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Application of lag-k autocorrelation coefficient and the TGA signals approach to detecting and quantifying adulterations of extra virgin olive oil with inferior edible oils
Analytica Chimica Acta 688 (2011) 140–145
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Analytica Chimica Acta journal homepage: www.elsevier.com/locate/aca
Application of lag-k autocorrelation coefficient and the TGA signals approach to detecting and quantifying adulterations of extra virgin olive oil with inferior edible oils José S. Torrecilla ∗ , Julián García, Silvia García, Francisco Rodríguez Department of Chemical Engineering, Faculty of Chemistry, University Complutense of Madrid, 28040 Madrid, Spain
a r t i c l e
i n f o
Article history: Received 5 July 2010 Received in revised form 29 December 2010 Accepted 10 January 2011 Available online 18 January 2011 Keywords: Low grade olive oils Corn oil Sunflower oil Thermogravimetric analyzer Chaotic parameter Adulteration
a b s t r a c t The combination of lag-k autocorrelation coefficients (LCCs) and thermogravimetric analyzer (TGA) equipment is defined here as a tool to detect and quantify adulterations of extra virgin olive oil (EVOO) with refined olive (ROO), refined olive pomace (ROPO), sunflower (SO) or corn (CO) oils, when the adulterating agents concentration are less than 14%. The LCC is calculated from TGA scans of adulterated EVOO samples. Then, the standardized skewness of this coefficient has been applied to classify pure and adulterated samples of EVOO. In addition, this chaotic parameter has also been used to quantify the concentration of adulterant agents, by using successful linear correlation of LCCs and ROO, ROPO, SO or CO in 462 EVOO adulterated samples. In the case of detection, more than 82% of adulterated samples have been correctly classified. In the case of quantification of adulterant concentration, by an external validation process, the LCC/TGA approach estimates the adulterant agents concentration with a mean correlation coefficient (estimated versus real adulterant agent concentration) greater than 0.90 and a mean square error less than 4.9%. © 2011 Elsevier B.V. All rights reserved.
1. Introduction The adulteration of foods means that the product does not meet with the health and safety standards established by governments or health authorities. This illegal deviation from the quality of the food is usually caused by the addition of cheaper elements which range from potentially poisonous (e.g. anomalous quantity of E. coli in apple cider or Listeria monocytogenes in Brie cheeses can cause death) to substances added only to increase the product’s bulk or weight which can be considered as economic and healthy adulteration. In the field of edible oils, adulterated oils have contributed to serious epidemics in the past. It is worth noting a case in Morocco in 1959 caused by the addition of ortho-cresyl phosphate to oil which was subsequently sold as edible oil. Another occurred in Japan in 1968 due to the accidental contamination of rice oil with polychlorinated biphenyls [1]. In the oleic sector, regretfully the “Spanish toxic oil syndrome” (STOS) must be mentioned. In May 1981, in a small area south of Madrid (Spain) a disease appeared which had its origin in the ingestion of oil fraudulently sold as pure olive oil [1]. Most symptoms started with respiratory distress, nausea and vomiting, headaches, various skin eruptions, general discomfort,
∗ Corresponding author. Tel.: +34 91 394 42 40; fax: +34 91 394 42 43. E-mail address: [email protected] (J.S. Torrecilla). 0003-2670/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2011.01.009
abdominal pain, and myalgias. Between May 1981 and October 1983, this adulteration resulted in more than 20,000 people being affected and claimed about 400 lives in Spain. Thirty years later, the aftermath is still being felt, afflicted patients are still dying, and the number of death attributed to STOS may exceed 500. Among survivors, the prevalence of some chronic conditions is high. Although certain clinical characteristics of STOS resembled those of other diseases such as scleroderma and graft-versus-host disease, a similar disease had never been seen previously [1]. Epidemiologic studies integrated with chemical analyses of case-related oils have shown that the disease is closely associated with the consumption of oils containing fatty acid esters of 3-(N-phenylamino)-1,2-propanediol [2]. At present, many questions related with STOS remain unresolved. As the main objective of these illegal processes is to achieve greater economic benefit, all adulterant agents are chemically similar and substantially cheaper [3–5]. Given the chemical similarities of extra virgin olive oil (EVOO) and refined olive-pomace oil (ROPO), refined olive oil (ROO) or hazelnut oil, these adulterations are difficult to detect, especially when their concentrations are less than 10% [3,4,6]. In order to fight against the increase in these fraudulent activities, two general measures are being taken: (i) The chemical compositions of specific olive oils have been qualified and protected by certificates of denomination of origin issued by a government body. Considering the constant increase of Quality Assurance and
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Certification schemes, the European Parliament has requested the European Commission to implement a Community legal framework for the protection against such schemes in the food chain. In addition, the European Community also regulates the protection of geographical indications and designations of origin for agricultural products and foodstuffs (Commission Regulation (EC) No 1898/2006); and (ii) Analytical techniques, physicochemical parameters, indexes, etc. [7–9] and their integration with chemmometric tools based on mathematical algorithms [10–13] have been also proposed. Focusing on the analytical method, to detect these fraudulent activities, researchers are looking for characteristic chemicals such as chlorophylls [14], tocopherols, volatile compounds [4] and phenolic compounds to facilitate early detection of adulteration [15]. To measure these compounds, among others spectroscopy techniques (FT-MIR and FT-Raman and fluorescence spectroscopy), chromatography, nuclear magnetic resonance (NMR) spectroscopy techniques are being used [15–18]. The chromatographic techniques include gas chromatography (GC) [5,19,20], GC–mass spectrometry [21–23], high-performance liquid chromatography [24–26]. Thermal characteristics of extra virgin olive oil can also be used to adequately detect the adulteration of this oil and to carry out quality control throughout its production chain [27]. Although these methods are widely used to detect low concentrations of adulterating agents, given that their sampling times are habitually higher than the sample preparation time, these techniques cannot be applied to control on-line the quality of the EVOO during the manufacturing process. Considering this important requirement and the associated risks of adulteration of foods, the development of a simple, cheap, and rapid alternative to detect the adulterating agent in EVOO is necessary. Regretfully, the ideal tool for the detection of minute concentrations of every possible adulterant product is not a reality. Nevertheless, at present, the combination of the aforementioned techniques and power chemometric tools based on principal component analysis [5], neural networks [6,10,28], or even chaotic parameters notably reduce the detectable concentration of adulterant agent in EVOO [13]. As most chaotic regions can represent dynamic systems, tools based on chaotic parameters can detect slight variations in initial experimental conditions [29–31]. Although models based on chaotic parameters can be suitable to determine trace chemicals in real samples, in the chemical field, these types of models have been described in few manuscripts [29,32]. To the best of our knowledge, in the oleic field, there is only one publication where models based on chaotic parameters are shown [13]. The successful results achieved lead us to believe that models based on chaotic parameters would be appropriate to quantify the adulteration of foods. The combination of the lag-k autocorrelation coefficient (LCC) as a chaotic parameter (CP) and TGA equipment has been here tested to detect adulterated EVOO and quantify the concentration of refined olive oil (ROO), refined olive pomace oil (ROPO), sunflower oil (SO) and corn oil (CO) as adulterant agents of EVOO when the adulterant concentration is between 0 and 14% (w/w).
