Raman of Edible Fats

Introduction    |    Prior Work    |    FAME Work    |    Edible Fats

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chemical structure of fatty acids

Lipids are perhaps the most diverse of the main food groups, with the accepted definition grouping together a huge range of types of molecule on the basis of their shared insolubility in water 1. The most common class of lipids that are found in foods are based on the fatty acid, which usually occurs in nature as straight, even-numbered chains, with or without unsaturated bonds. Three of the most common fatty acids; stearic acid, elaidic acid and oleic acids have 18-carbon chains, these are shown in Figure 1. It is the composition of the fatty acids that determine the properties and uses of many edible fats since the chain length and unsaturation level determines melting points, energy available for metabolism, polarity and crystal packing1-3.

The standard shorthand nomenclature for fatty acids is sufficient to inform the reader of the full chemical structure of the acid. The notation is 2:

Nc: Nu  i  DP

 Nc is the number of carbons in the acid/acyl chain, including the carbonyl and methyl terminals.

Nu is the number of unsaturated bonds in the chain. If 0 then formula is complete.

i is the isomerisation of the unsaturated bond.

P is the position of the first carbon of the unsaturated bond, counting from the carbonyl end. For centrally positioned monounsaturated bonds the positional number for these isomers is simply the chain length divided by 2.

In my studies two main classes of fatty acid based lipids were used; Fatty Acid Methyl Esters (FAME) were used as model systems4-6 while triglycerides were studied in edible fats7-9. Shorthand codes for triglycerides consist of one or two letter codes identifying each fatty acid in the order they are bound to the glyceride with position 1 first. For example, POS consists of a palmitoyl bound to position 1 on the glycerine, oleoyl bound to the middle position and stearoyl bound to the third position. A short list of the common abbreviations is given in Table 1

Fatty Acid

Code in Triglyceride

Fatty Acid

Code in Triglyceride












Table 1 List of the shorthand codes for selected fatty acids in triglycerides. The triglycerides’ codes are as found in Stauffer 1996.


Early applications of Raman spectroscopy to fat and oil analysis

Raman spectroscopy has been used in a number of investigations of lipid composition, crystal structure and purity assays. Early work was primarily focused on phospholipids, with extensive research into the effect of internal rotation of the fatty acid chains. In 1972 Bailey published a paper10 in which Raman spectroscopy was used to predict the cis/trans isomer ratio from model mixtures of pure triglycerides and FAMEs. During the next eighteen years one other paper was published on the application of Raman spectroscopy to the chemical analysis of edible fat, a short paper on the prediction of saturation levels 11. Sadeghi-Jorabchi re-ignited interest with a pair of papers in which he determined the unsaturation levels of oils and  fats 12, 13.  Since then, papers have been published on oil unsaturation parameters 14-17, oil adulteration 18-21, biopsy22, 23, frying oil deterioration 24and lypolysis 25.

My Raman spectroscopic investigation of fatty acids

I started the study of fatty acid based lipids with a critical evaluation of a number of homologous families of fatty acid methyl esters. A homologous (literally ‘same word’) family is a series of chemical compounds that differs in discrete chemical units. The homologous series chosen where saturated fatty acids (varying in chain length from 4 to 22 carbons long, in one carbon increments), monounsaturated fatty acids (each had a central unsaturated bond and chain length varied from 14 to 22) and 18 carbon (all fRaman spectroscopy lipids fatty acid methyl ester oleic stearicatty acids had 18 carbons in the chaiRaman spectroscopy table of assigments fatty acid methyl ester stearic oleicn, but the number of unsaturated bonds ranged from 0 to 3).

Figure 2 shows the Raman spectra of some fatty acids, illustrating a saturated and an unsaturated fatty acid in the liquid state, along with a fatty acid in the molten state. The relationship between the position of the Raman bands and the chemical unit giving rise to it are well understood, and the assignments for each of the bands are given in table 2. The band labelled 1, which ranged from 1730 to 1760 cm-1 in the samples investigated arises from the carbonyl mode at the ester linkage. This proved to be a very useful band for normalising the Raman signal as this chemical unit is present in every fatty acid chain, thus standardises against molar (per molecule) quantities. Band 2, found between 1610 and 1670 cm-1, is a particular importance for nutritional information as it arises from the unsaturated bonds in a fatty acid. Band3 and 4 arise from the methylene units that make up the length of the fatty acid chain, although they do differ significantly in their behaviour (band 3 contains contributions from all C-H bonds in the acid, band 4 only from CH2 groups not adjacent to either end of the molecule).

