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) 是衡量氧化程度的指标，它直接关系到油样的新鲜度。先前在文献中报道的几项研究将各种光谱技术与多变量分析相结合，以使用现场便携式和过程仪器快速确定 PV；这些努力提出了“最佳案例”场景，其中包含来自狭义训练和测试集的油。本文的目的是评估近红外和中红外吸收光谱和拉曼散射光谱对来自不同油类（包括季节性和供应商变化）的油样的使用，以确定哪种测量技术或其组合最适合预测PV。使用既定的基于滴定的方法对每种油类进行 PV 分析后 全球和全球子集校准模型是根据本研究中使用的 19 种油类收集的光谱数据构建的。来自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 光路长度 - 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 是由偶然相关性产生的。基于 NIR（8 和 24 毫米 OPL）数据和所有红外数据的低级融合的全球 PV 模型产生了相同的 RMSEP 5.1。全局子集模型，基于任何类别（纯、特轻、特级初榨）的任何光谱和橄榄油训练集，都未能外推到非橄榄油。然而，建立在特级初榨橄榄油上的近红外全球子集模型可以外推到其他橄榄油类别的测试样本。