1 H NMR combined with chemometrics for the rapid detection of adulteration in camellia oils

(1)

Contents lists available atScienceDirect

Food Chemistry

journal homepage:www.elsevier.com/locate/foodchem

1

H NMR combined with chemometrics for the rapid detection of adulteration in camellia oils

Ting Shi

^a

, MengTing Zhu

^a

, Yi Chen

^a,⁎

, XiaoLi Yan

^a

, Qian Chen

^a

, XiaoLin Wu

^a

, Jiangnan Lin

^b

, Mingyong Xie

^a

aState Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, People’s Republic of China

bCollege of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, People’s Republic of China

A R T I C L E I N F O

Keywords:

Camellia oil

1H NMR Adulteration Chemometrics

A B S T R A C T

Proton nuclear magnetic resonance (¹H NMR) and chemometrics were employed to detect the adulteration of camellia oil (CAO) with 3 different cheap vegetable oils. With the intensity of 15 selected¹H NMR signals as input variables, principal component analysis (PCA) showed good group clustering results for pure and nonpure CAO, but unsatisfied identification accuracy for the adulterated oil types, indicating relatively small difference among those oils. Whereas these difference could be revealed by orthogonal projection to latent structures discriminant analysis (OPLS-DA), with identification accuracy higher than 90%. Partial least squares (PLS) was further applied for the prediction of adulteration level in CAO. With less than 6 variables screened out by variable importance in the projection (VIP) scores as potential key markers, the developed PLS models showed better accuracy. The prediction results for 10 hold-out samples also confirmed that this method was accurate and fast for the detection of CAO adulteration.

1. Introduction

The genus Camellia originates from East Asia with many different breeds, and as one of the main species, the woody evergreen specie of Camellia oleifera is mainly produced in the southern provinces of China (Yang, Liu, Chen, Lin, & Wang, 2016). As one of popular edible vegetable oils, camellia oil (CAO) has become a significant ingredient in our daily diet due to its distinctiveflavor and taste, high nutritional value, medical function and better storage stability than other edible oils (Haiyan, Bedgood, Bishop, Prenzler, & Robards, 2006). CAO has a large content of unsaturated fatty acids including palmitoleic acid (C16:1), oleic acid (C18:1, OA), linoleic acid (C18:2, LA), linolenic acid (C18:3), eicosenoic acid (C20:1) and docosenoic acid (C22:1) (Li, Kong, Shi, & Shen, 2016). Especially the oleic acid typically accounts for 74–87%, followed by linoleic acid ranging from 7 to 14% (Li et al., 2012). Its fatty acid composition is similar to that of olive oil, thus it is often honored as‘‘Eastern Olive Oil’’. In addition to unsaturated fatty acids, camellia oil is also rich in Vitamins E, squalene and tea poly- phenol (He, Zhou, Zhang, & Liu, 2011). Since it contains such a lot of natural antioxidants with various beneficial biological activities, CAO is useful in reducing the risk of high blood pressure, coronary heart dis- ease, atherosclerosis, blood cholesterol, and regulating the nervous system, strengthening the immune system as well as preventing other

diseases (Zeb, 2012). All those distinct properties make the CAO being very popular to consumers and sold at much higher price than other vegetable oils. In order to seek high profit, adulteration of CAO with other cheap vegetable oils was always found in the market. Due to being apparently similar to commercially qualified CAO, corn oil (CO), sunflower oil (SO) and rapeseed oil (RO) were most likely added into it by the unscrupulous traders.

Several conventional techniques have been reported to identify the adulteration of camellia oil, such as gas chromatography (GC) (Li, Huang, et al., 2016) and gas chromatography–mass spectrometry (G- C–MS) (Xie, Liu, Yu, Song, & Hu, 2013). However, these methods are tedious, destructive, and often require complicated sample pretreat- ments. Thus, aiming to overcome these shortcomings, a lot of rapid, non-invasive and reproducible techniques have been applied recently including near infrared fourier transform raman spectroscopy (Weng, Weng, & Chen, 2006), total reflectance infrared spectroscopy (MIR- ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) (Wang, Lee, Wang, & He, 2006), electronic nose (Hai & Wang, 2006), ion mobility spectrometry (IMS)fingerprints (Liu et al., 2017), and differential scanning calorimetry (DSC) analysis (Li, Huang, et al., 2016). Compared with above mentioned methods, nuclear magnetic resonance (NMR) shows a lot of superiorities such as extreme speed, easy automation, remarkable selectivity, excellent

http://dx.doi.org/10.1016/j.foodchem.2017.09.061

Received 23 June 2017; Received in revised form 23 August 2017; Accepted 12 September 2017

⁎Corresponding author.

E-mail address:[email protected](Y. Chen).

Available online 14 September 2017

MARK

(2)

repeatability, and capable of giving a complete view of chemical compositions with qualitative and quantitative information (Agiomyrgianaki, Petrakis, & Dais, 2010; Monteiro et al., 2009). In combination with the use of chemometric methods, NMR has become a preferred technique for the assessment of food authenticity, such as saﬀron (Petrakis, Cagliani, Tarantilis, Polissiou, & Consonni, 2017), roasted coﬀee (de Moura Ribeiro, Boralle, Redigolo Pezza, Pezza, & Toci, 2017), edible oils (Zhu, Wang, & Chen, 2017), honey (Siddiqui, Musharraf, Choudhary, & Rahman, 2017), milk (Santos, Pereira-Filho, & Colnago, 2016). Besides, ¹H NMR combined with principal component analysis (PCA) and partial least squares (PLS) were employed to determine the physicochemical properties of Brazi- lian crude oil (Duarte et al., 2016).¹H NMR and orthogonal projections to latent structure-discriminant analysis (OPLS-DA) were applied for the discrimination of Korean and Chinese herbal medicines (Kang et al., 2008).

