- Email: [email protected]
Prediction of coumarin and ethyl vanillin in pure vanilla extracts using MID-FTIR spectroscopy and chemometrics
Talanta 197 (2019) 264–269
Contents lists available at ScienceDirect
Talanta journal homepage: www.elsevier.com/locate/talanta
Prediction of coumarin and ethyl vanillin in pure vanilla extracts using MIDFTIR spectroscopy and chemometrics
T
Cristina Montserrat Moreno-Leya, Diana Maylet Hernández-Martíneza, Guillermo Osorio-Revillab, ⁎ Adriana Patricia Tapia-Ochoateguib, Gloria Dávila-Ortizb, Tzayhri Gallardo-Velázqueza, a
Departamento de Biofísica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Lázaro Cárdenas, Prolongación de Carpio y Plan de Ayala S/N, Col. Santo Tomás, CP.11340 Ciudad de México, Mexico b Departamento de Ingeniería Bioquímica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Adolfo López Mateos, Av. Wilfrido Massieu Esq. Cda. Miguel Stampa s/n, C.P.07738 Ciudad de México, Mexico
ARTICLE INFO
ABSTRACT
Keywords: Vanilla extracts Coumarin Ethyl vanillin Adulteration Chemometrics MID-FTIR
Fourier transform mid-infrared (MID-FTIR) spectroscopy coupled with chemometric analysis was used to identify and quantify coumarin (CMR) and ethyl vanillin (EVA) adulterations in pure vanilla extracts. Forty samples adulterated with CMR (0.25–10 ppm) and forty with EVA (0.25–10%) were prepared from pure vanilla extracts and characterized by MID-FTIR spectroscopy to develop chemometric models. Additionally, six commercial vanilla samples were analyzed. A soft independent modeling of class analogy (SIMCA) model was developed to identify and classify the purity from EVA-adulterated or CMR-adulterated samples. Prediction models for CMR or EVA content were developed using the principal component regression (PCR), partial least squares with single y-variables (PLS1), and with multiple y-variables (PLS2) algorithms. Moreover, the predictions of the best quantification chemometric model were compared with the results of a high-performance liquid chromatography-diode array detector (HPLC-DAD) method to evaluate the accuracy of the prediction. The PLS1 algorithm had better performance using 3 and 8 factors for EVA and CMR, respectively. The SIMCA model showed 100% recognition and rejections rates. The results demonstrate that adulteration of pure vanilla with EVA and CMR could be successfully predicted by the developed technique.
1. Introduction The vanilla plant is a member of the family Orchidaceae and is native to México. Natural vanilla is a complex mixture of flavor components extracted mainly from the cured pods of the beans of Vanilla planifolia Andrews, Vanilla pompona Schiede, and Vanilla tahitensis J. W. Moore; V. planifolia is the most valued [1]. The major vanilla-producing countries are Indonesia, Madagascar, China, Papua New Guinea, and Mexico [2]. The special flavor and aroma of vanilla are due to more than 200 compounds, with vanillin (4-hydroxy-3-methoxybenzaldehyde) by far the most abundant [3]. The characteristic vanilla aroma is only released after the fermentation of green vanilla beans, called “curing,” where the glucoside of the vanillin, glucovanillin, is hydrolyzed by the enzyme βD-glucosidase [4]. Vanilla requires particular growth conditions, and the green beans require pollination by specialized insects, which means that in most places, pollination is performed by hand using a small, thin stick [5].
⁎
Additionally, the harvest occurs two to three years after planting. Due to the intensive processing procedures, natural vanilla extracts and vanilla pods are the second most expensive spice in the world, just behind saffron [6]. The price of natural vanilla ranges between $ 5262 USD/ton and $ 6528 USD/ton [2]. Vanilla is the world's most popular flavor, and it is used in innumerable food products, such as ice cream, bakery goods, cola-type drinks, and chocolates, as well as by fragrance and pharmaceutical manufacturing companies. Due to the massive consumption of vanillaflavored products, limited quantities of vanilla beans, and the high cost of vanilla production, the flavor is obtained synthetically to satisfy demand. Consequently, vanilla obtained through chemical processes is less expensive than natural vanilla extracts [7,8]. Furthermore, natural vanilla extracts have been adulterated with ethyl vanillin, synthetic vanillin or coumarin as less expensive supplies. As a result, the adulteration of spices, such as vanilla, has been a severe worldwide issue resulting in quality deterioration affecting the market and creating possible human health risks.
Corresponding author. E-mail address: [email protected] (T. Gallardo-Velázquez).
https://doi.org/10.1016/j.talanta.2019.01.033 Received 29 September 2018; Received in revised form 5 January 2019; Accepted 7 January 2019 Available online 09 January 2019 0039-9140/ © 2019 Elsevier B.V. All rights reserved.
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
Ethyl vanillin (EVA), or 3-ethoxy 4-hydroxybenzaldehyde, is synthetically produced from safrole and is approximately three to four times more potent as a flavoring agent than vanilla. As a result, the adulteration of natural vanilla extract can reach a maximum 10% EVA without objectionable notes [8]. Although EVA has not shown toxicity to humans when it is added to natural vanilla extracts, the label "imitation, artificial or synthetic product” must be added. If not properly label, it is considered food fraud. On the other hand, the presence of coumarin (CMR), or 1,2-benzopyrone, in natural vanilla extracts is attributed to adulteration with the tonka bean, Dipteryx odorata (Aubl.) Willd (Fabaceae family). The use of CMR has been limited after the discovery of its hepatoxicity and carcinogenic effects in experimental animals, though no genotoxic activity has been concluded [9]. The growing interest in natural products is a global trend. Therefore, to ensure the quality and authenticity of vanilla extracts, reliable techniques that verify the presence of adulterants are of interest. Analytical techniques for identification and quantification of EVA and CMR as adulterants in vanilla extracts have been reported, for example, high-performance liquid chromatography [10,11], ultrahighperformance liquid chromatography [12,13], liquid chromatography mass spectrometry [11,14], stable isotope ratio analysis mass spectrometry [15], and time-of-flight mass spectrometry [16,17]. Moreover, flow tube mass spectrometry has been used to quantify aromatic profiles of vanilla extracts such as EVA [3]. The latest techniques are sensitive and precise and have fast performance. Nevertheless, these techniques are expensive or require a long time for sample preparation. Infrared spectroscopy is an analytical technique that contributes to the easy and rapid analysis of vanilla extracts, and it does not require the use of either reagents or sample pretreatments. However, the quantification of adulterants by Fourier transform mid-infrared (MID-FTIR) spectroscopy has not been reported, and it has only been used to differentiate artificial and natural vanilla extracts [18] or to provide differentiation of vanilla extracts from different countries [3]. The aim of this study was to obtain chemometric models based on MID-FTIR spectroscopy to identify and quantify CMR and EVA as adulterants of pure vanilla extracts. To confirm the validity of the developed technique, a comparison between the best quantification chemometric model and HPLC-DAD method was conducted, and the classification chemometric model was applied to commercial vanilla extracts.
2.3. Preparation of adulterated samples The pure extracts were adulterated with EVA or CMR in a binary mixture. Forty pure extracts were adulterated with EVA in concentrations ranging from 0.25% to 10% (w/v) in increments of 0.25%. This range of adulteration was selected based on Singhal, Kulkarni, and Rege [22], who reported that EVA is added to the vanilla extracts at a maximum of 10% w/v; a higher dosage can produce a somewhat harsh ‘chemical’ character that is easily detected. Forty pure extracts were adulterated with CMR in concentrations ranging from 0.25 to 10 ppm in increments of 0.25 ppm. This range was selected based on the tolerable daily intake of 0.1 mg/kg body weight established by EFSA [9]. The samples were stored in amber bottles in a dark, cool place. 2.4. MID-FTIR spectra acquisition The spectra of unadulterated, CMR-adulterated and EVA-adulterated vanilla extracts, as well as commercial samples were collected in triplicate using a Fourier transform mid-infrared (MID-FTIR) spectrometer (Spectrum GX, Perkin Elmer Massachusetts, USA) over the range of 4000–550 cm−1 at a resolution of 4 cm−1 with a total of 64 scans. Two milliliters of extract spread onto the ATR crystal of zinc selenide was analyzed. A background air spectrum was scanned as a blank reference using the same instrumental conditions. 2.5. Discrimination model A discrimination model (SIMCA model) was obtained to identify pure extracts from adulterated samples. The soft independent modeling of class analogy (SIMCA) model was achieved using the Assure ID software, version 4.0.2.0175 (®2007 PerkinElmer, Inc.). The model is based on principal component analysis. Three classes were defined: a) pure vanilla samples, b) CMR-adulterated samples, and c) EVA-adulterated samples. A total of thirty spectra for the calibration set and ten spectra for the validation set were considered for each class. A total of 120 spectra were analyzed. Different pretreatments were tested such as smoothing, ambient filters (CO2 y H2O), baseline correction, and normalization using multiplicative scatter correction (MSC) and standard normal variate (SNV). Performance of the SIMCA model was evaluated as the interclass distance between the three classes, which is an indicator of similarity among classes, and it should be greater than 3. Moreover, with the recognition rate (sensitivity) and rejection rate (specificity), a value of 100% describes a model that differentiates the classes correctly. Validation was evaluated base on the following statistics: (a) total distance ratio, which value must be less than 1, and (b) residual distance, which must be less than 3 [23–25].
