A Fourier transform‐near infrared (FT‐NIR) method originally designed to determine the peroxide value (PV) of triacylglycerols at levels of 10–100 PV was improved upon to allow for the analysis of PV between 0 and 10 PV, a range of interest to the edible oil industry. The FT‐NIR method uses convenient disposable glass vials for sample handling, and PV is determined by spectroscopically measuring the conversion of triphenylphosphine (TPP) to triphenylphosphine oxide (TPPO) when reacted with hydroperoxides. A partial‐leastsquares calibration was developed for 8 mm o.d. vials by preparing randomized mixtures of TPP and TPPO in a zero‐PV oil. The method was validated with samples prepared by gravimetric dilution of oxidized oil with a zero‐PV oil. It was shown that the American Oil Chemists’ Society primary reference method was quite reproducible (±0.5 PV), but relatively insensitive to PV differences at lower (0–2) PV. The FT‐NIR method on the other hand was shown to be more accurate overall in tracking PV, but slightly less reproducible (0.9 PV) due to working close to the limit of detection. The sensitivity and reproducibility of the FT‐NIR method could be improved upon through the use of larger‐diameter vials combined with a detector having a wider dynamic range. The proposed FT‐NIR PV method is simple to calibrate and implement and can be automated to allow for routine quality control analysis of edible fats and oils.
A method for the determination of iodine value (IV) by Fourier transform-near infrared (FT-NIR) spectroscopy was developed and evaluated in an international collaborative study. The FT-NIR analyzer employed in this work uses disposable vials for sample handling and incorporates validation protocols designed to ensure that the calibration will give accurate results from analyzer to analyzer and stability over time without any further calibration development work. The global IV calibration was developed from over 1,200 animal, marine, and vegetable oils and fats, which were obtained on a number of different instruments worldwide. The Standard Error of Cross Validation measured from a range of 0-190 IV varied from ±0.2-1.4 IV (1 sigma). The repeatability for all models was on the order of 0.1 IV, which states that most of the error was inherited from the primary methods. Finally, an international interlaboratory study was carried out with 16 samples obtained from the AOCS Smalley Laboratory Proficiency Program and an oil processor. The average reproducibility error in any one lab was better than 0.15, and the average reproducibility between labs was better than 0.33. An uncertainty of 0.45 was calculated from the average FT-NIR values obtained from the collaborative study vs. the AOCS Certified Wijs method (Cd 1d-92).
This work demonstrates the application of partial least squares (PLS) analysis as a discriminant as well as a quantitative tool in the analysis of edible fats and oils by Fourier transform near-infrared (FT-NIR) spectroscopy. Edible fats and oils provided by a processor were used to calibrate a FT-NIR spectrometer to discriminate between four oil formulations and to determine iodine value (IV). Samples were premelted and analyzed in glass vials maintained at 75°C to ensure that the samples remained liquid. PLS calibrations for the prediction of IV were derived for each oil type by using a subset of the samples provided as the PLS training set. For each oil formulation (type), discrimination criteria were established based on the IV range, spectral residual, and PLS factor scores output from the PLS calibration model. It was found that all four oil types could be clearly differentiated from each other, and all the validation samples, including a set of blind validation samples provided by the processor, were correctly classified. The PLS-predicted IV for the validation samples were in good agreement with the gas chromatography IV values provided by the processor. Comparable predictive accuracy was obtained from a calibration derived by combining samples of all four oil types in the training set as well as a global IV calibration supplied by the instrument manufacturer. The results of this study demonstrate that by combining the rapid and convenient analytical capabilities of FT-NIR spectroscopy with the discriminant and predictive power of PLS, one can both identify oil type as well as predict IV with a high degree of confidence. These combined capabilities provide processors with better control over their process.Paper no. J9230 in JAOCS 77, 29-36 (January 2000)KEY WORDS: Discriminant analysis, fats and oils, Fourier transform near-infrared spectroscopy, iodine value, partial least squares, quality control.The application of near-infrared (NIR) spectroscopy in edible oil analysis has predominantly involved its use for the rapid quantitative determination of the oil content in oilseeds, with relatively little work being carried out on the analysis of oils per se. Most work on NIR oil analysis development has focused on classifying and/or discriminating between oil types as well as detecting adulteration, particularly of olive oil. Bewig et al.(1) used a filter-based NIR instrument to differentiate between four types of oils (cottonseed, canola, soybean, and peanut) using discriminant analysis based on Mahalanobis distance principles. Sato (2) used principal component analysis (PCA) to classify vegetable oils using second-derivative NIR spectra, with PCA providing the benefit of using all the spectral data collected rather than only the data at selected wavelengths. Wesley et al. (3,4) worked on olive oil adulteration and demonstrated that it is possible to effectively use NIR spectroscopy in conjunction with PCA to predict both the purity of olive oil and the type of adulterant as well as to quantitate the adulter...
A generalized partial-least-squares calibration for determination of the trans content of edible fats and oils by Fourier transform near-infrared (FT-NIR) spectroscopy using 8-mm disposable glass vials for sample handling and measurement was developed. The trans contents of a broad range of oils were determined using the American Oil Chemists' Society single-bounce horizontal attenuated total reflectance (SB-HATR) mid-infrared spectroscopic procedure, these trans reference data were used in the development of the generalized FT-NIR calibration. Additional refined and product-specific calibrations were also developed, and all the calibrations were assessed for their predictive capabilities using two sets of validation samples, one comprising a broad range of oil types and the other restricted to oils with specific characteristics. The FT-NIR trans predictions obtained using the generalized calibration were in good agreement with the SB-HATR results; the values were accurate and reproducible to within ±1.1 and ±0.5% trans, respectively, compared to a reproducibility of ±0.40% trans obtained for the SB-HATR method. The accuracy of the predictions obtained from the generalized FT-NIR calibration for particular oil types was not significantly improved by supplementing the base training set with samples of these specific types. Calibrating only these oil types did, however, produce a substantial improvement in predictive accuracy, approaching that of the SB-HATR method. These product specific calibrations produced serious predictive errors when nonrepresentative samples were analyzed. The incorporation of a supplementary discriminate analysis routine was found to be a powerful safeguard in flagging nonrepresentative samples as outliers and could also be used to select the calibration most appropriate for the characteristics of the sample being analyzed. Overall, it was concluded that FT-NIR spectroscopy provides a viable alternative to the SB-HATR/mid-Fourier transform infrared method for trans determination, making use of more industrially robust instrumentation and equipped with a simpler sample handling system. FIG. 4. A cross-validation plot of FT-NIR trans predictions obtained using the Cal-1 vs. the SB-HATR trans reference values. See Figure 2 for abbreviation.
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