However, there are few researches reporting about the detection of adulterated CAO by applying¹H NMR combined with PCA, OPLS-DA and PLS models. The objective of this research is to detect the adulteration of CAO with three cheaper vegetable oils including CO, SO and RO by combining application of ¹H NMR and chemometrics. In this research, 120 oil samples including 21 pure CAO, 89 nonpure CAO adulterated with other diﬀerent types of oil at varied adulteration levels, and 10 hold-out samples were prepared and submitted to¹H NMR experiment. PCA analysis was performed on their¹H NMR data to ex- plore the best bucket width, then on this basis, OPLS-DA and PLS were further employed to detect the adulteration and predict its adulteration levels as well.

2. Materials and methods 2.1. Samples

Four types of commercially available refined vegetable oils including CAO (n = 21, produced in different areas of Jiangxi Province), CO (n = 3, from different brands: Fulinmen, Jinlongyu and Changshouhua), SO (n = 3, from different brands: Fulinmen, Jinlongyu and Changshouhua), RO (n = 2, from different brands: Jinlongyu and Changshouhua), were provided by Jiangxi Province Bureau of Quality and Technical Supervision, and 10 samples for the out of sample validation were purchased from a local supermarket in Nanchang, China.

All those samples were produced during January 2016 to May 2016, and were stored at 4 °C between delivery and NMR measurements to avoid compositional changes.

Eighty-one binary blend oil samples were prepared by adding either CO, SO or RO into CAO at percentages ranging from 5% to 80% (5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, v/v). A total of 120 samples were obtained and their NMR spectra were recorded im- mediately after preparation.

2.2. ¹H NMR spectroscopy

For each NMR sample preparation, 200μL of the pure or blend oil sample was dissolved in 800μL of deuterated chloroform (CDCl3

99.8%-d) containing tetramethylsilane (TMS 0.3% v/v). Next, 600μL of the mixture was transferred into a standard 5 mm NMR tube for direct measurement. The reagent was purchased from Aladdin (Shanghai, China).

All one-dimensional¹H NMR spectra were recorded at 600.38 MHz and 298 K on a Bruker AV 600 spectrometer (Bruker Corporation, Switzerland) equipped with a cryoprobe and a z-gradient. The acquisition parameters were as follows: time domain 32 K, 90° pulse width of 6.5μs, spectral width of 13 ppm, acquisition time of 3 s and relaxation delay of 1 s; 32 scans and 4 dummy scans were accumulated for each free induction decay.

The NMR raw data sets were pre-processed in the MestReNova

software (Mestrelab Reserch, Santiago de Compostela, Spain). Chemical shifts were calibrated by setting the peak of TMS as internal reference at 0.00 ppm to obtain good peak alignment. For ensuring a better quantitative evaluation of the signals, phase correction and baseline correction were performed automatically.

In order to perform the statistical analysis, the spectra were integrated at four diﬀerent types of bucket width over the region from 10 to 0.5 ppm excluding residual solvent signal (7.60–6.90 ppm) in the MestReNova (Piccinonna et al., 2016). On one hand, the intensity of 15 selected signals inTable 1(except signal 9 that worked as a reference) were compared with the intensity of the signal ofα-methylene protons of all acyl chains (signal 9, 2.40–2.20 ppm) that was normalized to 1000 (Mannina, Marini, Gobbino, Sobolev, & Capitani, 2010). For each resonance, this normalizing procedure gives an index, which is pro- portional to the molar ratio between each compound and the total amount of the fatty chains. Therefore, 15 buckets were obtained. On the other hand, the spectra were integrated at equal width of 0.26 ppm, 0.04 ppm and 0.01 ppm respectively, then normalised to total sum, obtaining 34 buckets, 220 buckets and 879 buckets. The constant bucketing width of 0.01 ppm and 0.04 ppm are commonly used in NMR metabolomics, while 0.26 ppm was as well employed since it coincided with the largest peak range (1.40–1.14 ppm) found over all selected signals inTable 1.

2.3. Fatty acid composition analysis of 10 hold-out samples

Fatty acid composition analysis was carried out using a 6890N gas chromatograph equipped with a ﬂame ionization detector and a ca- pillary column CP-Sil 88 (100 m × 0.25 mm × 0.39 mm, 0.20μm).

The analysis procedure was performed according to the method reported in our previous research (Yang, Chen, Zhang, Nie, & Xie, 2012).

2.4. Statistical analyses

Multivariate analysis of the acquired data was carried out by PCA, OPLS-DA and PLS in SIMCA P+ version 12 software (Umetrics, Sweden).

Table 1

Assignment of the signals of camellia oil¹H NMR spectrum.

