Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)
Anjineyulu Kothakota,
Ravi Pandiselvam,
Kaliramesh Siliveru,
Jai Prakash Pandey,
Nukasani Sagarika,
Chintada H. Sai Srinivas,
Anil Kumar,
Anupama Singh,
Shivaprasad D. Prakash
Affiliations
Anjineyulu Kothakota
Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Thiruvananthapuram 695019, Kerala, India
Ravi Pandiselvam
Physiology, Biochemistry and Post-Harvest Technology Division, ICAR-Central Plantation Crops Research Institute, Chowki 671124, Kerala, India
Kaliramesh Siliveru
Department of Grain Science & Industry, Kansas State University, Manhattan, KS 66502, USA
Jai Prakash Pandey
Department of Post-Harvest Process and Food Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttarakhand, India
Nukasani Sagarika
Department of Food Process Engineering, College of Food Processing Technology & Bio-Energy, Anand Agricultural University, Anand 388110, Gujarat, India
Chintada H. Sai Srinivas
Foods Business Division, ITC Limited, Visakhapatnam 530001, Andhra Pradesh, India
Anil Kumar
Department of Food Science and Technology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnager 263145, India
Anupama Singh
Department of Post-Harvest Process and Food Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttarakhand, India
Shivaprasad D. Prakash
Department of Grain Science & Industry, Kansas State University, Manhattan, KS 66502, USA
This study involves information about the concentrations of nutrients (proteins, phenolic compounds, free amino acids, minerals (Ca, P, and Iron), hardness) in milled rice processed with enzymes; xylanase and cellulase produced by Aspergillus awamori, MTCC 9166 and Trichoderma reese, MTCC164. Brown rice was processed with 60–100% enzyme (40 mL buffer -undiluted) for 30 to 150 min at 30 °C to 50 °C followed by polishing for 20–100 s at a safe moisture level. Multiple linear regression (MLR) and artificial neural network (ANN) models were used for process optimization of enzymes. The MLR correlation coefficient (R2) varied between 0.87–0.90, and the sum of square (SSE) was placed within 0.008–8.25. While the ANN R2 (correlation coefficient) varied between 0.97 and 0.9999(1), MSE changed from 0.005 to 6.13 representing that the ANN method has better execution across MLR. The optimized cellulase process parameters (87.2% concentration, 80.1 min process time, 33.95 °C temperature and 21.8 s milling time) and xylanase process parameters (85.7% enzyme crude, 77.1 min process time, 35 °C temperature and 20 s) facilitated the increase of Ca (70%), P (64%), Iron (17%), free amino acids (34%), phenolic compounds (78%) and protein (84%) and decreased hardness (20%) in milled rice. Scanning electron micrographs showed an increased rupture attributing to enzymes action on milled rice.
