Record Details

Machine Learning Algorithms for Protein Physicochemical Component Prediction Using Near Infrared Spectroscopy in Chickpea Germplasm

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Title Machine Learning Algorithms for Protein Physicochemical Component Prediction Using Near Infrared Spectroscopy in Chickpea Germplasm
 
Creator Priyadarshi Madhu Bala
Anu Sharma
K. K. Chaturvedi
Rakesh Bhardwaj
SB Lal
MS Farooqi
Sanjeev Kumar
D C Mishra
Mohar Singh
 
Subject Artificial Neural Network
Chickpea
Machine learning
Near infrared spectroscopy
Random Forest
Spectroscopy
 
Description Not Available
Prediction of physicochemical components of chickpea flour using near infrared spectroscopy requires discovering exact wavelength regions that provide the most useful data before preprocessing. This study used six essential machine learning techniques to develop models for predicting proteinphysicochemical component in chickpea: Linear Regression (LR), Artificial Neural Network (ANN), Partial Least Squares Regression (PLSR), Random Forest (RF), Support Vector Regression (SVR) and Decision Tree Regression (DTR). Performance measurements such as Root Mean Square Error and Karl Pearson’s Correlation Coefficient and Coefficient of Determination were used to validate the models. RF and ANN models showed significant improvement over all other models in terms of accuracy.
Not Available
 
Date 2023-02-28T10:38:03Z
2023-02-28T10:38:03Z
2022-06-24
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/76500
 
Language English
 
Relation Not Available;
 
Publisher Indian Journal