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Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array
Accepted Manuscript Title: Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array Authors: Mohammad Mahdi Bordbar, Javad Tashkhourian, Bahram Hemmateenejad PII: DOI: Reference:
S0925-4005(17)32131-7 https://doi.org/10.1016/j.snb.2017.11.010 SNB 23504
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
22-1-2017 29-10-2017 2-11-2017
Please cite this article as: Mohammad Mahdi Bordbar, Javad Tashkhourian, Bahram Hemmateenejad, Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array, Sensors and Actuators B: Chemical https://doi.org/10.1016/j.snb.2017.11.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array Mohammad Mahdi Bordbar, Javad Tashkhourian,* Bahram Hemmateenejad* Chemistry Department, Shiraz University, Shiraz, 71454, Iran *
Email address: [email protected], [email protected]
Graphical abstract
Highlights
A colorimetric sensor array composed of pH and redox indicators was fabricated for analysis of pickles
Accurate classification of the fruit pickles of different sources was achieved
It could discriminate between the pure pickles and those contaminated with alum or acetic acid
Quantitative measurements of contaminants were also possible
Abstract 1
A simple and low cost method was presented for detection and determination of two major toxic materials including alum and synthetic acetic acid in fraud pickles based on a novel and sensitive colorimetric sensor array. This sensor was composed of a (4×5) array of pH and redox indicators. The color change profiles were individual fingerprints for each specifics analytes and can be monitored with an ordinary flatbed scanner followed by unsupervised pattern recognition method such as principal component analysis (PCA) and hierarchical clustering analysis (HCA). The produced color patterns were dependent on the type of fruit used for producing of pickle and hence they used for discrimination of the vinegar based on the type of fruits they originated. Also, the responses of the sensors were dependent on the amounts of alum and synthetic acetic acid added to the pickles. Partial least square (PLS) regression as a multivariate calibration method was used to estimate the content of alum and synthetic acetic acid in pickle samples through image analysis. A root mean square error for calibration and prediction of 0.469 and 0.446 for alum and also 1.34 and 0.933 for acetic acid were obtained, respectively. This colorimetric sensor array demonstrates excellent potential for qualitative and quantitative control of fruit pickle samples. Keywords: Sensor array; Pickle; Adulteration; Chemometrics; Alum; Discrimination
Introduction Toxicants are hazardous substances causing serious health effects when a person is exposed to it. A degree of toxicity is related to the type of toxic materials, their concentration and the rout of exposure to it [1]. Among some type of toxic entities, chemical toxicants including inorganic substances such as alum and organic compounds like synthetic acetic acid are more strongly dangerous because of their chemical interaction with various parts of the human body [2]. These toxic chemical compounds cause irritation of the skin and mucous membranes, lung damage, upset the ionic equilibrium in your bloodstream and degeneration of nervous system tissue [3]. These toxic material can be used in food and caused foodborne illnesses and sometimes can be fatal [4]. This dangerous phenomenon is known as food adulteration that faced by the food industry and consumers. The most common motivation for adulteration practices is increasing profitability [5]. Therefore, it is essential to investigate the quality of food because it is directly related to people’s health and social progress. 2
Fruit pickle is one of the foods that attracted the attention of adulterators because of consuming a large amount of it in the world as a famous traditional condiment and preservative [6]. It is also beneficial for health, such as preventing colds [6], treating gastropathy [7], and keeping tumors at bay [8]. Adding alum and synthetic acetic acid to fruit pickles is a way for adulterators for production of fake fruit pickles. Therefore, it is important to investigate the authentication of fruit pickles by a quality control process to ensure their safety and efficacy. Up to now, well established analytical procedures like mass spectrometry (MS)[9,10], gas chromatography (GC)[11], atomic absorption spectroscopy (AAS)[12] and near-infrared spectroscopy (NIR) [13,14] are able to accurately extract certain information in order to draw conclusions about quality of pickle. However, these methods might be destructive, expensive, complex and in some instances need time-consuming sample preparation. Thus, it would be beneficial to develop people-friendly methods such that they can be easily implemented by nonexpert consumers. Colorimetric sensor arrays (CSA) are examples of simple and cost-effective analytical devices that have been developed by Suslick et al in 2000 [15]. CSAs have been successfully developed for analysis of wide varieties of real samples [16–20]. These disposable devices are constructed by printing chemical responsive dyes on a polymeric substrate with desired properties such as inertness towards gases and liquids, high surface area (to incorporate sufficient colorant), optical transparency or high reflectivity, and stability over a wide pH range [21]. Recently, some electronic nose systems were used to investigate the pickle acetic fermentation [22] and detect different marked ages of a pickle [23]. However, their experimental method suffers from complexity. Also, according to the best of our knowledge, the qualitative and quantitative analysis of adulterated samples as well as the discrimination of fruit pickles by CSA has not been addressed yet. In the present study, a colorimetric sensor array was fabricated for qualitative discrimination of pickles prepared from different fruits. The sensors were fabricated by printing 20 pH and redox indicators on a silica gel plates. The discrimination was based the interaction of different chemical species in the vapor of the pickles (head-space) and the indicators deposited on the sensor substrate, which is resulted in distinctive color changes for each type of pickle. The effective parameters on sensor array responses such as temperature and time were optimized. Chemometrics pattern recognition methods like principle component analysis (PCA) and 3
hierarchical cluster analysis (HCA) were used for extraction of useful information and increasing the selectivity of the sensors. Furthermore, the amount of alum and synthetic acetic acid were determined in fruit pickle samples using partial least square (PLS) as a multivariate calibration method.
2. Materials and Methods 2.1. Reagent Commercial pH and redox indicators including methyl red (MR), cresol red (CR), titan yellow (TY), bromothymol blue (BPB), chlorophenol red (CPR), aniline Blue WS (AB-WS), alizarine (AZN), 4-(2-pyridylazo) resorcinol (PAR), Nile blue (NB), phenol red (PR), methylene blue (MTB), bromophenol blue (BPR), bromocresol purple (BCP), murexide (MUR), m-cresol purple (MCP), orange (IV) (OIV), methyl orange (MO), eriochrome cyanine R (ECR), thymol blue (TB), eosin yellowish (EY), which were of chemical purity or analytical grade, were purchase from Sigma-Aldrich (USA) and used without further purification. Ethanol as a solvent was obtained from Merck (Darmstadt, Germany). Reverse phase silica thin layer chromatography plates were purchased from Merck (Darmstadt, Germany).
