The peroxide value (PV) of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine PVs using field portable and process instrumentation; those efforts presented âbest-caseâ scenarios with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique, or combination thereof, is best for predicting PVs. Following PV assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression (PLSR) calibration models to predict the PV of unknown oil samples. A global PV model based on near-infrared (8 mm optical path length â OPL) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm OPL near infrared (5.1), Raman (6.9) and 50 μm OPL mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global PV models based on low-level fusion of the NIR (8 and 24 mm OPL) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes.

中文翻译：

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EXPRESS：测定 19 类自然陈化植物性食用油过氧化值的光谱技术比较


食用油的过氧化值（PV）是氧化程度的衡量标准，它直接关系到油样的新鲜度。文献中先前报道的几项研究将各种光谱技术与多变量分析相结合，使用现场便携式和过程仪器快速确定 PV；这些努力展示了来自狭义训练和测试集的油的“最佳情况”场景。本文的目的是评估近红外和中红外吸收和拉曼散射光谱对不同油类油样（包括季节性和供应商变化）的使用，以确定哪种测量技术或其组合最适合预测光伏发电。使用已建立的基于滴定的方法对每种油类进行 PV 测定后，根据本研究中使用的 19 种油类收集的光谱数据构建了全局和全局子集校准模型。每种光学技术的光谱用于创建偏最小二乘回归 (PLSR) 校准模型，以预测未知油样的 PV。基于近红外（8 mm 光程长度 – OPL）油测量的全球 PV 模型产生最低的 RMSEP (4.9)，其次是 24 mm OPL 近红外 (5.1)、拉曼 (6.9) 和 50 μm OPL 中红外（7.3）。然而，已确定拉曼 RMSEP 是由机会相关性产生的。基于 NIR（8 和 24 mm OPL）数据和所有红外数据低水平融合的全球光伏模型产生相同的 RMSEP 5.1。基于任何类别（纯、特淡、特级初榨）的任何光谱和橄榄油训练集的全局子集模型都未能推断出非橄榄油。 然而，基于特级初榨橄榄油构建的近红外全局子集模型可以推断出其他橄榄油类别的测试样品。