Artificial Neural Network Predictive Modelling of luffa cylindrica Seed Oil Antioxidant Yield

Year 2021, , 494 - 504, 30.12.2021

Kenechi Nwosu-obieogu

https://doi.org/10.54287/gujsa.972137

Cited By: 4

Abstract

This study applied artificial neural network (ANN) in evaluating the models for terpineol and polyphenol yield from luffa cylindrica seed oil. The experiment was carried out at a temperature (60-80oC), time (4-6 hours), and solvent/seed ratio (8-12 ml/g) with response as antioxidant yield. FTIR (Fourier Transform Infra-red Spectroscopy) revealed the presence of terpineol and polyphenol at peaks of 1461.1cm-1 and 3008.0cm-1 respectively. The ANN prediction indices are thus; terpineol (R2= 9.9999E-1, MSE=2.25766E-9) and polyphenol (R2=9.9999E-1, MSE=4.42588E-10). This study reveals that the ANN technique can successfully predict antioxidants from luffa cylindrica seed oil.

Keywords

ANN, Terpineol, Polyphenol, Antioxidant

References

• Adeniyi, A. G., Igwegbe, C. A. & Ighalo, J. O. (2021) ANN modelling of the adsorption of herbicides and pesticides based on sorbate-sorbent interphase. Chemistry Africa, 4, 443-449. doi:10.1007/s42250-020-00220-w
• Afolabi, T. J., Onifade, K. R., Akindipe, V. O. & Odetoye, T. E. (2014). Optimization of Solvent Extraction of Parinari polyandra Benth Seed Oil Using Response Surface Methodology. British Journal of Applied Science & Technology, 5(5), 436-446.
• Agatonovic-Kustrin, S., Ristivojevic, P., Gegechkori, V., Litvinova, T. M., Morton, D. W. (2020). Essential oil quality and purity evaluation via FT-IR spectroscopy and pattern recognition techniques. Applied sciences, 10(20), 1-12. doi:10.3390/app10207294
• Akinsanmi, A. O., Oboh, G., Akinyemi, J. A., & Adefagha, A. S. (2015). Assessment of the nutritional, antinutritional, and antioxidant capacity of unripe, ripe, and overripe plantain (Musa paradisiaca) peels. International Journal of Advanced Research, 3(2), 63-72.
• Almeida, J. S. (2002) Predictive Non-linear Modelling of Complex Data by Artificial Neural Networks. Current Opinion in Biotechnology, 13(1), 72-76. doi:10.1016/s0958-1669(02)00288-4
• Cabrera, A. C. & Prieto, J. M. (2010) Application of artificial neural networks to the prediction of the antioxidant activity of essential oils in two experimental in vitro models. Food Chemistry, 118(1), 141-146. doi:10.1016/j.foodchem.2009.04.070
• Campone, L., Celano, R., Rizzo, S., Piccinelli, A. L., Rastrelli, L., & Russo, M. (2020). Development of an Enriched Polyphenol (Natural Antioxidant) Extracts from Orange Juice (Citrus sinensis) by Adsorption on Macroporous Resins. Journal of Food Quality, 1251957, 1-9. doi:10.1155/2020/1251957
• Cimpoiu, C., Cristea, V-M., Hosu, A., Sandru, M., & Seserman, L. (2011) Antioxidant activity prediction and classification of some teas using artificial neural networks. Food Chemistry, 127(3), 1323-1328. doi:10.1016/j.foodchem.2011.01.091
• de Lima, R. K., Cardoso, M. das G., Andrade, M. A., Nascimento, E. A., de Morais, S. A. L., & Nelson, D. L. (2010). Composition of the essential oil from the leaves of tree domestic varieties and one wild variety of the guava plant (Psidium guajava L., Myrtaceae). Revista Brasileira de Farmacognosia, 20(1), 41-44. doi:10.1590/S0102-695X2010000100009
• Ferhat, M. A., Meklati, B. Y., & Chemat, F. (2007). Comparison of different isolation methods of essential oil from Citrus fruits: cold pressing, hydrodistillation and microwave ‘dry’ distillation. Flavour and Fragrance Journal, 22(6), 494-504. https://doi.org/10.1002/ffj.1829
• Ghorai, N., Chakraborty, S., Gucchait, S., Saha, S. K., & Biswas, S. (2012). Estimation of total terpenoids concentration in plant tissues using a monoterpene, linalool as the standard reagent. Protocol Exchange. doi:10.1038/PROTEX.2012.055
