首页|Kwame Nkrumah University of Science and Technology Reports Findings in Machine L earning (Non-invasive prediction of maca powder adulteration using a pocket-size d spectrophotometer and machine learning techniques)
Kwame Nkrumah University of Science and Technology Reports Findings in Machine L earning (Non-invasive prediction of maca powder adulteration using a pocket-size d spectrophotometer and machine learning techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Kumasi, Ghana, by News Rx correspondents, research stated, “Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adul terants such as maize and soy flour is a challenge that has not been studied wit h non-invasive techniques such as near infrared spectroscopy (NIRS). This study developed models to rapidly classify and predict 0, 10, 20, 30, 40, and 50% w/w of soybean and maize flour in red, black and yellow maca cultivars using a h andheld spectrophotometer and chemometrics.”
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