偏最小二乘回归
数学
傅里叶变换红外光谱
油菜
预处理器
葵花籽油
植物油
化学
分析化学(期刊)
生物系统
色谱法
统计
人工智能
食品科学
计算机科学
工程类
化学工程
生物
作者
Rasool Khodabakhshian,Hajarsadat Seyedalibeyk Lavasani,Philipp Weller
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-09-01
卷期号:419: 136055-136055
被引量:2
标识
DOI:10.1016/j.foodchem.2023.136055
摘要
Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R2p) range of 0.910 to 0.971 for different adulterants.
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