**4.2. Research example: Application of PCA on 1H-NMR spectra for the study of the thermal stability of edible oils**

Numerous chemical reactions contribute to the chemical and physical aging of oils. During heating, the oils undergo degradation and their functional and organoleptic features are significantly modified. The heating induces chemical reactions such as oxidation, polymerization, hydrolysis and cis/trans isomerization, which have an impact not only on the nutritional value of oils but which may also generate toxic compounds injurious to health [*24, 25*]. The most important cause of the deterioration of oils is oxidation. Among the oxidation products, our interest has focused especially on secondary oxidation products, such as aldehydes, because they are rarely present in natural unheated oil, as described by Choe et al. [*26*] from the study of secondary oxidation products proposed by Frankel [*27*]. In this work, an analytical approach was first adopted to calculate a new semi-quantitative criterion of the thermal stability of oils. This new test is based on the assumption that by focusing on a selected portion of the 1H-NMR spectra and for a relatively short time, we can model the appearance of aldehydes by a kinetic law of order 1, knowing that the mechanisms actually at work are more complex and associated with radical reactions. In the following pages is presented only that part of this work related to the application of PCA to 1H-NMR data to characterize and follow the effect of temperature and time of heating on the chemical quality of edible oils. For further information about the kinetic study and multiway treatments see [*28*].

#### *4.2.1. Samples*

16 Analytical Chemistry

**Step 5.** Calculating factorial contributions - Loadings

problem in the plane formed by the first two PCs**.** 

the groups of samples from a molecular point of view.

**Figure 5.** Scores & loadings on the PC1xPC2 plane.

**the thermal stability of edible oils** 

samples has low values for the same pair of variables, and vice versa.

A complete interpretation of the results of PCA involves the graph of the loadings, i.e. the projection of the variables in the sample space. But how does one get this? Consider what has been calculated so far: the eigenvalues and eigenvectors of the matrix samples and their factorial coordinates in the space of principal components. We need to calculate the factorial coordinates of variables in the sample space. The results are called "*loadings*" or "*factorial contributions*". You just have to transpose the initial data matrix and repeat the entire calculation. Figure 5 shows the results representing the position of the variables of the

The graph of the loadings allows us to understand what the characteristic variables of each group of samples on the graph of the scores are. We see, in particular, that the samples of Group 1 are distinguished from the others by variables 420 and 474 while the Group 2 samples are distinguished from the others by variables 520 and 570. In other words, for these two sets of variables, the groups of samples have opposite values in quantitative terms: when a group has high values for the two pairs of variables, then the other group of

When the measured variables are of structural (mass spectrometry data) or spectral types (infrared bands or chemical shifts in NMR), the joint interpretation of scores and loadings can be extremely interesting because one can be in a position to know what distinguishes

**4.2. Research example: Application of PCA on 1H-NMR spectra for the study of** 

Numerous chemical reactions contribute to the chemical and physical aging of oils. During heating, the oils undergo degradation and their functional and organoleptic features are significantly modified. The heating induces chemical reactions such as oxidation, polymerization, hydrolysis and cis/trans isomerization, which have an impact not only on the Three types of edible oils were analysed. Rapeseed, sunflower and virgin olive oils were purchased at a local supermarket and used in a thermal oxidation study. Approximately 12 mL of oil was placed in 10 cm diameter glass dishes and subjected to heating in a laboratory oven with temperature control. Each of the three types of oil was heated at 170 °C, 190 °C and 210 °C, each of which is close to home-cooking temperatures. Three samples of 1 g were collected every 30 min until the end of the heating process, fixed at 180 min, resulting in a total of 189 samples to be analysed. Samples were cooled in an ice-water bath for 4 min, in order to stop thermal-oxidative reactions, and then directly analysed.

#### *4.2.2. 1H-NMR spectroscopy*

Between 0.3 and 0.5 g of oil was introduced into an NMR tube (I.D. 5 mm) with 700 µL of deuterated chloroform for the sample to reach a filling height of approximately 5 cm. The proton NMR spectrum was acquired at 300.13 MHz on a Bruker 300 Advance Ultrashield spectrometer with a 7.05 T magnetic field. A basic spin echo sequence was applied. The acquisition parameters were: spectral width 6172.8 Hz; pulse angle 90–180°; pulse delay 4.4 µs; relaxation delay 3 s; number of scans 64; plus 2 dummy scans, acquisition time 5.308 s, with a total acquisition time of about 9 min. The experiment was carried out at 25 °C. Spectra were acquired periodically throughout the thermal oxidation process. All plots of 1H NMR spectra or spectral regions were plotted with a fixed value of absolute intensity for comparison.

#### *4.2.3. Data & data processing*

The initial matrix (189 samples × 1001 variables) contains 1H-NMR spectra of three oils at three heating temperatures and seven heating times. The computations were performed using the MATLAB environment, version R2007b (Mathworks, Natick, MA, USA).