Signal Chemical shift (ppm)

Multiplicity Compound

1 5.42–5.29 m eCH]CHe(all unsaturated fatty acids) 2 5.29–5.22 m > CHOCOR (triacylglycerols) 3 4.36–4.24 dd eCH2OCOR (triacylglycerols)

4 4.20–4.05

4.20–4.10 dd eCH2OCOR (triacylglycerols) 4.10–4.05 m sn-1,3-diacylglycerols

5 4.04–3.98 m sn-1,3-diacylglycerols

6 3.76–3.68 d sn-1,2-diacylglycerols

7 2.84–2.79 t ]CHeCH2eCH](linolenyl group)

8 2.79–2.70 t ]CHeCH2eCH](linoleyl group)

9 2.40–2.20 dt eOCOeCH2e(all acyl groups)

10 2.08–1.94 m eCH2eCH]CHe(oleyl, linoleyl and linolenyl groups)

11 1.70–1.50

1.70–1.67 s Squalene

1.67–1.50 m eOCOeCH2eCH2e(all acyl groups) 12 1.40–1.14 m e(CH2)ne(all acyl groups) 13 1.02–0.92 t eCH]CHeCH2eCH3(linolenyl group) 14 0.92–0.80 t eCH2eCH2eCH2eCH3(all acyl groups

except linolenyl) 15 0.72–0.66

0.70 s Stigmasterol

0.68 s β-Sitosterol

16 0.60–0.50 d Triterpene alcohol (Cycloartenol) s: single; d: doublet; t: triplet; m: multiplet; dt: double of triplet; dd: doublet of doublet.

(3)

2.4.1. Principle component analysis

As one of the unsupervised models and the foundation of most chemometric algorithms, PCA is ﬁrstly used here to reduce the dimensionality of the whole data system by dividing a large number of variables into new orthogonal axes. On this basis, PCA always results in a score plot and a loading plot (Chen et al., 2014; Chen, Xie, et al., 2008).

2.4.2. Orthogonal projection to latent structures discriminant analysis The supervised model, OPLS-DA as a statistical methodology combining the capabilities of PLS-DA and SIMCA classification, was also used in this study. OPLS-DA is a rotated model by decomposing the systematic variation in the X matrix into two distinct parts, Y-predictive (TpPp^T) block and Y-orthogonal (ToPo^T) block. Since the systematic orthogonal variation to the response is removed from the data matrix X, the discriminative information is mainly observed in thefirst predictive component (Bylesjö et al., 2006). Therefore, both dimensionality re- duction and data fusion step make OPLS-DA more accurate and straight forward to interpret. Its discriminating accuracy can be evaluated according to the number of significant components, R²(cumulative), Q² (cumulative), PCV-ANOVA and permutation test results. R²explains the total variations in the data to evaluate the goodness of fit, while Q² indicates the predictability of the model. Q²value higher than 0.5 is generally admitted for good predictability, moreover, the model is perfect when the value of them are close to 1 (Triba et al., 2015). R² will approach to 1 with the number of components increasing mono- tonically, while Q²is not necessary, thus resulting in an over-fitting of the model when there is a great discrepancy between R²and Q². Per- mutation test is subsequently used to assess thefitness of this model through random permutations of theyvariable, which is performed in PLS-DA (partial least squares-discriminant analysis) with the same number of components. The result is valid since both PLS-DA and OPLS- DA models have the same solutions (Chen. Zhu, et al., 2008; Kang et al., 2008). Among 200 permutations test, the intercept value of R²Y should not be greater than 0.3–0.4 and Q²Y should be no more than 0.05, otherwise the model is overfitting and poor quality (And & Dahlman, 2002; Fang, Goh, Tay, Lau, & Li, 2013). CV-ANOVA (Cross-Validation- Analysis Of Variance) procedure is used for calculating a p-value to evaluate the significance of OPLS-DA model (Sadeghi-Bazargani, Bangdiwala, Mohammad, Maghsoudi, & Mohammadi, 2011). P≤0.05 is considered to be statistically significant.

2.4.3. Partial least squares

As a multivariate calibration technique, PLS construct a mathema- tical model based on the features of PCA and multiple regression. Such a model can efficiently quantify the concentration of adulteration from a large number of data. The performance of the PLS model is evaluated on the basis of accuracy and linearity. Its accuracy can be evaluated with the root mean square error of estimation (RMSEE) and root mean square error of prediction (RMSEP). The smaller of both statistical values, the better prediction ability of the PLS model is indicated (And & Dahlman, 2002; Chen et al., 2012; Mabood et al., 2017). The linearity of the regression model is evaluated byfitting the reference adulteration value versus predicted ones. PLS model with correlation coefficient R²> 0.99, slope closer to 1 and intercept value closer to 0 was considered good with high linearity (Santos et al., 2016).

2.5. Calibration and validation set selection

All oil samples with diﬀerent brands and adulterated ratios were divided into two groups. 2/3 of the samples were randomly selected as the calibration set to construct the supervised model, and the remaining 1/3 samples were used as the independent validation set.