Raman spectroscopy fatty acid methyl esters saturated stearic butyric palmitic arachadonic

The relationship between chemical structure and the Raman spectrum can readily be seen in Figure 3, which compares the chemical structure of four saturated fatty acids with their respective Raman spectra. A number of bands can be observed to increase with the chain length and the inset plots the intensity of one of these (normalised to the carbonyl mode) against chain length. There is a clear correlation between the chain length and the intensity of this scissor mode at 1440 cm-1. However, given that these were pure reference compounds the correlation was disappointing, with R2 = 0.95. above 14 carbons in length the scatter increases, and appears to have a see-saw effect about the central trend lines. Splitting the trend into odd and even length fatty acids improves the trends to R2 = 0.99. The mystery behind this see-saw was unveiled by density functional theory simulation of the Raman spectrum based on the chemical structure. The modes affected by the see-saw in fact involved all the methylene groups moving in synch with each other, i.e. adding each methylene unit affected the vibration along the entire chain, rather than each methylene unit acting in isolation. Odd and even chains have different centres of symmetry as odd chains are symmetric about the central methylene unit, whereas even chains are symmetric about the central C-C bond. Symmetry of the molecule determines vibrational modes, which explains why the odd and even molecules do not fit the same trend. From this result we can see that Raman should work well for analysing edible fats (the vast majority are even chains) but care must be taken in more unusual fats. It would be expected the difference between odd and even chains would actually be of benefit in multivariate analysis as it creates additional variance.

In comparison the trends observed in the 18 carbon homologous series weRaman spectroscopy spectra spectrum fatty acids oleic stearic linoleic linolenic acidre much more straightforward. Figure 4 shows the Raman spectra (normalised about the carbonyl mode) and chemical structure of the 18 carbon fatty acids studied. It is readily apparent that several bands increase with unsaturation, while several decrease. The reason why the CH2 modes decrease is that each unsaturated bond eliminates two CH2 groups, replacing them with two CH groups. The inset shows that the trends between the Raman bands for unsaturation and the degree of unsaturation are very smooth, in contrast to the chain length trend. This is because the unsaturated bonds are isolated from each other and their vibrations are uncoupled. However, it is important to use th correct band for normalising; the carbonyl mode for per molecule (molar) determination and a CH2 mode for mass (molal/degree of) unsaturation. As aluded to above the CH2 modes are not equal and the scissor mode is preferable as it also contains contributions from CH2 modes adjacent to the ends as well as CH, CH3.

My Raman spectroscopic investigation of edible fats

Raman spectra adipose tissue fat lipid pork chicken beef lamb

We have seen above the close relationship between the chemical composition of a lipid sample and its Raman spectrum. These relationships strongly suggest the possibility of employing Raman spectroscopy to analyse the fatty acid profile of more complex fat samples. In order to assess this capability I measured the fatty acid profile of butters and adipose tissue using gas chromatography and used these results to regress against the recorded Raman spectra from the same samples.

Figure 5 shows the mean Raman spectrum recorded from each of the four species studied in the adipose investigation, pork, chicken, lamb and beef. Pork had the highest unsaturation per fatty acid chain, while chicken had the shortest chain length. The beef and lamb spectra were very similar, with both being much more saturated than either the pork or chicken, while having an average chain length comparable to the pork. These differences are borne out across the range of samples studied, as is evidenced by the principal component analysis (PCA) score plot shown in Figure 6. PCRaman spectroscopy PCA principal component analysis adipose chicken beef lamb porkA summarises the differences in the data, with the consequence that samples that show little difference lie close together while samples that are very different are widely separated. The chicken samples are very isolated in the scatter plot, forming a very tight group remote from any other samples. This indicates that this species is radically different from the others, and indeed this is unsurprising as the chicken is, of course, avian while the remaining species are mammalian. Within the mammalian species there is another distinct separation, with the pork samples barely touching the lamb samples, while the separation of lamb and beef is limited. Unlike pigs, cows and sheep are both ruminant animals which proves to be important for fatty acid accumulation. The ruminant digestion depends on bacteria fermenting difficult to digest molecules such as cellulose, but these bacteria also hydrogenate the unsaturated fatty acids present in the food. This action was critical in the context of the butter study, in which the level of polyunsaturated fatty acids was enhanced by dietary intervention by coating the linseeds so Raman spectroscopy adipose kohonen network chicken lamb beef porkthat they were only digested in the true stomach after rumination.

PCA is an unsupervised method, meaning that it is given no prior information on the samples before the analysis. This means that its information is directed to the largest effects that occur naturally in the data, but these may not be the same effects that best describe the differences between the samples. The differences between poultry and mammal (or between ruminant/non-ruminant) are sufficiently large that PCA captures this separation, but the differences between beef and lamb are too slight for it to pick up. A number of approaches were used that included training a statistical method by defining the groups each sample belonged to. More information can be found in reference 7, but Figure 7 shows one of these ‘supervised’ techniques known as the Kohonen neural network. In this plot the data space is divided into a grid, with each intersection representing a spectrum that varies along each axis. The data is plotted closest to the spectra it is most similar to. Thus samples within each box are grouped together, and this is found to be largely successful. Only four samples, indicated by arrows, were incorrectly classified and all these were ruminant samples.Raman spectroscopy PLS regression partial least squares adipose chicken beef lamb pork

The Raman signals were also correlated with the concentrations of individual fatty acids, and Figure 8 shows the regression plots between the Raman spectrum and the GC measured fatty acid content for two major fatty acids, oleic (18:1c) and linoleic (18:2c) acid. The samples have been colour coded to demonstrate where each species lies within the range. The Raman spectrum could be used to predict the content of 15 fatty acids, giving a comprehensive summary of the fatty acid composition of these edible fats in a single rapid measurement from an unextracted sample.