2. Materials and methods
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Table 1 Types of edible oils used to adulterate the extra virgin olive oil samples provided by Aceites Borges Pont SAU, brand and number of adulterated oil samples used in the learning, verification and validation samples. Adulterant agent ROO ROPO SO CO
Number of samples
Brand
Learning and verification
Validation
108 108 108 108
29 29 29 29
KOIPE, SOS Cuétara SA Aceites Pina SA KOIPE, SOS Cuétara SA KOIPE, SOS Cuétara SA
were used. The mass of the sample in the TGA analysis was from 9 to 12 mg [33]. Extra virgin olive oil, refined olive oil, refined olive-pomace oil, sunflower oil and corn oil were provided by Spanish companies, and their botanical origin and quality were guaranteed by the suppliers, Table 1. All were stored in the dark at room temperature until the day of analysis which was prior to their expiry date. To estimate and detect the adulteration of EVOO with ROO, ROPO, SO or CO, binary mixtures were prepared. Then, to create the necessary databases to calculate LCCs, the losses of the weight of the oleic samples were measured during the heating process. 2.2. Chaotic parameter used To detect low concentrations of edible oil as an adulterant in EVOO samples, lag-k autocorrelation coefficients have been calculated from TGA scans of adulterated EVOO samples (vide supra). As models based on chaotic parameters can detect slight variations in the initial experimental conditions [29–31], these must be maintained constant in every TGA scan. Lag-k autocorrelation coefficient (Rk ) was originally called the correlogram. Since its value is often similar to the largest Lyapunov exponent, it is also called “poor man’s Lyapunov Exponent” or serial correlation function. The autocorrelation function is a linear measure which quantifies the extent to which XT versus XT-k (here, X represent the weight of the sample at temperature T) is a straight line [30]. The lag-k autocorrelation coefficient measures how strongly on average each data point is correlated with one k temperature steps away. The mean subtracted from each data point so that Rk = 0 for uncorrelated data. These coefficients measure linearly how strongly on average each data point is correlated with temperature lag (T), Eq. (1) [30,34]. These are the ratio of the autocovariance to the variance of the data. In general, RT is between 1 (k = 0) and 0 (large k) [30]. For Xn nearly periodic, the correlation function will be an oscillated decline Eq. (1) [30]. RT =
2
N−K (Xn − X(Xn−k − X) ˙n=1 N−K N−K ˙n=1 (Xn − X)˙n=1 (Xn−k − X)
(1)
where X, X and N represent the weight set of the measurements by TGA equipment, their average and the total number of data sets, respectively. Given that the global lag T has been assumed as equal to 200 ◦ C with an individual T, k = 5 ◦ C, the number of coefficients (LCCs) is 39 [30,35]. For instance, in the case of k = 50 ◦ C (ninth lag-k autocorrelation coefficient), throughout the work Rk have been referred to as R50 .
2.1. Instrumentation 2.3. Learning, verification and validation sample The thermogravimetric analyzer used in this work was a Mettler Toledo TGA/SDTA851e. Two melting points (of indium and aluminum) and heating rates of 10 ◦ C min−1 were used to calibrate the equipment. The accuracy of temperature and mass measurements was 0.1 ◦ C and 0.001 mg, respectively. Here, the temperature range was from 30 to 600 ◦ C while purging with 70 mL min−1 of air. For every TGA analysis, alumina pans (Al2 O3 ) with a capacity of 70 L
Every data set of the learning and verification samples is composed of thirty-nine lag-k autocorrelation coefficients (vide supra) with their respective concentrations of low grade olive or seed oils in weight percentage. The formats of these matrixes of databases are as many rows as numbers of datasets and 40 columns (39 LCCs and 1 adulterant agent concentration). The concentration ranges in
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learning range, the data were randomly distributed between both samples [36]. On the other hand, with relation to the external validation process, the above mentioned chaotic parameter has been calculated using different TGA scans from binary mixtures composed of EVOO and ROO, ROPO, SO or CO (116 samples), Table 1. Although the concentrations of these components in the external validation sample are within the range of learning sample, their values are different. The format of the external validation sample is 116 rows (samples) and 40 columns (39 LCCs and one concentration of adulterant agent) [36]. 2.4. Linear models The linear models tested in this work are statistically linear [37]. Linear and multiple linear regressions are the most widely used and known modelling method. They have been adapted to a broad range of situations. In a multivariate case, a linear equation containing more than one independent variable can be constructed, Eq. (2). [AC] = ˛0 +
n
˛i · LCCi + ε
(2)
i=1
where AC, n, ˛i (˛0 , ˛1 ,. . ., ˛n ), LCCi (i = 1, 2, . . ., n) and ε represent response variable (adulterant agent concentration), number of observations, parameters of the model, independent variables and random error, respectively [38]. The error term is an unobservable random variable that represents the residual variation and will be assumed to have zero mean, constant variance and a normal distribution. The linear models are not limited to lines or planes, but include a fairly wide range of shapes [37]. Due to its simplicity (Eq. (2)), this type of linear model has been used here. The design of the linear models and statistical analyses were carried out by SPSS version 17. 3. Results and discussion The results of this work have been classified in four sections, viz. preliminary calculations, detector of adulterations, design of the linear models and external validation of the models. 3.1. Preliminary calculations
Fig. 1. TGA scans of mixtures (—; temperature vs. weight) and weight versus lag-k weigh of samples (·····) k = 50 ◦ C; (- - -) k = 100 ◦ C and (– – –) k = 150 ◦ C. (a) Extra virgin olive oil; (b) adulterated EVOO with ROO (3.23%, w/w); (c) adulterated EVOO with ROPO (3.20%, w/w).