2. Materials and methods 2.1. Reagents Vanillin (≥99%), coumarin (≥99%), and ethyl vanillin (≥98%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). All chemicals were HPLC grade.
2.6. Quantification model
2.2. Vanilla pure extracts and characterization
For the calibration, models were constructed as a matrix that mathematically relates the different concentrations of adulterants EVA or CMR in the pure samples with their MID-FTIR spectra through partial least squares with single y-variable (PLS1), partial least squares with multiple y-variables (PLS2) and principal component regression (PCR) algorithms. Two optimized chemometric models were obtained for each algorithm. One model quantifies EVA, and the second model quantifies CMR. The information of forty samples (i.e., their MID-FTIR spectra and their EVA or CMR content) was input to the Spectrum Quant+® software, version 4.51.02 (®2000 Perkin Elmer, Inc.). For each adulterated system, thirty spectra of adulterated samples were used in the calibration set, and the validation set was analyzed with the remaining ten spectra. The model development was based on selecting the adequate spectral region, spectral pretreatments, and optimal number of factors
Vanilla pods were collected from Papantla de Olarte, Veracruz, México (20°26'52"N 97°19'12"W). The pod quality was determined by the color and odor according to the Mexican Standard for vanilla [19], and humidity was calculated using a thermobalance (Ohaus, MB45, New Jersey) by analyzing 0.5 g at 50 °C for 60 min [20]. The pure vanilla extracts (1-fold) were obtained from 380 g of beneficiated vanilla pods mixed with 3.78 L of ethanol-water at 65% and incubated for three months in darkness. The pure extracts were then filtered. The quantification of vanillin in the pure extracts was according to spectrophotometric methods 19.011–19.012 of AOAC [21] through the use of a vanillin calibration curve and reading at 348 nm. The corresponding lineal regression equation was as follows: Vanillin [mg/mL] = (ABS − 0.0237) / 137.24, R2 = 0.999. The pure vanilla extracts were obtained once to develop the calibration models. 265
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
or principal components. The spectra pretreatments included smoothing using the Savitzky-Golay algorithm, first and second derivative, and baseline correction (offset), and they were normalized using SNV or MSC. The optimum number of factors, or principal components, was set to the lowest number of factors that provided the closest to minimum standard error in calibration (SEC). The election of the more accurate and reliable algorithm for the quantification of the concentrations of EVA and CMR was based on the following: (a) coefficient of determination of calibration (Rc2) for predicted versus actual values, which should be as close to 1, and (b) the SEC, which should be as low as possible. The prediction accuracy of the models was assessed using (a) the coefficient of determination of validation (Rv2), (b) the standard error of prediction (SEP), which should be as low as possible, (c) the Mahalanobis total distance, which must be less than 1, and (d) the residual ratio, which must be less than 3 [24,25]. Additionally, the relative difference percentage (%RD) values were estimated for each parameter with the following formula:
O-H stretching vibrations from alcohol were in the region of 3670–3580 cm−1, and phenols vibrate between 3620 and 3530 cm−1 [28]. According to Kus et al. [29], some bands between 3051 and 3099 cm−1 were due to C-H stretching vibrations of benzene rings. The bands between 3000 and 2860 cm−1 are due to methyl (-CH3) and methylene (-CH2-) asymmetric and symmetric stretching of aldehydic hydrogen [18,28] from many compounds present in vanilla such as vanillin or p-hydroxybenzaldehyde. The bands between 1700 and 1600 correspond to C˭C aromatic stretching vibrations and combinations of C˭O and C˭C stretching vibrations in pyrones [28]. Moreover, the increase in the concentration of CMR was identified in a specific region between 1600 and 1500 cm−1, with the bands corresponding to aromatic ring C-C stretching and bending vibrations [3]. Additionally, the bands at 1088 and 1045 cm−1 corresponding to C-O-C and C-O stretching vibrations are attributed to ethers and alcohols [18], and the peak at 826 cm−1 corresponds to bending vibrations of the pyrone ring [29]. Fig. 1b depicts the spectra of pure vanilla and EVA-adulterated vanilla; Fig. 1a and b share some bands, but the main differences were obtained in the region between 1700 and 1100 cm−1. The band observed at 1700–1620 cm−1 corresponds to C˭O stretching vibrations belonging to aromatic aldehydes, ketones or carboxylic acids, such as acetic acid and hexanoic acid [18]; the latter two compounds are usually present at high concentrations in Mexican vanilla pods [30]. Moreover, the 1700–1620 cm−1 band overlapped with C˭C stretching vibrations from aromatic compounds [28]. The adulterated vanilla spectrum showed changes in this band. The bands between 1700 and 1520 cm−1 correspond to C-C stretching or bending vibrations in benzene rings from compounds of vanillin, anisyl alcohol, isovaleric acid, or guaiacol [3], which are important flavor compounds for the overall vanilla aroma profile [30]. The peaks observed at 1590 and 1515 cm−1 are present only in the adulterated vanilla spectrum. The bands in the 1460–1383 cm−1 region are due to the O-H in-plane and out-of-plane deformation of alcohols and O-H out-of-plane deformation of phenols [28]. The peaks at 1300–1250 cm−1 correspond to vibrations of aryl aldehydes [28]. The adulterated vanilla spectrum showed an increase in the intensity of these bands. Finally, the bands between 1200 and 1135 cm−1 correspond to ester C-O-C stretching vibrations and rocking vibrations of the methyl and ethoxy groups [28].
%RD = (Actual value–Predicted value)/(Actual value) × 100. 2.7. HPLC-DAD reference method The quantification of EVA and CMR adulterants was performed using high-performance liquid chromatography (HPLC) according to a method based on Huesgen [12] and Margraf [26] using a PerkinElmer HPLC chromatograph, model Flexar (PerkinElmer, Inc.) comprising a diode array detector (DAD), autosampler, quaternary pump, and vacuum degasser. The separations were performed on a Hypersil™ BDS C18 (100 mm × 4.0 mm, 3.0 µm) column (Waters Corporation, Massachusetts, USA); 1 μL of sample or standard was injected, and the oven temperature was 30 °C for EVA and 40 °C for CMR. The samples were detected at 230, 260 and 280 nm; however, the maximum absorption was at 230 nm for EVA and 280 nm for CMR. Acetonitrile and acid water (pH 2.3) were used as the mobile phase under the elution gradient shown in Supplementary Table 1S. Nine EVA-adulterated samples (dilution 1:100 v/v) and nine CMRadulterated samples (without dilution) were analyzed in triplicate and the peak area reported. The identity of the peaks in the standards and samples were confirmed by the UV spectra, which were similar to the standards reported by Huesgen [12]. The standards’ retention times were 4.462, 4.783, and 5.007 min for vanillin, EVA, and CMR, respectively. The concentration of adulterant was calculated using calibration curves for EVA (0.2–l.0 mg mL–1) and CMR (0.01–0.1 mg mL–1) in ethanol. EVA = (Area + 186,065) / 1 × 107, R2 = 0.9894; and CMR = (Area − 3787.1) / 9 × 106, R2 = 0.9994.
3.3. Discrimination model In the SIMCA model, the spectral region between 3000 and 1100 cm−1 showed adequate spectral variability to discriminate the samples into their respective classes. The selected spectral pretreatments were multiplicative scatter correction (MSC), offset and the Savitzky-Golay filter with five smoothing points. The pretreatments improve the relationship between the spectral signals and type of classes. Fig. 2a shows part of the fingerprint region used, where the spectral changes were more evident. The interclass distance between the pure and EVA-adulterated extracts was 65.6, which was due to the significant difference between the two spectra. On the other hand, due to the spectral similarity between the pure vanilla extract and CMR-adulterated spectra, the interclass distance was 4.28. The larger the interclass distance, the better the separation was between groups. According to He, Rodríguez-Saona, and Gusti [31], a class distance greater than 3 indicates an absence of overlap between the classes. The analysis of the SIMCA model performance was done by the recognition and rejection rates. Rates of 100% were obtained for the three classes. Thus, the model classified samples in the correct category and properly rejected the foreign samples. Fig. 2b presents the 3D principal components scores plot generated by the model. This figure shows the excellent separation between the hyperboxes of adulterated samples and pure vanilla samples. The ellipse that encloses the clusters describes the 99% confidence interval.
