Zhejiang University Researcher Provides New Study Findings on Machine Learning ( Near-Infrared Spectroscopy Analysis of the Phytic Acid Content in Fuzzy Cottonse ed Based on Machine Learning Algorithms)
Zhejiang University Researcher Provides New Study Findings on Machine Learning ( Near-Infrared Spectroscopy Analysis of the Phytic Acid Content in Fuzzy Cottonse ed Based on Machine Learning Algorithms)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on artificial intelligence is now ava ilable. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Cottonseed is rich in oil and protein.” Financial supporters for this research include National Key Technology R& D Program of China; Jiangsu Collaborative Innovation Center For Modern Crop Prod uction; National Science Foundation of China. The news editors obtained a quote from the research from Zhejiang University: “H owever, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is a n efficient and eco-friendly analytical technique for crop quality analysis. Des pite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatmen ts, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear suppo rt vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonsee d. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best pre diction performance.”
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