the adulteration processes of EVOO with four adulterated agents are between 0 and 14% (w/w). As an example, TGA scans of pure and adulterated samples composed of EVOO and ROPO (3.20%, w/w) and EVOO with ROO (3.23%, w/w) are shown in Fig. 1. Although the EVOO samples are adulterated with different types of low grade olive oils, both profiles with almost equal concentration values are similar. The learning and verification samples were composed of 432 data sets, which were distributed in 108 for EVOO + ROO, 108 for EVOO + ROPO, 108 for EVOO + SO and 108 for EVOO + CO. The only difference between the verification and learning samples is that the latter is composed of 80% (346 datasets) of data and the former of the remaining 20%. Taking into account that every datum of the verification sample should be interpolated within the
In the first step, to guarantee the reliability of the estimations calculated by the models to be designed, the applicability domain of the experimental measurements has been evaluated selecting the data set with cross-validated standardized residuals greater than three standard deviations [39,40]. In this evaluation, no response outlier was determined. Once the domain of the whole data used given by TGA equipment has been tested, the lag-k autocorrelation coefficients were calculated following Eq. (1). With this information, depending on the adulterant agent, four matrixes composed of 108 rows and 40 columns (vide supra) were created to carry out the learning and verification processes of the models studied here. In addition, another matrix composed of 116 rows and 40 columns has been created to externally validate the models designed. 3.2. Detection of adulterations Some statistical properties of the LCCs calculated from the EVOO samples, which were adulterated with the lowest concentration of the adulterant agent and pure EVOO, have been calculated. Lower and higher quartile (box) median, average and range of data from every sample (whisker) have been plotted in a “Box and Whisker
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Table 2 Standardized moments of TGA scans of pure EVOO and mixtures composed of EVOO and ROO, ROPO, SO and CO.
Standardized skewness Standardized kurtosis
EVOO
ROO
ROPO
SO
CO
−0.90910 −1.56512
−1.10072 −1.44549
−1.12366 −1.42503
−1.14362 −1.42691
−1.08048 −1.45606
Autocorrelation coefficient
1
been correctly detected. Therefore, this method is a promising way to detect adulterated EVOO. These statistical results lead us to think that the lag-k autocorrelation coefficient could be adequate to quantify the adulteration of EVOO, and therefore, the next step is to know the concentration of adulterant agents used in the fraudulent activities. With this aim, simple linear models will be designed to estimate the concentration of adulterant agent in EVOO samples.
0,9 0,8 0,7 0,6
3.3. Quantification of the concentration of adulterant agents
0,5 EVOO
EVOO&
EVOO&
EVOO
EVOO
ROPO
ROO
& SO
& CO
Fig. 2. Statistical characteristics of lag-k autocorrelation coefficients of TGA signal from pure EVOO, and EVOO adulterated with ROPO, ROO SO and CO.
plot” (Fig. 2). From observation of the differences of box sizes, averages and medians, the pure EVOO could be distinguished from the other samples. Studying these properties in depth, in particular in the third and fourth statistical moments, standardizing skewness (SS) and kurtosis (SK) of the LCCs calculated from EVOO samples adulterated with the lowest concentration of the adulterant agent and pure EVOO have been calculated. There are no significant deviations from the normal distribution, Table 2. Comparing the SS and SK calculated from LCCs of pure EVOO and adulterated EVOO with the minimum quantity of adulterant, differences higher than 19 and 7% have been found, respectively. Using these differences between statistical properties of LCCs from pure and adulterated EVOO samples, adulterations of EVOO samples have been detected. The procedure for the detection of adulterated samples consists of calculating the SS of 45 new samples. Then, the adulterant agent concentration and SS were plotted. Finally depending on their values, the samples were classified as pure (the adulterant agent concentration is equal or less than 1% (w/w)) or adulterated EVOO (the adulterant agent concentration is higher than 1% (w/w)), Fig. 3. In this figure, the SS range was established according to the magnitudes of SS values shown in Table 2 (0.909 for pure EVOO and 1.1 for adulterated EVOO). In over 82% of cases, the adulterations have
Fig. 3. Detection of adulterated EVOO samples. The EVOO samples, considered as pure, are surrounded with a rectangle. (: pure samples; *: adulterated samples).
As TGA profiles of adulterated samples are similar (Fig. 1) and the linear models used are simple, a power coefficient is required to quantify the concentration of the adulterant agents. Because of this, a chaotic parameter has been used here. Finding the best linear relation between lag-k autocorrelation coefficients and adulterating concentrations is now the main objective. Firstly, the adulteration of EVOO with refined olive oil, refined pomade olive oil, sunflower and corn oil was individually modelled and four lineal models were proposed. Then, one global model to estimate the adulteration with ROPO, ROO, SO or CO simultaneously was designed. Finally, the global model was externally validated using an external validation sample [36].
3.4. Application of individual models to detect the adulteration of EVOO Using the aforementioned learning and verification samples, the adulterant agent concentration will be estimated. Looking for the most suitable and reliable model to estimate the concentration of edible oil (dependent variable) and as a consequence of the combination of the aforementioned 39 lag-k autocorrelation coefficients (independent variables), more than 140,000 models were designed. Here, the models with the best statistical results to estimate the concentration of ROO, ROPO, SO or CO in EVOO using 8, 8, 7 and 5 independent variables, respectively, Table 3. The correlation coefficients and mean square error (MSE, real vs. estimated values) are shown in Table 3 and these have been calculated using the verification sample. Employing 8 independent variables there are nearly 14 data sets (108/8) for each parameter to be optimized, and so the model can be reliably used. More independent variables could be used to make linear models, but the statistical results did not improve sufficiently to justify this step. Nevertheless, it is worth pointing out that this chemometric tool would have to be re-optimized previously to be applied in every specific situation maintaining constant the initial experimental conditions (vide supra) and following the same method explained here. Different combinations of all LCCs presented here can adequately quantify the edible oil concentration as adulterating agents of EVOO when the former concentration is between 0 and 14%. To sum up, the TGA scans of adulterated oil samples and the LCCs calculated from them can detect and quantify the adulterating agent concentrations using this simple method. Nevertheless, better statistical results can be achieved establishing non linear models between the aforementioned LCCs and adulterated agent concentrations, but the model, and so, its calculation procedure is more complex.