2.2. Pickles and adulterated samples Pickle samples prepared from 9 different fruits or trees including blackcurrant, date palm, pomegranate, persimmon, sugar beet, raspberry, orange balsamic, apple and quince, spanning an important range of types of pickle that is of public interest, were used. For each type of pickle, three different samples were collected; one sample was produced in the laboratory and two samples were purchased from traditional pickle factories and stored in the laboratory at ambient temperature. In addition, 5 brands of blackcurrant pickle were obtained from five commercial pickle factories. Three replicate samples were used for each brand of blackcurrant pickle. Adulterated blackcurrant pickle samples were provided by mixing of synthetic acetic acid and pickle with different volume-volume percentages (V/V %) in the range of 0 % to 100 %. Also, mixtures of alum and pickle with different weight-volume percentages (W/V %) in the range of 0 % to 45 % were prepared.
2.3. Apparatus and software 4
An automatic micropipette (BRAND Transferpette® S, Germany) was used to spot indicators on a plate and an oven (MEMMERT UN 30, Germany) was used for heating samples. Recording the color changes of the sensor array was performed by an ordinary flatbed scanner (CanoScanLiDE 700F, with a resolution of 300 dpi, USA). Calculation of the color values of each spot, multivariate image analysis, and chemometrics data analysis methods were performed by MATLAB R2013 scientific software (The Mathworks, Inc., MA, USA).
2.4. Sensor array fabrication The sensing elements of the sensor array are composed of some pH and redox indicators, which are cheap reagent and are available with high purity. The presence of acidic constitutes in the vapor of fruit pickles and oxidizing or reducing agents in pickle are the main reasons for using these indicators in the sensor array. Solutions of the indicators were prepared by transferring 5.0 mg portion of each indicator in 5.0 mL volumetric flask and diluting to the mark with ethanol. To prevent colorant leaching in hydrophilic media, the solutions of the dyes were mixed with 0.1 M ethanolic solution of tetrabutylammonium hydroxide (TBAH) in 4:1 volume ratio (Vindicator:VTBAH). A 1.0 µL portion of this mixture was spotted on a reverse phase silica gel plates using automatic micropipette. The fabricated sensor array was stored in a desiccator under vacuum condition for 12 hours before further usage. The color map of the prepared sensor array is shown in Fig. 1.
2.5. Measurement procedures and data collection A schematic diagram of the procedure used for data collection with colorimetric sensor array is shown in supplementary materials Fig. S1. For analysis of each pickle sample, a plate of sensor array is first scanned and its image is recorded. Then, it is placed on the top of a 25mL beaker containing 10 mL portion of the pickle sample. The beaker and sensor array are transferred in an oven with the temperature adjusted at a certain value for a specified time duration. The color map of the indicator after exposure to the vapor of pickle sample is recorded by a scanner. A written program in the form of graphical user interface (GUI) in MATLAB environment aligns the color spot of the sensor array before and after exposure to the vapor of the pickle sample and then calculates changes in the red, green and blue (RGB) color values of each element of the array by subtracting the averaged color intensity of each spot of the array before and after exposure to the 5
fruit pickles. Averaging avoids artifacts from non-uniformity of the dye spots. The differences in the individual color values as well as their combinations are saved as a spreadsheet. The response of each sample is represented as the change in the RGB values of each of the 20 dyes (i.e. a 60 dimensional vector). For each pickle sample, 3 replicate measurements are used.
2.6. Data analysis tools 2.6.1. Qualitative multivariate statistical analysis For pattern recognition, principal component analysis (PCA) is one of the most frequently methods that consists of the dimension reduction of a multi-dimensional data set into a new data space in such a way that the most significant characteristic of the data (the variance) is preserved in the first few dimensions of the new data space [24]. Each column describes a principal component, PC, which lies in the direction of the maximum variance within that data set. PCA can show clustering and classification of similar sample by plotting them in the two-dimensional space of the PCs (score plot). Like PCA, hierarchical cluster analysis (HCA) is an unsupervised method of multivariate analysis, which is an agglomerative clustering technique [25]. The clusters are determined from the distance between experimental data. Ward’s method is used to define the linkage between the clusters which is the minimum amount of the variance between the samples [26,27]. In this method, the nearest-neighbor points are paired into a single cluster which is then paired with other nearestneighbor points or clusters until all points and clusters are connected to each other [28,29]. HCA explains what samples are similar to each other and how are the similarities between them.
2.6.2. Quantitative multivariate statistical analysis Partial least square (PLS) is a popular multivariate calibration method that is particularly useful to create the relationship between a set of dependent variable (e.g. concentration) and a large set of independent variables (e.g., color values of sensor array) [30]. In the present study, the contents of alum and synthetic acid are the dependent variables and the changes in the RGB values of each sensing element in sensor array are known as independent variables. The root mean square error of cross-validation (RMSECV) is used to determine the optimum number of PLS latent variables. To assess the capability of the models to fit the calibration data and to calculate the deviation of
6
the model, root mean square error of calibration (RMSEC) and , root mean square error of prediction (RMSEP) are used [31].
