Agricultural & Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.
Agricultural & Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.
Food Chem. 2017 Apr 15;221:379-385. doi: 10.1016/j.foodchem.2016.10.089. Epub 2016 Oct 21.
An electronic nose (e-nose), having 18 metal oxide semiconductor (MOS) sensors, guided determination of frying disposal time of sunflower oil is reported. The ranking and screening of MOS sensors, specific for volatile organic compounds, was performed using fuzzy logic. A correlation was examined between rancidity indices of fried oil (total polar compounds (TPC), and triglyceride dimers-polymers (TGDP), among others) and e-nose based odor index. Fuzzy logic screened 6 MOS sensors (LY2/G, LY2/AA, LY2/GH, LY2/gCT1, T30/1, and P30/1) to deconvolute the rancid fried oils using hierarchical clustering on principal component space. A good relationship was noted between rancidity indices and odor index (R>0.85). Based on maximum discard limits of rancidity indices (25% TPC and 10% TGDP), the frying disposal time of 15.2h (TPC) vs. 15.8h (e-nose) and 15.5h (TGDP) vs. 16.3h (e-nose) was determined. The demonstrated methodology holds a potential extension to different fried oils and products.
一种具有 18 个金属氧化物半导体 (MOS) 传感器的电子鼻（e-nose）被用于指导葵花籽油的煎炸处理时间的确定。使用模糊逻辑对挥发性有机化合物专用的 MOS 传感器进行了排序和筛选。考察了煎炸油的酸败指标（总极性化合物（TPC）、甘油二聚体-聚合物（TGDP）等）与基于电子鼻的气味指数之间的相关性。模糊逻辑筛选了 6 个 MOS 传感器（LY2/G、LY2/AA、LY2/GH、LY2/gCT1、T30/1 和 P30/1），用于在主成分空间上进行层次聚类，以解析酸败的煎炸油。酸败指数与气味指数之间存在良好的相关性（R>0.85）。基于酸败指数的最大丢弃限制（25% TPC 和 10% TGDP），确定了 15.2h（TPC）与 15.8h（电子鼻）和 15.5h（TGDP）与 16.3h（电子鼻）的煎炸处理时间。所展示的方法具有扩展到不同煎炸油和产品的潜力。