MSEa
4.3 2.1 3.0 5.1
Given that the chaotic parameter is defined for specific initial experimental conditions, every equation is only adequate in the detection of the given adulterated oil for which it has been established. 3.5. Application of a global model to detect the adulteration of EVOO
R35 : −2.42071 × 107 R40 : 737363 R195 : −47052.8 R30 : 2.39206 × 107 R30 : −7.09771 × 106 R175 : 345414
R40 : 6.05662 × 106 R50 : 103072
˛6 ˛5
˛7
0.903 0.971 0.968 0.881
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R2 a
144
Bearing in mind that the type of adulteration of EVOO is a priori unknown, the estimation of a unique concentration ROO, ROPO, SO or CO simultaneously is recommended. With this objective, a new mathematical relation between LCCs calculated from TGA scans of binary mixtures of EVOO and ROO, ROPO, SO and CO and their respective concentrations of adulterating compounds of EVOO samples using ten independent variables has been proposed, Eq. (3). This is the most suitable lineal model possible. As expected, due to the chemical differences between the edible oils used as adulterant agents, the statistical results are the poorest of all groups studied (individual models, Table 3). [Adulterant agent] = 1.56839 × 106 − 9.72164 × 106 · R10 + 1.6812 × 107 · R15 − 9.7689 × 106 · R20 + 2.58826 × 106 · R40 − 2.18167 × 106 · R50 + 2.38091 × 106 · R65 − 2.85641 × 106 · R70
+ 3.70024 × 106 · R90 − 356932 · R100 (R2 > 0.872; MSE < 9%)
(3)
The most accurate quantification of every adulterating oil is determined using their respective individual models, Table 3. Nevertheless, taking into account the impossibility of previously determining which type of adulterating agent is present and in line with the statistical results (R2 > 0.872; MSE < 9%), the model described by Eq. (3) is suitable to estimate simultaneously the concentrations of ROO, ROPO, CO or SO as adulterating agents in EVOO samples. Finally, in order to verify the applicability and how reliable this global model is in estimating the concentration of every adulterated agent, the external validation process was carried out.
[Adulterant agent] =
i=0
7
˛i · Rn .
3.6. External validation of the global model
a
R20 : −1.0709 × 108 R20 : 3.61701 × 107 R100 : 695354 R185 : 60738.3
R25 : 3.49296 × 107 R25 : 2.22711 × 107 R170 : −315193 R200 : −22766.4
˛3
R15 : 1.08146 × 108 R15 : 3.40302 × 107 R95 : −1.27503 × 106 R165 : −139403 R10 : −4.94233 × 107 R10 : −1.66479 × 107 R90 : 599374 R160 : 102060 7.66789 × 106 2.9801 × 106 −2942.92 −688.537 ROO ROPO SO CO
˛2 ˛1 ˛0 Adulterant agent
Table 3 Coefficients of the linear models (˛i ) used to estimate the adulterant agents concentration of EVOO.
˛4
+ 5.36789 × 106 · R80 − 7.85322 × 106 · R85
To validate externally the model described by Eq. (3), a new database has been used (vide supra). The estimations (Eq. (3)) and real values of the external validation sample are plotted in Fig. 4. The correlation coefficients and MSE values of estimated versus real values are 0.891 and 5%, 0.939 and 3.6%, 0.903 and 4.6% and 0.876 and 6.3% for ROO, ROPO, SO and CO, respectively. In the light of these results, every adulterant agent concentration studied is adequately estimated. Given the similarities between ROO and EVOO, lower correlation coefficient and higher MSE are expected. These statistical results lead us to think that linear models based on the LCCs can be used to determine on-line adulteration with ROO, ROPO, CO or SO. One of the most important specifications for detecting adulterations is the lower limit of detection. Here, using only one model (Eq. (3)), the lower limit of detection of adulteration during the external validation process was 1.2, 1.2, 1.1 and 1.2% (w/w) for refined olive, refined olive pomace, sunflower and corn oils concentrations, respectively. These values are less than other models, such as nuclear magnetic resonance spectroscopy (RMN)/multivariate statistical analysis (5% (w/w), [16]) or the synchronous fluorescence
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Acknowledgements The authors thank Ms. Deborah Demiaux for her contribution to this work and are grateful to the Spanish “Comunidad Autónoma de Madrid” for financial support of project S2009/PPQ-1545. References
Fig. 4. Experimental values from external validation samples versus their respective estimated values calculated by Eq. (3); (a) refined olive oil () and refined olive pomace oil (䊉); (b) sunflower oil () and corn oil ().
method/partial least-squares regression (2.6% (w/w), [41]). Given the simplicity of the analytical equipment used here in comparison with the RMN apparatus, this difference in the lower limit of detection shows the power of lag-k autocorrelation coefficient concerning adulteration of EVOO. This widens the horizon to design tools which solve the problem of illegal adulterants, which in some cases can cause serious health problems aside from economic fraud [42]. In the light of these results, the proposed method is a reliable tool when detection of ROO, ROPO, CO or SO concentrations of less than 1.2% (w/w) is required. Therefore, it is suitable not only to detect adulterations, but also to measure impurities when high grade olive oil is transferred to other storage tanks which had contained lower grade olive oils and was not adequately prepared. In the latter application, given that the nature of the impurity is known, an individual model can be used, Table 3. The most important advantage of the proposed method consists of detecting and quantifying adulteration by only calculating one chaotic parameter. In the statistical field, these autocorrelation coefficients, which can be calculated easily (Eq. (1)), extract the essential information from huge databases such as TGA scans. The detection of refined olive oil and refined olive pomace, corn or sunflower oils as adulterating compounds of EVOO samples using this simple method represents the first step in the design of a more general and simple tool which can detect even more adulterating agents simultaneously.