3. Results and discussion 3.1. ¹H NMR analysis

Table 1depicts 600.38 MHz ¹H NMR spectra of camellia oil and those chemical moieties corresponding to the chemical shift are listed by referring to previous literatures (Alonso-Salces et al., 2010;

D’Imperio et al., 2007; Laincer et al., 2016; Salinero et al., 2012). All those spectra of four vegetable oils were similar in the shape but different in the peak intensities. Since triglycerides (TGs) are the main component of all vegetable oils, the intensities of their signals (1.40–1.14 ppm, 0.80–0.92 ppm, 1.50–1.70, 2.20–2.40 ppm, 5.29–5.42 ppm) covered a large proportion of the spectrum for all the four tested oils. Especially for 1.40–1.14 ppm peaks, representing unsaturated fatty acids and saturated fatty acids methylene protons, whose intensities were the highest. Besides of TGs components, there were also a number of minor components, such as sterols, squalene and di-glycerides (DGs) in edible oils, as shown inTable 1.

In order to analyze the diversity of fatty acids in diﬀerent types of oil, the contents of oleic acid, linoleic acid, linolenic acid and saturated fatty acid were calculated by the integral values of signal 1, 8, 13 and 14 based on the previous literature (Salinero et al., 2012; Vigli, Philippidis, Spyros, & Dais, 2003). As presented inTable S1, CAO had the highest content of oleic acid (80%), followed by RO with the value at approximately 54%, while CO and SO presented the lowest value.

The highest level of linoleic acid was found in CO and SO (> 57%) but lowest in CAO (8%). Linolenic acid was particularly high in RO (12%), while low in CAO (1%). These results were consistent with that of previous researches (Popescu et al., 2015).

3.2. Discrimination between diﬀerent types of oils with exploratory PCA

PCA was served to reduce the dimensionality of input variables as well as to better visualize the separation of different vegetable oils in score plot.Fig. 1(a)–(d) shows PCA scores of the above 110 oil samples, where the corresponding number of input variables were 15, 34, 220 and 879 respectively. Thefirst two principal components contributed to above 65% of total variance, which interpreted a majority variance from the raw data. As shown in all the four score plots, the adulterated CAO with RO samples were located comparatively far from other samples. This may be due to its particular high content of linolenic acid as we discussed above. While the locations for the pure CAO and other two adulterated CAO samples crossed each other. Especially for the groups of adulterated CAO with CO and SO, the overlapping was ob- viously high due to their similar fatty acid compositions. Although the four sample groups could not be completely differed from each other in all the four score plots, the separation inFig. 1(a) based on 15 selected signals as input variables was comparatively better. Thus 15 selected signals were chosen as input variables in following studies.

In order to screen variables most responsible for the observed classiﬁcation, the loadings plot (Fig. 1(e)) of 15 variables were also obtained in parallel with score plot from PCA analysis. From the - loading plot, the relationships between variables in the space of theﬁrst two components could be revealed. As shown inFig. 1(e), TGs, linolenic acid and sterol had similar heavy loadings for principal component 1.

DGs, linoleic acid, as well as unsaturated fatty acid, however, had similar heavy loadings for principal component 2. If we put the loadings plot and scores plot overlaid together, we could see that the adulterated CAO with RO samples diﬀer from those of other samples. CAO + RO samples relied on linolenic acid and sterols, while CAO + CO as well as CAO + SO samples preferred DGs and linoleic acid. Pure CAO samples were closely related with TGs and unsaturated fatty acid (total sum of oleic acid, linoleic acid and linolenic acid).

(4)

Fig. 1.The score plot PCA performed on 15 variables (a), 34 variables (b), 220 variables (c), 879 variables (d); the overlay of score plot and loadings plot (e). CAO: camellia oil; CO: corn oil; SO: sunﬂower oil; RO: rapeseed oil.

(5)

3.3. Discrimination of adulteration by OPLS-DA

Since PCA is unable to separate all the adulterated CAO samples completely, the data set was further submitted to one supervised chemometric method OPLS-DA. Supervised OPLS-DA with Pareto scaling ﬁltered out the systematic variations in the X matrix which were not related to Y response, thus maximising the separation among diﬀerent samples. As shown in Fig. 2(a), the pure CAO and adulterated CAO

samples were clearly separated into three groups: CAO, CAO + RO, CAO + CO/SO and the separation was quite better than that in PCA score. The corresponding parameters R²X (cum) , R²Y (cum) and Q² (cum) of the model were 0.99, 0.62 and 0.55 respectively, which indicated a good fitness and high predictive ability. In this case, CAO samples located particularly far from adulterated groups. However, overlapping occurred in the adulterated oil samples. Especially CAO + CO and CAO + SO samples were classified into one cluster owing to Fig. 2.OPLS-DA and PLS models of camellia oil samples adulterated with other vegetable oils. The clustering of pure camellia oil (CAO) and adulterated camellia oil (CAO + CO, CAO + SO, CAO + RO) is represented in thefirst OPLS-DA model (a). A second OPLS-DA model shows the clustering of pure camellia oil (CAO) and adulterated camellia oil (CAO + CO, CAO + SO) (b). Results obtained by calibration set using PLS2 models for camellia oil adulterated with corn oil (c), sunflower oil (d), and rapeseed oil (e). CAO: camellia oil; CO: corn oil; SO:

sunﬂower oil; RO: rapeseed oil.

(6)

the small difference in the abundance of chemical component. Besides, one CAO + RO sample at concentration of 10% was misclassified as CAO, probably due to the similar content of oleic acid in the two groups, which suggested that CAO may be more likely to be adulterated with RO than CO or SO at lower adulterated concentration. The goodness of fit and the predictability of OPLS-DA model were fre- quently validated by 200 random permutations test. As shown inFig.

