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Analytical Methods

Quantiﬁcation of adulterations in extra virgin ﬂaxseed oil using MIR and PLS Letícia Maria de Souza a,⇑, Felipe Bachion de Santana a, Lucas Caixeta Gontijo a,b, Sarmento Júnior Mazivila a,c, Waldomiro Borges Neto a a b c

Institute of Chemistry, Federal University of Uberlândia, Santa Mônica Campus, 38408-100 Uberlândia, Minas Gerais, Brazil Goiano Federal Institute of Education, Science and Technology, Geraldo Silva Nascimento Road, km 2.5, 75790-000 Urutaí, GO, Brazil Josina Machel Secondary School of Belane, Vilankulo, Inhambane, Mozambique

a r t i c l e

i n f o

Article history: Received 15 October 2014 Received in revised form 20 January 2015 Accepted 14 February 2015 Available online 27 February 2015 Keywords: Extra virgin ﬂaxseed oil Adulteration of edible oils Infrared spectroscopy PLS Analytical validation

a b s t r a c t This paper proposes a new method for the quantitative analysis of soybean oil (SO) and sunﬂower oil (SFO) as adulterants in extra virgin ﬂaxseed oil (EFO) by applying Mid Infrared Spectroscopy (MIR) associated with chemometric technique of Partial Least Squares (PLS). The PLS models were built in accordance with standard method ASTM E1655-05 and these showed good correlation between thereference values and those calculated using the PLS models with low error values, with R = 0.998 for SFO and R = 0.999 for SO in EFO. These models were validated analytically in accordance with Brazilian and international guidelines through the estimate of ﬁgures of merit parameters, thus showing an effective and feasible method to control the quality of extra virgin ﬂaxseed oil. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Flaxseed (Linum usitatissimum L.) is an oilseed crop considered a functional food (Shima, Gui, Arnison, Wang, & Reaney, 2014) that has a high concentration of a-tocopherol (Oomah & Sitter, 2009) and an extremely high concentration of a-linolenic acid (Khattab & Zeitoun, 2013) and plant lignans, such as Secoisolariciresinol diglucoside 3699 lg/g as reported by Patel, Vaghasiya, Pancholi, and Paul (2012), considered anticancer effects compounds (Daleprane et al., 2010). Several studies have pointed to beneﬁts of including extra virgin ﬂaxseed oil (EFO) in the human diet (Pilar et al., 2014). Previous studies showed that inclusion of EFO in the diet can inhibit cancer cell growth and spontaneous metastasis (Chen, Wang, & Thompson, 2006; Dabrosin, Chen, Wang, & Thompson, 2002) and, thus, is a great ally in the treatment of breast cancer (Mason, Chen, & Thompson, 2010) and ovarian cancer (Eilati, Bahr, & Hales, 2013). Good results were demonstrated in controlling the rates of LDL and Total Cholesterol Concentrations (Patade et al., 2008) and ⇑ Corresponding author. Tel.: +55 34 32916338; fax: +55 34 32394385. E-mail addresses: [email protected], [email protected] (L.M. de Souza), [email protected] (F.B. de Santana), [email protected]. com (L.C. Gontijo), [email protected], [email protected]. com.br (S.J. Mazivila), [email protected] (W. Borges Neto). http://dx.doi.org/10.1016/j.foodchem.2015.02.081 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.

treating cardiovascular diseases (Dodin & et al., 2008; Leyva et al., 2011) related to the consumption of EFO. Due to their nutraceutical properties and high cost, EFO can be adulterated by the addition of edible oils with lower nutritional and commercial value, generating not only economical losses, but also a decrease in health beneﬁts. This fraud is difﬁcult to detect since reﬁned soybean oil (SO) and reﬁned sunﬂower oil (SFO), the most used adulterant oils, show similar physicochemical characteristics to EFO. Thus, it is necessary to establish efﬁcient and feasible methods for the identiﬁcation and quantiﬁcation of adulteration in EFO (Yang, Irudayaraj, & Paradkar, 2005). Adulteration detection in edible oils is usually done by chromatography (Cserháti, Forgács, Deyl, & Miksik, 2005) however, infrared spectroscopy has advantages because the use of an accessory attenuated total reﬂectance (ATR) gives fast and direct analysis, reduced volume is required, which does not require pre-treatment of the samples and requires a reduced volume. IR spectroscopy is widely used on analysis of adulteration in various food products (Kartheek, Smith, Muthu, & Manavalan, 2011), edible oils such as olive oil (Gurdeniz & Ozen, 2009), coconut oil (Rohman & Che Man, 2011) and avocado oil (Quiñones-Islas, Meza-Márquez, Osorio-Revilla, & Gallardo-Velazquez, 2013) among others, as well as for the quality control of milk and dairy products (Santos, Pereira-Filho, & Rodriguez-Saona, 2013) and fruits and vegetables (Nicolai & et al., 2007), etc.