1.         Hui, Y. H., Bailey's Industrial Oil and Fat Products. 5 ed.; John Wiley & Sons Inc.: New York, 1996; Vol. 1.

2.         Stauffer, C. E., Fats and Oils. 1 ed.; Eagan Press: St Paul, Minnisota, 1996.

3.         Groff, J. L., et al., Advanced Nutrition and Human Metabolism. 2 ed.; West publishing Company: St Paul, 1995; p 575.

4.         Oakes, R. E., et al., DFT Studies Of Long-Chain Fames: Theoretical Justification For Determining Chain Length And Unsaturation From Experimental Raman Spectra. Journal of Molecular Structure-Theochem 2003, 626, 27-45.

5.         Oakes, R. E., et al., Conformations, Vibrational Frequencies And Raman Intensities Of Short Chain Fatty Acid Methyl Esters Using DFT With 6-31 G(d) And Sadlej pVTZ Basis Sets. Journal of Molecular Structure 2002, 586, (1-3), 91-110.

6.         Beattie, J. R., et al., A Critical Evaluation of Raman Spectroscopy for the Analysis of Lipids: Fatty Acid Methyl Esters. Lipids 2004, 39, 407-419.

7.         Beattie, J. R., et al., Classification of adipose tissue species using raman spectroscopy. Lipids 2007, 42, (7), 679-685.

8.         Beattie, J. R., et al., Prediction of adipose tissue composition using Raman spectroscopy: Average properties and individual fatty acids. Lipids 2006, 41, (3), 287-294.

9.         Beattie, J. R., et al., Multivariate prediction of clarified butter composition using Raman spectroscopy. Lipids 2004, 39, (9), 897-906.

10.       Bailey, G. F.; Horvat, R. J., Raman Spectroscopy Analysis of the Cis/Trans Isomer Composition of Edible Vegetable Oils. JAOCS 1972, 49, 494-498.

11.       Butler, M., et al., Raman Spectral Analysis of the 1300 cm-1 Region for Lipid and Membrane Studies. Chemistry and Physics of Lipids 1979, 29, 99-102.

12.       Sadeghi-Jorabchi, H., et al., Determination Of The Total Unsaturation In Oils And Fats By Fourier Transform Raman Spectroscopy. JAOCS 1990, 67, 483-486.

13.       Sadeghi-Jorabchi, H., et al., Quantitative Analysis Of Oils And Fats By Fourier Transform Raman Spectroscopy. Spectrochimica Acta 1991, 47A, 1449-1458.

14.       Baeten, V., et al., Oil and fat classification by FT-Raman spectroscopy. Journal of Agricultural and Food Chemistry 1998, 46, (7), 2638-2646.

15.       Chmielarz, B., et al., Studies On The Double-Bond Positional Isomerization Process In Linseed Oil By UV, IR And Raman-Spectroscopy. Journal of Molecular Structure 1995, 348, 313-316.

16.       da Silva, M. I. P., et al., Castor oil catalytic hydrogenation reaction monitored by Raman spectroscopy. Materials Letters 2000, 45, (3-4), 197-202.

17.       Lerner, B., et al. In Characterisation of Polyunsaturation in Cooking Oils by the 910 FT-Raman., 2nd International on FT-IR, Antwerp,Belgium, 1992; Vansant, E. F., Ed. University of Antwerp: Antwerp,Belgium, 1992; pp 75-82.

18.       Aparicio, R.; Baeten, V., Fats and Oils authentication by FT-Raman. Oleagineux Corps Gras Lipides 1998, 5, (4), 293-295.

19.       Davies, A. N., et al., Study of the use of molecular spectroscopy for the authentication of extra virgin olive oils. part I: Fourier transform Raman spectroscopy. Applied Spectroscopy 2000, 54, (12), 1864-1867.

20.       Li-Chan, E., Developments in detection of adulteration of olive oil. Trends in food science & technology 1994, 5, 3-11.

21.       Marigheto, N. A., et al., A comparison of mid-infrared and Raman spectroscopies for the authentication of edible oils. Journal of the American Oil Chemists Society 1998, 75, (8), 987-992.

22.       Baraga, J. J., et al., In-situ optical histochemistry of human artery using near infrared Fourier transform Raman spectroscopy. Proccedings of the National Acedemy of Sciences USA 1992, 89, 3473-3477.

23.       Frank, C. J., et al., Characterization of Human Breast Biopsy Specimens with near-Ir Raman-Spectroscopy. Analytical Chemistry 1994, 66, (3), 319-326.

24.       Engelsen, S. B., Explorative spectrometric evaluations of frying oil deterioration. Journal of the American Oil Chemists Society 1997, 74, (12), 1495-1508.

25.       Weldon, M. K.; Morris, M. D., Surface-enhanced Raman spectroscopic investigation of bacterial lipolysis in a skin pore phantom. Applied Spectroscopy 2000, 54, (1), 20-23.



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