Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow

Main Article Content

Jiang Caiyan https://orcid.org/0000-0003-4730-8750
Shang Shan https://orcid.org/0000-0002-0577-9952
Zheng Jie https://orcid.org/0000-0001-5478-6027
Fu Baoshang
GUO Minqiang
Shen Pengbo
Jiang Pengfei https://orcid.org/0000-0002-2975-2527

Keywords

artificial neural network, hot-air drying, moisture content, sturgeon bone marrow, predict

Abstract

In the experiment of this article, the artificial neural network (ANN) was used to establish the sturgeon bone marrow drying model. Further, the effects of different temperatures (40, 60, and 80°C), humidities (0, 20, and 40%), and air velocities (8, 16, and 25 m/s) on the drying characteristics of sturgeon bone marrow were studied. The studies had shown that with the increase of drying temperature, the acceleration of air velocity, and the decrease of humidity, the sturgeon bone marrow can be dried in the shortest period of 100 min. This study used ANN to feasibly predict dried sturgeon bone marrow moisture ratio, based on the time, temperature, humidity, and air velocity drying inputs. The results revealed that 11 hidden neurons were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R value 0.996. This model correctly predicted the optimal drying conditions and established that temperature is the single most significant factor in determining the drying time of sturgeon bone marrow. It is expected that this system will have broader application in other food drying requirements.

Abstract 439 | PDF Downloads 378 HTML Downloads 29 XML Downloads 6

References

Akin, A., Gurlek, G., Ozbalta, N., 2014. Mathematical model of solar drying characteristics for pepper (capsicum annuum). Isi Bilimi ve Teknigi Dergisi / Journal of Thermal Science & Technology 34(2): 99–109.

Ali, M., Shahin, R., Alireza, K., Zahra, E.D., 2008. Estimation of thin-layer drying characteristics of kiwifruit (cv. Hayward) with use of Page’s model. American-Eurasian Journal of Agricultural and Environmental Sciences 3(5): 802–805.

Camila, B., Fernando, N.S., Jorge, F., Jorge, S., Ângelo, S., 2011. Predicting the drying kinetics of salted codfish (Gadus Morhua): semi-empirical, diffusive and neural network models. Inter-national Journal of Food Science and Technology 46(3): 509–515. 10.1111/j.1365-2621.2010.02513.x

Dariush, Z., Hossein, N., Mohsen, R., 2015. Energy and Quality Attributes of Combined Hot-Air/Infrared Drying of Paddy. Drying Technology 33(5): 570–582. 10.1080/07373937.2014.962143

Guo, L., Wang, P., Liu, B., Ai, C.Q., Zhou, D.Y., Song, S., et al., 2017. Identification and quantification of uronic acid-containing polysaccharides in tissues of Russian sturgeon (Acipenser gueldenstaedtii) by HPLC-MS/MS and HPLC-MS. European Food Research and Technology 243(7): 1201–1209. 10.1007/s00217-016-2834-6

Guo, M.Q., Jiang, P.F., Fu, B.S., Bai, F., Qi, L.B., Shen, P.B., 2019. Analysis and evaluation of nutritional components of different kinds of freeze drying sturgeon’s bone marrow. Food Research and Development 40(12): 194–199.

Gui, M., Song, J., Zhang, Z., Hui, P., Li, P., 2014. Biogenic amines formation, nucleotide degradation and TVB-N accumulation of vacuum-packed minced sturgeon (Acipenser schrencki) stored at 4°C and their relation to microbiological attributes. Journal of The Science of Food and Agriculture 94(10): 2057–2063. 10.1002/jsfa.6524

Gui, M., Song, J.Y., Zhang, L., Wang, S., Wu, R.Y., Ma, C.W., et al., 2015. Chemical characteristics and antithrombotic effect of chondroitin sulfates from sturgeon skull and sturgeon backbone. Carbohydrate Polymers 123: 454–460. 10.1016/j.carbpol.2015.01.046