3. Results and discussion 3.1. Quality of vanilla pods and vanilla extracts The vanilla pods used to obtain the extracts were classified as Gourmet II since the color ranged between 1545M “solid coated” and 438 EC “solid to process Euro.” A woody sweet smell was identified in the olfactory test. The moisture obtained in the vanilla pods was 4.31 ± 0.12%. The content of vanillin in the obtained vanilla extracts was 2.01 mg vanillin/mL extract (1-fold), which is in the range established by official Mexican methods [27]. 3.2. MID-FTIR spectra analysis The CMR-adulterated vanilla spectrum showed very similar peaks to the pure vanilla spectrum. In Fig. 1a, the pure vanilla and CMR-adulterated spectra are presented in the region of 3800–800 cm−1. The broad and strong band of 3650–3050 cm−1 is due to overlapping of O-H peaks belonging to water, ethanol or phenols in the vanilla extract. The 266
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
Fig. 1. MID-FTIR spectra for (a) pure vanilla ( ) and CMR-adulterated vanilla ( ) extracts, and (b) pure vanilla ( ) and EVAadulterated vanilla ( ) extracts.
In the external validation, ten samples of each class (pure, CMRadulterated, and EVA-adulterated) were correctly identified due to the total distance ratio ranging from 0.5292 to 0.9070, the value of which must be less than 1.0 for a sample to be classified. Moreover, the residual distance ranged from 0.8215 to 1.4810, confirming the samples’ spectral variations were successfully modeled since the value was less than 3.
phenolic compounds. Table 1 shows the statistics for the six developed models, as well as the pretreatments used in the models. These pretreatments are useful to minimize unwanted contributions in the analytical signal that interfere in the construction of the model in addition to systematic errors during the procedure. An Rc2 value greater than 0.9 suggests excellent quantitative information [32]. Additionally, low SEC and SEP values indicate low error in the regression since they have the same unit as the actual value. According to Table 1, for the EVA prediction models, the models based on PLS2 showed higher Rc2, lower SEC and SEP values than PLS1 or PCR. However, PLS1 was selected as the better model since fewer factors were required, and the %RD values between the reference and predicted values were slightly less for PLS1 (0.11–12.0%) than PLS2
3.4. Quantification models The development of the optimized EVA prediction model was based on the 1700–1110 cm−1 region. For the CMR prediction, the regions between 3175 and 2800 cm−1 and 1800–850 cm−1 were selected for the construction of the model. The selected regions are related to
Fig. 2. SIMCA model results. (a) Pre-treated MID-FTIR spectra of pure vanilla (green), CMR-adulterated vanilla extracts (ocher), and EVA-adulterated vanilla extracts (blue) in the fingerprint region. (b) 3D distribution of pure vanilla (PURE), EVA and CMR samples obtained with 3 principal components in the SIMCA model. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article)
267
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
Table 1 Calibration results for optimized models, based on MID-FTIR spectroscopy, to quantify the adulterants ethyl vanillin (EVA) and coumarin (CMR). Adulterant
Algorithm
Pre-treatments
Factorsa
Rc2b
SECc
SEPd
EVA
PLS1 PLS2 PCR PLS1 PLS2 PCR
MSC and first derivative with 9 smoothing points SNV and first derivative with 9 smoothing points SNV and second derivative with 13 smoothing points MSC and offset MSC and offset Offset
3 4 3 8 9 6
0.9992 0.9995 0.9992 0.9963 0.9476 0.9847
0.0837 0.0750 0.0861 0.2146 0.7896 0.4162
0.0883 0.0795 0.0923 0.2427 0.9200 0.4882
CMR
a b c d
Optimum number of factors (PLS1 and PLS2) or optimum number of principal components (PCR). Rc2, coefficient of determination of calibration, should be close to 1. SEC, standard error of calibration, should be as low as possible. SEP, standard error of prediction, should be as low as possible; MSC, multiplicative scatter correction; SNV, standard normal variate.
(0.89–12.8%). For the CMR prediction models, the PLS1 model was better due to high Rc2 and low SEC and SEP values with the least number of factors. Overall, the CMR models required a higher number of factors or principal components for calibrations than the EVA models as the CMRadulterated samples showed subtle spectral differences with respect to pure vanilla samples, and the concentrations evaluated were on the order of ppm. The robustness of the PLS1 models was investigated with the prediction of ten new samples for each model. According to the scatter plots of predicted and actual values (Supplementary Figs. 1Sa and 1Sb), good performance of the models was achieved for EVA and CMR since the Rv2 and slopes were close to 1. The Mahalanobis total distance and residual ratios for EVA ranged from 0.1561 to 0.8495 and 0.4598–2.355, respectively, while those from CMR ranged from 0.2305 to 0.9371 and 0.3906–2.866. The Mahalanobis total distance should be less than 1 to guarantee the spectral similarities among calibration and external validation samples. The residual ratio value greater than 3 suggests that the residual spectrum from external validation samples contained features suitably described and modeled during calibration [24,25,33].
Table 2 Comparative results of adulterated samples quantify by HPLC-DAD and PLS1 methods. Adulterant
EVA (%)
R2 Slope CMR (ppm)
3.5. Limit of quantification of the chemometric model R2 Slope
For the PLS1 models, the limit of quantification (LOQ) was obtained by an external validation system of four samples whose values were less than 0.25% EVA or less than 0.25 ppm CMR. This validation set was predicted by the chemometric models to identify the minimum values that models can be able to predict. Moreover, the Mahalanobis total distance and residual ratio parameters were analyzed, if these parameters are adequate, it is expected that the predictions of the model are reliable. The results are presented in Supplementary Table 2S. The LOQ was 0.20% for EVA and 0.1 ppm for CMR. Thus, the CMR model is able to quantify the tolerable daily intake of 0.1 mg/kg body [9].
a
Actual value
HPLC-DAD
0.5 0.75 1.0 2.0 3.0 4.0 5.0 6.0 7.0
0.46 0.72 0.94 1.88 2.87 4.18 4.78 6.26 6.97 0.997 1.019 1.29 2.4 3.58 4.92 5.75 6.4 7.57 8.22 9.28 0.993 0.981
1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5
Predicted value
PLS1 %RDa −8.00 −4.00 −6.00 −6.00 −4.33 4.50 −4.40 4.33 −0.43 −14.00 −4.00 2.29 9.33 4.55 −1.54 0.93 −3.29 −2.32
Predicted value
%RDa
0.49 0.72 0.90 1.96 3.03 4.08 5.26 6.14 7.02 0.999 1.029 1.27 2.21 2.92 4.02 5.05 6.15 7.63 8.44 9.37 0.996 1.044
−2.00 −4.00 −10.00 −2.00 1.00 2.00 5.20 2.33 0.29 −15.33 −11.60 −16.57 −10.67 −8.18 −5.38 1.73 −0.71 −1.37
%RD, percentage relative difference.
Mexico City that were marketed by Mexican producers as “Pure 100% vanilla extract”. Two out of six samples were identified as pure vanilla by the SIMCA model, but none tested positive for EVA or CMR. Therefore, the PLS1 model cannot be applied to these commercial samples since they show adulterants different from those that the developed models can predict, i.e., EVA and CMR. Additional studies are needed to analyze others adulterant systems in vanilla. Supplementary Table 3S shows the statistical results of SIMCA model for commercial samples.
3.6. Comparison of the PLS1 chemometric model and HPLC-DAD method The EVA and CMR content from eighteen adulterated samples was quantified by HPLC-DAD and the PLS1 chemometric model. Table 2 shows the %RD values for both methods with respect to actual values. Moreover, the scatter plot of predicted versus actual values for the PLS1 model and HPLC-DAD method were linear and had slopes close to 1 (Table 2). This result suggests that the developed PLS1 model is able to predict and quantify adulterations of EVA or CMR in vanilla extracts in the same way as the HPLC-DAD method.
4. Conclusions The results show that MID-FTIR spectroscopy can be successfully applied for prediction of adulteration of pure vanilla extracts. The SIMCA model was able to efficiently identify and classify pure vanilla samples from those adulterated with EVA or CMR. Similarly, the chemometric model based on the PLS1 algorithm was able to quantify EVA and CMR in the range of 0.20–10% and 0.1–10 ppm, respectively. The EVA predictions were better than CMR since CMR adulteration was more difficult to identify because of the spectral similarity among CMRadulterated vanilla and vanilla samples. The analysis of new adulterated samples showed no significant difference between the PLS1 method and HPLC-DAD method. The chemometric model offers
3.7. Application of chemometric models After successful validation of the SIMCA and PLS1 models, six 1-fold commercial samples of vanilla were analyzed by the models to prove applicability. The samples were acquired in commercial stores in 268
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
advantages of being faster and easier to execute than HPLC. The SIMCA model showed its applicability with the analysis of commercial samples. The results revealed that some manufacturers use fraudulent labeling since the label states pure vanilla product when it is not. Further developments of chemometric models should include vanilla samples from other origins in order to obtain an analytical method that predicts adulteration in different types of vanilla extracts. Nevertheless, the analytical approach may help design regulations for strict quality control, because this technique is simple, fast, sensitive and perfect for laboratories that demand immediate results.
