3. Results and Discussion
3.1 Single-Factor Experiments of Camellia seed
scCO2 extraction
The loading of crushed Camellia seeds and the flow rate of
CO2 were set fixed in the experiments. Varied extraction
temperature and pressure were investigated and discussed in detail.
3.1.1 Effect of Extraction Temperature
Rising of the temperature could enhance both the saturated vapor
pressure and the diffusivity coefficient of the solute, resulting in
better solubility behavior in scCO2, while at the same
time, higher temperature also reduced the density of
scCO2 decreasing the dissolving capacity of
scCO2 (Bogdanovic et al., 2015; Pilavtepe et al., 2012).
These two factors have opposing effects on the oil extraction yield, as
a result, an optimum temperature could be predicted in theory.
Temperature effects are shown in Fig. 1 (a). As the temperature
increased, the oil yield decreased rapidly. This indicates that the
optimal temperature might appear lower than 313.15 K. Dissolving power
of scCO2 plays an more important role in the extraction
process. Higher yield was obtained with longer extraction time.
3.1.2 Effect of Extraction Pressure
As the pressure increased, the density and polarity of
scCO2 also increased which led to stronger dissolving
capacity indicating higher extraction yield. Pressure effects are
displayed in Fig. 1 (b). The oil yield increased gradually as the
pressure increased, and the yield increased less at higher pressure. In
a longer-time dynamic extraction process, more oils were extracted and
the highest yield 22.60% was achieved at 30 MPa and 8 h. Within the
first 4 hours, the oil yield increased fast, and after 4 hours, it
enhanced slowly in a upward trend.
3.2 Oil physicochemical characteristics
Lower AV and POV represents better quality of oil. These two indicators
of all the oil samples were determined and compared in Table 3. AVs of
commercial pressed oils are all less than 1, while for samples extracted
by n-hexane and scCO2, the values are much higher,
distributing between 3 and 1. The AV value contributes to distinguishing
sourness tastes. SCCE samples were further investigated in Fig. 2. AVs
declined obviously as extraction time increased, e.g., from 3.48 mg/g
(0-2 h) to 0.64 mg/g (4-6 h) at the condition of 25 MPa and 333.15 K.
While for POV, sample extracted by n-hexane was the highest up to 0.493
g/100 g, indicating that solvent reflux process is unfavorable for the
oil quality. For SCCE samples, POV were ranging from 0.012
~ 0.029 g/100g, significantly lower than other oil
prepared samples, indicating scCO2 extraction could be a
better preparation method keeping oil quality. After further optimizing
the supercritical extraction process, it seems promising to prepareCamellia oil of which AV and POV meet the first-grade standard
(GBT11765-2018, AV ≤ 0.50 mg/g, POV ≤ 0.25 g/100g).
3.3 Taste characteristics
3.3.1
E-tongue profiles ofCamellia oils
Fig. 3 shows the radar chart of taste variations determined by
electronic tongue. Pressed and n-hexane extracted oils samples are
displayed in Fig. 3 (a), and scCO2 extracted samples are
shown in Fig. 3 (b) (varied temperature) and Fig. 3 (c) (varied
pressure). There were no obvious differences in bitterness, astringency,
aftertaste-bitterness (aftertaste-B), aftertaste-astringency
(aftertaste-A) and saltiness among these samples. However, for sourness,
umami and richness, there were significant differences among these oil
samples, especially in the SCCE samples. Negative correlations were
found between umami and sourness (R2 = -0.997, p
< 0.001), richness and sourness (R2 =
-0.912, p < 0.001). E-tongue value of reference tartaric acid
solution was set as zero, so oil sourness taste value were all negative.
The larger the absolute value, the weaker sourness taste. Sourness taste
of three major categories oil samples (in descending order) was:
scCO2 extracted oils, n-hexane extracted oil and pressed
oils. The highest sourness was found in S13 (-0.99), and the lowest was
in S2 (-10.32). For the SCCE samples at 25 MPa and different
temperatures, shown in Fig. 3 (b), sourness gradually increased in
ascending order: Samples (313.15 K) < Samples (323.15 K)
< Samples (333.15 K). While for the SCCE samples at 313.15 K
and different pressures (Fig. 3 (c)), sourness was highest in oils at 30
MPa. This may be due to that sourness substances are more easier to be
extracted under higher temperature and pressure.
Differences of bitterness and astringency taste responses are relative
small. For bitterness, the maximum value was in solvent extracted S6
(-1.05). Pressed oils have the smallest bitterness and astringency among
all the samples. Similarly, high temperature (S13-S15) and high pressure
(S19-S21) contribute to the astringency taste value.