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L.M. de Souza et al. / Food Chemistry 182 (2015) 35–40

The IR spectra of EFO, SO and SFO show a high degree of overlap and low variation in the absorbance values due to the similarities in the nature of their constituents. Thus, it is necessary to use PLS to quantify the adulterants present in EFO (Forina, Lanteri, & Casale, 2007; Javidnia, Parish, Karimi, & Hemmateenejad, 2013). Analytical validation is a requirement of the ISO Guide standard (NBR ISO/IEC 17025: 2005) so that the model can be considered robust and reliable (Poppi, Braga, & Valderrama, 2009). The validation of multivariate calibration models can be obtained through calculation of parameters like accuracy, selectivity, sensitivity, analytical sensitivity, limit of detection, limit of quantiﬁcation, linearity, the standard deviation and test of systematic errors. This set of parameters is usually known as ‘‘ﬁgures of merit’’ (Souza, Mitsutake, Gontijo, & Borges Neto, 2014). In this work, we have focused on development and validation according international and national guidelines by estimation of the ﬁgures of merit of a PLS model using mid-infrared (MIR) spectroscopy applied to the quantiﬁcation of SO and SFO oil adulterations in EFO samples in the concentration range of 3.50–30.0% (w/w) (ASTM Standard, 2012; Thompson, Ellison, & Wood, 2006).

data (where k is the number of latent variables), and the prediction set must be equal to 4k (ASTM Standard, 2012). The number of latent variables was selected according to the percentage of variance that was observed in the X (absorbance) and Y (adulterant concentration) blocks on the joint comparison containing the plot of the ‘‘Root Means Square Error of Cross Validation’’ (RMSECV). The accuracy of the models was determined by calculating the ‘‘Root Means Square Error of Calibration’’ (RMSEC), RMSECV and ‘‘Root Means Square Error of Prediction’’ (RMSEP). The elliptical joint conﬁdence region was performed for both PLS models for the evaluation of systematic and random errors (Souza et al., 2014). The Matlab 6.1 (Mathworks Inc.) software and PLS Toolbox 3.5 (Eigenvector Research) were used to derive the PLS models. 2.4. Outlier detection

2. Experiment

The outlier was detected by comparing high values of the absolute errors in individual samples with the RMSEC and evaluating high leverage values and Q residuals at 95% conﬁdence (Silva, Ferreira, Braga, & Sena, 2012; Valderrama, Braga, & Poppi, 2007; Walczak & Massart, 1998).

2.1. Sample preparation

2.5. Figures of merit

EFO was obtained with certiﬁed purity from Brazilian industry of vegetable oil extraction while SFO and SO used in artiﬁcial adulterations of the samples were obtained at local supermarkets. For the sample preparation, we used SO and SFO to detect artiﬁcial tampering of EFO. 68 samples of EFO tampered with known proportions of SO were prepared, and 59 samples of EFO tampered with known proportions of SFO, both sample sets in adulteration proportions of 3.50 to 30.00% (w/w) and stored in amber glass vials with 5.0 ml ﬁnal volume each sample. Samples were mechanically homogenized using PHOENIX AP-56/8821 mechanical agitator (speed approximately 200 rpm) and kept standing for a period of 36 h at room temperature (25.0 °C) and under the protection of the incident light (ISO 5555:2001(E), 2001).

The Net Analyte Signal (NAS) ðra Þ (Ferré, Boqué, FernándezBand, Larrechi, & Rius, 1997; Souza et al., 2014) was used in determining the ﬁgure of merit and was calculated for analyte a according to Eq. (1) (Faber & Kowalski, 1997) in which PNAS;a is the orthogonal projection to a given vector space for NAS, a identiﬁes the analyte of interest, r represents the spectrum of a given sample R a is a matrix of the spectral signals generated by all other analytes except a; I is an appropriately dimensioned unit matrix; and ðR a Þþ is the pseudo-inverse of R a usually computed by singular value decomposition using A factors:

2.2. HATR–FTIR measurements The MIR spectra were acquired in triplicate using a Perkin Elmer Spectrum Two spectrometer equipped with a horizontal attenuated total reﬂectance (HATR) accessory with a ZnSe crystal, over the region of 4000–600 cm1 at 4 cm1 resolution over 16 scans. Approximately 0.5 ml of each sample was placed on the HATRZnSe crystal to obtain the spectra. HATR was cleaned with isopropyl alcohol after each scan to avoid contamination of preloaded samples. The spectral baselines were corrected using the baseline method for the ranges of 1900–2600 cm1 and 3100–4000 cm1 for both data matrices. 2.3. Multivariate analysis The data set was mean centered and the leave-one-out method was employed for cross-validation. The PLS models were constructed using 37 and 43 calibration samples and 22 and 25 prediction samples for EFO with SFO PLS and EFO with SO PLS, respectively. The prediction set was completely independent of the calibration set. The number of samples in the calibration and validation sets and the number of latent variables used in the PLS models were determined according standard method ASTM E1655-05, which indicates that the minimum number of samples for a calibration set must be equal to 6(k + 1) for mean centered

ra ¼ PNAS;a r ¼ I R a ðR a Þþ r

ð1Þ

The concentration of a in unknown samples was obtained from the MIR spectrum r using Eq. (2) (Rocha, Nogueira, & Vaz, 2012):

Yun;a ¼

sTa PNAS;a r sT PNAS;a PNAS;a r ðs ÞT r ¼ Ta ¼ a 2a T sa PNAS;a sa sa PNAS;a PNAS;a sa jjsa jj

ð2Þ

where sa is a spectrum containing analyte a at unit concentration, and sa is its corresponding NAS. The equations used to calculate the ﬁgures of merit parameters are all shown in Table 1. The accuracy of the results (Eq. (3)) corresponds to the degree of agreement between the reference value and the predicted value using the PLS model (Souza et al., 2014). The selectivity parameter was deﬁned using NAS calculations as shown in Eq. (4) (Silva et al., 2012; Souza et al., 2014). The sensitivity (Eq. (5)) (Rocha et al., 2012; Souza et al., 2014) was deﬁned as the signal fraction responsible for an increase of one unit of concentration for the ownership of interest (Lorber, Faber & Kowalski, 1997). The analytical sensitivity (Eq. (6)) was deﬁned as the ratio between sensitivity and the estimated standard deviation of instrumental noise (dx), and the inverse of this parameter (c1 ) expresses the minimum difference in concentration that can be discernible by the method, considering the instrumental noise as the only source of error whatever the technique employed (Lorber, Faber, & Kowalski, 1997; Rocha et al., 2012; Souza et al., 2014). dx is obtained through nine replicate spectra of the EFO free of SO or SFO, following the IUPAC recommendations (Thompson et al., 2006).

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L.M. de Souza et al. / Food Chemistry 182 (2015) 35–40 Table 1 Figures of merit calculated for the PLS models prediction SO and SFO in EFO samples. Figures of merit

Equations

Accuracy

Parameter

rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ P

EFO with SO PLS 0.23 0.66 0.36 0.007

Limit of detection (% w/w)

1 LD ¼ 3:3dx SEN ^

Eq. (7)

Limit of quantiﬁcation (% w/w)

LQ ¼ 10dx

1 ^ SEN

RMSE ¼

2

^i Þ ðy y i¼1 i n

RMSEC (% w/w) RMSECV (% w/w) RMSEP (% w/w)

EFO with SFO PLS 0.34 0.49 0.41 0.009

n

Eq. (3)

Value

Eq. (8)

0.028

0.021

c n as i jjxi jj

Eq. (4)

0.02

0.02

Sensitivity

^ ¼ 1 SEN jjb jj

Eq. (5)

0.088

0.114

Analytical Sensitivity Inverse of analytical sensitivity Systematic error

^ SEN c ¼ jjdxjj

Eq. (6)

456.344 0.002 0.031 0.377 25 0.393 2.059

Selectivity (%)

SELi ¼

k

bias ¼ SDV ¼ tbias ¼

Pnval

ðyi ^ yi Þ nval

i¼1

P

^i Þbias ½ðyi y nval 1 pﬃﬃﬃﬃﬃﬃ jbiasj nval SDV

2

Eq. (9)

Bias

351.066 0.002 0.043

Eq. (10)

Standard deviation Degree of freedom tcalc tcrit

0.412 22 0.492 2.073

Eq. (11)

^si is the norm of the NAS vector ^i is the predicted value using the PLS model; yi is the reference value for the sample I; and n is the number of samples. In Eq. (4) na In Eq. (3) y and jjxi jj is the norm of each spectrum. In Eq. (5) b is the vector of ﬁnal regression coefﬁcients, which can be obtained by any multivariate method.

Fig. 1. MIR spectra of virgin ﬂaxseed oil (EFO extra), sunﬂower oil (SFO) and soybean oil (SO).