#### *4.2.4. Results and discussion*

The chemical changes taking place in the oils over time and at different temperatures, were monitored by 1H nuclear magnetic resonance spectroscopy. The spectral region between 0 and 7.2 was not used in this work because the changes observed were not exclusively specific to heating by-products, as were those in the spectral region between 9 and 10. Figs. 6.1–6.3 present this spectral region for rapeseed oil for different heating times at 170 °C, 190 °C and 210 °C. The increase in the aldehydic protons peaks over time and as a function of temperature is clearly visible. It is to be noted that the temperature has a similar effect in this spectral region for sunflower oil, but with a different patterns of peaks. This reflects differences in the structure of the aldehydes formed in the two oils. The effect of temperature is much smaller for olive oil due to the composition of the fatty acids and a higher concentration of natural antioxidants such as tocopherols.

PCA: The Basic Building Block of Chemometrics 19

**Figure 6.** Expanded region between 9 and 10 of the 1H NMR spectra of rapeseed oil at different

times during heating (F5.1) at 170°C, (F5.2) at 190°C, (F5.3) at 210°C.

PCA score plots show classical trajectories with time and temperature of the three oils, and 66% of the total variance is explained by the first 2 components (see Fig. 7). The score plots show that the thermal degradations are quite different, with that of olive oil being much less extensive, with that of rapeseed being more similar to olive oil than sunflower oil. This observation makes sense because olive oil and rapeseed oil have approximately the same range of oleic acid (respectively 55.0–83.0% and 52.0–67.0% [*29*]), which is one of the three major unsaturated fatty acids of edible oils, along with linoleic and linolenic acids, while sunflower oil has a lower content (14.0–38.0%). The smaller spread of scores for olive oil at a given temperature indicates that chemical changes caused by heating over time are more pronounced in rapeseed and sunflower oil than in olive oil. Olive oil is also different from the other two oils in that the orientation of its scores' trajectory is different for each temperature. The plot of the PC1 and PC2 loadings suggests that the chemical evolution of the sunflower oil and olive oil is not characterized by the formation of the same aldehyde protons. The scores for PC1 characterize the time factor for sunflower, rapeseed and olive oils at 170 °C, while the loadings for PC1 characterize the progressive appearance of trans-2 alkenals and E,E-2,4-dialkadienals associated with peaks at 9.534, 9.507 and 9.554 (see Fig. 7) and the short chain alkanals associated with the peak at 9.766.

The scores for PC2 are related more to the time factor for rapeseed oil and olive oil at 190 and 210 °C. The loadings for PC2 are characterized by several peaks: 9.497 and 9.506 (possibly related to the increase of trans-2-alkenals), 9.763 and 9.786 (N-alkanals and short chain alkanals or oxo-alkanals) and 9.553 (possibly 4,5-epoxy trans-2-alkenals). According to Guillén et al. [*30, 31*], these compounds are among the secondary oxidation products. The loadings of each PC give a clear idea of which peaks increase or decrease during the heating and allow a better understanding of what differentiates the oil samples from a chemical point of view. The Euclidean distance in the multivariate space of three first principal components (i.e. those that explain the majority of the variance of the data studied (70%)) allows the proposal of a thermal stability order of studied edible oils. This order is presented in table 7.

tocopherols.

presented in table 7.

*4.2.4. Results and discussion* 

The chemical changes taking place in the oils over time and at different temperatures, were monitored by 1H nuclear magnetic resonance spectroscopy. The spectral region between 0 and 7.2 was not used in this work because the changes observed were not exclusively specific to heating by-products, as were those in the spectral region between 9 and 10. Figs. 6.1–6.3 present this spectral region for rapeseed oil for different heating times at 170 °C, 190 °C and 210 °C. The increase in the aldehydic protons peaks over time and as a function of temperature is clearly visible. It is to be noted that the temperature has a similar effect in this spectral region for sunflower oil, but with a different patterns of peaks. This reflects differences in the structure of the aldehydes formed in the two oils. The effect of temperature is much smaller for olive oil due to the composition of the fatty acids and a higher concentration of natural antioxidants such as

PCA score plots show classical trajectories with time and temperature of the three oils, and 66% of the total variance is explained by the first 2 components (see Fig. 7). The score plots show that the thermal degradations are quite different, with that of olive oil being much less extensive, with that of rapeseed being more similar to olive oil than sunflower oil. This observation makes sense because olive oil and rapeseed oil have approximately the same range of oleic acid (respectively 55.0–83.0% and 52.0–67.0% [*29*]), which is one of the three major unsaturated fatty acids of edible oils, along with linoleic and linolenic acids, while sunflower oil has a lower content (14.0–38.0%). The smaller spread of scores for olive oil at a given temperature indicates that chemical changes caused by heating over time are more pronounced in rapeseed and sunflower oil than in olive oil. Olive oil is also different from the other two oils in that the orientation of its scores' trajectory is different for each temperature. The plot of the PC1 and PC2 loadings suggests that the chemical evolution of the sunflower oil and olive oil is not characterized by the formation of the same aldehyde protons. The scores for PC1 characterize the time factor for sunflower, rapeseed and olive oils at 170 °C, while the loadings for PC1 characterize the progressive appearance of trans-2 alkenals and E,E-2,4-dialkadienals associated with peaks at 9.534, 9.507 and 9.554 (see Fig.