3. Result and discussion 3.1. Discrimination of the pickles based on the type of fruits source Before using the sensor array for quantitative measurements, it was used for discrimination between the pickles obtained from different fruit sources. To obtain the best sensing and discriminatory properties of the proposed sensor array, optimization of the effective parameters such as temperature and time were carried out after fabrication of the sensor array. Because of the rate of reactions and the concentration of the volatiles in the head space are temperature dependent, it is essential to adjust the performance of the sensor array by changing the working temperature. In this regard, the effect of temperature on the sensor array responses was investigated by increasing the oven temperature from 30 ºC to 90 ºC. As shown in Fig. 2, for three kinds of fruit pickle including blackcurrant, date palm and apple, the response of the sensor was increased by increasing the oven temperature to up to 70 ˚C and after that no significant effect of temperature was observed. It should be noted that for other kinds of pickle similar results were obtained. By increasing the temperature, more volatile species escape from the pickle and reach to the CSA. To investigate the time required that the reaction between indicator dyes and the chemical species in the pickle to be equilibrated, the color map of the sensors were recorded over the time (up to 35 min in 5 min intervals). It is evident from Fig. 3 that for black currant, date palm and apple pickles, 25 min is required that the chemical species in the vapor of pickle penetrate into the sensor texture and to react with the indicators. Similar results were obtained for other kinds of pickles. To investigate the reproducibility of the designed sensor array in this study, the responses of the sensors to 4 types of pickles including black currant, apple, acetic acid and data palm were measured with 5 replications. The results are given as the Euclidean distance between the responses of all sensor elements in Fig. 4. As seen, there are very small differences between the responses of the repeated sensors. The calculated coefficient of variation are 4.26%, 3.58%, 4.46 and 3.36% for black currant, apple, acetic acid and data palm, respectively. The results show that the repeatability is satisfactory.
7
After finding the optimum temperature and time (70 ˚C and 25 min exposure time), the responses of the designed colorimetric sensor array to vapor of the pickles were used for discrimination of pickles. The generated colorimetric maps for different types of pickles are given in Fig. 5. Obviously, for each type of pickle, a specific color map has been produced and these color maps can be used as discrimination criteria for identification of the fruit source of the pickle. Actually, difference in the chemical constituents of the pickle’s vapor is translated in the difference in the generated color maps. As pointed out by Suslick et al [15], this type of colorimetric sensors behaves as optical nose (optoelectric nose) such that they visualize odor (or smell) of the pickle as a color map. To investigate the discriminatory ability of the sensor array in a quantitative manner, PCA and HCA as unsupervised pattern recognition methods were used for visual inspection of classification. As reported in Fig. 6a, the 2-dimensional PCA scores plot which constructed using the first two principle components, gives a cluster trend but with partial overlapping between some pickles (e.g, a high overlap between apple, orange balsamic, sugar beet and blackcurrant). This figure shows that 78.77 % of total variances in row data were extracted with the first two PCs and the results led to not good clustering because some information in original data is not considered by these two PCs. HCA was another tested method used to characterize different type of fruit pickle samples. HCA is the simplest and free model approach that provides a better discrimination of high dimensional data unlike linear discriminant analysis or support vector machine analysis as supervised and dependent analysis methods [32]. As shown in Fig. 6b, the resulting dendrogram represented excellent discrimination without misclassification among all type of fruit pickles. In addition to discrimination of the pickles, from the shown score plots and dendrogram in Fig. 6 one can explain what extent the pickles are different or similar. For example, the pickles from Quince and pomegranate are totally different from other pickles whereas those from Apple, Orange balsamic, Raspberry and sugar beet are more similar than others.
3.2. Evaluation of brand discrimination of fruit pickles To identify the different brands of fruit pickle, five brands of blackcurrant pickle as a popular drink for people, was evaluated by using constructed colorimetric sensor array. In the HCA dendrogram shown in the Fig. 6b, the results for different brands of blackcurrant pickle are also represented. 8
Although the differences in different brands of the same pickle are much less than those in different types of fruit pickle, It was evident that the sensor array shows the excellent performance to facial discrimination of samples with similar compositions, such as these five brands of black currant pickle. Black currant varieties, different environmental conditions in the production of pickles and adding food additives, coloring agents or various taste enhancers is some important reasons for difference in internal quality of different brands [33]. According to Fig. 6b, it can be understood that blackcurrant pickle from brand #4 company is strongly similar to natural blackcurrant pickle and the other from brand #1 company is significantly different from natural sample probably because of using some counterfeit materials such as alum in their pickle products.
3.3. Adulteration detection of fruit pickles To investigate the feasibility of the fabricated sensor array in identification of adulteration in fruit pickle, black currant pickle was selected and it was contaminated with different amounts of alum and synthetic acetic acid, separately. Fig. 7 shows the color change profiles for adulterated samples consist of 1.0, 3.0, 5.0, 15.0, 25.0, 35.0 and 45 % (W/V) of alum and 1.0, 3.0, 5.0, 10.0, 20.0, 40.0, 60.0 and 80.0% (V/V) of synthetic acetic acid. All samples provided the variety patterns with unique characteristic. As illustrated in Fig.7, the interesting result is that the colorimetric patterns are changed from pure black currant pickle to pure alum or synthetic acetic acid. It is obvious from Fig. 7a that the changes in color of thymol blue and methyl red (sensor no. S8 and S14, respectively) in the color difference map of pure blackcurrant is different from which one containing very low amounts (1.0%) of alum. Also, the infected blackcurrant pickles by 3.0 % synthetic acetic acid are visually different from pure pickles for sensing elements of orange (IV), chlorophenol red and methyl orange (sensor no. S3, S4 and S7, respectively). These results confirm the high sensitivity of the suggested CSA to detect trace amount of alum and synthetic acetic acid in the pickle. The projection of the color maps of the response of infected samples in the 2-dimentional space of their PCs, shown in Fig. 7c clearly demonstrates how the sensor can discriminate the alum-infected from the acetic acid-infected samples. Also, it is obvious from Fig. 7c that the there is a direct relationship between the magnitude of the PCs and the amount of contaminants. This led us to develop quantitative models that are able to estimate the amount of contaminants in the pickles using the designed CSA. 9
3.4. Quantitative determination of adulteration in fruit pickle In this part, it was investigated whether the designed CSA can be used to measure the amounts of contaminants in the pickles samples adulterated with alum or industrial acetic acid. To do so, the response of the CSA for mixtures of black currant pickles-alum and black currant pickle-acetic acid were used as predictor variables of partial least square (PLS) regression model. For each type of adulterant, separate regression model was developed. In each case, a total of 30 mixtures were prepared in the concentration range of 0.0 % -100.0 %. (V/V) of pickle-acetic acid and 0.0 % 45.0 % (W/V) pickles-alum. In the former 6 samples and in the latter 7 samples were selected as test set, randomly. Leave-one-out cross validation was used to refine the optimum number of PLS latent variables. To diminish the effect of systematic errors, a lot of high frequency random noise and light scattering noise information during imaging, column mean-centering was done on the response data matrices [26,34]. To evaluate the performance of the calibration models, root mean square error of calibration based on cross-validation (RMSEC), root mean square error of prediction (RMSEP) and the respective correlation coefficients (R2) were calculated. The statistical parameters of the obtained PLS calibration models are given in Table 1. To show the impact mean-centering on the performance of the models, the results of before and after mean-centering are given. It is obvious from Table 1 that mean-centering not only decreased the model dimensionality but also resulted in better statistical quality of the models. It is observed that the developed multivariate calibration models have excellent performances in predicting the amount of adulteration in pickles by acetic acid and alum with prediction errors of as low as 1.0% (V/V) and 0.5 % (W/V), respectively. Also, Fig. 8 shows how the predicted concentrations of alum and acetic acid by PLS calibration are highly correlated with the real amounts in both training and test set samples.