The limit of detection (LD) (Eq. (7)) establishes the minimum detectable value of the net signal or concentration difference that can be distinguished by the method (Silva et al., 2012; Souza et al., 2014). The limit of quantiﬁcation (LQ) (Eq. (8)) deﬁnes the lowest analyte concentration that can be measured with appropriate accuracy and precision (Souza et al., 2014; Valderrama et al., 2007). The ﬁt of the PLS models were evaluated by correlating the real values with the predicted values from the prediction set and by the graph of real values versus the NAS norms (Lorber et al., 1997; Silva et al., 2012; Souza et al., 2014). The value of bias (Eq. (9)) refers to systematic errors, calculated from the difference between the mean and references values which are the components of error that are not random. The standard deviation validation (SDV) value that characterizes the dispersion of the predicted values of adulteration by validation set was calculated using Eq. (10) (Silva et al., 2012; Souza et al., 2014). The ASTM E1655-05 standard addressed the investigation of the systematic errors using a t-test (Eq. (11)) for validation samples at 95% conﬁdence and degrees of freedom equal to the number of prediction samples (ASTM E1655-05, 2012).

The PLS models were constructed with ﬁve and six latent variables for EFO contaminated with SFO and SO, respectively and showed a good ﬁt between the predicted and measured values. Both PLS models presented R values higher than 0.99 (Fig. 2). The Q residual verses leverage graph was used to verify the presence of outliers in the PLS models (Fig. 3). We observed that none of the samples was considered an outlier in the IR region evaluated. Also, no outlier was detected from the high absolute errors of the individual samples upon comparing the RMSEC value for both models. The ﬁgures of merit estimated to validate the PLS models are shown in Table 1, and the results conﬁrmed the accuracy of the models. The models showed low values of RMSEC, RMSECV and RMSEP and so were considered acceptable. Table 1 shows the estimated values of LQ, which correspond to proportions P0.02% (w/w) of SFO and SO in EFO. The results indicate that the methods are appropriate for quality control of EFO with a working range of 3.50–30.00% (w/w). The estimated intercept (i) and slope (s) were compared with their ideal values of 0 and 1, respectively, using the elliptical joint conﬁdence region (EJCR) test; in this case, an ordinary least squares ﬁtting of the prepared concentration values versus the predicted concentration values for each model was used. Fig. 4 shows the EJCR for both PLS model results and shows there was no signiﬁcant difference between the prepared concentration values and the PLS predicted concentration values and that there was no evidence of bias within the 95% conﬁdence level with i = 0.2695 and s = 1.0122 for SFO in EFO and i = 0.0969 and s = 0.9938 for SO in EFO. Therefore, the PLS models were capable of quantifying SFO and SO in EFO. The linearity parameter was evaluated using errors obtained using the PLS models. The equations in Fig. 5 are particularly useful in routine analysis, where the analyst can visualize the PLS models in a univariate way. A t-test was applied to assess whether the ‘bias’ included in the model is signiﬁcant or not. In the PLS models, tcalc < tcrit for the degree of freedom is equal to the number of prediction samples. Therefore, the PLS models showed no systematic errors.

3. Results and discussion 4. Conclusion The overlapping MIR spectra of EFO, SO and SFO are shown Fig. 1. The spectra show many similarities in absorbance bands and vary slightly, resulting in difﬁculties to quantify SO and SFO in EFO without the use of chemometric tools.

This work proposes an efﬁcient, low-cost, fast and sample preparation free methodology to determine adulteration by addition of sunﬂower and soybean oils in extra virgin ﬂaxseed oil.

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L.M. de Souza et al. / Food Chemistry 182 (2015) 35–40

Fig. 2. PLS models ﬁt for (a) EFO samples adulterated by SFO, and (b) EFO samples adulterated by SO.

Fig. 3. Q residual values versus leverage of EFO samples adulterated by (a) SFO, and (b) SO at 95% conﬁdence.

Fig. 4. The elliptical joint conﬁdence region (EJCR) for the slope and intercept of the regression of predicted concentration versus the reference values. The ideal result consisting of intercept = 0 and slope = 1 is show by point (), whereas the experimental result is indicated by asterisk (⁄), (a) for PLS EFO with SFO, and (b) for PLS EFO with SO.

L.M. de Souza et al. / Food Chemistry 182 (2015) 35–40

39

Fig. 5. Pseudo-univariate calibration curve. Plot of the norm of the NAS versus reference values of adulterant for the calibration and validation samples for (a) PLS EFO with SFO, and (b) PLS EFO with SO.

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