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Analytica Chimica Acta journal homepage: www.elsevier.com/locate/aca
Application of lag-k autocorrelation coefficient and the TGA signals approach to detecting and quantifying adulterations of extra virgin olive oil with inferior edible oils José S. Torrecilla ∗ , Julián García, Silvia García, Francisco Rodríguez Department of Chemical Engineering, Faculty of Chemistry, University Complutense of Madrid, 28040 Madrid, Spain
a r t i c l e
i n f o
Article history: Received 5 July 2010 Received in revised form 29 December 2010 Accepted 10 January 2011 Available online 18 January 2011 Keywords: Low grade olive oils Corn oil Sunflower oil Thermogravimetric analyzer Chaotic parameter Adulteration
a b s t r a c t The combination of lag-k autocorrelation coefficients (LCCs) and thermogravimetric analyzer (TGA) equipment is defined here as a tool to detect and quantify adulterations of extra virgin olive oil (EVOO) with refined olive (ROO), refined olive pomace (ROPO), sunflower (SO) or corn (CO) oils, when the adulterating agents concentration are less than 14%. The LCC is calculated from TGA scans of adulterated EVOO samples. Then, the standardized skewness of this coefficient has been applied to classify pure and adulterated samples of EVOO. In addition, this chaotic parameter has also been used to quantify the concentration of adulterant agents, by using successful linear correlation of LCCs and ROO, ROPO, SO or CO in 462 EVOO adulterated samples. In the case of detection, more than 82% of adulterated samples have been correctly classified. In the case of quantification of adulterant concentration, by an external validation process, the LCC/TGA approach estimates the adulterant agents concentration with a mean correlation coefficient (estimated versus real adulterant agent concentration) greater than 0.90 and a mean square error less than 4.9%. © 2011 Elsevier B.V. All rights reserved.
1. Introduction The adulteration of foods means that the product does not meet with the health and safety standards established by governments or health authorities. This illegal deviation from the quality of the food is usually caused by the addition of cheaper elements which range from potentially poisonous (e.g. anomalous quantity of E. coli in apple cider or Listeria monocytogenes in Brie cheeses can cause death) to substances added only to increase the product’s bulk or weight which can be considered as economic and healthy adulteration. In the field of edible oils, adulterated oils have contributed to serious epidemics in the past. It is worth noting a case in Morocco in 1959 caused by the addition of ortho-cresyl phosphate to oil which was subsequently sold as edible oil. Another occurred in Japan in 1968 due to the accidental contamination of rice oil with polychlorinated biphenyls [1]. In the oleic sector, regretfully the “Spanish toxic oil syndrome” (STOS) must be mentioned. In May 1981, in a small area south of Madrid (Spain) a disease appeared which had its origin in the ingestion of oil fraudulently sold as pure olive oil [1]. Most symptoms started with respiratory distress, nausea and vomiting, headaches, various skin eruptions, general discomfort,
∗ Corresponding author. Tel.: +34 91 394 42 40; fax: +34 91 394 42 43. E-mail address: [email protected] (J.S. Torrecilla). 0003-2670/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2011.01.009
abdominal pain, and myalgias. Between May 1981 and October 1983, this adulteration resulted in more than 20,000 people being affected and claimed about 400 lives in Spain. Thirty years later, the aftermath is still being felt, afflicted patients are still dying, and the number of death attributed to STOS may exceed 500. Among survivors, the prevalence of some chronic conditions is high. Although certain clinical characteristics of STOS resembled those of other diseases such as scleroderma and graft-versus-host disease, a similar disease had never been seen previously [1]. Epidemiologic studies integrated with chemical analyses of case-related oils have shown that the disease is closely associated with the consumption of oils containing fatty acid esters of 3-(N-phenylamino)-1,2-propanediol [2]. At present, many questions related with STOS remain unresolved. As the main objective of these illegal processes is to achieve greater economic benefit, all adulterant agents are chemically similar and substantially cheaper [3–5]. Given the chemical similarities of extra virgin olive oil (EVOO) and refined olive-pomace oil (ROPO), refined olive oil (ROO) or hazelnut oil, these adulterations are difficult to detect, especially when their concentrations are less than 10% [3,4,6]. In order to fight against the increase in these fraudulent activities, two general measures are being taken: (i) The chemical compositions of specific olive oils have been qualified and protected by certificates of denomination of origin issued by a government body. Considering the constant increase of Quality Assurance and
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Certification schemes, the European Parliament has requested the European Commission to implement a Community legal framework for the protection against such schemes in the food chain. In addition, the European Community also regulates the protection of geographical indications and designations of origin for agricultural products and foodstuffs (Commission Regulation (EC) No 1898/2006); and (ii) Analytical techniques, physicochemical parameters, indexes, etc. [7–9] and their integration with chemmometric tools based on mathematical algorithms [10–13] have been also proposed. Focusing on the analytical method, to detect these fraudulent activities, researchers are looking for characteristic chemicals such as chlorophylls [14], tocopherols, volatile compounds [4] and phenolic compounds to facilitate early detection of adulteration [15]. To measure these compounds, among others spectroscopy techniques (FT-MIR and FT-Raman and fluorescence spectroscopy), chromatography, nuclear magnetic resonance (NMR) spectroscopy techniques are being used [15–18]. The chromatographic techniques include gas chromatography (GC) [5,19,20], GC–mass spectrometry [21–23], high-performance liquid chromatography [24–26]. Thermal characteristics of extra virgin olive oil can also be used to adequately detect the adulteration of this oil and to carry out quality control throughout its production chain [27]. Although these methods are widely used to detect low concentrations of adulterating agents, given that their sampling times are habitually higher than the sample preparation time, these techniques cannot be applied to control on-line the quality of the EVOO during the manufacturing process. Considering this important requirement and the associated risks of adulteration of foods, the development of a simple, cheap, and rapid alternative to detect the adulterating agent in EVOO is necessary. Regretfully, the ideal tool for the detection of minute concentrations of every possible adulterant product is not a reality. Nevertheless, at present, the combination of the aforementioned techniques and power chemometric tools based on principal component analysis [5], neural networks [6,10,28], or even chaotic parameters notably reduce the detectable concentration of adulterant agent in EVOO [13]. As most chaotic regions can represent dynamic systems, tools based on chaotic parameters can detect slight variations in initial experimental conditions [29–31]. Although models based on chaotic parameters can be suitable to determine trace chemicals in real samples, in the chemical field, these types of models have been described in few manuscripts [29,32]. To the best of our knowledge, in the oleic field, there is only one publication where models based on chaotic parameters are shown [13]. The successful results achieved lead us to believe that models based on chaotic parameters would be appropriate to quantify the adulteration of foods. The combination of the lag-k autocorrelation coefficient (LCC) as a chaotic parameter (CP) and TGA equipment has been here tested to detect adulterated EVOO and quantify the concentration of refined olive oil (ROO), refined olive pomace oil (ROPO), sunflower oil (SO) and corn oil (CO) as adulterant agents of EVOO when the adulterant concentration is between 0 and 14% (w/w).
