and Technology, 17, 114-123.
Rocchetti G, Senizza A, Gallo A, Lucini L, Giuberti G, & Patrone V. (2019). In vitro large intestine fermentation of gluten-free rice cookies containing alfalfa seed (Medicago sativa L.) flour: A combined metagenomic/metabolomic approach. Food Research International, 120, 312-321. https://doi.org/https://doi.org/10.1016/j.foodres.2019.03.003
Schmelter L, Rohm H, & Struck S. (2021). Gluten-free bakery products: Cookies made from different Vicia faba bean varieties. Future Foods, 4, 100038. https://doi.org/https://doi.org/10.1016/j.fufo.2021.100038
Bisgin H, Bera T, Ding H, Semey HG, Wu L, Liu Z, Barnes AE, Langley DA, Pava-Ripoll M, Vyas HJ, Tong W, & Xu J. (2018). Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles. Sci Rep, 8(1), 6532. https://doi.org/10.1038/s41598-018-24926-7
Granfeldt Y, Bjorck I, Drews A, & Tovar J. (1992). An in vitro procedure based on chewing to predict metabolic response to starch in cereal and legume products. European Journal of Clinical Nutrition, 46(9), 649-660.
Olawoye B, Adeniyi DM, Oyekunle AO, Kadiri O, & Fawale SO. (2017). Economic Evaluation of Cookie made from blend of Brewers? Spent Grain (BSG), Groundnut cake and Sorghum Flour. Open Agriculture, 2(1), 401-410. https://doi.org/https://doi.org/10.1515/opag-2017-0043
Olawoye B, Gbadamosi SO, Otemuyiwa IO, & Akanbi CT. (2020). Gluten-free cookies with low glycemic index and glycemic load: optimization of the process variables via response surface methodology and artificial neural network. Heliyon, 6(10), e05117. https://doi.org/https://doi.org/10.1016/j.heliyon.2020.e05117
Remya R., Jyothi AN, & Sreekumar J. (2018). Effect of chemical modification with citric acid on the physicochemical properties and resistant starch formation in different starches. Carbohydrate Polymers, 202, 29-38. https://doi.org/https://doi.org/10.1016/j.carbpol.2018.08.128
Saha D & Manickavasagan A. (2021). Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Curr Res Food Sci, 4, 28-44. https://doi.org/10.1016/j.crfs.2021.01.002
Shao M, Wang X., Bu Z, Chen X, & Wang Y (2020). Prediction of energy consumption in hotel buildings via support vector machines. Sustainable Cities and Society, 57, 102128. https://doi.org/https://doi.org/10.1016/j.scs.2020.102128
Article Metrics
Abstract View
: 197 times
Download : 74 times
DOI: 10.57046/CIDZ9008
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Proceedings of the Nigerian Academy of Science
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
.png)