The scores for PC2 are related more to the time factor for rapeseed oil and olive oil at 190 and 210 °C. The loadings for PC2 are characterized by several peaks: 9.497 and 9.506 (possibly related to the increase of trans-2-alkenals), 9.763 and 9.786 (N-alkanals and short chain alkanals or oxo-alkanals) and 9.553 (possibly 4,5-epoxy trans-2-alkenals). According to Guillén et al. [*30, 31*], these compounds are among the secondary oxidation products. The loadings of each PC give a clear idea of which peaks increase or decrease during the heating and allow a better understanding of what differentiates the oil samples from a chemical point of view. The Euclidean distance in the multivariate space of three first principal components (i.e. those that explain the majority of the variance of the data studied (70%)) allows the proposal of a thermal stability order of studied edible oils. This order is

7) and the short chain alkanals associated with the peak at 9.766.

**Figure 6.** Expanded region between 9 and 10 of the 1H NMR spectra of rapeseed oil at different times during heating (F5.1) at 170°C, (F5.2) at 190°C, (F5.3) at 210°C.

PCA: The Basic Building Block of Chemometrics 21

Observed thermal stability order

Temperature

190 ◦C 170 ◦C 210 ◦C

OO > SO >= RO OO > RO >= SO OO > SO >> RO

**Table 7.** Thermal stability order of the studied oils given by multivariate distances calculated from

The conclusion of this work indicates that olive oil is more stable than the others (rapeseed and sunflower oil), whatever the heating temperature applied. One reason for this stability is its higher concentration of natural antioxidants, such as tocopherols, and its content of oleic acid with unsaturated bonds, which allow it to participate more easily in the radical oxidation mechanisms which occur during heating and thus protect other fragile

This application example of the study of the stability of heated oils shows the power of such tool as PCA in the treatment of multivariate spectroscopic data as used with the characteristic fingerprints of chemical and physical phenomena occurring in the samples over time. The PCA allows the direct use of spectra (or pieces of spectra) or analytical curves instead of the integration values typically used in NMR spectroscopy. The principle presented here is now very common in the field of spectroscopists in general and many monitoring tools or processes for monitoring chemical reactions involve PCA or more robust variants (Robust PCA, RPCA based on robust covariance estimators, RPBA based on

**5. PARAFAC for parallel factor analysis, a generalization of PCA to** 

three-way data or a "data cube". Figure 8 below illustrates what has been said.

Usually, physicochemical analysis - or else the more generally parametric monitoring of a number of physicochemical properties of a set of samples - resulted in the construction of a data matrix with samples in rows and physicochemical properties in columns. This data matrix is a mathematical representation of the characteristics of the sample set at time *t* in preparatory and analytical conditions attached to the instant action. Incidentally, the fact of working on a matrix which is a two-dimensional array allows us to speak of two modes or 2-way data. Now, imagine that you reproduce these measurements on several dates *t*1, *t*2, ..., *t*n. You no longer have a matrix *X* (*n*, *p*) but the **N** matrices *X*i (*n*, *p*) of the same size where *i* is the number of the matrix corresponding to the time *t*i. This is known as 3-mode data or

coordinates on 3 first PCs. OO: olive oil, RO: rapeseed oil, SO: sunflower oil.

*4.2.5. Conclusion* 

compounds in the oil for longer.

projection pursuit, Kernel PCA, etc.).

**3-way data** 

**5.1. Introduction** 

**Figure 7.** PCA scores (up) and loadings (bottom) of (63 x 1001) data matrix for 3 types of oils and 3 heating temperatures. In both cases, only PC1 and PC2 are represented.


**Table 7.** Thermal stability order of the studied oils given by multivariate distances calculated from coordinates on 3 first PCs. OO: olive oil, RO: rapeseed oil, SO: sunflower oil.

#### *4.2.5. Conclusion*

20 Analytical Chemistry

**Figure 7.** PCA scores (up) and loadings (bottom) of (63 x 1001) data matrix for 3 types of oils and 3

heating temperatures. In both cases, only PC1 and PC2 are represented.

The conclusion of this work indicates that olive oil is more stable than the others (rapeseed and sunflower oil), whatever the heating temperature applied. One reason for this stability is its higher concentration of natural antioxidants, such as tocopherols, and its content of oleic acid with unsaturated bonds, which allow it to participate more easily in the radical oxidation mechanisms which occur during heating and thus protect other fragile compounds in the oil for longer.

This application example of the study of the stability of heated oils shows the power of such tool as PCA in the treatment of multivariate spectroscopic data as used with the characteristic fingerprints of chemical and physical phenomena occurring in the samples over time. The PCA allows the direct use of spectra (or pieces of spectra) or analytical curves instead of the integration values typically used in NMR spectroscopy. The principle presented here is now very common in the field of spectroscopists in general and many monitoring tools or processes for monitoring chemical reactions involve PCA or more robust variants (Robust PCA, RPCA based on robust covariance estimators, RPBA based on projection pursuit, Kernel PCA, etc.).
