Meiho University Institutional Repository:Item 987654321/4317
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    题名: Open-Source Visual Programming Software for Introducing Principal Component Analysis to the Analytical Curriculum
    作者: 葉泰聖
    贡献者: 健康科學管理學院
    关键词: analytical chemistry
    principal component analysis
    FTIR, GC, NMR
    日期: 2025-04-09
    上传时间: 2025-04-09T06:13:09Z (UTC)
    摘要: With the increasing complexity of analytical datanowadays, great reliance on statistical and chemometric software isquite common for scientists. Powerful open-source software, such
    as Python, R, and the commercial software MATLAB, demands good coding skills. Writing original code could be challenging for students with no prior programming experience. Orange Data Mining is a Python based visual programming software that has been used widely in many scientific publications. Principal component analysis (PCA) is one of the most common exploratory data analysis techniques with applications in outlier detection, dimensionality reduction, graphical clustering, and classification. By using a program workflow based on widgets (a computational unit within Orange), the task of PCA can be done very quickly. The same workflow could be used for different types of analytical data
    without the need for reprogramming again. The application of Orange Data Mining software to PCA exploratory analysis of sugar NIR spectral data from a portable NIR spectrometer will be demonstrated. Further data sets including multivariate coffee composition data, instant coffee FTIR spectra, vegetable oil fatty acid composition, and vegetable oil NMR spectra were given as Supporting Information to enhance the learning of software through repetition. From the demonstration, it can be easily seen how
    Orange Data Mining software will be useful for introducing PCA to the analytical curriculum.
    显示于类别:[食品營養系] 期刊論文

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