S1, the intercept value of R²Y was less than 0.3 and Q²Y was less than 0.05, which indicated a statistical significance and not overfitting with high predictive value of the model. Moreover, the statistical significance was also validated with PCV-ANOVA value close to 0 in CA- ANOVA test. As shown inTable 2, when the validation set was used to further evaluate the OPLS-DA model, a perfect discriminant accuracy of 100% and 90% were obtained for CAO + CO/SO and CAO + RO respectively, moreover, the overall discriminant accuracy was above 94.59% for the calibration and validation set, proving a better classification prediction ability.

In order to further discriminate the CAO + CO and CAO + SO samples, a second OPLS-DA model was specially developed to classify these two groups. The total explained variance and predictive ability for second OPLS-DA model were 0.997, 0.736 and 0.547 respectively. The intercept value of R²Y and Q²Y (Fig. S2) indicated a good level of this model. As shown inFig. 2(b), pure CAO and most adulterated CAO with CO or SO samples were clearly separated into three groups. A part of CAO + CO and CAO + SO samples overlapped in the graph as labeled, where the adulterated level was less than 20%. When the concentration of CO or SO increased, the distance between the adulterated oil samples increased accordingly, and the higher content of linolenic acid in CO samples, as reported inTable S1, may be accounted for this result. As the validation results reported inTable 2, a 90% correct classiﬁcation

rate was achieved for CAO + SO and CAO + CO, demonstrating the feasibility of OPLS-DA model.

3.4. Prediction of the adulteration levels by PLS models

OPLS-DA was applicable in identifying the specific type of adulterated oil, but it could not predict the level of adulteration in CAO, therefore, the data were subsequently subjected to PLS model. In PLS model, the information about the discriminating power of each variables were obtained from the variable importance in the projection (VIP). VIP values larger than 1 were considered to be significant for the observed classification (Mannina, Marini, Gobbino, Sobolev, & Capitani, 2010). According to the VIP values showed inFig.

S3, the unsaturated fatty acid (signal 1), linolenic acid (signal 7), linoleic acid (signal 8), and signal 12 (total sum of unsaturated fatty acids and saturated fatty acids) were found to contribute significantly to the model of CAO + CO (Fig. S3(a)), CAO + SO (Fig. S3(b)) and CAO + RO (Fig. S3(c)). Besides, signal 14 (all acids except linolenyl) was also confirmed to be important in the models of CAO + CO and CAO + RO, while TGs (signal 4) was significant in both CAO + SO and CAO + RO. In order to validate the potential discrimination ability of those significant variables, they were extracted and used as input data for PLS2 model, which was compared with PLS1 model built on 15 variables. The prediction performance for the models were summarized in Table 3. Both PLS1 and PLS2 model (Fig. 2(c)–(e)) showed good linearity and accuracy with R²> 0.99, slope close to 1, intercept close to 0, low RMSEE and RMSEP (close to 0). In comparison with PLS1, PLS2 model showed better accuracy with lower RMSEP. Thus our results indicated that those selected variables could be regarded as the potential key markers for the verification of adulteration of CAO.

3.5. Out of sample validation

A good way to test the forecasting and prediction performance of the models is to perform out-of-sample validation, which means to withhold some of the sample data from the model calibration and estimation process, then use the model to make predictions for the hold- out data in order to see how accurate they are. In this study, the re- liability of OPLS-DA and PLS model were further validated by 10 hold- out samples. As presented inTable 4,among them, samples 1–5 were labeled as pure CAO, samples 6–8 were sold as blend CAO but without information of the blended oil name, and samples 9–10 were labeled as CAO blended with RO. However, from the detection results from OPLS- DA and PLS, 3 of the labeled pure CAO (samples no. 1–3) were iden- tiﬁed as CAO adulterated with RO at adulteration level of 94–97%.

Samples no. 6–8 were detected as CAO blended with RO at percentage of 78–92%. Samples no. 9–10 were validated as CAO blended with RO at the ratio of 81%. In order to verify this prediction results, the fatty acids composition of these samples were further determined with GC Table 2

Discrimination accuracy of the OPLS-DA model analysis.

First model Calibration Validation

Members Correct Members Correct

CAO 14 100% 7 85.71%

CAO + RO 19 94.74% 10 90%

CAO + CO/SO 40 100% 20 100%

Total 73 98.63% 37 94.59%

Second model Members Correct Members Correct

CAO 14 100% 7 85.71%

CAO + CO 20 95% 10 90%

CAO + SO 20 90% 10 90%

Total 54 94.44% 27 88.89%

CAO: camellia oil; CAO + RO: camellia oil adulterated with rapeseed oil; CAO + CO/SO:

camellia oil adulterated with corn oil or sunﬂower oil; CAO + CO: camellia oil adulterated with corn oil; CAO + SO: camellia oil adulterated with corn oil.

Table 3

Performance of PLS models for adulteration of camellia oil using 15 variables (PLS1) and extracted variables (PLS2).

