Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils.

The peroxide value 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 peroxide values using field portable and process instrumentation; those efforts presented "best-case 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 peroxide values. Following peroxide value 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 calibration models to predict the peroxide value of unknown oil samples. A global peroxide value model based on near-infrared (8 mm optical path length) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm optical path length near infrared (5.1), Raman (6.9) and 50 µm optical path length mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global peroxide value models based on low-level fusion of the NIR (8 and 24 mm optical path length) 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. This work demonstrates the difficulty of developing a truly global method for determining peroxide value of oils.

Hyperspectral Raman Imaging Using a Spatial Heterodyne Raman Spectrometer with a Microlens Array.

A new hyperspectral Raman imaging technique is described using a spatial heterodyne Raman spectrometer (SHRS) and a microlens array (MLA). The new technique enables the simultaneous acquisition of Raman spectra over a wide spectral range at spatially isolated locations within two spatial dimensions (x, y) using a single exposure on a charge-coupled device (CCD) or other detector types such as a complementary metal-oxide semiconductor (CMOS) detector. In the SHRS system described here, a 4 × 4 mm MLA with 1600, 100 µm diameter lenslets is used to image the sample, with each lenslet illuminating a different region of the SHRS diffraction gratings and forming independent fringe images on the CCD. The fringe images from each lenslet contain the fully encoded Raman spectrum of the region of the sample "seen" by the lenslet. Since the SHRS requires no moving parts, all fringe images can be measured simultaneously with a single detector exposure, and in principle using a single laser shot, in the case of a pulsed laser. In this proof of concept paper, hyperspectral Raman spectra of a wide variety of heterogeneous samples are used to characterize the technique in terms of spatial and spectral resolution tradeoffs. It is shown that the spatial resolution is a function of the diameter of the MLA lenslets, while the number of spatial elements that can be resolved is equal to the number of MLA lenslets that can be imaged onto the SHRS detector. The spectral resolution depends on the spatial resolution desired, and the number of grooves illuminated on both diffraction gratings by each lenslet, or combination of lenslets in cases where they are grouped.