Predictive modeling of allowable storage time of finger millet grains using artificial neural network and support vector regression approaches

被引:0
|
作者
Joshi, T. Jayasree [1 ]
Rao, P. Srinivasa [1 ]
机构
[1] Indian Inst Technol Kharagpur, Agr & Food Engn Dept, Kharagpur 721302, West Bengal, India
关键词
Finger millet; Safe storage time; Levenberg-marquardt; Bayesian regularization; Scaled conjugated gradient; Support vector regression; MOISTURE; TEMPERATURE;
D O I
10.1016/j.jfoodeng.2024.112224
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The study aimed to establish safe storage guidelines for long-term preservation of finger millet grains and to develop a model for predicting the allowable storage time. The experiment was conducted at various temperature (15, 25, 35 and 45 degrees C) and moisture contents (8, 11, 14, 17 and 20% wb). Changes in response variables such as germination, free fatty acid and mold growth were systematically monitored. Finger millet with a moisture content above 14% should be dried to safe moisture levels within 3-5 weeks at 30 degrees C or within 5-10 weeks at 15 degrees C to preserve grain quality. To maintain high quality and seed viability of finger millet, moisture content and storage temperature should be below 12% and 20 degrees C, respectively for up to 34 weeks. The study also assessed the use of artificial neural network and support vector regression models in predicting the safe storage period for finger millet grains.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Predictive modelling of allowable storage time for pearl millet using multilayer perception neural network
    Joshi, Jayasree T.
    Rao, P. Srinivasa
    [J]. JOURNAL OF STORED PRODUCTS RESEARCH, 2024, 108
  • [2] Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression
    Alade, Ibrahim Olanrewaju
    Rahman, Mohd Amiruddin Abd
    Hassan, Amjed
    Saleh, Tawfik A.
    [J]. Journal of Applied Physics, 2020, 128 (08):
  • [3] Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression
    Alade, Ibrahim Olanrewaju
    Abd Rahman, Mohd Amiruddin
    Hassan, Amjed
    Saleh, Tawfik A.
    [J]. JOURNAL OF APPLIED PHYSICS, 2020, 128 (08)
  • [4] Artificial Neural Network and Support Vector Regression Modeling for Prediction of Mixing Time in Wet Granulation
    Chamnanthongpaivanh, Boonyasith
    Chatchawalsaisin, Jittima
    Kittithreerapronchai, Oran
    [J]. JOURNAL OF PHARMACEUTICAL INNOVATION, 2022, 17 (04) : 1235 - 1246
  • [5] Artificial Neural Network and Support Vector Regression Modeling for Prediction of Mixing Time in Wet Granulation
    Boonyasith Chamnanthongpaivanh
    Jittima Chatchawalsaisin
    Oran Kittithreerapronchai
    [J]. Journal of Pharmaceutical Innovation, 2022, 17 : 1235 - 1246
  • [6] Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm
    Zhang, Di
    Lin, Junqiang
    Peng, Qidong
    Wang, Dongsheng
    Yang, Tiantian
    Sorooshian, Soroosh
    Liu, Xuefei
    Zhuang, Jiangbo
    [J]. JOURNAL OF HYDROLOGY, 2018, 565 : 720 - 736
  • [7] Determination of ‘Hass’ avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression
    Byeong-Hyo Cho
    Kento Koyama
    Shigenobu Koseki
    [J]. Journal of Food Measurement and Characterization, 2021, 15 : 2021 - 2030
  • [8] Determination of 'Hass' avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression
    Cho, Byeong-Hyo
    Koyama, Kento
    Koseki, Shigenobu
    [J]. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION, 2021, 15 (02) : 2021 - 2030
  • [9] DIMENSIONAL PREDICTION FOR FDM MACHINES USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR REGRESSION
    Lyu, Jiaqi
    Manoochehri, Souran
    [J]. PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2019, VOL 1, 2020,
  • [10] Modeling pile capacity using support vector machines and generalized regression neural network
    Pal, Mahesh
    Deswal, Surinder
    [J]. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING, 2008, 134 (07) : 1021 - 1024