• Gonçalves, F. J., Rocha, S. M., & Coimbra, M. A. (2012) Study of the retention capacity of anthocyanins by wine polymeric material. Food Chemistry, 134(2), 957-963. doi:10.1016/j.foodchem.2012.02.214
• Guiné, R. P. F., Barroca, M. J., Gonçalves, F. J., Alves, M., Oliveira, S., & Mendes, M. (2015) Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to drying treatments. Food Chemistry, 168, 454-459. doi:10.1016/j.foodchem.2014.07.094
• Guiné, R. P. F, Matos, S., Goncalves, F. J., Costa, D. & Mendes M. (2018) Evaluation of phenolic compounds and antioxidant activity of blueberries and modelization by artificial neural networks. International Journal of Fruit Science, 18(2), 199-214. doi:10.1080/15538362.2018.1425653
• Karadžić Banjac, M. Ž., Kovačević, S. Z., Jevrić, L. R., Podunavac‐Kuzmanović, S. O., Tepić Horecki, A. N., Vidović, S. S., Šumić, Z. M., Ilin, Ž. M., Adamović, B. D., & Kuljanin, T. A. (2018). Artificial neural network modelling of the antioxidant activity of lettuce submitted to different postharvest conditions. Journal of Food Processing and Preservation, 43(3), e13878. doi:10.1111/jfpp.13878
• Khaleel, C., Tabanca, N., & Buchbauer, G., (2018). α-Terpineol, a natural monoterpene: A review of its biological properties. Open Chemistry, 16(1), 349-361. doi:10.1515/chem-2018-0040
• Kovacević, S. Z., Jevrić, L. R., Podunavac‐Kuzmanović, S. O., Kalaidiziia, N. D., & Loncar, E. S. (2015) Quantitative structure-retention relationship analysis of some xylofuranose derivatives by linear multivariate method. Acta Chimica Slovenica, 60(2), 420-428. doi:10.17344/acsi.2014.888
• Liu, L., Chen, L., Abbasi, A. M., Wang, Z., Li, D., & Shen, Y. (2018) Optimization of extraction of polyphenols from Sorghum Moench using response surface methodology, and determination of their antioxidant activities. Tropical Journal of Pharmaceutical Research, 17(4), 619-626. doi:10.4314/tjpr.v17i4.8
• Liyana-Pathirana, C. M., Shahidi, F., & Alasalvar, C. (2006). Antioxidant activity of cherry laurel fruit (Laurocerasus officinalis Roem.) and its concentrated juice. Food Chemistry, 99(1), 121-128. doi:10.1016/j.foodchem.2005.06.046
• Maosudi, S., Sima, M., & Tolouei-Rad, M. (2018). Comparative study of ANN and ANFIS models for predicting temperature in machining. Journal of Engineering Science and Technology, 13(1), 211-225.
• Molina, G., Pessôa, M. G., Bicas, J. L., Fontanille, P., Larroche, C., & Pastore, G. M. (2019). Optimization of limonene biotransformation for the production of bulk amounts of α-terpineol. Bioresource Technology, 294, 122180. doi:10.1016/j.biortech.2019.122180
• Nwosu-Obieogu, K., Aguele, F. & Chiemenem, L. I. (2020) Soft computing prediction of oil extraction from huracrepitan seeds. Kem. Ind., 69(12), 653-658. doi:10.15255/KUI.2020.006
• Oboh, I. O. & Aluyor, E. O. (2009). Luffa cylindrica-an emerging cash crop. African Journal of Agricultural Research, 4(8), 684-688. doi:10.5897/AJAR.9000476
• Ohlsson, T. & Bengtsson, N. (2002). Minimal processing technologies in the food industry. Woodhead Publishing.
• Ojediran, J. O., Okonkwo, C. E., Adeyi, A. J., Adeyi, O., Olaniran, A. F., George, N. E., & Olayanju, A. T. (2020) Drying characteristics of yam slices (Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics. Heliyon, 6(3), e03555. doi:10.1016/j.heliyon.2020.e03555
• Oke, E. O., Nwosu-Obieogu, K., & Ude, J. C. (2020) Experimental Study and Exergy Efficiency Prediction of Three-Leaved Yam (Dioscorea Dumetorum) Starch Drying. International Journal of Exergy, 33(4), 427-443. doi:10.1504/IJEX.2020.111690
• Oke, E. O., Nwosu-Obieogu, K., Okolo, B., I., Adeyi, O., Omotoso, A. O., & Ude, C. U. (2021) Hevea brasiliensis oil epoxidation: hybrid genetic algorithm–neural fuzzy–Box–Behnken (GA–ANFIS–BB) modelling with sensitivity and uncertainty analyses. Multiscale and Multidisciplinary Modelling, Experiments and Design, 4, 131-144. doi:10.1007/s41939-020-00086-y