2. Materials and methods
141
Table 1 Types of edible oils used to adulterate the extra virgin olive oil samples provided by Aceites Borges Pont SAU, brand and number of adulterated oil samples used in the learning, verification and validation samples. Adulterant agent ROO ROPO SO CO
Number of samples
Brand
Learning and verification
Validation
108 108 108 108
29 29 29 29
KOIPE, SOS Cuétara SA Aceites Pina SA KOIPE, SOS Cuétara SA KOIPE, SOS Cuétara SA
were used. The mass of the sample in the TGA analysis was from 9 to 12 mg [33]. Extra virgin olive oil, refined olive oil, refined olive-pomace oil, sunflower oil and corn oil were provided by Spanish companies, and their botanical origin and quality were guaranteed by the suppliers, Table 1. All were stored in the dark at room temperature until the day of analysis which was prior to their expiry date. To estimate and detect the adulteration of EVOO with ROO, ROPO, SO or CO, binary mixtures were prepared. Then, to create the necessary databases to calculate LCCs, the losses of the weight of the oleic samples were measured during the heating process. 2.2. Chaotic parameter used To detect low concentrations of edible oil as an adulterant in EVOO samples, lag-k autocorrelation coefficients have been calculated from TGA scans of adulterated EVOO samples (vide supra). As models based on chaotic parameters can detect slight variations in the initial experimental conditions [29–31], these must be maintained constant in every TGA scan. Lag-k autocorrelation coefficient (Rk ) was originally called the correlogram. Since its value is often similar to the largest Lyapunov exponent, it is also called “poor man’s Lyapunov Exponent” or serial correlation function. The autocorrelation function is a linear measure which quantifies the extent to which XT versus XT-k (here, X represent the weight of the sample at temperature T) is a straight line [30]. The lag-k autocorrelation coefficient measures how strongly on average each data point is correlated with one k temperature steps away. The mean subtracted from each data point so that Rk = 0 for uncorrelated data. These coefficients measure linearly how strongly on average each data point is correlated with temperature lag (T), Eq. (1) [30,34]. These are the ratio of the autocovariance to the variance of the data. In general, RT is between 1 (k = 0) and 0 (large k) [30]. For Xn nearly periodic, the correlation function will be an oscillated decline Eq. (1) [30]. RT =
2
N−K (Xn − X(Xn−k − X) ˙n=1 N−K N−K ˙n=1 (Xn − X)˙n=1 (Xn−k − X)
(1)
where X, X and N represent the weight set of the measurements by TGA equipment, their average and the total number of data sets, respectively. Given that the global lag T has been assumed as equal to 200 ◦ C with an individual T, k = 5 ◦ C, the number of coefficients (LCCs) is 39 [30,35]. For instance, in the case of k = 50 ◦ C (ninth lag-k autocorrelation coefficient), throughout the work Rk have been referred to as R50 .
2.1. Instrumentation 2.3. Learning, verification and validation sample The thermogravimetric analyzer used in this work was a Mettler Toledo TGA/SDTA851e. Two melting points (of indium and aluminum) and heating rates of 10 ◦ C min−1 were used to calibrate the equipment. The accuracy of temperature and mass measurements was 0.1 ◦ C and 0.001 mg, respectively. Here, the temperature range was from 30 to 600 ◦ C while purging with 70 mL min−1 of air. For every TGA analysis, alumina pans (Al2 O3 ) with a capacity of 70 L
Every data set of the learning and verification samples is composed of thirty-nine lag-k autocorrelation coefficients (vide supra) with their respective concentrations of low grade olive or seed oils in weight percentage. The formats of these matrixes of databases are as many rows as numbers of datasets and 40 columns (39 LCCs and 1 adulterant agent concentration). The concentration ranges in
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learning range, the data were randomly distributed between both samples [36]. On the other hand, with relation to the external validation process, the above mentioned chaotic parameter has been calculated using different TGA scans from binary mixtures composed of EVOO and ROO, ROPO, SO or CO (116 samples), Table 1. Although the concentrations of these components in the external validation sample are within the range of learning sample, their values are different. The format of the external validation sample is 116 rows (samples) and 40 columns (39 LCCs and one concentration of adulterant agent) [36]. 2.4. Linear models The linear models tested in this work are statistically linear [37]. Linear and multiple linear regressions are the most widely used and known modelling method. They have been adapted to a broad range of situations. In a multivariate case, a linear equation containing more than one independent variable can be constructed, Eq. (2). [AC] = ˛0 +
n
˛i · LCCi + ε
(2)
i=1
where AC, n, ˛i (˛0 , ˛1 ,. . ., ˛n ), LCCi (i = 1, 2, . . ., n) and ε represent response variable (adulterant agent concentration), number of observations, parameters of the model, independent variables and random error, respectively [38]. The error term is an unobservable random variable that represents the residual variation and will be assumed to have zero mean, constant variance and a normal distribution. The linear models are not limited to lines or planes, but include a fairly wide range of shapes [37]. Due to its simplicity (Eq. (2)), this type of linear model has been used here. The design of the linear models and statistical analyses were carried out by SPSS version 17. 3. Results and discussion The results of this work have been classified in four sections, viz. preliminary calculations, detector of adulterations, design of the linear models and external validation of the models. 3.1. Preliminary calculations
Fig. 1. TGA scans of mixtures (—; temperature vs. weight) and weight versus lag-k weigh of samples (·····) k = 50 ◦ C; (- - -) k = 100 ◦ C and (– – –) k = 150 ◦ C. (a) Extra virgin olive oil; (b) adulterated EVOO with ROO (3.23%, w/w); (c) adulterated EVOO with ROPO (3.20%, w/w).