[11] B.K. Lavine, D.T. Corona, U.D.N.T. Perera, Analysis of vanilla extract by reversed phase liquid chromatography using water rich mobile phases, Microchem J. 103 (2012) 49–61, https://doi.org/10.1016/j.microc.2012.01.004. [12] A.G. Huesgen, Analysis of natural and artificial vanilla preparations, Application Note, Agilent Technologies, Inc, Waldbronn, Germany, 2011. [13] C.M. Ng, W.M. Reuter, Analysis of vanillin, ethyl vanillin and coumarin in vanilla extract products by UHPLC with PDA detection, Application Note, PerkinElmer, Inc, CT, USA, 2015. [14] L.S. De Jager, G.A. Perfetti, G.W. Diachenko, Determination of coumarin, vanillin, and ethyl vanillin in vanilla extract products: liquid chromatography mass spectrometry method development and validation studies, J. Chromatogr. A 1145 (1–2) (2007) 83–88, https://doi.org/10.1016/j.chroma.2007.01.039. [15] C. Cordella, I. Moussa, A.C. Martel, N. Sbirrazzuoli, L. Lizzani-Cuvelier, Recent developments in food characterization and adulteration detection: technique-oriented perspectives, J. Agr. Food Chem. 50 (2002) 1751–1764, https://doi.org/10. 1021/jf011096z. [16] A. Dalmia, Rapid detection of vanilla bean extract adulteration with tonka bean extracts with no chromatography or sample preparation, Aplication note, PerkinElmer, Inc, CT, USA, 2013. [17] A. Dalmia, G.L. Perkins, C. Whitehouse, Rapid Differentiation between natural and artificial vanilla flavorings for determining food fraud using AxION DSA/TOF with no chromatography or sample preparation, Aplication note, PerkinElmer, Inc, USA, 2013. [18] P. Aljaff, B.O. Rasheed, T.A. Omer, A comparison between Natural and synthetic food flavoring extracts using infrared spectra and optical activity, J. Appl. Phys. (2013) 01–06. [19] NMX-FF-074-SCFI-2009 Mexican standard. Productos no industrializados para uso humano- vainilla- (Vanilla fragans (Salisbury) Ames) –Especificaciones y métodos de prueba. 〈http://dof.gob.mx/nota_detalle.php?Codigo=5119518&fecha=13/ 11/2009〉, (accessed 20 January 2018). [20] E. Odoux, S. Languille, J.P. Leblé, A. Lombard, The effect of the nature of the soil amendments on the main aromatic compounds in Vanilla fragans beans, Fruits 55 (2000) 63–71. [21] AOAC, Association of Official Analytical Chemists, AOAC 19.011-19.012, Vanillin in vanilla extract: rapid approximate method spectrophotometric method, Official Methods of Aanalysis of A.O.A.C, 17th ed., Association of Official Analytical Chemists, Gaithersburg, MD, 2000. [22] R.S. Singhal, P.R. Kulkarni, D.V. Rege, Handbook of Indices of Food Quatlity and Authenticity, Woodhead Publishing Ltd, Cambridge, England, 1997. [23] PerkinElmer, Assure ID tutorial. Version 4.0.2.0175, PerkinElmer, Inc, 2007. [24] N. Quiñones-Islas, O.G. Meza-Márquez, G. Osorio-Revilla, T. Gallardo-Velazquez, Detection of adulterants in avocado oil by Mid-FTIR spectroscopy and multivariate analysis, Food Res. Int. 51 (1) (2013) 148–154, https://doi.org/10.1016/j.foodres. 2012.11.037. [25] P. Jiménez-Sotelo, M. Hernández-Martínez, G. Osorio-Revilla, O.G. Meza-Márquez, F. García-Ochoa, T. Gallardo-Velázquez, Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils, Food Addit. Contam. Part A Chem. Anal. Control Expo. Risk Assess. 33 (7) (2016) 1105–1115. [26] M. Margraf, Comparision of compounds in Bourbon Vanilla extract and vanilla flavour, The Application Notebook, Knauer, Inc, Berlin, Germany, 2015. [27] NOM-182-SCFI-2011 Mexican standard. Vainilla de Papantla, extractos y derivadosespecificaciones, información comercial y métodos de ensayo (prueba). 〈http:// www.dof.gob.mx/normasOficiales/4477/seeco/seeco.htm〉, (accessed 20 January 2018). [28] G. Socrates, Infrared and Raman Characteristic Groups Frequencies, Tables and Charts, John Wiley and Sons, Chichester, 2001. [29] N. Kus, S. Breda, I. Reva, E. Tasal, C. Ogretir, R. Fausto, FTIR spectroscopic and theoretical study of the photochemistry of matrix-isolated coumarin, Photochem. Biol. 83 (2007) 1237–1253, https://doi.org/10.1111/j.1751-1097.2007.00152.x. [30] D. Havkin-Frenkel, F.C. Belanger, Handbook of Vanilla Science and Technology, Wiley-Blackwell, UK, 2011. [31] J. He, L.E. Rodríguez-Saona, M.M. Gusti, Mid infrared spectroscopy for juice authentication-rapid differentiation of commercial juices, J. Agr. Food Chem. 55 (11) (2007) 4443–4452, https://doi.org/10.1021/jf062715c. [32] J.S. Shenk, M.O. Westerhaus, Calibration the ISI way, in: A.M.C. Davies, P.C. Williams (Eds.), Near Infrared Spectroscopy. NIR Publications, Chichester, UK, 1996. [33] PerkinElmer, Spectrum Quant+ tutorial. Version 4.51.02, PerkinElmer, Inc, 2000.
Acknowledgements The authors welcome the financial support from the Consejo Nacional de Ciencia y Tecnología (CONACyT, Mexico) (grant: 702208) and the Instituto Politécnico Nacional through its Secretaría de Investigación y Posgrado (SIP-IPN, Mexico) (projects: 20161077, 20171634). Conflict of interest The authors declare that they have no conflict of interest on this work. Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.talanta.2019.01.033. References [1] K. Anuradha, B. Shyamala, M. Naidu, Vanilla-its science of cultivation, curing, chemistry, and nutraceutical propertiaes, Crit. Rev. Food Sci. Nutr. 53 (12) (2013) 1250–1276, https://doi.org/10.1080/10408398.2011.563879. [2] FAOSTAT, Vanilla producer prices statistics, 2014–2016 period, 2016. 〈http:// www.fao.org/faostat/en/#data/PP〉, (accessed 15 January 2018). [3] M.D. Sharp, N.A. Kocaoglu-Vurma, V. Langford, L.E. Rodriguez-Saona, W.J. Harper, Rapid discrimination and characterization of vanilla bean extracts by attenuated total reflection infrared spectroscopy and selected ion flow tube mass spectrometry, J. Food Sci. 77 (3) (2012) C284–C292, https://doi.org/10.1111/j.1750-3841.2011. 02544.x. [4] E. Odoux, J. Escoute, J.L. Verdeil, J.M. Brillouet, Localization of ß-D- glucosidase activity and glucovanillin in vanilla beans (Vanilla planifora Andrews), Ann. Bot. 92 (3) (2003) 437–444, https://doi.org/10.1093/aob/mcg150. [5] H. Korthou, R. Velpoorte, Vanilla, in: R.G. Berger (Ed.), Flavours and Fragances, Springer International Publishing, Berlin, 2007, pp. 2003–2017 (AG). [6] U. Schweiggert, R. Carle, A. Schieber, Conventional and alternative processes for spice production: a review, Trends Food Sci. Technol. 18 (2007) 260–268, https:// doi.org/10.1016/j.tifs.2007.01.005. [7] J.D. Bythrow, Vanilla as a medicinal plant, Semin. Integr. Med. 3 (2005) 129–131, https://doi.org/10.1016/j.sigm.2006.03.001. [8] C.C. De Guzman, R.R. Zara, Vanilla, in: K.V. Peter (Ed.), Handbook of herbs and spices, second edition, Woodhead Publishing Ltd, UK, 2012, pp. 547–589. [9] EFSA, Scientific opinion of the panel on food additives, flavourings, processing aids and materials in contact with food on a request from the European Commission on coumarin in flavourings and other food ingredients with flavouring properties. question No. EFSA-Q-2008-677, EFSA J. 793 (2008) 1–15. [10] T. Jenkins, M. Waite, Screening of commercial vanilla extract for authenticity using the Breeze 2 Modular HPLC System, Application note, Waters, Corp, MA, USA, 2008.