Guine, R.P.F., Almeida, C.F.F., Correia, P.M.R., Mendes, M., 2015. Modelling the Influence of Origin, Packing and Storage on Water Activity, Colour and Texture of Almonds, Hazelnuts and Walnuts Using Artificial Neural Networks. Food and Bioprocess Technology 8(5): 1113–1125. 10.1007/s11947-015-1474-3

Hao, S.X., Wei, Y., Li, L.H., 2015. The effects of different extraction methods on composition and storage stability of sturgeon oil. Food Chemistry 173: 274–282. 10.1016/j.foodchem.2014.09.154

Imran, A., Chawalit, J., Pisit, C., Somrote, K., 2014. Prediction of Physical Quality Parameters of Frozen Shrimp (Litopenaeus vannamei): An Artificial Neural Networks and Genetic Algorithm Approach. Food and Bioprocess Technology 7(5): 1433–1444. 10.1007/s11947-013-1135-3

Jiang, P.F., Qi, L.B., Guo, M.Q., Bai, F., Shen, P.b., Dong, X.P., 2019. Effect of different heating temperatures on quality of sturgeon bone marrow. The Food Industry 40(07): 195–198.

Jiang, P.F., Guo, M.Q., Chen, Y., Yu, W.J., Pei, L.Y., Shen, P.B., 2021. Quality Characteristics of Dried Sturgeon Bone Marrow under Different Rehydration Temperatures. The Food Industry 42(01): 10–14.

Jiang, P.F., Zhu, K.Y., Shang, S., Jin, W.G., Yu, W.Y., Li, S., Wang, S., Dong, X.P., 2022. Application of Artificial Neural Network in the Baking Process of Salmon. Journal of Food Quality 10.1155/2022/3226892

Jafari, S.M., Ganje, M., Dehnad, D., Ghanbari, V., 2016. Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion. Journal of Food Processing and Preservation 40(2): 329–339. 10.1111/jfpp.12610

Lato, L.P., Danijela, Z.S., Ljubinko, B.L., Biljana, R.C., Olgica, A.K., 2014. Effects of temperature and immersion time on rehydration of osmotically treated pork meat. Journal of Food and Nutrition Research 53(3): 260–270.

Li, S., Hu, Y., Hong, Y.M., Xu, L.B., Zhou, M.Z., Fu, C.X., et al., 2016. Analysis of the hydrolytic capacities of Aspergillus oryzae proteases on soybean protein using artificial neural networks. Journal of Food Processing and Preservation 40(5): 918–924. 10.1111/jfpp.12670

Martinez, O., Salmeron, J., Guillen, M.D., Pin, C., Casas, C., 2012. Physicochemical, sensorial and textural characteristics of liquid-smoked salmon (Salmo salar) as affected by salting treatment and sugar addition. International Journal of Food Science and Technology 47: 1086–1096. 10.1111/j.1365-2621.2012.02945.x

Malekjani, N., Jafari, S.M., Rahmati, M.H., Zadeh, E.E., Mirzaee, H., 2013. Evaluation of Thin-Layer Drying Models and Artificial Neural Networks for Describing Drying Kinetics of Canola Seed in a Heat Pump Assisted Fluidized Bed Dryer. International Journal of Food Engineering 9(4): 375–384. 10.1515/ijfe-2012-0136

Mansour, G.M., Mostafa, K., 2011. Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles. Food and Nutrition Sciences 2(8): 803–808. 10.4236/fns.2011.28110

Menlik, T., Özdemir, M.B., Kirmaci, V., 2010. Determination of freeze-drying behaviors of apples using artificial neural network. Expert Systems with Applications 37(12): 7669–7677. 10.1016/j.eswa.2010.04.075

