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Updated: Jul 20, 2025

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优化变量选择和机器学习模型用于橄油质量评估,使用便携式近红外光谱学

Rabie Reda1, Taoufiq Saffaj2, Ilham Bouzida3

  • 1University Sidi Mohamed Ben Abdellah, Faculty of Sciences and Techniques of Fez, Laboratory of Applied Organic Chemistry, Fez, Morocco; Moroccan Foundation for Advanced Science, Innovation & Research, MAScIR Rabat, Morocco.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|July 31, 2023
PubMed
概括

近红外光谱学 (NIRS) 准确预测橄油的酸度和质量参数. 这种具有成本效益的方法减少了评估特黄,初和普通初橄油质量的分析时间.

关键词:
酸度 酸度 酸度 酸度K232 / K270 K232 / K270 是一个很大的问题.机器学习 机器学习接近红外光谱学近红外光谱学橄油质量 橄油质量变量选择 变量选择

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