PLS1 PLS2

CAO + CO CAO + SO CAO + RO CAO + CO CAO + SO CAO + RO

Number of input X-variables 15 15 15 6 5 3

Number of latent variables 3 3 2 2 2 2

R²X 0.92 0.819 0.739 0.974 0.977 0.998

R²Y 0.997 0.993 0.991 0.998 0.991 0.989

Q² 0.973 0.984 0.981 0.997 0.987 0.985

R² 0.997 0.993 0.991 0.999 0.991 0.989

Slope 1 1 1 1 1 1

Intercept 3.856 e⁻⁶ 1.24 e⁻⁶ −9.793 e⁻⁷ 1.235 e⁻⁶ −9.353 e⁻⁷ −4.605 e⁻⁶

RMSEE 0.018 0.028 0.031 0.013 0.033 0.033

RMSEP 0.028 0.059 0.053 0.020 0.032 0.034

CAO: camellia oil; CO: corn oil; SO: sunﬂower oil; RO: rapeseed oil.

(7)

and showed inTable 4. According to the Chinese national standard for camellia oil (GB 11765-2003) and rapeseed oil (GB 1536-2004), the percentage of oleic acid, linoleic acid, and saturated fatty acid in camellia oil should be in the range of 74%–87%, 7%–14%, and 7%–11%, respectively, while the percentage of oleic acid, linoleic acid, and linolenic acid in RO should be 51–70%, 15–30% and 5–14% respectively.

In such case, only the sample 4 and 5 could be identiﬁed as pure CAO owing to the similar fatty acid compositions to pure CAO. The remaining 8 samples were determined to be CAO adulterated with RO.

This conﬁrmed our results obtained above from the OPLS-DA, indicating that our method was robust and applicable for the determination of CAO adulteration.

4. Conclusions

1H NMR spectra combined with PCA, OPLS-DA and PLS was proven to be useful for the authentication of CAO adulteration in qualitative and quantitative analysis. Pure CAO could be separated clearly from adulterated samples in PCA scores plot, while the CAO + SO and CAO + CO samples were overlapped. The PCA loading plot indicated that 15 variables such as linolenic acid, sitosterol, unsaturated fatty acids, TGs, linoleic acid and DGs played important roles for the determination of CAO adulteration. With these 15 variables as input data, 2 developed OPLS-DA models could discriminate the CAO + SO samples from CAO + CO samples. VIP scores of PLS screened out less than 6 potential key markers for the prediction of adulteration level in CAO. Its prediction accuracy was satisfied with low RMSEE and RMSEP. The results from out of sample validation also confirmed that these developed method was accurate and fast for the authentication of CAO as well as pre- dicting the specific adulterated oil types and its adulteration levels.

Our above results strongly support the capability of¹H NMR and chemometrics to be applied in authentication of CAO adulteration.

However, it should be noted that in this research only corn oils, sun- ﬂower oils, rapeseed oils were involved as adulterated oil, further studies with more other oil samples could be performed to improve the accuracy and promote the application of this method.

Acknowledgements

The ﬁnancial supports from the National Natural Science Foundation of China (No: 31471647, 21467016), the Key Technologies R & D Program of Jiangxi Province (No. 20152ACF60012), Young Scientists of Jiangxi Province (No. 20142BCB23005) and State Key Laboratory of Food Science and Technology, Nanchang University Support Program (SKLF-ZZB-201513) are gratefully acknowledged.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, athttp://dx.doi.org/10.1016/j.foodchem.2017.09.061.

References

Agiomyrgianaki, A., Petrakis, P. V., & Dais, P. (2010). Detection of reﬁned olive oil adulteration with reﬁned hazelnut oil by employing NMR spectroscopy and multivariate statistical analysis.Talanta, 80(5), 2165–2171.

Alonso-Salces, R. M., Héberger, K., Holland, M. V., Moreno-Rojas, J. M., Mariani, C., Bellan, G., ... Guillou, C. (2010). Multivariate analysis of NMRﬁngerprint of the unsaponiﬁable fraction of virgin olive oils for authentication purposes.Food Chemistry, 118(4), 956–965.

And, S. B., & Dahlman, O. (2002). Chemical compositions of hardwood and softwood pulps employing photoacoustic Fourier transform infrared spectroscopy in combination with partial least-squares analysis.Analytical Chemistry, 74(22), 5851–5858.

Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006).

OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classiﬁ- cation.Journal of Chemometrics, 20(8–10), 341–351.

Chen, Y., Deng, J., Wang, Y., Liu, B., Ding, J., Mao, X., ... Li, J. (2014). Study on discrimination of white tea and albino tea based on near-infrared spectroscopy and chemometrics.Journal of the Science of Food and Agriculture, 94(5), 1026–1033.

Chen, Y., Xie, M.-Y., Yan, Y., Zhu, S.-B., Nie, S.-P., Li, C., ... Gong, X.-F. (2008).

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diﬀuse reﬂectance spectroscopy and pattern recognition techniques.

Analytica Chimica Acta, 618(2), 121–130.

Chen, Y., Xie, M., Zhang, H., Wang, Y., Nie, S., & Li, C. (2012). Quantiﬁcation of total polysaccharides and triterpenoids in Ganoderma lucidum and Ganoderma atrum by near infrared spectroscopy and chemometrics.Food Chemistry, 135(1), 268–275.

Chen, Y., Zhu, S.-B., Xie, M.-Y., Nie, S.-P., Liu, W., Li, C., ... Wang, Y.-X. (2008). Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographicﬁngerprints and combined chemometrics methods.

Analytica Chimica Acta, 623(2), 146–156.

D’Imperio, M., Mannina, L., Capitani, D., Bidet, O., Rossi, E., Bucarelli, F. M., ... Segre, A.