4. Conclusion In summary, a novel and sensitive colorimetric sensor array successfully used to discriminate both 9 types and 5 brands of fruit pickle based on the reaction between twenty chemical dyes and vapor compounds of fruit pickles that provide fingerprint color change profiles for each sample. The process performed by image analysis technique and the excellent discrimination obtained by HCA in the optimum conditions. It was interesting that the proposed sensor array has high potential to 10
quantitative analysis of adulterated samples contaminated with alum and synthetic acetic acid in fruit pickles combined with PLS-1 model after applying mean centering as a data preprocessing method. Owing to several advantageous like simple, low cost, non-destructive and no sample preparation, this sensor is a reliable tool for real application compared the several analytical techniques that are expensive and time consuming.
Acknowledgement The authors gratefully acknowledge the financial support from the University of Shiraz.
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Legend for the Figures Fig. 1. The colorimetric map of the colorimetric sensor array used in this study and the name of the chemical responsive dyes used in each cell array. Each dye was combined with tetrabutyl ammonium hydroxide before deposition on sensor substrate. Fig. 2. (a) Changing in the response of CSA for Black currant pickle as function of temperature, (b) The effect of oven temperature on the response of array sensor for three fruit pickles (Black currant, Data palm and Apple). The Euclidean distance is the total length of the full dimensional color-difference vector that can be considered as the overall array response. Fig. 3. (a) Changing in the response of CSA for Black currant pickle as function of heating time at temperature of 70°C, (b) Response time of the array of sensors for three fruit pickles (Black currant, Data palm and Apple). Fig. 4. The repeatability of the response of colorimetric sensor array for detection of blackcurrant pickle, acetic acid, apple pickle and data palm pickle after 25 min heating at 70°C. The Euclidean distance is the total length of the full dimensional color-difference vector, that is, the total array 14
response. The coefficient of variation of the array response was about 4.26% for blackcurrant pickle, 3.58% for acetic acid, 4.46% for apple pickle and 3.36% for data palm pickle. Fig. 5. Color difference maps of the CSA for nine studied fruit pickles and synthetic acetic acid at 70 ° C and 25 min. Fig. 6. (a) Classification results achieved by (a) PCA score plot and (b) HCA dendrogram based on the color difference maps shown in Fig. 5. Fig. 7. Color difference maps for black currant pickle contaminated with different amounts of (a) alum and (b) synthetic acetic acid. The distribution of the samples in the two-dimensional PCA score plot are given in the lower panel: yellow marker shows the pure black currant while red and blue markers denotes contaminated samples with alum and acetic acid, respectively. The color gradients show the samples of different concentrations of contaminants such that more intense colors higher concentration. Fig. 8. The results of the PLS regression after applying mean centering preprocessing method for quantitative analysis of (a) alum, (b) synthetic acetic acid. (Training and Test set is shown by blue and red markers, respectively).
Table 1. The results of prediction model of alum and synthetic acetic acid before and after applying mean centering preprocessing methods. Determination of Alum Preprocessing method
Determination of Synthetic acetic acid
RMSEC
RMSEP
R2c
R2p
Nothing
PLS factor 3
RMSEC
RMSEP
R2c
R2p
0.934
PLS factor 4
0.565
0.523
0.898
1.68
1.021
0.991
0.993
Mean centering
2
0.464
0.446
0.997
0.998
2
1.34
0.933
0.998
0.999
15
16
Fig. 1
17
Fig. 2
18
Fig. 3
19
Fig. 4
20
Fig. 5
21
Fig. 6
22
Fig. 7
23
Fig. 8
24
Bibliography Bahram Hemmateenejad is a professor of Chemistry at Chemistry Department of Shiraz University. He received his B.S. degree in pure chemistry from Shiraz University in 1996, M.S. and Ph.D. in Analytical Chemistry from Isfahan University of Technology and Shiraz University in 1998 and 2002, respectively. As fellow of Alexander von Humboldt Foundation, he visits different research institutes and universities in Germany. He runs a Lab on the development and applications of different chemometrics methods in the chemical disciplines of bioanalytical chemistry, chemo-biological interactions, computer-aided drug design and colorimetric sensors. Currently, he is large and active group working on paper-based sensors.
Mohammad Mahdi Bordbar received the BSC degree in applied chemistry from Kashan University in 2010 and M.Sc. degree in analytical chemistry from Yasouj University in 2012. He is currently a graduate student at the Department of Chemistry, Shiraz University, Shiraz, Iran. Javad Tashkhourian received his M.Sc. and Ph.D. degrees in analytical chemistry from Shiraz University in 1999 and 2004, respectively. He was a member of chemistry department of Persian Gulf University (2004–2010). He joined the chemistry department of Shiraz University in 2010 where he is now an Associate Professor. His research is focused on design and construction of chemical sensors (optical and electrochemical) and synthesis and applications of nanomaterials in electrochemical analysis.