3.3.2 Discrimination of Camellia oil
Principal component analysis (PCA) is mainly used to exhibit a clear
visualization of multidimensional taste profiles in a reduced-dimension
plot (Zhu et al., 2020). The relative variances of components suggest
the relevant importance of the component expressed as percentages in
Table 6. In the results from PCA with eigenvalue greater than 1 (Fig. S1
in supplementary material), the first two principal components (PC1 and
PC2) accounted for 65.4% and 22.8% of total variance, respectively,
with the total cumulative variance contribution for 88.2%. Although the
pressed oils were successfully separated from extracted oils, the
difference between SCCE samples and n-hexane extracted samples was not
obvious. Fig. 4 illustrates three-dimensional score plot (a) and loading
plot (b) in PCA for the difference Camellia oils by the first
three principal components (PC1, PC2 and PC3), with the total cumulative
variance contribution for 97.4%. The samples are well clustered and no
overlap was observed among the three groups, implying the taste of three
groups were different. Pressed oils showed relative narrow spatial
distribution, while SCCE samples, exhibited a wide range of positive to
negative scores, almost splitting into 3 subgroups along both PC1 and
PC2 directions: G1 (S13~S15 and
S19~S21), G2 (S10~S12), and G3
(S7~S9 and S16~S18). (Fig. S2 in
supplementary material ). These three groups well illustrated the
scCO2 extraction condition differences among the
samples, as G1 represents better solubility of solute in
scCO2 due to high temperature (S13~S15),
or stronger solvent power due to high pressure
(S19~S21); G2 represents medium temperature and pressure
(S10~S11); G3 represents relative low temperature and
pressure (S7~S9 and S16~S18).
8 kinds of taste loading vectors were shown in Fig. 4 (b). Similar
loading vectors suggest redundancy in the potential responses and high
loading parameters contribute to discriminating among the samples (Saidi
et al., 2018). The umami, richness, saltness, sourness, and astringency
were mainly responsible for the oil discrimination in the direction of
PC1 , whereas aftertaste-A and Aftertaste-B contributed a lot to the
separation along with the direction of PC2, and for PC3, bitterness and
astringency play an important role in the classification (Fig. S3 in
supplementary material). The pressed, n-hexane extracted and
scCO2 extracted samples were mainly differentiated by
umami, richness, saltness, aftertaste-A and Aftertaste-B with positive
score values along PC1 and PC2. Combined the varied extraction
processed, PC2 in general may be responsible for special flavor
substances, as pressed and strong scCO2 extraction
method offered more abundant flavor chemicals in the oils. These
clusters are clearly distinguished from each other (Fig. S2 (a) in
supplementary material). To be noteworthy, most PC3 may represent free
fatty acid, for the acid values of S6 and S13 were relative large in
accordance with their high positive score along PC3. Also, peroxide
value of the oil samples may involve in PC1, as their increasing trend
is consistent. Not all the data fit these regulations, and the reason is
hard to suggest because of the complicated multivariate nature of the
score plots.
Thus for the particular dataset, reliable discrimination of oil samples
of different extraction method can be illustrated by the e-tongue
technique.
3.3.3 Cluster analysis
Hierarchical cluster analysis (HCA) discovered and identified
relationships between oil varieties by the distance of taste response of
the samples (Zhu et al., 2020). HCA provides insight into the taste
profiles by dividing similar samples into groups (clusters) (Liu et al.,
2020). In this study, HCA was conducted by Ward’s method for aggregation
and the squared Euclidean distance as diversity test. Dendrogram of
clustered oil samples base on similarities is shown in Fig. 5. The
clustering result is almost the same with that of PCA analysis. All the
samples were first divided into two main clusters. Cluster I is SCCE
samples and Cluster II involved pressed oils and n-hexane extracted
oils. ScCO2 conditions related with solvent extraction
power contributes a lot to Cluster I. Cluster II was further divided
into two sub-clusters, one was consist of pressed oils and the other was
n-hexane extracted oil S6, when the number of overall cluster is set to
5 or more, indicating the difference between the pressed and n-hexane
extracted samples is less than the SCCE samples of varied conditions.
Thus the HCA method was able to distinguish oil samples of different
extraction processes base on the e-tongue profiles.
3.4 Correlation between E-tongue analysis and chemical properties
Acid value (AV) and peroxide value (POV) are key parameters to assessCamellia oil quality and gradation. The possibility of estimating
the AV and POV level based on e-tongue profiles processed with Multi
Factor Linear Regression Model (MLRM) was evaluated, applying the AV and
POV data of Camellia oils produced from different extraction
methods determined by titration according to National Standard. The
model results were clearly shown in Table 7. Based on 8 kind of
potential signals recorded by 5 sensors, which was further selected
using stepwise regression method, MLRM models were established with
certain ability predicting AV and PV of Camellia oils (AV:
R2 = 0.702, p < 0.001; POV:
R2 = 0.632, p < 0.001). Lack of the
linearity of the models may be due to the inhomogeneity in 1 kg scale
scCO2 extraction process which resulted AV and POV
fluctuating. Fig. 6 shows “determined value versus predicted value”
plot of the MLRM derived for acid value (a) and peroxide value (b). For
assessing these physiochemical characterizations using e-tongue
technique coupled with chemometrics, satisfactory results were also
reported (Rodrigues et al., 2019; Semenov et al., 2019). Although the
precision of the models could be further improved, the method still
seems attractive for obtaining two key quality values in one
measurement. This indicates that e-tongue system would be a promising
technique for quantification of Camellia oil quality parameters.