(2007). NMR and statistical study of olive oils from Lazio: A geographical, ecological and agronomic characterization.Food Chemistry, 105(3), 1256–1267.

de Moura Ribeiro, M. V., Boralle, N., Redigolo Pezza, H., Pezza, L., & Toci, A. T. (2017).

Authenticity of roasted coﬀee using¹H NMR spectroscopy.Journal of Food Composition and Analysis, 57, 24–30.

Duarte, L. M., Filgueiras, P. R., Silva, S. R., Dias, J. C., Oliveira, L. M., Castro, E. V., & de Oliveira, M. A. (2016). Determination of some physicochemical properties in Brazilian crude oil by¹H NMR spectroscopy associated to chemometric approach.

Fuel, 181, 660–669.

Fang, G., Goh, J. Y., Tay, M., Lau, H. F., & Li, S. F. Y. (2013). Characterization of oils and fats by¹H NMR and GC/MSﬁngerprinting: Classiﬁcation, prediction and detection of adulteration.Food Chemistry, 138(2–3), 1461–1469.

Hai, Z., & Wang, J. (2006). Detection of adulteration in camellia seed oil and sesame oil using an electronic nose.European Journal of Lipid Science and Technology, 108(2), 116–124.

Haiyan, Z., Bedgood, D. R., Bishop, A. G., Prenzler, P. D., & Robards, K. (2006). Effect of added caffeic acid and tyrosol on the fatty acid and volatile profiles of camellia oil following heating.Journal of Agricultural and Food Chemistry, 54(25), 9551–9558.

He, L., Zhou, G. Y., Zhang, H. Y., & Liu, J. A. (2011). Research progress on the health function of tea oil.Journal of Medicinal Plants Research, 5(4), 485–489.

Kang, J., Choi, M. Y., Kang, S., Kwon, H. N., Wen, H., Lee, C. H., ... Kwon, S. W. (2008).

Application of a¹H nuclear magnetic resonance (NMR) metabolomics approach combined with orthogonal projections to latent structure-discriminant analysis as an eﬃcient tool for discriminating between Korean and Chinese herbal medicines.

Journal of Agricultural and Food Chemistry, 56(24), 11589–11595.

Laincer, F., Iaccarino, N., Amato, J., Pagano, B., Pagano, A., Tenore, G., ... Randazzo, A.

(2016). Characterization of monovarietal extra virgin olive oils from the province of Béjaïa (Algeria).Food Research International, 89, 1123–1133.

Li, R., Huang, J., Huang, L., Teng, J., Xia, N., Wei, B., & Zhao, M. (2016). Comparison of GC and DSC monitoring the adulteration of camellia oil with selected vegetable oils.

Journal of Thermal Analysis and Calorimetry, 126(3), 1735–1746.

Li, X., Kong, W., Shi, W., & Shen, Q. (2016). A combination of chemometrics methods and GC–MS for the classiﬁcation of edible vegetable oils.Chemometrics and Intelligent Laboratory Systems, 155, 145–150.

Table 4

The average contents of fatty acid in 10 hold-out samples.

Code Label SFA (%) C16:1 (%) C18:1 (%) C18:2 (%) C18:3 (%) GC OPLS-DA PLS (% v/v)^a

1 pure 6.79 ± 0.01 0.21 ± 0.00 63.93 ± 0.01 19.51 ± 0.01 9.56 ± 0.01 CAO + RO CAO + RO 97

2 pure 6.80 ± 0.01 0.21 ± 0.00 64.81 ± 0.06 18.92 ± 0.01 9.26 ± 0.07 CAO + RO CAO + RO 94

3 pure 6.81 ± 0.02 0.21 ± 0.00 64.64 ± 0.1 19.01 ± 0.13 9.33 ± 0.02 CAO + RO CAO + RO 95

4 pure 11.90 ± 0.08 0.14 ± 0.00 78.80 ± 0.06 8.47 ± 0.01 0.70 ± 0.04 CAO CAO 0

5 pure 10.52 ± 0.02 0.08 ± 0.00 80.12 ± 0.02 8.46 ± 0.01 0.83 ± 0.00 CAO CAO 0

6 blend oil 7.35 ± 0.01 0.20 ± 0.00 66.81 ± 0.04 18.73 ± 0.00 6.91 ± 0.04 CAO + RO CAO + RO 78

9 CAO + RO 6.91 ± 0.02 0.21 ± 0.00 65.97 ± 0.08 19.28 ± 0.10 7.63 ± 0.00 CAO + RO CAO + RO 81

10 CAO + RO 6.93 ± 0.00 0.21 ± 0.00 65.89 ± 0.02 19.33 ± 0.02 7.64 ± 0.00 CAO + RO CAO + RO 81

SFA: saturated fatty acids; C16:1: palmitoleic acid; C18:1: oleic acid; C18:2: linoleic acid; C18:3: linolenic acid. CAO: camellia oil; RO: rapeseed oil.

aThe predicted volume percentage of adulterated oil in total oil volume by PLS model.

(8)

Li, S., Zhu, X., Zhang, J., Li, G., Su, D., & Shan, Y. (2012). Authentication of pure camellia oil by using near infrared spectroscopy and pattern recognition techniques.Journal of Food Science, 77(4), C374–C380.