25
S0925-4005(17)32131-7 https://doi.org/10.1016/j.snb.2017.11.010 SNB 23504
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
22-1-2017 29-10-2017 2-11-2017
Please cite this article as: Mohammad Mahdi Bordbar, Javad Tashkhourian, Bahram Hemmateenejad, Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array, Sensors and Actuators B: Chemical https://doi.org/10.1016/j.snb.2017.11.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array Mohammad Mahdi Bordbar, Javad Tashkhourian,* Bahram Hemmateenejad* Chemistry Department, Shiraz University, Shiraz, 71454, Iran *
Email address: [email protected], [email protected]
Graphical abstract
Highlights
A colorimetric sensor array composed of pH and redox indicators was fabricated for analysis of pickles
Accurate classification of the fruit pickles of different sources was achieved
It could discriminate between the pure pickles and those contaminated with alum or acetic acid
Quantitative measurements of contaminants were also possible
Abstract 1
A simple and low cost method was presented for detection and determination of two major toxic materials including alum and synthetic acetic acid in fraud pickles based on a novel and sensitive colorimetric sensor array. This sensor was composed of a (4×5) array of pH and redox indicators. The color change profiles were individual fingerprints for each specifics analytes and can be monitored with an ordinary flatbed scanner followed by unsupervised pattern recognition method such as principal component analysis (PCA) and hierarchical clustering analysis (HCA). The produced color patterns were dependent on the type of fruit used for producing of pickle and hence they used for discrimination of the vinegar based on the type of fruits they originated. Also, the responses of the sensors were dependent on the amounts of alum and synthetic acetic acid added to the pickles. Partial least square (PLS) regression as a multivariate calibration method was used to estimate the content of alum and synthetic acetic acid in pickle samples through image analysis. A root mean square error for calibration and prediction of 0.469 and 0.446 for alum and also 1.34 and 0.933 for acetic acid were obtained, respectively. This colorimetric sensor array demonstrates excellent potential for qualitative and quantitative control of fruit pickle samples. Keywords: Sensor array; Pickle; Adulteration; Chemometrics; Alum; Discrimination
Introduction Toxicants are hazardous substances causing serious health effects when a person is exposed to it. A degree of toxicity is related to the type of toxic materials, their concentration and the rout of exposure to it [1]. Among some type of toxic entities, chemical toxicants including inorganic substances such as alum and organic compounds like synthetic acetic acid are more strongly dangerous because of their chemical interaction with various parts of the human body [2]. These toxic chemical compounds cause irritation of the skin and mucous membranes, lung damage, upset the ionic equilibrium in your bloodstream and degeneration of nervous system tissue [3]. These toxic material can be used in food and caused foodborne illnesses and sometimes can be fatal [4]. This dangerous phenomenon is known as food adulteration that faced by the food industry and consumers. The most common motivation for adulteration practices is increasing profitability [5]. Therefore, it is essential to investigate the quality of food because it is directly related to people’s health and social progress. 2
Fruit pickle is one of the foods that attracted the attention of adulterators because of consuming a large amount of it in the world as a famous traditional condiment and preservative [6]. It is also beneficial for health, such as preventing colds [6], treating gastropathy [7], and keeping tumors at bay [8]. Adding alum and synthetic acetic acid to fruit pickles is a way for adulterators for production of fake fruit pickles. Therefore, it is important to investigate the authentication of fruit pickles by a quality control process to ensure their safety and efficacy. Up to now, well established analytical procedures like mass spectrometry (MS)[9,10], gas chromatography (GC)[11], atomic absorption spectroscopy (AAS)[12] and near-infrared spectroscopy (NIR) [13,14] are able to accurately extract certain information in order to draw conclusions about quality of pickle. However, these methods might be destructive, expensive, complex and in some instances need time-consuming sample preparation. Thus, it would be beneficial to develop people-friendly methods such that they can be easily implemented by nonexpert consumers. Colorimetric sensor arrays (CSA) are examples of simple and cost-effective analytical devices that have been developed by Suslick et al in 2000 [15]. CSAs have been successfully developed for analysis of wide varieties of real samples [16–20]. These disposable devices are constructed by printing chemical responsive dyes on a polymeric substrate with desired properties such as inertness towards gases and liquids, high surface area (to incorporate sufficient colorant), optical transparency or high reflectivity, and stability over a wide pH range [21]. Recently, some electronic nose systems were used to investigate the pickle acetic fermentation [22] and detect different marked ages of a pickle [23]. However, their experimental method suffers from complexity. Also, according to the best of our knowledge, the qualitative and quantitative analysis of adulterated samples as well as the discrimination of fruit pickles by CSA has not been addressed yet. In the present study, a colorimetric sensor array was fabricated for qualitative discrimination of pickles prepared from different fruits. The sensors were fabricated by printing 20 pH and redox indicators on a silica gel plates. The discrimination was based the interaction of different chemical species in the vapor of the pickles (head-space) and the indicators deposited on the sensor substrate, which is resulted in distinctive color changes for each type of pickle. The effective parameters on sensor array responses such as temperature and time were optimized. Chemometrics pattern recognition methods like principle component analysis (PCA) and 3
hierarchical cluster analysis (HCA) were used for extraction of useful information and increasing the selectivity of the sensors. Furthermore, the amount of alum and synthetic acetic acid were determined in fruit pickle samples using partial least square (PLS) as a multivariate calibration method.
2. Materials and Methods 2.1. Reagent Commercial pH and redox indicators including methyl red (MR), cresol red (CR), titan yellow (TY), bromothymol blue (BPB), chlorophenol red (CPR), aniline Blue WS (AB-WS), alizarine (AZN), 4-(2-pyridylazo) resorcinol (PAR), Nile blue (NB), phenol red (PR), methylene blue (MTB), bromophenol blue (BPR), bromocresol purple (BCP), murexide (MUR), m-cresol purple (MCP), orange (IV) (OIV), methyl orange (MO), eriochrome cyanine R (ECR), thymol blue (TB), eosin yellowish (EY), which were of chemical purity or analytical grade, were purchase from Sigma-Aldrich (USA) and used without further purification. Ethanol as a solvent was obtained from Merck (Darmstadt, Germany). Reverse phase silica thin layer chromatography plates were purchased from Merck (Darmstadt, Germany).