269
Contents lists available at ScienceDirect
Talanta journal homepage: www.elsevier.com/locate/talanta
Prediction of coumarin and ethyl vanillin in pure vanilla extracts using MIDFTIR spectroscopy and chemometrics
T
Cristina Montserrat Moreno-Leya, Diana Maylet Hernández-Martíneza, Guillermo Osorio-Revillab, ⁎ Adriana Patricia Tapia-Ochoateguib, Gloria Dávila-Ortizb, Tzayhri Gallardo-Velázqueza, a
Departamento de Biofísica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Lázaro Cárdenas, Prolongación de Carpio y Plan de Ayala S/N, Col. Santo Tomás, CP.11340 Ciudad de México, Mexico b Departamento de Ingeniería Bioquímica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Adolfo López Mateos, Av. Wilfrido Massieu Esq. Cda. Miguel Stampa s/n, C.P.07738 Ciudad de México, Mexico
ARTICLE INFO
ABSTRACT
Keywords: Vanilla extracts Coumarin Ethyl vanillin Adulteration Chemometrics MID-FTIR
Fourier transform mid-infrared (MID-FTIR) spectroscopy coupled with chemometric analysis was used to identify and quantify coumarin (CMR) and ethyl vanillin (EVA) adulterations in pure vanilla extracts. Forty samples adulterated with CMR (0.25–10 ppm) and forty with EVA (0.25–10%) were prepared from pure vanilla extracts and characterized by MID-FTIR spectroscopy to develop chemometric models. Additionally, six commercial vanilla samples were analyzed. A soft independent modeling of class analogy (SIMCA) model was developed to identify and classify the purity from EVA-adulterated or CMR-adulterated samples. Prediction models for CMR or EVA content were developed using the principal component regression (PCR), partial least squares with single y-variables (PLS1), and with multiple y-variables (PLS2) algorithms. Moreover, the predictions of the best quantification chemometric model were compared with the results of a high-performance liquid chromatography-diode array detector (HPLC-DAD) method to evaluate the accuracy of the prediction. The PLS1 algorithm had better performance using 3 and 8 factors for EVA and CMR, respectively. The SIMCA model showed 100% recognition and rejections rates. The results demonstrate that adulteration of pure vanilla with EVA and CMR could be successfully predicted by the developed technique.
1. Introduction The vanilla plant is a member of the family Orchidaceae and is native to México. Natural vanilla is a complex mixture of flavor components extracted mainly from the cured pods of the beans of Vanilla planifolia Andrews, Vanilla pompona Schiede, and Vanilla tahitensis J. W. Moore; V. planifolia is the most valued [1]. The major vanilla-producing countries are Indonesia, Madagascar, China, Papua New Guinea, and Mexico [2]. The special flavor and aroma of vanilla are due to more than 200 compounds, with vanillin (4-hydroxy-3-methoxybenzaldehyde) by far the most abundant [3]. The characteristic vanilla aroma is only released after the fermentation of green vanilla beans, called “curing,” where the glucoside of the vanillin, glucovanillin, is hydrolyzed by the enzyme βD-glucosidase [4]. Vanilla requires particular growth conditions, and the green beans require pollination by specialized insects, which means that in most places, pollination is performed by hand using a small, thin stick [5].
⁎
Additionally, the harvest occurs two to three years after planting. Due to the intensive processing procedures, natural vanilla extracts and vanilla pods are the second most expensive spice in the world, just behind saffron [6]. The price of natural vanilla ranges between $ 5262 USD/ton and $ 6528 USD/ton [2]. Vanilla is the world's most popular flavor, and it is used in innumerable food products, such as ice cream, bakery goods, cola-type drinks, and chocolates, as well as by fragrance and pharmaceutical manufacturing companies. Due to the massive consumption of vanillaflavored products, limited quantities of vanilla beans, and the high cost of vanilla production, the flavor is obtained synthetically to satisfy demand. Consequently, vanilla obtained through chemical processes is less expensive than natural vanilla extracts [7,8]. Furthermore, natural vanilla extracts have been adulterated with ethyl vanillin, synthetic vanillin or coumarin as less expensive supplies. As a result, the adulteration of spices, such as vanilla, has been a severe worldwide issue resulting in quality deterioration affecting the market and creating possible human health risks.
Corresponding author. E-mail address: [email protected] (T. Gallardo-Velázquez).
https://doi.org/10.1016/j.talanta.2019.01.033 Received 29 September 2018; Received in revised form 5 January 2019; Accepted 7 January 2019 Available online 09 January 2019 0039-9140/ © 2019 Elsevier B.V. All rights reserved.
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
Ethyl vanillin (EVA), or 3-ethoxy 4-hydroxybenzaldehyde, is synthetically produced from safrole and is approximately three to four times more potent as a flavoring agent than vanilla. As a result, the adulteration of natural vanilla extract can reach a maximum 10% EVA without objectionable notes [8]. Although EVA has not shown toxicity to humans when it is added to natural vanilla extracts, the label "imitation, artificial or synthetic product” must be added. If not properly label, it is considered food fraud. On the other hand, the presence of coumarin (CMR), or 1,2-benzopyrone, in natural vanilla extracts is attributed to adulteration with the tonka bean, Dipteryx odorata (Aubl.) Willd (Fabaceae family). The use of CMR has been limited after the discovery of its hepatoxicity and carcinogenic effects in experimental animals, though no genotoxic activity has been concluded [9]. The growing interest in natural products is a global trend. Therefore, to ensure the quality and authenticity of vanilla extracts, reliable techniques that verify the presence of adulterants are of interest. Analytical techniques for identification and quantification of EVA and CMR as adulterants in vanilla extracts have been reported, for example, high-performance liquid chromatography [10,11], ultrahighperformance liquid chromatography [12,13], liquid chromatography mass spectrometry [11,14], stable isotope ratio analysis mass spectrometry [15], and time-of-flight mass spectrometry [16,17]. Moreover, flow tube mass spectrometry has been used to quantify aromatic profiles of vanilla extracts such as EVA [3]. The latest techniques are sensitive and precise and have fast performance. Nevertheless, these techniques are expensive or require a long time for sample preparation. Infrared spectroscopy is an analytical technique that contributes to the easy and rapid analysis of vanilla extracts, and it does not require the use of either reagents or sample pretreatments. However, the quantification of adulterants by Fourier transform mid-infrared (MID-FTIR) spectroscopy has not been reported, and it has only been used to differentiate artificial and natural vanilla extracts [18] or to provide differentiation of vanilla extracts from different countries [3]. The aim of this study was to obtain chemometric models based on MID-FTIR spectroscopy to identify and quantify CMR and EVA as adulterants of pure vanilla extracts. To confirm the validity of the developed technique, a comparison between the best quantification chemometric model and HPLC-DAD method was conducted, and the classification chemometric model was applied to commercial vanilla extracts.
2.3. Preparation of adulterated samples The pure extracts were adulterated with EVA or CMR in a binary mixture. Forty pure extracts were adulterated with EVA in concentrations ranging from 0.25% to 10% (w/v) in increments of 0.25%. This range of adulteration was selected based on Singhal, Kulkarni, and Rege [22], who reported that EVA is added to the vanilla extracts at a maximum of 10% w/v; a higher dosage can produce a somewhat harsh ‘chemical’ character that is easily detected. Forty pure extracts were adulterated with CMR in concentrations ranging from 0.25 to 10 ppm in increments of 0.25 ppm. This range was selected based on the tolerable daily intake of 0.1 mg/kg body weight established by EFSA [9]. The samples were stored in amber bottles in a dark, cool place. 2.4. MID-FTIR spectra acquisition The spectra of unadulterated, CMR-adulterated and EVA-adulterated vanilla extracts, as well as commercial samples were collected in triplicate using a Fourier transform mid-infrared (MID-FTIR) spectrometer (Spectrum GX, Perkin Elmer Massachusetts, USA) over the range of 4000–550 cm−1 at a resolution of 4 cm−1 with a total of 64 scans. Two milliliters of extract spread onto the ATR crystal of zinc selenide was analyzed. A background air spectrum was scanned as a blank reference using the same instrumental conditions. 2.5. Discrimination model A discrimination model (SIMCA model) was obtained to identify pure extracts from adulterated samples. The soft independent modeling of class analogy (SIMCA) model was achieved using the Assure ID software, version 4.0.2.0175 (®2007 PerkinElmer, Inc.). The model is based on principal component analysis. Three classes were defined: a) pure vanilla samples, b) CMR-adulterated samples, and c) EVA-adulterated samples. A total of thirty spectra for the calibration set and ten spectra for the validation set were considered for each class. A total of 120 spectra were analyzed. Different pretreatments were tested such as smoothing, ambient filters (CO2 y H2O), baseline correction, and normalization using multiplicative scatter correction (MSC) and standard normal variate (SNV). Performance of the SIMCA model was evaluated as the interclass distance between the three classes, which is an indicator of similarity among classes, and it should be greater than 3. Moreover, with the recognition rate (sensitivity) and rejection rate (specificity), a value of 100% describes a model that differentiates the classes correctly. Validation was evaluated base on the following statistics: (a) total distance ratio, which value must be less than 1, and (b) residual distance, which must be less than 3 [23–25].