Liu, L., Hu, C., Liu, L., Zhang, S., Chen, K., & He, D. (2017). Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometryﬁngerprints and chemometric models.European Journal of Lipid Science and Technology, 119(3), 1500463.

Mabood, F., Gilani, S. A., Albroumi, M., Alameri, S., Al Nabhani, M. M. O., Jabeen, F., ...

Hussain, Z. (2017). Detection and estimation of Super premium 95 gasoline adulteration with Premium 91 gasoline using new NIR spectroscopy combined with multivariate methods.Fuel, 197, 388–396.

Mannina, L., Marini, F., Gobbino, M., Sobolev, A. P., & Capitani, D. (2010). NMR and chemometrics in tracing European olive oils: The case study of Ligurian samples.

Talanta, 80(5), 2141–2148.

Monteiro, M. R., Ambrozin, A. R. P., da Silva Santos, M., Boﬀo, E. F., Pereira-Filho, E. R., Lião, L. M., & Ferreira, A. G. (2009). Evaluation of biodiesel–diesel blends quality using¹H NMR and chemometrics.Talanta, 78(3), 660–664.

Petrakis, E. A., Cagliani, L. R., Tarantilis, P. A., Polissiou, M. G., & Consonni, R. (2017).

Sudan dyes in adulterated saffron (Crocus sativus L.): Identification and quantifica- tion by¹H NMR.Food Chemistry, 217, 418–424.

Piccinonna, S., Ragone, R., Stocchero, M., Del Coco, L., De Pascali, S. A., Schena, F. P., &

Fanizzi, F. P. (2016). Robustness of NMR-based metabolomics to generate compar- able data sets for olive oil cultivar classiﬁcation. An inter-laboratory study on Apulian olive oils.Food Chemistry, 199, 675–683.

Popescu, R., Costinel, D., Dinca, O. R., Marinescu, A., Stefanescu, I., & Ionete, R. E.

(2015). Discrimination of vegetable oils using NMR spectroscopy and chemometrics.

Food Control, 48, 84–90.

Sadeghi-Bazargani, H., Bangdiwala, S. I., Mohammad, K., Maghsoudi, H., & Mohammadi, R. (2011). Compared application of the new OPLS-DA statistical model versus partial least squares regression to manage large numbers of variables in an injury case- control study.Scientiﬁc Research and Essays, 6(20), 4369–4377.

Salinero, C., Feás, X., Mansilla, J. P., Seijas, J. A., Vázquez-Tato, M. P., Vela, P., & Sainz, M. J. (2012).¹H-nuclear magnetic resonance analysis of the triacylglyceride composition of cold-pressed oil from Camellia japonica.Molecules, 17(6), 6716.

Santos, P. M., Pereira-Filho, E. R., & Colnago, L. A. (2016). Detection and quantiﬁcation of milk adulteration using time domain nuclear magnetic resonance (TD-NMR).

Microchemical Journal, 124, 15–19.

Siddiqui, A. J., Musharraf, S. G., Choudhary, M. I., & Rahman, A.-U. (2017). Application of analytical methods in authentication and adulteration of honey.Food Chemistry, 217, 687–698.

Triba, M. N., Le Moyec, L., Amathieu, R., Goossens, C., Bouchemal, N., Nahon, P., ...

Savarin, P. (2015). PLS/OPLS models in metabolomics: The impact of permutation of dataset rows on the K-fold cross-validation quality parameters.Molecular Biosystems, 11(1), 13–19.

Vigli, G., Philippidis, A., Spyros, A., & Dais, P. (2003). Classiﬁcation of edible oils by employing³¹P and¹H NMR spectroscopy in combination with multivariate statistical analysis. A proposal for the detection of seed oil adulteration in virgin olive oils.

Journal of Agricultural and Food Chemistry, 51(19), 5715–5722.

Wang, L., Lee, F. S. C., Wang, X., & He, Y. (2006). Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR andfiber optic diffuse reflectance NIR.Food Chemistry, 95(3), 529–536.

Weng, R.-H., Weng, Y.-M., & Chen, W.-L. (2006). Authentication of Camellia oleifera Abel oil by near infrared Fourier transform Raman spectroscopy.Journal of the Chinese Chemical Society, 53(3), 597–603.

Xie, J., Liu, T., Yu, Y., Song, G., & Hu, Y. (2013). Rapid detection and quantiﬁcation by GC–MS of Camellia seed oil adulterated with soybean oil.Journal of the American Oil Chemists’' Society, 90(5), 641–646.

Yang, Y., Chen, Y., Zhang, Z. F., Nie, S. P., & Xie, M. Y. (2012). Changes in fatty acid composition and quality of three kinds of vegetable oils during frying.Food Science, 33(23), 36–41.

Yang, C., Liu, X., Chen, Z., Lin, Y., & Wang, S. (2016). Comparison of oil content and fatty acid proﬁle of ten new Camellia oleifera cultivars.Journal of Lipids, 2016, 6.

Zeb, A. (2012). Triacylglycerols composition, oxidation and oxidation compounds in camellia oil using liquid chromatography–mass spectrometry.Chemistry and Physics of Lipids, 165(5), 608–614.

Zhu, W., Wang, X., & Chen, L. (2017). Rapid detection of peanut oil adulteration using low-ﬁeld nuclear magnetic resonance and chemometrics.Food Chemistry, 216, 268–274.