2.2. Pickles and adulterated samples Pickle samples prepared from 9 different fruits or trees including blackcurrant, date palm, pomegranate, persimmon, sugar beet, raspberry, orange balsamic, apple and quince, spanning an important range of types of pickle that is of public interest, were used. For each type of pickle, three different samples were collected; one sample was produced in the laboratory and two samples were purchased from traditional pickle factories and stored in the laboratory at ambient temperature. In addition, 5 brands of blackcurrant pickle were obtained from five commercial pickle factories. Three replicate samples were used for each brand of blackcurrant pickle. Adulterated blackcurrant pickle samples were provided by mixing of synthetic acetic acid and pickle with different volume-volume percentages (V/V %) in the range of 0 % to 100 %. Also, mixtures of alum and pickle with different weight-volume percentages (W/V %) in the range of 0 % to 45 % were prepared.
2.3. Apparatus and software 4
An automatic micropipette (BRAND Transferpette® S, Germany) was used to spot indicators on a plate and an oven (MEMMERT UN 30, Germany) was used for heating samples. Recording the color changes of the sensor array was performed by an ordinary flatbed scanner (CanoScanLiDE 700F, with a resolution of 300 dpi, USA). Calculation of the color values of each spot, multivariate image analysis, and chemometrics data analysis methods were performed by MATLAB R2013 scientific software (The Mathworks, Inc., MA, USA).
2.4. Sensor array fabrication The sensing elements of the sensor array are composed of some pH and redox indicators, which are cheap reagent and are available with high purity. The presence of acidic constitutes in the vapor of fruit pickles and oxidizing or reducing agents in pickle are the main reasons for using these indicators in the sensor array. Solutions of the indicators were prepared by transferring 5.0 mg portion of each indicator in 5.0 mL volumetric flask and diluting to the mark with ethanol. To prevent colorant leaching in hydrophilic media, the solutions of the dyes were mixed with 0.1 M ethanolic solution of tetrabutylammonium hydroxide (TBAH) in 4:1 volume ratio (Vindicator:VTBAH). A 1.0 µL portion of this mixture was spotted on a reverse phase silica gel plates using automatic micropipette. The fabricated sensor array was stored in a desiccator under vacuum condition for 12 hours before further usage. The color map of the prepared sensor array is shown in Fig. 1.
2.5. Measurement procedures and data collection A schematic diagram of the procedure used for data collection with colorimetric sensor array is shown in supplementary materials Fig. S1. For analysis of each pickle sample, a plate of sensor array is first scanned and its image is recorded. Then, it is placed on the top of a 25mL beaker containing 10 mL portion of the pickle sample. The beaker and sensor array are transferred in an oven with the temperature adjusted at a certain value for a specified time duration. The color map of the indicator after exposure to the vapor of pickle sample is recorded by a scanner. A written program in the form of graphical user interface (GUI) in MATLAB environment aligns the color spot of the sensor array before and after exposure to the vapor of the pickle sample and then calculates changes in the red, green and blue (RGB) color values of each element of the array by subtracting the averaged color intensity of each spot of the array before and after exposure to the 5
fruit pickles. Averaging avoids artifacts from non-uniformity of the dye spots. The differences in the individual color values as well as their combinations are saved as a spreadsheet. The response of each sample is represented as the change in the RGB values of each of the 20 dyes (i.e. a 60 dimensional vector). For each pickle sample, 3 replicate measurements are used.
2.6. Data analysis tools 2.6.1. Qualitative multivariate statistical analysis For pattern recognition, principal component analysis (PCA) is one of the most frequently methods that consists of the dimension reduction of a multi-dimensional data set into a new data space in such a way that the most significant characteristic of the data (the variance) is preserved in the first few dimensions of the new data space [24]. Each column describes a principal component, PC, which lies in the direction of the maximum variance within that data set. PCA can show clustering and classification of similar sample by plotting them in the two-dimensional space of the PCs (score plot). Like PCA, hierarchical cluster analysis (HCA) is an unsupervised method of multivariate analysis, which is an agglomerative clustering technique [25]. The clusters are determined from the distance between experimental data. Ward’s method is used to define the linkage between the clusters which is the minimum amount of the variance between the samples [26,27]. In this method, the nearest-neighbor points are paired into a single cluster which is then paired with other nearestneighbor points or clusters until all points and clusters are connected to each other [28,29]. HCA explains what samples are similar to each other and how are the similarities between them.
2.6.2. Quantitative multivariate statistical analysis Partial least square (PLS) is a popular multivariate calibration method that is particularly useful to create the relationship between a set of dependent variable (e.g. concentration) and a large set of independent variables (e.g., color values of sensor array) [30]. In the present study, the contents of alum and synthetic acid are the dependent variables and the changes in the RGB values of each sensing element in sensor array are known as independent variables. The root mean square error of cross-validation (RMSECV) is used to determine the optimum number of PLS latent variables. To assess the capability of the models to fit the calibration data and to calculate the deviation of
6
the model, root mean square error of calibration (RMSEC) and , root mean square error of prediction (RMSEP) are used [31].