Mohammad, F.D., Morteza, G., Reza, P., Mehdi, R.H., Hashemi, S.J., 2015. On the Characteristics of Thin-Layer Drying Models for Intermittent Drying of Rough Rice. Chemical Engineering Communications 202(8): 1024–1035. 10.1080/00986445.2014.900049

Mortaza, A., Soleiman, H., Arun, S.M., 2015. Application of Artificial Neural Networks (ANNs) in Drying Technology: A Comprehensive Review. Drying Technology: An International Journal 33(12): 1397–1462. 10.1080/07373937.2015.1036288

Poonpat, P., Panupong, Y., Akkarin, S., Pongpol, U., Saowanit, I. 2014. Classification of Boiled Shrimp’s Shape Using Image Analysis and Artificial Neural Network Model. Journal of Food Process Engineering 37(3): 257–263. 10.1111/jfpe.12081

Rai, P., Majumdar, G.C., Dasgupta, S., De, S., 2005. Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature. Journal of Food Engineering 68(4): 527–533. 10.1016/j.jfoodeng.2004.07.003

Sarimeseli, A., Coskun, M.A., Yuceer, M., 2014. Modeling microwave drying kinetics of thyme (thymus vulgaris L.) leaves using ANN methodology and dried product quality. Journal of Food Processing & Preservation 38(1): 558–564. 10.1111/jfpp.12003

Shi, C., Cui, J.Y., Zhang, Y.M., Qin, N., Luo, Y.K., 2017. Application of artificial neural network to predict the change of inosine monophosphate for lightly salted silver carp (hypophthalmichthys molitrix) during thermal treatment and storage. Journal of Food Processing and Preservation 41(6). 10.1111/jfpp.13246

Shrestha, B.L., Wood, H.C., Tabil, L., Baik, O.D., Sokhansanj, S., 2017. Microwave permittivity-assisted artificial neural networks for determining moisture content of chopped alfalfa forage. IEEE Instrumentation and Measurement Magazine 20(3): 37–42. 10.1109/MIM.2017.7951691

Tatar, F., Cengiz, A., Kahyaoglu, T., 2014. Effect of hemicellulose as a coating material on water sorption thermodynamics of the microencapsulated fish oil and Artificial Neural Network (ANN) modeling of isotherms. Food and Bioprocess Technology 7(10): 2793–2802. 10.1007/s11947-014-1291-0

Tarafdar, A., Shahi, N.C., Singh, A., Sirohi, R. 2018. Artificial neural network modeling of water activity: a low energy approach to freeze drying. Food and Bioprocess Technology 11(1): 164–171. 10.1007/s11947-017-2002-4

Thrupathihalli, P.K.M., Balaraman, M., 2012. Microwave drying of mango ginger (Curcuma amadaRoxb): prediction of drying kinetics by mathematical modelling and artificial neural network. International Journal of Food Science and Technology 47(6): 1229–1236. 10.1111/j.1365-2621.2012.02963.x

Thirupathihalli, P.K.M., Balaraman, M., 2014. Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network. J Food Sci Technol 51(12): 3712–3721. 10.1007/s13197-013-0941-y

Turkay, K., Zhou, W.B., 2017. Recent applications of advanced control techniques in food industry. Food and Bioprocess Technology 10(3): 522–542. 10.1007/s11947-016-1831-x

Viviana, C.M., Camila, A.P., Adriano, C., Toni, J.L., Adriano, S., 2014. Hot-Air Drying Characteristics of Soybeans and Influence of Tempera-ture and Velocity on Kinetic Parameters. Journal of Food Process Engineering 37(6): 619–627. 10.1111/jfpe.12118

Xu, L.J., Lu, Y.H. 2018. China fishery statistical yearbook 2018. Beijing: China Agriculture Press.

Yu, H.M., Zuo, C.C., Xie, Q.J. 2015. Drying Characteristics and Model of Chinese Hawthorn Using Microwave Coupled with Hot Air. Mathematical Problems in Engineering 1: 1–15. 10.1155/2015/480752