Differentiation of Edible Oils by Type Using Raman Spectroscopy and Pattern Recognition Methods

Kwofie, Francis; Lavine, Barry K.; Ottaway, J. M.; Booksh, Karl S.

doi:10.1177/0003702819888220

Cited by 22 publications

(8 citation statements)

References 33 publications

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“…Edible oils are an excellent proof of concept because of pervasive fluorescence and shared peaks in the Raman spectrum: each unique oil contains signature triglyceride peaks at 1270, 1325, 1440, 1660, and 1750 cm -1 . 13 Software matching (MIRA Cal DS from Metrohm) of the spectra in Figure 4 supports the claim that best SNR leads to accurate identification. Using the Hit Quality Index (HQI), which indicates the degree of correlation between a sample and library spectra (0.00 equals no match, 1.00 is perfect correlation, threshold of 0.20), each spectrum was matched using a library containing dozens of edible oils.…”

Section: Methods Comparisonsupporting

confidence: 61%

Fluorescence rejection in Raman spectroscopy: an algorithmic approach and applications

Gelwicks,

Zemtsop,

Allen

2024

Photonic Instrumentation Engineering XI

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For accurate Raman measurements, emission spectra must be free of fluorescence that obscures the critical information given by spectral "fingerprint" Raman peaks. The ideal Raman instrument can quickly and automatically generate a robust and fluorescence-free signal at the sampling point. Both mechanical and mathematical methods exist to reduce fluorescence, but with shortcomings and particular emphasis on spectral features. XTR is an algorithmic fluorescencerejection technique that enhances the Raman spectrum for accurate analysis, identification, and verification of materials. Here, we present the fundamentals of the XTR method and provide application examples of XTR for identifying materials that have traditionally confounded Raman, like cellulose, oils, polymers, paints, and dyes.

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Section: Methods Comparisonsupporting

confidence: 61%

Fluorescence rejection in Raman spectroscopy: an algorithmic approach and applications

Gelwicks,

Zemtsop,

Allen

2024

Photonic Instrumentation Engineering XI

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“…These Raman spectra were previously shown to provide good classifications of edible oil class despite the presence of significant oxidation variation within the data collection. 31,32 The Raman spectra have very little C–O bond information. The spectra are dominated by C–C and C–H vibrational bands in the region observed.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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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 Î¼m 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.

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“…Raman spectroscopy uses inelastic scattering to analyze the sample, the sample does not need pretreatment, and the instrument is simple and convenient to operate [ 12 ]. In recent years, Raman spectroscopy combined with chemometric methods has been widely used in the fields of variety identification, quality analysis, and adulteration detection of edible oils [ 13 , 14 , 15 , 16 , 17 , 18 ]. However, in the detection of mycotoxins in edible oils [ 19 ], the existing research has used the synthesis of highly specific substrate materials to enhance the absorption intensity of specific Raman characteristic peaks.…”

Section: Introductionmentioning

confidence: 99%

High Precisive Prediction of Aflatoxin B1 in Pressing Peanut Oil Using Raman Spectra Combined with Multivariate Data Analysis

Zhu

Jiang

Chen

2022

Foods

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This study proposes a label-free rapid detection method for aflatoxin B1 (AFB1) in pressing peanut oil based on Raman spectroscopy technology combined with appropriate chemometric methods. A DXR laser Raman spectrometer was used to acquire the Raman spectra of the pressed peanut oil samples, and the obtained spectra were preprocessed by wavelet transform (WT) combined with adaptive iteratively reweighted penalized least squares (airPLS). The competitive adaptive reweighted sampling (CARS) method was used to optimize the characteristic bands of the Raman spectra pretreated by the WT + airPLS, and a partial least squares (PLS) detection model for the AFB1 content was established based on the features optimized. The results obtained showed that the root mean square error of prediction (RMSEP) and determination coefficient of prediction (RP2) of the optimal CARS-PLS model in the prediction set were 22.6 µg/kg and 0.99, respectively. The results demonstrate that the Raman spectroscopy combined with appropriate chemometrics can be used to quickly detect the safety of edible oil with high precision. The overall results can provide a technical basis and method reference for the design and development of the portable Raman spectroscopy system for the quality and safety detection of edible oil storage, and also provide a green tool for fast on-site analysis for regulatory authorities of edible oil and production enterprises of edible oil.

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Differentiation of Edible Oils by Type Using Raman Spectroscopy and Pattern Recognition Methods

Cited by 22 publications

References 33 publications

Fluorescence rejection in Raman spectroscopy: an algorithmic approach and applications

Fluorescence rejection in Raman spectroscopy: an algorithmic approach and applications

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

High Precisive Prediction of Aflatoxin B1 in Pressing Peanut Oil Using Raman Spectra Combined with Multivariate Data Analysis

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