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| 基于衰减全反射傅里叶变换红外光谱的餐饮废油掺假检测 |
| Adulteration detection of waste cooking oil based on attenuated total reflection Fourier transform infrared spectroscopy |
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| DOI： |
| 中文关键词: ATR-FTIR 餐饮废油 掺假检测 |
| 英文关键词:ATR-FTIR waste cooking oil adulteration detection |
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| 中文摘要: |
| 为了建立基于衰减全反射傅里叶变换红外光谱（ATR-FTIR）的餐饮废油掺假检测方法，以常见食用油和餐饮废油为原料，收集8个餐饮废油和25个食用油样品，制备30个掺假油样品，共63个油样进行红外光谱扫描。随机取48个油样作为校正集样品，15个油样作为验证集样品，建立餐饮废油定性分析模型，并对定性模型进行验证；从30个掺假油样品中，随机取20个油样作为校正集样品，10个油样作为验证集样品，建立餐饮废油定量分析模型，并对定量模型进行验证。结果表明：在红外光谱范围为1 550～650 cm-1条件下，采用原始光谱结合判别分析建立定性分析模型，其识别率可达100%；采用偏最小二乘法（PLS）建立定量分析模型，在掺假比例1%～10%时，模型预测值与实际掺假比例呈良好的线性关系，相关系数（R）为0.982 2，标准偏差（SD）为0.47。表明基于ATR-FTIR的餐饮废油掺假检测是可行的。 |
| 英文摘要: |
| In order to establish adulteration detection method of waste cooking oil based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), the infrared spectrum of oil samples were collected, with 63 oil samples as raw materials, including 8 waste cooking oil samples, 25 edible oil samples and 30 adulteration oil samples. From 63 oil samples, 48 oil samples were taken as calibration set samples randomly, 15 oil samples as validation set. The qualitative model for identification of waste cooking oil was established and validated. From 30 adulteration oil samples, 20 oil samples were taken as calibration set samples randomly, 10 oil samples as validation set. The quantitative model for identification of waste cooking oil was established and validated. The results showed that when spectral range was 1 550-650 cm-1, the qualitative model was established using the original spectrum combined with discrimination analysis and the recognition rate reached 100%. The quantitative analysis model was established using partial least square (PLS) with adulteration ratio of 1%-10%. The model predictive value and the actual adulteration ratio had good linear relationship, and the correlation coefficient (R) and standard deviation (SD) were 0.982 2 and 0.47, respectively. Adulteration detection of waste cooking oil based on ATR-FTIR was workable. |
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