3. Result and discussion 3.1. Discrimination of the pickles based on the type of fruits source Before using the sensor array for quantitative measurements, it was used for discrimination between the pickles obtained from different fruit sources. To obtain the best sensing and discriminatory properties of the proposed sensor array, optimization of the effective parameters such as temperature and time were carried out after fabrication of the sensor array. Because of the rate of reactions and the concentration of the volatiles in the head space are temperature dependent, it is essential to adjust the performance of the sensor array by changing the working temperature. In this regard, the effect of temperature on the sensor array responses was investigated by increasing the oven temperature from 30 ºC to 90 ºC. As shown in Fig. 2, for three kinds of fruit pickle including blackcurrant, date palm and apple, the response of the sensor was increased by increasing the oven temperature to up to 70 ˚C and after that no significant effect of temperature was observed. It should be noted that for other kinds of pickle similar results were obtained. By increasing the temperature, more volatile species escape from the pickle and reach to the CSA. To investigate the time required that the reaction between indicator dyes and the chemical species in the pickle to be equilibrated, the color map of the sensors were recorded over the time (up to 35 min in 5 min intervals). It is evident from Fig. 3 that for black currant, date palm and apple pickles, 25 min is required that the chemical species in the vapor of pickle penetrate into the sensor texture and to react with the indicators. Similar results were obtained for other kinds of pickles. To investigate the reproducibility of the designed sensor array in this study, the responses of the sensors to 4 types of pickles including black currant, apple, acetic acid and data palm were measured with 5 replications. The results are given as the Euclidean distance between the responses of all sensor elements in Fig. 4. As seen, there are very small differences between the responses of the repeated sensors. The calculated coefficient of variation are 4.26%, 3.58%, 4.46 and 3.36% for black currant, apple, acetic acid and data palm, respectively. The results show that the repeatability is satisfactory.
7
After finding the optimum temperature and time (70 ˚C and 25 min exposure time), the responses of the designed colorimetric sensor array to vapor of the pickles were used for discrimination of pickles. The generated colorimetric maps for different types of pickles are given in Fig. 5. Obviously, for each type of pickle, a specific color map has been produced and these color maps can be used as discrimination criteria for identification of the fruit source of the pickle. Actually, difference in the chemical constituents of the pickle’s vapor is translated in the difference in the generated color maps. As pointed out by Suslick et al [15], this type of colorimetric sensors behaves as optical nose (optoelectric nose) such that they visualize odor (or smell) of the pickle as a color map. To investigate the discriminatory ability of the sensor array in a quantitative manner, PCA and HCA as unsupervised pattern recognition methods were used for visual inspection of classification. As reported in Fig. 6a, the 2-dimensional PCA scores plot which constructed using the first two principle components, gives a cluster trend but with partial overlapping between some pickles (e.g, a high overlap between apple, orange balsamic, sugar beet and blackcurrant). This figure shows that 78.77 % of total variances in row data were extracted with the first two PCs and the results led to not good clustering because some information in original data is not considered by these two PCs. HCA was another tested method used to characterize different type of fruit pickle samples. HCA is the simplest and free model approach that provides a better discrimination of high dimensional data unlike linear discriminant analysis or support vector machine analysis as supervised and dependent analysis methods [32]. As shown in Fig. 6b, the resulting dendrogram represented excellent discrimination without misclassification among all type of fruit pickles. In addition to discrimination of the pickles, from the shown score plots and dendrogram in Fig. 6 one can explain what extent the pickles are different or similar. For example, the pickles from Quince and pomegranate are totally different from other pickles whereas those from Apple, Orange balsamic, Raspberry and sugar beet are more similar than others.
3.2. Evaluation of brand discrimination of fruit pickles To identify the different brands of fruit pickle, five brands of blackcurrant pickle as a popular drink for people, was evaluated by using constructed colorimetric sensor array. In the HCA dendrogram shown in the Fig. 6b, the results for different brands of blackcurrant pickle are also represented. 8
Although the differences in different brands of the same pickle are much less than those in different types of fruit pickle, It was evident that the sensor array shows the excellent performance to facial discrimination of samples with similar compositions, such as these five brands of black currant pickle. Black currant varieties, different environmental conditions in the production of pickles and adding food additives, coloring agents or various taste enhancers is some important reasons for difference in internal quality of different brands [33]. According to Fig. 6b, it can be understood that blackcurrant pickle from brand #4 company is strongly similar to natural blackcurrant pickle and the other from brand #1 company is significantly different from natural sample probably because of using some counterfeit materials such as alum in their pickle products.
3.3. Adulteration detection of fruit pickles To investigate the feasibility of the fabricated sensor array in identification of adulteration in fruit pickle, black currant pickle was selected and it was contaminated with different amounts of alum and synthetic acetic acid, separately. Fig. 7 shows the color change profiles for adulterated samples consist of 1.0, 3.0, 5.0, 15.0, 25.0, 35.0 and 45 % (W/V) of alum and 1.0, 3.0, 5.0, 10.0, 20.0, 40.0, 60.0 and 80.0% (V/V) of synthetic acetic acid. All samples provided the variety patterns with unique characteristic. As illustrated in Fig.7, the interesting result is that the colorimetric patterns are changed from pure black currant pickle to pure alum or synthetic acetic acid. It is obvious from Fig. 7a that the changes in color of thymol blue and methyl red (sensor no. S8 and S14, respectively) in the color difference map of pure blackcurrant is different from which one containing very low amounts (1.0%) of alum. Also, the infected blackcurrant pickles by 3.0 % synthetic acetic acid are visually different from pure pickles for sensing elements of orange (IV), chlorophenol red and methyl orange (sensor no. S3, S4 and S7, respectively). These results confirm the high sensitivity of the suggested CSA to detect trace amount of alum and synthetic acetic acid in the pickle. The projection of the color maps of the response of infected samples in the 2-dimentional space of their PCs, shown in Fig. 7c clearly demonstrates how the sensor can discriminate the alum-infected from the acetic acid-infected samples. Also, it is obvious from Fig. 7c that the there is a direct relationship between the magnitude of the PCs and the amount of contaminants. This led us to develop quantitative models that are able to estimate the amount of contaminants in the pickles using the designed CSA. 9
3.4. Quantitative determination of adulteration in fruit pickle In this part, it was investigated whether the designed CSA can be used to measure the amounts of contaminants in the pickles samples adulterated with alum or industrial acetic acid. To do so, the response of the CSA for mixtures of black currant pickles-alum and black currant pickle-acetic acid were used as predictor variables of partial least square (PLS) regression model. For each type of adulterant, separate regression model was developed. In each case, a total of 30 mixtures were prepared in the concentration range of 0.0 % -100.0 %. (V/V) of pickle-acetic acid and 0.0 % 45.0 % (W/V) pickles-alum. In the former 6 samples and in the latter 7 samples were selected as test set, randomly. Leave-one-out cross validation was used to refine the optimum number of PLS latent variables. To diminish the effect of systematic errors, a lot of high frequency random noise and light scattering noise information during imaging, column mean-centering was done on the response data matrices [26,34]. To evaluate the performance of the calibration models, root mean square error of calibration based on cross-validation (RMSEC), root mean square error of prediction (RMSEP) and the respective correlation coefficients (R2) were calculated. The statistical parameters of the obtained PLS calibration models are given in Table 1. To show the impact mean-centering on the performance of the models, the results of before and after mean-centering are given. It is obvious from Table 1 that mean-centering not only decreased the model dimensionality but also resulted in better statistical quality of the models. It is observed that the developed multivariate calibration models have excellent performances in predicting the amount of adulteration in pickles by acetic acid and alum with prediction errors of as low as 1.0% (V/V) and 0.5 % (W/V), respectively. Also, Fig. 8 shows how the predicted concentrations of alum and acetic acid by PLS calibration are highly correlated with the real amounts in both training and test set samples.
