Abstract
In this paper, intelligent technology of combined low field NMR (LF-NMR) and back propagation artificial neural network (BP-ANN) was used to monitor moisture content in carrot during microwave vacuum drying. The relationship between different drying powers (200, 300, and 400 W) and NMR signals (A21, A22, A23, and Atotal) was investigated. Results show that as the drying time elapsed, the NMR signals of Atotal and A23 decrease all drying conditions, A21 and A22 tend to increase at high moisture content and then decrease, which is consistent with the state of water while changes during drying. NMR signals can be used as indicators for online monitoring of moisture and control of the drying process. With NMR signals as input variables, a BP-ANN model was built optimized by transfer function, training function, and the number of neurons to model the moisture content (output). Compared with a linear regression model and multiple linear regression model, the BP-ANN model with the topology of 4-25-1, transfer function of tansig and purelin, and training function of trainlm outperformed the fitting performance and accuracy. This shows that the combined approach of utilizing LF-NMR and BP-ANN has great potential in intelligent online monitoring and control applications for carrot drying.
Similar content being viewed by others
References
Aguilo, I., Downey, G., Keenan, D. F., Lyng, J. G., Brunton, N., & Rai, D. K. (2014). Observations on the water distribution and extractable sugar content in carrot slices after pulsed electric field treatment. Food Research International, 64, 18–24.
Badea, E., Şendrea, C., Carşote, C., Adams, A., Blümich, B., & Iovu, H. (2016). Unilateral NMR and thermal microscopy studies of vegetable tanned leather exposed to dehydrothermal treatment and light irradiation. Microchemical Journal, 129, 158–165.
Chen, Y., Cai, K., Tu, Z., Nie, W., Ji, T., Hu, B., Chen, C., & Jiang, S. (2018). Prediction of benzo[a]pyrene content of smoked sausage using back-propagation artificial neural network. Journal of the Science of Food and Agriculture, 98(8), 3022–3030.
Cheng, S. S., Tang, Y. Q., Zhang, T., Song, Y. k., Wang, X. H., Wang, H. H., Wang, H. T., & Tan, M. Q. (2017). Approach for monitoring the dynamic states of water in shrimp during drying process with LF-NMR and MRI. Drying Technology, 36(7), 841–848.
China. (2016). GB5009.3-2016 Determination of moisture in food. Beijing: National Health and Family Planning Commission of the People’s Republic of China.
Duan, X. M., Feng, X. Q., Song, L., Zhang, B., Cai, X. T., Li, M. M., Yang, F. W., & Fan, L. L. (2013). Advances on development of fruit and vegetable drying by MVD technology. Food and Fermentiin Industries., 39(9), 156–164.
Hu, X. Y., Lan, W. Q., Zhang, N. N., & Xie, J. (2017). Research progress of low-field nuclear magnetic resonance technology in food. Science and Technology of Food Industry, 38(6), 386–396.
Li, L. L., Zhang, M., Bhandari, B., & Zhou, L. Q. (2018). LF-NMR online detection of water dynamics in apple cubes during microwave vacuum drying. Drying Technology, 36(16), 2006–2015.
Lv, W. Q., Zhang, M., Bhandari, B., Yang, Z., & Wang, Y. (2016). Analysis of drying properties and vacuum-impregnated qualities of edamame (Glycine max (L.) Merrill). Drying Technology, 35(9), 1075–1084.
Lv, W. Q., Zhang, M., Bhandari, B., Li, L. L., & Wang, Y. C. (2017a). Smart NMR method of measurement of moisture content of vegetables during microwave vacuum drying. Food and Bioprocess Technology, 10(12), 2251–2260.
Lv, W. Q., Zhang, M., Bhandari, B., Wang, Y. C., & Liu, C. Q. (2017b). Freeze drying and vacuum impregnating characteristics of Nostoc sphaeroides Kützing. Drying Technology, 35(11), 1379–1387.
Lv, W. Q., Zhang, M., Wang, Y. C., & Adhikari, B. (2018). Online measurement of moisture content, moisture distribution, and state of water in corn kernels during microwave vacuum drying using novel smart NMR/MRI detection system. Drying Technology, 36(13), 1592–1602.
Meng, X., Zhang, M., & Adhikari, B. (2012). Prediction of storage quality of fresh-cut green peppers using artificial neural network. International Journal of Food Science and Technology, 47(8), 1586–1592.
Mohammad, H. N., Shahin, R., Mortaza, A., Soleiman, H., & Seyed, S. M. (2015). Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food and Bioprocess Processing., 94, 263–274.
Momenzadeh, L., Zomorodian, A., & Mowla, D. (2012). Applying artificial neural network for drying time prediction of green pea in a microwave assisted fluidized bed dryer. Journal of Agricultural Science and Technology, 14, 513–522.
Nadian, M. H., Abbaspour-Fard, M. H., Martynenko, A., & Golzarian, M. R. (2017). An intelligent integrated control of hybrid hot air-infrared dryer based on fuzzy logic and computer vision system. Computers and Electronics in Agriculture, 137, 138–149.
Ordukaya, E., & Karlik, B. (2016). Fruit juice-alcohol mixture analysis using machine learning and electronic nose. IEEJ Transactions on Electrical and Electronic Engineering, 11, S171–S176.
Pariyani, R., Ismail, I. S., Ahmad Azam, A., Abas, F., & Shaari, K. (2017). Identification of the compositional changes in Orthosiphon stamineus leaves triggered by different drying techniques using (1) H NMR metabolomics. Journal of the Science of Food and Agriculture, 97(12), 4169–4179.
