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

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.

___

  • 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