首页|Yunnan Academy of Agricultural Sciences Reports Findings in Chemicals and Chemis try (A rapid method for identification of Lanxangia tsaoko origin and fruit shap e: FT-NIR combined with chemometrics and image recognition)
Yunnan Academy of Agricultural Sciences Reports Findings in Chemicals and Chemis try (A rapid method for identification of Lanxangia tsaoko origin and fruit shap e: FT-NIR combined with chemometrics and image recognition)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Chemicals and Chemistr y is the subject of a report. According to news originating from Kunming, People 's Republic of China, by NewsRx correspondents, research stated, "Lanxangia tsao ko's accurate classifications of different origins and fruit shapes are signific ant for research in L. tsaoko difference between origin and species as well as f or variety breeding, cultivation, and market management. In this work, Fourier t ransform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensiona l and three-dimensional correlation spectroscopies to further investigate the sp ectral characteristics of L. tsaoko." Our news journalists obtained a quote from the research from the Yunnan Academy of Agricultural Sciences, "Before building the classification model, the raw FT- NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algori thm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant anal ysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS- DA and SVM models can achieve 100% classification in origin classi fication, but what is difficult to avoid is the complex process of model optimiz ation."
KunmingPeople's Republic of ChinaAsi aChemicals and ChemistryChemometricEmerging TechnologiesMachine Learning
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