2. Materials and methods 2.1. Reagents Vanillin (≥99%), coumarin (≥99%), and ethyl vanillin (≥98%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). All chemicals were HPLC grade.
2.6. Quantification model
2.2. Vanilla pure extracts and characterization
For the calibration, models were constructed as a matrix that mathematically relates the different concentrations of adulterants EVA or CMR in the pure samples with their MID-FTIR spectra through partial least squares with single y-variable (PLS1), partial least squares with multiple y-variables (PLS2) and principal component regression (PCR) algorithms. Two optimized chemometric models were obtained for each algorithm. One model quantifies EVA, and the second model quantifies CMR. The information of forty samples (i.e., their MID-FTIR spectra and their EVA or CMR content) was input to the Spectrum Quant+® software, version 4.51.02 (®2000 Perkin Elmer, Inc.). For each adulterated system, thirty spectra of adulterated samples were used in the calibration set, and the validation set was analyzed with the remaining ten spectra. The model development was based on selecting the adequate spectral region, spectral pretreatments, and optimal number of factors
Vanilla pods were collected from Papantla de Olarte, Veracruz, México (20°26'52"N 97°19'12"W). The pod quality was determined by the color and odor according to the Mexican Standard for vanilla [19], and humidity was calculated using a thermobalance (Ohaus, MB45, New Jersey) by analyzing 0.5 g at 50 °C for 60 min [20]. The pure vanilla extracts (1-fold) were obtained from 380 g of beneficiated vanilla pods mixed with 3.78 L of ethanol-water at 65% and incubated for three months in darkness. The pure extracts were then filtered. The quantification of vanillin in the pure extracts was according to spectrophotometric methods 19.011–19.012 of AOAC [21] through the use of a vanillin calibration curve and reading at 348 nm. The corresponding lineal regression equation was as follows: Vanillin [mg/mL] = (ABS − 0.0237) / 137.24, R2 = 0.999. The pure vanilla extracts were obtained once to develop the calibration models. 265
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
or principal components. The spectra pretreatments included smoothing using the Savitzky-Golay algorithm, first and second derivative, and baseline correction (offset), and they were normalized using SNV or MSC. The optimum number of factors, or principal components, was set to the lowest number of factors that provided the closest to minimum standard error in calibration (SEC). The election of the more accurate and reliable algorithm for the quantification of the concentrations of EVA and CMR was based on the following: (a) coefficient of determination of calibration (Rc2) for predicted versus actual values, which should be as close to 1, and (b) the SEC, which should be as low as possible. The prediction accuracy of the models was assessed using (a) the coefficient of determination of validation (Rv2), (b) the standard error of prediction (SEP), which should be as low as possible, (c) the Mahalanobis total distance, which must be less than 1, and (d) the residual ratio, which must be less than 3 [24,25]. Additionally, the relative difference percentage (%RD) values were estimated for each parameter with the following formula:
O-H stretching vibrations from alcohol were in the region of 3670–3580 cm−1, and phenols vibrate between 3620 and 3530 cm−1 [28]. According to Kus et al. [29], some bands between 3051 and 3099 cm−1 were due to C-H stretching vibrations of benzene rings. The bands between 3000 and 2860 cm−1 are due to methyl (-CH3) and methylene (-CH2-) asymmetric and symmetric stretching of aldehydic hydrogen [18,28] from many compounds present in vanilla such as vanillin or p-hydroxybenzaldehyde. The bands between 1700 and 1600 correspond to C˭C aromatic stretching vibrations and combinations of C˭O and C˭C stretching vibrations in pyrones [28]. Moreover, the increase in the concentration of CMR was identified in a specific region between 1600 and 1500 cm−1, with the bands corresponding to aromatic ring C-C stretching and bending vibrations [3]. Additionally, the bands at 1088 and 1045 cm−1 corresponding to C-O-C and C-O stretching vibrations are attributed to ethers and alcohols [18], and the peak at 826 cm−1 corresponds to bending vibrations of the pyrone ring [29]. Fig. 1b depicts the spectra of pure vanilla and EVA-adulterated vanilla; Fig. 1a and b share some bands, but the main differences were obtained in the region between 1700 and 1100 cm−1. The band observed at 1700–1620 cm−1 corresponds to C˭O stretching vibrations belonging to aromatic aldehydes, ketones or carboxylic acids, such as acetic acid and hexanoic acid [18]; the latter two compounds are usually present at high concentrations in Mexican vanilla pods [30]. Moreover, the 1700–1620 cm−1 band overlapped with C˭C stretching vibrations from aromatic compounds [28]. The adulterated vanilla spectrum showed changes in this band. The bands between 1700 and 1520 cm−1 correspond to C-C stretching or bending vibrations in benzene rings from compounds of vanillin, anisyl alcohol, isovaleric acid, or guaiacol [3], which are important flavor compounds for the overall vanilla aroma profile [30]. The peaks observed at 1590 and 1515 cm−1 are present only in the adulterated vanilla spectrum. The bands in the 1460–1383 cm−1 region are due to the O-H in-plane and out-of-plane deformation of alcohols and O-H out-of-plane deformation of phenols [28]. The peaks at 1300–1250 cm−1 correspond to vibrations of aryl aldehydes [28]. The adulterated vanilla spectrum showed an increase in the intensity of these bands. Finally, the bands between 1200 and 1135 cm−1 correspond to ester C-O-C stretching vibrations and rocking vibrations of the methyl and ethoxy groups [28].
%RD = (Actual value–Predicted value)/(Actual value) × 100. 2.7. HPLC-DAD reference method The quantification of EVA and CMR adulterants was performed using high-performance liquid chromatography (HPLC) according to a method based on Huesgen [12] and Margraf [26] using a PerkinElmer HPLC chromatograph, model Flexar (PerkinElmer, Inc.) comprising a diode array detector (DAD), autosampler, quaternary pump, and vacuum degasser. The separations were performed on a Hypersil™ BDS C18 (100 mm × 4.0 mm, 3.0 µm) column (Waters Corporation, Massachusetts, USA); 1 μL of sample or standard was injected, and the oven temperature was 30 °C for EVA and 40 °C for CMR. The samples were detected at 230, 260 and 280 nm; however, the maximum absorption was at 230 nm for EVA and 280 nm for CMR. Acetonitrile and acid water (pH 2.3) were used as the mobile phase under the elution gradient shown in Supplementary Table 1S. Nine EVA-adulterated samples (dilution 1:100 v/v) and nine CMRadulterated samples (without dilution) were analyzed in triplicate and the peak area reported. The identity of the peaks in the standards and samples were confirmed by the UV spectra, which were similar to the standards reported by Huesgen [12]. The standards’ retention times were 4.462, 4.783, and 5.007 min for vanillin, EVA, and CMR, respectively. The concentration of adulterant was calculated using calibration curves for EVA (0.2–l.0 mg mL–1) and CMR (0.01–0.1 mg mL–1) in ethanol. EVA = (Area + 186,065) / 1 × 107, R2 = 0.9894; and CMR = (Area − 3787.1) / 9 × 106, R2 = 0.9994.
3.3. Discrimination model In the SIMCA model, the spectral region between 3000 and 1100 cm−1 showed adequate spectral variability to discriminate the samples into their respective classes. The selected spectral pretreatments were multiplicative scatter correction (MSC), offset and the Savitzky-Golay filter with five smoothing points. The pretreatments improve the relationship between the spectral signals and type of classes. Fig. 2a shows part of the fingerprint region used, where the spectral changes were more evident. The interclass distance between the pure and EVA-adulterated extracts was 65.6, which was due to the significant difference between the two spectra. On the other hand, due to the spectral similarity between the pure vanilla extract and CMR-adulterated spectra, the interclass distance was 4.28. The larger the interclass distance, the better the separation was between groups. According to He, Rodríguez-Saona, and Gusti [31], a class distance greater than 3 indicates an absence of overlap between the classes. The analysis of the SIMCA model performance was done by the recognition and rejection rates. Rates of 100% were obtained for the three classes. Thus, the model classified samples in the correct category and properly rejected the foreign samples. Fig. 2b presents the 3D principal components scores plot generated by the model. This figure shows the excellent separation between the hyperboxes of adulterated samples and pure vanilla samples. The ellipse that encloses the clusters describes the 99% confidence interval.