• Okla, M. K., Alamri, S. A., Salem, M. Z. M., Ali, H. M., Behiry, S. I., Nasser, R. A., Alaraidh, I. A., Al-Ghtani, S. M., & Soufan, W. (2019). Yield, Phytochemical Constituents, and Antibacterial Activity of Essential Oils from the Leaves/Twigs, Branches, Branch Wood, and Branch Bark of Sour Orange (Citrus aurantium L.). Processes, 7(6), 363. doi:10.3390/pr7060363
• Oli, C. C., Onuegbu, T. U., & Ezeudu. E. C. (2014). Proximate composition, characterization, and spectroscopic analysis of luffa aegyptiaca seed. International Journal of Life Sciences Biotechnology and pharma Research, 3(4), 194-200.
• Oniya, O. O., Oyelade, J. O., Ogunkunle, O., & Idowu, D. O. (2017) Optimization of Solvent extraction of Oil from Sandbox Kernels (Hura crepitans L.) by a Response Surface Method. Energy and Policy Research, 4(1), 36-43. doi:10.1080/23815639.2017.1324332
• Oyetayo, F. L., & Ojo, B. A., (2012). Food value and phytochemical composition of Luffa cylindrica seed flour. American Journal of Biochemistry, 2(6), 98-103. doi:10.5923/j.ajb.20120206.02
• Park, S-N., Lim, Y. K., Friere, M. O., Cho, E., Jin, D., & Kook, J-K. (2012). Antimicrobial effect of linalool and α-terpineol against periodontopathic and cariogenic bacteria. Anaerobe, 18(3) 369-372. doi:10.1016/j.anaerobe.2012.04.001
• Sales, A., Felipe, L. de O., & Bicas, J. L. (2020). Production, Properties, and Applications of α-Terpineol. Food and Bioprocess Technology, 13, 1261-1279. doi:10.1007/s11947-020-02461-6
• Shendge, P. N., & Belemkar, S., (2018). Therapeutic Potential of Luffa acutangula: A Review on Its Traditional Uses, Phytochemistry, Pharmacology and Toxicological Aspects. Frontiers in Pharmacology, 9. doi:10.3389/fphar.2018.01177
• Singleton, V. L., & Rossi, J. A., (1965). Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. Am J Enol Vitic, 16(3), 144-158.
• Skrypnik, L., & Novikova, A. (2020) Response Surface Modeling and Optimization of Polyphenols Extraction from Apple Pomace Based on Nonionic Emulsifiers. Agronomy, 10(1), 92. doi:10.3390/agronomy10010092
• Soto, J., Castilo, O., Melin, P., & Pedrycz, W. (2019) A new approach to multiple time series predictions using MIMO fuzzy aggregation models with modular neural networks. International Journal of Fuzzy Systems, 21, 1629-1648. doi:10.1007/s40815-019-00642-w
• Uzuner, S., & Cekmecelioglu, D. (2016). Comparison of Artificial neural networks (ANN) and Adaptive Neuro-fuzzy inference system (ANFIS) models in simulating polygalacturonase production. Bioresources, 11(4), 8676-8685. doi:10.15376/biores.11.4.8676-8685
• Vats, S. & Negi, S. (2013) Use of artificial neural network (ANN) for the development of bioprocess using Pinus roxburghii fallen foliages for the release of polyphenols and reducing sugars. Bioresource Technology, 140, 392-398. doi:10.1016/j.biortech.2013.04.106
• Vladimir-Knežević, S., Blažeković, B., Štefan, M. B. & Babac, M. (2011). Plant polyphenols as antioxidants influencing the human health. In: V. Rao (Eds.), Phytochemicals as Nutraceuticals - Global Approaches to Their Role in Nutrition and Health (pp. 155-180), IntechOpen. doi:10.5772/27843
• Xi, J., Xue, Y., Xu, Y. & Shen, Y. (2013) Artificial neural network modelling and optimization of ultrahigh-pressure extraction of green tea polyphenols. Food Chemistry, 141(1), 320-326. doi:10.1016/j.foodchem.2013.02.084
• Yu, L., Jin, W., Li, X., & Zhang, Y. (2018). Optimization of bioactive ingredient extraction from Chinese herbal medicine Glycyrrhiza glabra: a comparative study of three optimization models. Evidence-Based Complementary and Alternative Medicine, 6391414. doi:10.1155/2018/6391414
• Zengin, H., & Baysal, A. H. (2014). Antibacterial and antioxidant activity of essential oil terpenes against pathogenic and spoilage-forming bacteria and cell structure-activity relationships evaluated by SEM microscopy. Molecules, 19(11), 17773-17798. doi:10.3390/molecules191117773