the adulteration processes of EVOO with four adulterated agents are between 0 and 14% (w/w). As an example, TGA scans of pure and adulterated samples composed of EVOO and ROPO (3.20%, w/w) and EVOO with ROO (3.23%, w/w) are shown in Fig. 1. Although the EVOO samples are adulterated with different types of low grade olive oils, both profiles with almost equal concentration values are similar. The learning and verification samples were composed of 432 data sets, which were distributed in 108 for EVOO + ROO, 108 for EVOO + ROPO, 108 for EVOO + SO and 108 for EVOO + CO. The only difference between the verification and learning samples is that the latter is composed of 80% (346 datasets) of data and the former of the remaining 20%. Taking into account that every datum of the verification sample should be interpolated within the
In the first step, to guarantee the reliability of the estimations calculated by the models to be designed, the applicability domain of the experimental measurements has been evaluated selecting the data set with cross-validated standardized residuals greater than three standard deviations [39,40]. In this evaluation, no response outlier was determined. Once the domain of the whole data used given by TGA equipment has been tested, the lag-k autocorrelation coefficients were calculated following Eq. (1). With this information, depending on the adulterant agent, four matrixes composed of 108 rows and 40 columns (vide supra) were created to carry out the learning and verification processes of the models studied here. In addition, another matrix composed of 116 rows and 40 columns has been created to externally validate the models designed. 3.2. Detection of adulterations Some statistical properties of the LCCs calculated from the EVOO samples, which were adulterated with the lowest concentration of the adulterant agent and pure EVOO, have been calculated. Lower and higher quartile (box) median, average and range of data from every sample (whisker) have been plotted in a “Box and Whisker
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143
Table 2 Standardized moments of TGA scans of pure EVOO and mixtures composed of EVOO and ROO, ROPO, SO and CO.
Standardized skewness Standardized kurtosis
EVOO
ROO
ROPO
SO
CO
−0.90910 −1.56512
−1.10072 −1.44549
−1.12366 −1.42503
−1.14362 −1.42691
−1.08048 −1.45606
Autocorrelation coefficient
1
been correctly detected. Therefore, this method is a promising way to detect adulterated EVOO. These statistical results lead us to think that the lag-k autocorrelation coefficient could be adequate to quantify the adulteration of EVOO, and therefore, the next step is to know the concentration of adulterant agents used in the fraudulent activities. With this aim, simple linear models will be designed to estimate the concentration of adulterant agent in EVOO samples.
0,9 0,8 0,7 0,6
3.3. Quantification of the concentration of adulterant agents
0,5 EVOO
EVOO&
EVOO&
EVOO
EVOO
ROPO
ROO
& SO
& CO
Fig. 2. Statistical characteristics of lag-k autocorrelation coefficients of TGA signal from pure EVOO, and EVOO adulterated with ROPO, ROO SO and CO.
plot” (Fig. 2). From observation of the differences of box sizes, averages and medians, the pure EVOO could be distinguished from the other samples. Studying these properties in depth, in particular in the third and fourth statistical moments, standardizing skewness (SS) and kurtosis (SK) of the LCCs calculated from EVOO samples adulterated with the lowest concentration of the adulterant agent and pure EVOO have been calculated. There are no significant deviations from the normal distribution, Table 2. Comparing the SS and SK calculated from LCCs of pure EVOO and adulterated EVOO with the minimum quantity of adulterant, differences higher than 19 and 7% have been found, respectively. Using these differences between statistical properties of LCCs from pure and adulterated EVOO samples, adulterations of EVOO samples have been detected. The procedure for the detection of adulterated samples consists of calculating the SS of 45 new samples. Then, the adulterant agent concentration and SS were plotted. Finally depending on their values, the samples were classified as pure (the adulterant agent concentration is equal or less than 1% (w/w)) or adulterated EVOO (the adulterant agent concentration is higher than 1% (w/w)), Fig. 3. In this figure, the SS range was established according to the magnitudes of SS values shown in Table 2 (0.909 for pure EVOO and 1.1 for adulterated EVOO). In over 82% of cases, the adulterations have
Fig. 3. Detection of adulterated EVOO samples. The EVOO samples, considered as pure, are surrounded with a rectangle. (: pure samples; *: adulterated samples).
As TGA profiles of adulterated samples are similar (Fig. 1) and the linear models used are simple, a power coefficient is required to quantify the concentration of the adulterant agents. Because of this, a chaotic parameter has been used here. Finding the best linear relation between lag-k autocorrelation coefficients and adulterating concentrations is now the main objective. Firstly, the adulteration of EVOO with refined olive oil, refined pomade olive oil, sunflower and corn oil was individually modelled and four lineal models were proposed. Then, one global model to estimate the adulteration with ROPO, ROO, SO or CO simultaneously was designed. Finally, the global model was externally validated using an external validation sample [36].
3.4. Application of individual models to detect the adulteration of EVOO Using the aforementioned learning and verification samples, the adulterant agent concentration will be estimated. Looking for the most suitable and reliable model to estimate the concentration of edible oil (dependent variable) and as a consequence of the combination of the aforementioned 39 lag-k autocorrelation coefficients (independent variables), more than 140,000 models were designed. Here, the models with the best statistical results to estimate the concentration of ROO, ROPO, SO or CO in EVOO using 8, 8, 7 and 5 independent variables, respectively, Table 3. The correlation coefficients and mean square error (MSE, real vs. estimated values) are shown in Table 3 and these have been calculated using the verification sample. Employing 8 independent variables there are nearly 14 data sets (108/8) for each parameter to be optimized, and so the model can be reliably used. More independent variables could be used to make linear models, but the statistical results did not improve sufficiently to justify this step. Nevertheless, it is worth pointing out that this chemometric tool would have to be re-optimized previously to be applied in every specific situation maintaining constant the initial experimental conditions (vide supra) and following the same method explained here. Different combinations of all LCCs presented here can adequately quantify the edible oil concentration as adulterating agents of EVOO when the former concentration is between 0 and 14%. To sum up, the TGA scans of adulterated oil samples and the LCCs calculated from them can detect and quantify the adulterating agent concentrations using this simple method. Nevertheless, better statistical results can be achieved establishing non linear models between the aforementioned LCCs and adulterated agent concentrations, but the model, and so, its calculation procedure is more complex.