Seremet Ceclu, L., Botez, E., Nistor, O. V., Andronoiu, D. G., & Mocanu, G. D. (2016). Effect of different drying methods on moisture ratio and rehydration of pumpkin slices. Food Chemistry, 195, 104–109.
Song, Y., Zang, X., Kamal, T., Bi, J., Cong, S., Zhu, B., & Tan, M. (2017). Real-time detection of water dynamics in abalone (Haliotis discus hannai Ino) during drying and rehydration processes assessed by LF-NMR and MRI. Drying Technology, 36(1), 72–83.
Sun, Q., Zhang, M., & Mujumdar, A. S. (2018). Recent developments of artificial intelligence in drying of freshfood: a review. Critical Reviews in Food Science and Nutrition. https://doi.org/10.1080/10408398.2018.1446900.
Ting, X. (2014). Nondestructive detection of fruit quality based on low-field magnetic resonance technology. Hangzhou: D, China Jiliang University.
Tylewicz, U., Aganovic, K., Vannini, M., Toepfl, S., Bortolotti, V., Dalla Rosa, M., Oey, I., & Heinz, V. (2016). Effect of pulsed electric field treatment on water distribution of freeze-dried apple tissue evaluated with DSC and TD-NMR techniques. Innovative Food Science and Emerging Technologies, 37, 352–358.
Winiczenko, R., Górnicki, K., Kaleta, A., Martynenko, A., Janaszek-Mańkowska, M., & Trajer, J. (2018). Multi-objective optimization of convective drying of apple cubes. Computers and Electronics in Agriculture, 145, 341–348.
Xiao, Q. (2018). Drying process of sodium alginate edible films forming solutions studied by LF NMR. Food Chemistry, 250, 83–88.
Xin, Y., Zhang, M., & Adhikari, B. (2013). Effect of trehalose and ultrasound-assisted osmotic dehydration on the state of water and glass transition temperature of broccoli (Brassica oleracea L. var. botrytis L.). Journal of Food Engineering, 119(3), 640–647.
Xu, F., Jin, X., Zhang, L., & Chen, X. D. (2017a). Investigation on water status and distribution in broccoli and the effects of drying on water status using NMR and MRI methods. Food Research International, 96, 191–197.
Xu, J. C., Zhang, M., Mujumdar, A. S., & Adhikari, B. (2017b). Recent developments in smart freezing technology applied to fresh foods. Critical Reviews in Food Science and Nutrition, 57(13), 2835–2843.
Yaghoubi, M., Askari, B., Mokhtarian, M., Tavakolipour, H., Elhamirad, A. H., Jafarpour, A., & HeidarianS. (2013). Possibility of using neural networks for moisture ratio prediction in dried potatoes by means of different drying methods and evaluating physicochemical properties. Agricultural Engineering International: CIGR Journal, 15(4), 258–269.
Yan, K. J., Chu, Y., Huang, J. H., Jiang, M. M., Li, W., Wang, Y. F., Huang, H. Y., Qin, Y. H., Ma, X. H., Zhou, S. P., Sun, H., & Wang, W. (2016). Qualitative and quantitative analyses of Compound Danshen extract based on (1)H NMR method and its application for quality control. Journal of Pharmaceutical and Biomedical Analysis, 131, 183–187.
Zhang, M., Tang, J., Mujumdar, A. S., & Wang, S. (2006). Trends in microwave-related drying of fruits and vegetables. Trends in Food Science and Technology, 17(10), 524–534.
Zhang, M., Chen, H. Z., Mujumdar, A. S., Zhong, Q., & Sun, J. (2015). Recent developments in high-quality drying with energy-saving characteristic for fresh foods. Drying Technology, 33(13), 1590–1600.
Zhang, M., Chen, H. Z., Mujumdar, A. S., Tang, J., Miao, S., & Wang, Y. (2017). Recent developments in high-quality drying of vegetables, fruits, and aquatic products. Critical Reviews in Food Science and Nutrition, 57(6), 1239–1255.
Zhao, Y., Wang, W., Zheng, B., Miao, S., & Tian, Y. (2016). Mathematical modeling and influence of ultrasonic pretreatment on microwave vacuum drying kinetics of lotus (Nelumbo nucifera Gaertn.) seeds. Drying Technology, 35(5), 553–563.
Zhou, K. l., & Kang, Y. H. (2005). Neural network model and MTLAB simulation program design. Beijing: Peking University Press.
Zou, H. Q., Li, S., Huang, Y. H., Liu, Y., Bauer, R., Peng, L., Tao, O., Yan, S. R., & Yan, Y. H. (2014). Rapid identification of Asteraceae plants with improved RBF-ANN classification models based on MOS sensor E-nose. Evidence-based Complementary and Alternative Medicine, 2014, 425341.
Funding
We acknowledge the financial support from National Key R&D Program of China (Contract No. 2017YFD0400901), Jiangsu Province (China) Agricultural Innovation Project (Contract No. CX(17)2017), Jiangsu Province Key Laboratory Project of Advanced Food Manufacturing Equipment and Technology (No. FMZ201803), and Jiangsu Province (China) “Collaborative Innovation Center for Food Safety and Quality Control” Industry Development Program, National First-class Discipline Program of Food Science and Technology (No. JUFSTR20180205), all of which enabled us to carry out this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflicts of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sun, Q., Zhang, M., Mujumdar, A.S. et al. Combined LF-NMR and Artificial Intelligence for Continuous Real-Time Monitoring of Carrot in Microwave Vacuum Drying. Food Bioprocess Technol 12, 551–562 (2019). https://doi.org/10.1007/s11947-018-2231-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11947-018-2231-1