4. Conclusion In summary, a novel and sensitive colorimetric sensor array successfully used to discriminate both 9 types and 5 brands of fruit pickle based on the reaction between twenty chemical dyes and vapor compounds of fruit pickles that provide fingerprint color change profiles for each sample. The process performed by image analysis technique and the excellent discrimination obtained by HCA in the optimum conditions. It was interesting that the proposed sensor array has high potential to 10
quantitative analysis of adulterated samples contaminated with alum and synthetic acetic acid in fruit pickles combined with PLS-1 model after applying mean centering as a data preprocessing method. Owing to several advantageous like simple, low cost, non-destructive and no sample preparation, this sensor is a reliable tool for real application compared the several analytical techniques that are expensive and time consuming.
Acknowledgement The authors gratefully acknowledge the financial support from the University of Shiraz.
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Legend for the Figures Fig. 1. The colorimetric map of the colorimetric sensor array used in this study and the name of the chemical responsive dyes used in each cell array. Each dye was combined with tetrabutyl ammonium hydroxide before deposition on sensor substrate. Fig. 2. (a) Changing in the response of CSA for Black currant pickle as function of temperature, (b) The effect of oven temperature on the response of array sensor for three fruit pickles (Black currant, Data palm and Apple). The Euclidean distance is the total length of the full dimensional color-difference vector that can be considered as the overall array response. Fig. 3. (a) Changing in the response of CSA for Black currant pickle as function of heating time at temperature of 70°C, (b) Response time of the array of sensors for three fruit pickles (Black currant, Data palm and Apple). Fig. 4. The repeatability of the response of colorimetric sensor array for detection of blackcurrant pickle, acetic acid, apple pickle and data palm pickle after 25 min heating at 70°C. The Euclidean distance is the total length of the full dimensional color-difference vector, that is, the total array 14
response. The coefficient of variation of the array response was about 4.26% for blackcurrant pickle, 3.58% for acetic acid, 4.46% for apple pickle and 3.36% for data palm pickle. Fig. 5. Color difference maps of the CSA for nine studied fruit pickles and synthetic acetic acid at 70 ° C and 25 min. Fig. 6. (a) Classification results achieved by (a) PCA score plot and (b) HCA dendrogram based on the color difference maps shown in Fig. 5. Fig. 7. Color difference maps for black currant pickle contaminated with different amounts of (a) alum and (b) synthetic acetic acid. The distribution of the samples in the two-dimensional PCA score plot are given in the lower panel: yellow marker shows the pure black currant while red and blue markers denotes contaminated samples with alum and acetic acid, respectively. The color gradients show the samples of different concentrations of contaminants such that more intense colors higher concentration. Fig. 8. The results of the PLS regression after applying mean centering preprocessing method for quantitative analysis of (a) alum, (b) synthetic acetic acid. (Training and Test set is shown by blue and red markers, respectively).
Table 1. The results of prediction model of alum and synthetic acetic acid before and after applying mean centering preprocessing methods. Determination of Alum Preprocessing method
Determination of Synthetic acetic acid
RMSEC
RMSEP
R2c
R2p
Nothing
PLS factor 3
RMSEC
RMSEP
R2c
R2p
0.934
PLS factor 4
0.565
0.523
0.898
1.68
1.021
0.991
0.993
Mean centering
2
0.464
0.446
0.997
0.998
2
1.34
0.933
0.998
0.999
15
16
Fig. 1
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Fig. 2
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Fig. 3
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Fig. 4
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Fig. 5
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Fig. 6
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Fig. 7
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Fig. 8
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Bibliography Bahram Hemmateenejad is a professor of Chemistry at Chemistry Department of Shiraz University. He received his B.S. degree in pure chemistry from Shiraz University in 1996, M.S. and Ph.D. in Analytical Chemistry from Isfahan University of Technology and Shiraz University in 1998 and 2002, respectively. As fellow of Alexander von Humboldt Foundation, he visits different research institutes and universities in Germany. He runs a Lab on the development and applications of different chemometrics methods in the chemical disciplines of bioanalytical chemistry, chemo-biological interactions, computer-aided drug design and colorimetric sensors. Currently, he is large and active group working on paper-based sensors.
Mohammad Mahdi Bordbar received the BSC degree in applied chemistry from Kashan University in 2010 and M.Sc. degree in analytical chemistry from Yasouj University in 2012. He is currently a graduate student at the Department of Chemistry, Shiraz University, Shiraz, Iran. Javad Tashkhourian received his M.Sc. and Ph.D. degrees in analytical chemistry from Shiraz University in 1999 and 2004, respectively. He was a member of chemistry department of Persian Gulf University (2004–2010). He joined the chemistry department of Shiraz University in 2010 where he is now an Associate Professor. His research is focused on design and construction of chemical sensors (optical and electrochemical) and synthesis and applications of nanomaterials in electrochemical analysis.
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