3. Results and discussion 3.1. Quality of vanilla pods and vanilla extracts The vanilla pods used to obtain the extracts were classified as Gourmet II since the color ranged between 1545M “solid coated” and 438 EC “solid to process Euro.” A woody sweet smell was identified in the olfactory test. The moisture obtained in the vanilla pods was 4.31 ± 0.12%. The content of vanillin in the obtained vanilla extracts was 2.01 mg vanillin/mL extract (1-fold), which is in the range established by official Mexican methods [27]. 3.2. MID-FTIR spectra analysis The CMR-adulterated vanilla spectrum showed very similar peaks to the pure vanilla spectrum. In Fig. 1a, the pure vanilla and CMR-adulterated spectra are presented in the region of 3800–800 cm−1. The broad and strong band of 3650–3050 cm−1 is due to overlapping of O-H peaks belonging to water, ethanol or phenols in the vanilla extract. The 266
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
Fig. 1. MID-FTIR spectra for (a) pure vanilla ( ) and CMR-adulterated vanilla ( ) extracts, and (b) pure vanilla ( ) and EVAadulterated vanilla ( ) extracts.
In the external validation, ten samples of each class (pure, CMRadulterated, and EVA-adulterated) were correctly identified due to the total distance ratio ranging from 0.5292 to 0.9070, the value of which must be less than 1.0 for a sample to be classified. Moreover, the residual distance ranged from 0.8215 to 1.4810, confirming the samples’ spectral variations were successfully modeled since the value was less than 3.
phenolic compounds. Table 1 shows the statistics for the six developed models, as well as the pretreatments used in the models. These pretreatments are useful to minimize unwanted contributions in the analytical signal that interfere in the construction of the model in addition to systematic errors during the procedure. An Rc2 value greater than 0.9 suggests excellent quantitative information [32]. Additionally, low SEC and SEP values indicate low error in the regression since they have the same unit as the actual value. According to Table 1, for the EVA prediction models, the models based on PLS2 showed higher Rc2, lower SEC and SEP values than PLS1 or PCR. However, PLS1 was selected as the better model since fewer factors were required, and the %RD values between the reference and predicted values were slightly less for PLS1 (0.11–12.0%) than PLS2
3.4. Quantification models The development of the optimized EVA prediction model was based on the 1700–1110 cm−1 region. For the CMR prediction, the regions between 3175 and 2800 cm−1 and 1800–850 cm−1 were selected for the construction of the model. The selected regions are related to
Fig. 2. SIMCA model results. (a) Pre-treated MID-FTIR spectra of pure vanilla (green), CMR-adulterated vanilla extracts (ocher), and EVA-adulterated vanilla extracts (blue) in the fingerprint region. (b) 3D distribution of pure vanilla (PURE), EVA and CMR samples obtained with 3 principal components in the SIMCA model. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article)
267
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
Table 1 Calibration results for optimized models, based on MID-FTIR spectroscopy, to quantify the adulterants ethyl vanillin (EVA) and coumarin (CMR). Adulterant
Algorithm
Pre-treatments
Factorsa
Rc2b
SECc
SEPd
EVA
PLS1 PLS2 PCR PLS1 PLS2 PCR
MSC and first derivative with 9 smoothing points SNV and first derivative with 9 smoothing points SNV and second derivative with 13 smoothing points MSC and offset MSC and offset Offset
3 4 3 8 9 6
0.9992 0.9995 0.9992 0.9963 0.9476 0.9847
0.0837 0.0750 0.0861 0.2146 0.7896 0.4162
0.0883 0.0795 0.0923 0.2427 0.9200 0.4882
CMR
a b c d
Optimum number of factors (PLS1 and PLS2) or optimum number of principal components (PCR). Rc2, coefficient of determination of calibration, should be close to 1. SEC, standard error of calibration, should be as low as possible. SEP, standard error of prediction, should be as low as possible; MSC, multiplicative scatter correction; SNV, standard normal variate.
(0.89–12.8%). For the CMR prediction models, the PLS1 model was better due to high Rc2 and low SEC and SEP values with the least number of factors. Overall, the CMR models required a higher number of factors or principal components for calibrations than the EVA models as the CMRadulterated samples showed subtle spectral differences with respect to pure vanilla samples, and the concentrations evaluated were on the order of ppm. The robustness of the PLS1 models was investigated with the prediction of ten new samples for each model. According to the scatter plots of predicted and actual values (Supplementary Figs. 1Sa and 1Sb), good performance of the models was achieved for EVA and CMR since the Rv2 and slopes were close to 1. The Mahalanobis total distance and residual ratios for EVA ranged from 0.1561 to 0.8495 and 0.4598–2.355, respectively, while those from CMR ranged from 0.2305 to 0.9371 and 0.3906–2.866. The Mahalanobis total distance should be less than 1 to guarantee the spectral similarities among calibration and external validation samples. The residual ratio value greater than 3 suggests that the residual spectrum from external validation samples contained features suitably described and modeled during calibration [24,25,33].
Table 2 Comparative results of adulterated samples quantify by HPLC-DAD and PLS1 methods. Adulterant
EVA (%)
R2 Slope CMR (ppm)
3.5. Limit of quantification of the chemometric model R2 Slope
For the PLS1 models, the limit of quantification (LOQ) was obtained by an external validation system of four samples whose values were less than 0.25% EVA or less than 0.25 ppm CMR. This validation set was predicted by the chemometric models to identify the minimum values that models can be able to predict. Moreover, the Mahalanobis total distance and residual ratio parameters were analyzed, if these parameters are adequate, it is expected that the predictions of the model are reliable. The results are presented in Supplementary Table 2S. The LOQ was 0.20% for EVA and 0.1 ppm for CMR. Thus, the CMR model is able to quantify the tolerable daily intake of 0.1 mg/kg body [9].
a
Actual value
HPLC-DAD
0.5 0.75 1.0 2.0 3.0 4.0 5.0 6.0 7.0
0.46 0.72 0.94 1.88 2.87 4.18 4.78 6.26 6.97 0.997 1.019 1.29 2.4 3.58 4.92 5.75 6.4 7.57 8.22 9.28 0.993 0.981
1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5
Predicted value
PLS1 %RDa −8.00 −4.00 −6.00 −6.00 −4.33 4.50 −4.40 4.33 −0.43 −14.00 −4.00 2.29 9.33 4.55 −1.54 0.93 −3.29 −2.32
Predicted value
%RDa
0.49 0.72 0.90 1.96 3.03 4.08 5.26 6.14 7.02 0.999 1.029 1.27 2.21 2.92 4.02 5.05 6.15 7.63 8.44 9.37 0.996 1.044
−2.00 −4.00 −10.00 −2.00 1.00 2.00 5.20 2.33 0.29 −15.33 −11.60 −16.57 −10.67 −8.18 −5.38 1.73 −0.71 −1.37
%RD, percentage relative difference.
Mexico City that were marketed by Mexican producers as “Pure 100% vanilla extract”. Two out of six samples were identified as pure vanilla by the SIMCA model, but none tested positive for EVA or CMR. Therefore, the PLS1 model cannot be applied to these commercial samples since they show adulterants different from those that the developed models can predict, i.e., EVA and CMR. Additional studies are needed to analyze others adulterant systems in vanilla. Supplementary Table 3S shows the statistical results of SIMCA model for commercial samples.
3.6. Comparison of the PLS1 chemometric model and HPLC-DAD method The EVA and CMR content from eighteen adulterated samples was quantified by HPLC-DAD and the PLS1 chemometric model. Table 2 shows the %RD values for both methods with respect to actual values. Moreover, the scatter plot of predicted versus actual values for the PLS1 model and HPLC-DAD method were linear and had slopes close to 1 (Table 2). This result suggests that the developed PLS1 model is able to predict and quantify adulterations of EVA or CMR in vanilla extracts in the same way as the HPLC-DAD method.
4. Conclusions The results show that MID-FTIR spectroscopy can be successfully applied for prediction of adulteration of pure vanilla extracts. The SIMCA model was able to efficiently identify and classify pure vanilla samples from those adulterated with EVA or CMR. Similarly, the chemometric model based on the PLS1 algorithm was able to quantify EVA and CMR in the range of 0.20–10% and 0.1–10 ppm, respectively. The EVA predictions were better than CMR since CMR adulteration was more difficult to identify because of the spectral similarity among CMRadulterated vanilla and vanilla samples. The analysis of new adulterated samples showed no significant difference between the PLS1 method and HPLC-DAD method. The chemometric model offers
3.7. Application of chemometric models After successful validation of the SIMCA and PLS1 models, six 1-fold commercial samples of vanilla were analyzed by the models to prove applicability. The samples were acquired in commercial stores in 268
Talanta 197 (2019) 264–269
C.M. Moreno-Ley et al.
advantages of being faster and easier to execute than HPLC. The SIMCA model showed its applicability with the analysis of commercial samples. The results revealed that some manufacturers use fraudulent labeling since the label states pure vanilla product when it is not. Further developments of chemometric models should include vanilla samples from other origins in order to obtain an analytical method that predicts adulteration in different types of vanilla extracts. Nevertheless, the analytical approach may help design regulations for strict quality control, because this technique is simple, fast, sensitive and perfect for laboratories that demand immediate results.