MSEa
4.3 2.1 3.0 5.1
Given that the chaotic parameter is defined for specific initial experimental conditions, every equation is only adequate in the detection of the given adulterated oil for which it has been established. 3.5. Application of a global model to detect the adulteration of EVOO
R35 : −2.42071 × 107 R40 : 737363 R195 : −47052.8 R30 : 2.39206 × 107 R30 : −7.09771 × 106 R175 : 345414
R40 : 6.05662 × 106 R50 : 103072
˛6 ˛5
˛7
0.903 0.971 0.968 0.881
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R2 a
144
Bearing in mind that the type of adulteration of EVOO is a priori unknown, the estimation of a unique concentration ROO, ROPO, SO or CO simultaneously is recommended. With this objective, a new mathematical relation between LCCs calculated from TGA scans of binary mixtures of EVOO and ROO, ROPO, SO and CO and their respective concentrations of adulterating compounds of EVOO samples using ten independent variables has been proposed, Eq. (3). This is the most suitable lineal model possible. As expected, due to the chemical differences between the edible oils used as adulterant agents, the statistical results are the poorest of all groups studied (individual models, Table 3). [Adulterant agent] = 1.56839 × 106 − 9.72164 × 106 · R10 + 1.6812 × 107 · R15 − 9.7689 × 106 · R20 + 2.58826 × 106 · R40 − 2.18167 × 106 · R50 + 2.38091 × 106 · R65 − 2.85641 × 106 · R70
+ 3.70024 × 106 · R90 − 356932 · R100 (R2 > 0.872; MSE < 9%)
(3)
The most accurate quantification of every adulterating oil is determined using their respective individual models, Table 3. Nevertheless, taking into account the impossibility of previously determining which type of adulterating agent is present and in line with the statistical results (R2 > 0.872; MSE < 9%), the model described by Eq. (3) is suitable to estimate simultaneously the concentrations of ROO, ROPO, CO or SO as adulterating agents in EVOO samples. Finally, in order to verify the applicability and how reliable this global model is in estimating the concentration of every adulterated agent, the external validation process was carried out.
[Adulterant agent] =
i=0
7
˛i · Rn .
3.6. External validation of the global model
a
R20 : −1.0709 × 108 R20 : 3.61701 × 107 R100 : 695354 R185 : 60738.3
R25 : 3.49296 × 107 R25 : 2.22711 × 107 R170 : −315193 R200 : −22766.4
˛3
R15 : 1.08146 × 108 R15 : 3.40302 × 107 R95 : −1.27503 × 106 R165 : −139403 R10 : −4.94233 × 107 R10 : −1.66479 × 107 R90 : 599374 R160 : 102060 7.66789 × 106 2.9801 × 106 −2942.92 −688.537 ROO ROPO SO CO
˛2 ˛1 ˛0 Adulterant agent
Table 3 Coefficients of the linear models (˛i ) used to estimate the adulterant agents concentration of EVOO.
˛4
+ 5.36789 × 106 · R80 − 7.85322 × 106 · R85
To validate externally the model described by Eq. (3), a new database has been used (vide supra). The estimations (Eq. (3)) and real values of the external validation sample are plotted in Fig. 4. The correlation coefficients and MSE values of estimated versus real values are 0.891 and 5%, 0.939 and 3.6%, 0.903 and 4.6% and 0.876 and 6.3% for ROO, ROPO, SO and CO, respectively. In the light of these results, every adulterant agent concentration studied is adequately estimated. Given the similarities between ROO and EVOO, lower correlation coefficient and higher MSE are expected. These statistical results lead us to think that linear models based on the LCCs can be used to determine on-line adulteration with ROO, ROPO, CO or SO. One of the most important specifications for detecting adulterations is the lower limit of detection. Here, using only one model (Eq. (3)), the lower limit of detection of adulteration during the external validation process was 1.2, 1.2, 1.1 and 1.2% (w/w) for refined olive, refined olive pomace, sunflower and corn oils concentrations, respectively. These values are less than other models, such as nuclear magnetic resonance spectroscopy (RMN)/multivariate statistical analysis (5% (w/w), [16]) or the synchronous fluorescence
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Acknowledgements The authors thank Ms. Deborah Demiaux for her contribution to this work and are grateful to the Spanish “Comunidad Autónoma de Madrid” for financial support of project S2009/PPQ-1545. References
Fig. 4. Experimental values from external validation samples versus their respective estimated values calculated by Eq. (3); (a) refined olive oil () and refined olive pomace oil (䊉); (b) sunflower oil () and corn oil ().
method/partial least-squares regression (2.6% (w/w), [41]). Given the simplicity of the analytical equipment used here in comparison with the RMN apparatus, this difference in the lower limit of detection shows the power of lag-k autocorrelation coefficient concerning adulteration of EVOO. This widens the horizon to design tools which solve the problem of illegal adulterants, which in some cases can cause serious health problems aside from economic fraud [42]. In the light of these results, the proposed method is a reliable tool when detection of ROO, ROPO, CO or SO concentrations of less than 1.2% (w/w) is required. Therefore, it is suitable not only to detect adulterations, but also to measure impurities when high grade olive oil is transferred to other storage tanks which had contained lower grade olive oils and was not adequately prepared. In the latter application, given that the nature of the impurity is known, an individual model can be used, Table 3. The most important advantage of the proposed method consists of detecting and quantifying adulteration by only calculating one chaotic parameter. In the statistical field, these autocorrelation coefficients, which can be calculated easily (Eq. (1)), extract the essential information from huge databases such as TGA scans. The detection of refined olive oil and refined olive pomace, corn or sunflower oils as adulterating compounds of EVOO samples using this simple method represents the first step in the design of a more general and simple tool which can detect even more adulterating agents simultaneously.
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