| 彭丹，苏敏，徐一川，周琪，郑少帅，李军.基于低场核磁共振技术建立大豆油和葵花籽油
p-茴香胺值检测模型[J].中国油脂,2025,50(8):.[PENG Dan, SU Min, XU Yichuan, ZHOU Qi, ZHENG Shaoshuai, LI Jun.Construction of detection models for p-anisidine value of soybean oil and sunflower seed oil based on low-field nuclear magnetic resonance[J].China Oils and Fats,2025,50(8):.] |
| 基于低场核磁共振技术建立大豆油和葵花籽油
p-茴香胺值检测模型 |
| Construction of detection models for p-anisidine value of soybean oil and sunflower seed oil based on low-field nuclear magnetic resonance |
| |
| DOI：10.19902/j.cnki.zgyz.1003-7969.240266 |
| 中文关键词: 低场核磁共振 植物油 p-茴香胺值 模型 |
| 英文关键词:low-field nuclear magnetic resonance vegetable oil p-anisidine value model |
| 基金项目:河南省科技攻关项目（242102320278）；河南省A类专业创建建设专项（HN-HautFood-100）；河南工业大学“双一流”本科生科技创新能力提升专项项目（HN-HautFood IAEG-012） |
|
|
| 摘要点击次数: 111 |
| 全文下载次数: 96 |
| 中文摘要: |
| 为实现植物油氧化品质的快速检测，以大豆油和葵花籽油为研究对象，系统分析油脂氧化过程中低场核磁共振（LF-NMR）弛豫信号的变化规律，研究植物油p-茴香胺值（p-AV）与弛豫特性指标间的相关性，基于LF-NMR建立大豆油、葵花籽油及两种油脂总样本的p-AV模型，分别考察建模数据、预处理方法及建模方法对p-AV模型性能的影响，优化建模参数。结果表明：随着加热时间的延长，大豆油和葵花籽油的p-AV逐渐增加，弛豫时间则呈下降趋势，其峰面积变化幅度大小顺序均为S23>S22>S21，油脂p-AV与弛豫特性指标之间具有较好的相关性；大豆油、葵花籽油以及总样本p-AV模型的最佳建模数据均为1~1 000 ，预处理方法分别为正交信号校正（OSC）、无、OSC，建模方法均为偏最小二乘法（PLS）；最佳建模条件下大豆油和葵花籽油p-AV模型预测均方根误差（RMSEP）分别为3.448和2.572，总样本p-AV模型预测大豆油和葵花籽油p-AV的RMSEP分别为4.523和4.437。综上，基于LF-NMR检测植物油p-AV是可行的，单一油脂模型的预测准确性较高，而两种油脂总样本所建模型的通用性较好。 |
| 英文摘要: |
| In order to achieve the rapid detection of oxidative quality of vegetable oils, taking soybean oil and sunflower seed oil as the research objects, the changing rules of low-field nuclear magnetic resonance(LF-NMR) relaxation signals during the oxidation process were systematically analyzed, and the correlations between the p-anisidine value (p-AV) and relaxation characteristic indicators were studied. The calibration models for p-AV of soybean oil, sunflower seed oil and total samples (soybean oil + sunflower seed oil) were constructed based on LF-NMR, and the effects of modeling data, preprocessing methods and modeling method on p-AV model performance were examined. The results showed that the p-AV of soybean oil and sunflower seed oil gradually increased, while the relaxation time gradually decreased during the heating process. The order of peak area variation was S23>S22>S21, and there was a significant correlation between the p-AV and the relaxation characteristic indicators. The optimal modeling conditions for the p-AV models of soybean oil, sunflower seed oil and total samples were as follows: modeling data 1-1 000, preprocessing method orthogonal signal corretion(OSC), none and OSC, and modeling method partial least square (PLS). Under the optimal modeling conditions, the p-AV model prediction precision for soybean oil and sunflower seed oil were 3.448 and 2.572 respectively, while those were 4.523 and 4.437 using the total sample model. In conclusion, it is feasible to detect the p-AV of vegetable oils based on LF-NMR, and the prediction precision of the single vegetable oil model is higher, while the total sample model of two vegetable oils has better universality. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