[11] B.K. Lavine, D.T. Corona, U.D.N.T. Perera, Analysis of vanilla extract by reversed phase liquid chromatography using water rich mobile phases, Microchem J. 103 (2012) 49–61, https://doi.org/10.1016/j.microc.2012.01.004. [12] A.G. Huesgen, Analysis of natural and artificial vanilla preparations, Application Note, Agilent Technologies, Inc, Waldbronn, Germany, 2011. [13] C.M. Ng, W.M. Reuter, Analysis of vanillin, ethyl vanillin and coumarin in vanilla extract products by UHPLC with PDA detection, Application Note, PerkinElmer, Inc, CT, USA, 2015. [14] L.S. De Jager, G.A. Perfetti, G.W. Diachenko, Determination of coumarin, vanillin, and ethyl vanillin in vanilla extract products: liquid chromatography mass spectrometry method development and validation studies, J. Chromatogr. A 1145 (1–2) (2007) 83–88, https://doi.org/10.1016/j.chroma.2007.01.039. [15] C. Cordella, I. Moussa, A.C. Martel, N. Sbirrazzuoli, L. Lizzani-Cuvelier, Recent developments in food characterization and adulteration detection: technique-oriented perspectives, J. Agr. Food Chem. 50 (2002) 1751–1764, https://doi.org/10. 1021/jf011096z. [16] A. Dalmia, Rapid detection of vanilla bean extract adulteration with tonka bean extracts with no chromatography or sample preparation, Aplication note, PerkinElmer, Inc, CT, USA, 2013. [17] A. Dalmia, G.L. Perkins, C. Whitehouse, Rapid Differentiation between natural and artificial vanilla flavorings for determining food fraud using AxION DSA/TOF with no chromatography or sample preparation, Aplication note, PerkinElmer, Inc, USA, 2013. [18] P. Aljaff, B.O. Rasheed, T.A. Omer, A comparison between Natural and synthetic food flavoring extracts using infrared spectra and optical activity, J. Appl. Phys. (2013) 01–06. [19] NMX-FF-074-SCFI-2009 Mexican standard. Productos no industrializados para uso humano- vainilla- (Vanilla fragans (Salisbury) Ames) –Especificaciones y métodos de prueba. 〈http://dof.gob.mx/nota_detalle.php?Codigo=5119518&fecha=13/ 11/2009〉, (accessed 20 January 2018). [20] E. Odoux, S. Languille, J.P. Leblé, A. Lombard, The effect of the nature of the soil amendments on the main aromatic compounds in Vanilla fragans beans, Fruits 55 (2000) 63–71. [21] AOAC, Association of Official Analytical Chemists, AOAC 19.011-19.012, Vanillin in vanilla extract: rapid approximate method spectrophotometric method, Official Methods of Aanalysis of A.O.A.C, 17th ed., Association of Official Analytical Chemists, Gaithersburg, MD, 2000. [22] R.S. Singhal, P.R. Kulkarni, D.V. Rege, Handbook of Indices of Food Quatlity and Authenticity, Woodhead Publishing Ltd, Cambridge, England, 1997. [23] PerkinElmer, Assure ID tutorial. Version 4.0.2.0175, PerkinElmer, Inc, 2007. [24] N. Quiñones-Islas, O.G. Meza-Márquez, G. Osorio-Revilla, T. Gallardo-Velazquez, Detection of adulterants in avocado oil by Mid-FTIR spectroscopy and multivariate analysis, Food Res. Int. 51 (1) (2013) 148–154, https://doi.org/10.1016/j.foodres. 2012.11.037. [25] P. Jiménez-Sotelo, M. Hernández-Martínez, G. Osorio-Revilla, O.G. Meza-Márquez, F. García-Ochoa, T. Gallardo-Velázquez, Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils, Food Addit. Contam. Part A Chem. Anal. Control Expo. Risk Assess. 33 (7) (2016) 1105–1115. [26] M. Margraf, Comparision of compounds in Bourbon Vanilla extract and vanilla flavour, The Application Notebook, Knauer, Inc, Berlin, Germany, 2015. [27] NOM-182-SCFI-2011 Mexican standard. Vainilla de Papantla, extractos y derivadosespecificaciones, información comercial y métodos de ensayo (prueba). 〈http:// www.dof.gob.mx/normasOficiales/4477/seeco/seeco.htm〉, (accessed 20 January 2018). [28] G. Socrates, Infrared and Raman Characteristic Groups Frequencies, Tables and Charts, John Wiley and Sons, Chichester, 2001. [29] N. Kus, S. Breda, I. Reva, E. Tasal, C. Ogretir, R. Fausto, FTIR spectroscopic and theoretical study of the photochemistry of matrix-isolated coumarin, Photochem. Biol. 83 (2007) 1237–1253, https://doi.org/10.1111/j.1751-1097.2007.00152.x. [30] D. Havkin-Frenkel, F.C. Belanger, Handbook of Vanilla Science and Technology, Wiley-Blackwell, UK, 2011. [31] J. He, L.E. Rodríguez-Saona, M.M. Gusti, Mid infrared spectroscopy for juice authentication-rapid differentiation of commercial juices, J. Agr. Food Chem. 55 (11) (2007) 4443–4452, https://doi.org/10.1021/jf062715c. [32] J.S. Shenk, M.O. Westerhaus, Calibration the ISI way, in: A.M.C. Davies, P.C. Williams (Eds.), Near Infrared Spectroscopy. NIR Publications, Chichester, UK, 1996. [33] PerkinElmer, Spectrum Quant+ tutorial. Version 4.51.02, PerkinElmer, Inc, 2000.
Acknowledgements The authors welcome the financial support from the Consejo Nacional de Ciencia y Tecnología (CONACyT, Mexico) (grant: 702208) and the Instituto Politécnico Nacional through its Secretaría de Investigación y Posgrado (SIP-IPN, Mexico) (projects: 20161077, 20171634). Conflict of interest The authors declare that they have no conflict of interest on this work. Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.talanta.2019.01.033. References [1] K. Anuradha, B. Shyamala, M. Naidu, Vanilla-its science of cultivation, curing, chemistry, and nutraceutical propertiaes, Crit. Rev. Food Sci. Nutr. 53 (12) (2013) 1250–1276, https://doi.org/10.1080/10408398.2011.563879. [2] FAOSTAT, Vanilla producer prices statistics, 2014–2016 period, 2016. 〈http:// www.fao.org/faostat/en/#data/PP〉, (accessed 15 January 2018). [3] M.D. Sharp, N.A. Kocaoglu-Vurma, V. Langford, L.E. Rodriguez-Saona, W.J. Harper, Rapid discrimination and characterization of vanilla bean extracts by attenuated total reflection infrared spectroscopy and selected ion flow tube mass spectrometry, J. Food Sci. 77 (3) (2012) C284–C292, https://doi.org/10.1111/j.1750-3841.2011. 02544.x. [4] E. Odoux, J. Escoute, J.L. Verdeil, J.M. Brillouet, Localization of ß-D- glucosidase activity and glucovanillin in vanilla beans (Vanilla planifora Andrews), Ann. Bot. 92 (3) (2003) 437–444, https://doi.org/10.1093/aob/mcg150. [5] H. Korthou, R. Velpoorte, Vanilla, in: R.G. Berger (Ed.), Flavours and Fragances, Springer International Publishing, Berlin, 2007, pp. 2003–2017 (AG). [6] U. Schweiggert, R. Carle, A. Schieber, Conventional and alternative processes for spice production: a review, Trends Food Sci. Technol. 18 (2007) 260–268, https:// doi.org/10.1016/j.tifs.2007.01.005. [7] J.D. Bythrow, Vanilla as a medicinal plant, Semin. Integr. Med. 3 (2005) 129–131, https://doi.org/10.1016/j.sigm.2006.03.001. [8] C.C. De Guzman, R.R. Zara, Vanilla, in: K.V. Peter (Ed.), Handbook of herbs and spices, second edition, Woodhead Publishing Ltd, UK, 2012, pp. 547–589. [9] EFSA, Scientific opinion of the panel on food additives, flavourings, processing aids and materials in contact with food on a request from the European Commission on coumarin in flavourings and other food ingredients with flavouring properties. question No. EFSA-Q-2008-677, EFSA J. 793 (2008) 1–15. [10] T. Jenkins, M. Waite, Screening of commercial vanilla extract for authenticity using the Breeze 2 Modular HPLC System, Application note, Waters, Corp, MA, USA, 2008.
269