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Preliminary study of ion mobility based electronic nose MGD-1 for discrimination of hard cheeses
Journal of Food Engineering 92 (2009) 202–207
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Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng
Preliminary study of ion mobility based electronic nose MGD-1 for discrimination of hard cheeses Oguz Gursoy a,*, Panu Somervuo b, Tapani Alatossava c a
Department of Food Engineering, Faculty of Engineering, Pamukkale University, Kinikli, TR-20070 Denizli, Turkey Department of Applied Biology, University of Helsinki, FIN-00014 Helsinki, Finland c Department of Food Technology, University of Helsinki, FIN-00014 Helsinki, Finland b
a r t i c l e
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Article history: Received 14 April 2008 Received in revised form 28 October 2008 Accepted 1 November 2008 Available online 12 November 2008 Keywords: Gas sensor Electronic nose Cheese Quality Control
a b s t r a c t Electronic nose presents a rapid method to determine quality characteristics of foodstuffs. Ion mobility spectrometry allows rapid and simple on-site determination of gaseous compounds. Ion mobility based electronic nose system ‘‘MGD-1” was used to determine the separation of various hard and extra-hard cheese samples via headspace analysis. The verification of the electronic nose system was performed for different applications such as the discrimination of Emmental cheeses based on either age or geographical origin, and the discrimination of different cheeses based on variety. Nine month ripened Emmental cheeses were successfully discriminated from Emmental cheeses with 3 or 6 month ripening time. The results obtained from Emmental cheeses and other hard and extra-hard cheese varieties showed that the MGD-1 system is of a significant potential for discrimination of the cheese samples. Ó 2008 Published by Elsevier Ltd.
1. Introduction An electronic nose is an instrument comprising an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of comparing and recognizing single or complex volatile samples (Gardner and Bartlett, 1994). Electronic noses attracted great deal of interest throughout the last decade. A key search for ‘‘electronic nose”, ‘‘artificial nose” or ‘‘sensor arrays” in the Web of ScienceÒ database reveals more than 2000 papers during the last 10 years (from 1998 to 2008). Electronic noses offer a cheap, quick, and reliable alternative for determination of the quality of foods to other expensive methods such as gas chromatography–mass spectrometry, infrared spectrometry or even a sensory panel (Bargon et al., 2003). Sensory evaluation methods are generally time-consuming and labor-intensive for routine quality control applications and require panelist training, or high numbers of consumer panelists. An electronic nose combines the response profiles of the various sensors that react to different types of volatiles in foodstuffs (Ramajaki et al., 2006). Since the early developments in electronic noses, food analysis has been considered as one of its most useful applications (Di Natale et al., 1997). Electronic noses have been used for different applications in dairy industry including the determination of microbial quality of milk (Marsili, 1999; Haugen et al., 2006), the differentiation of ice cream samples (Miettinen * Corresponding author. Tel.: +90 258 296 3120; fax: +90 258 296 3262. E-mail address: [email protected] (O. Gursoy). 0260-8774/$ - see front matter Ó 2008 Published by Elsevier Ltd. doi:10.1016/j.jfoodeng.2008.11.002
et al., 2002; Jiamyangyuen and Harper, 2004), the on-line monitoring of yogurt fermentation (Navratil et al., 2004), the characterization of Cheddar cheese flavor (O’Riordan and Delahunty, 2003), the ripening process monitoring of Danish blue cheese (Trihaas, 2004; Trihaas and Nielsen, 2005; Trihaas et al., 2005), the determination of shelf life of Crescenza cheese (Benedetti et al., 2005), the pattern recognition of Swiss cheese aroma compounds (Jou and Harper, 1998), quality control of cheeses (Schaller, 2000), the detection of rind taste in Swiss Emmental cheese (Schaller et al., 2000), and the determination of the geographical origin of Emmental cheese (Pillonel et al., 2003a). Although the results of these studies are challenging, results are usually limited to the number of samples studied. High number of products should be evaluated before an electronic nose system is introduced as a routine tool to determine cheese quality in dairy industry. Ion mobility spectrometry (IMS) permits rapid and simple onsite determination of gaseous compounds by field-able and handheld devices. MGD-1 is a fast, versatile and commercially available IMS-based gas detector manufactured by Environics Ltd. (Mikkeli, Finland). The detection is based on the ionization of gas molecules in an open-type ionization cell, IMCELLTM. Schematic diagram of the IMCELLTM sensor part of ion mobility based spectrometry MGD-1 gas detector is shown in Fig. 1. Operating principle of MGD-1 is similar to the military model gas sensor M90 described by Kotiaho et al. (1995). Briefly, IMCELLTM contains a radioactive source 160 lC of 241Americium, which is responsible for the ionization of the incoming sample gas molecules in the ionization chamber. The ion clusters formed are brought into different electrical fields
O. Gursoy et al. / Journal of Food Engineering 92 (2009) 202–207
Fig. 1. Schematic diagram of the IMCELLTM sensor part of ion mobility based spectrometry MGD-1 gas detector. There are three positive and negative channels detecting the particles, of which only the positive channels are shown in the figure (modified from Kolehmainen et al. (2003)).
perpendicular to the sample flow, and collected to the six separate electrodes forming a specific identifiable signal pattern for each gas. The cell of the MGD-1 sensor has six detection channels, three for positive and three for negative ions (Anonymous, 2000; Miettinen et al., 2002b). A background signal level was obtained by passing ambient air or control sample through the MGD-1 sensor. Ion mobility distribution changes in the presence of the volatile compounds present in a sample gas, and the responses of the channels are collected (Tuovinen et al., 2000). The MGD-1 has another semiconductor metal oxide sensor, which is mainly used for further confirmation of the responses of electrodes. The objective of this preliminary study is to investigate the potential use of an IMS-based electronic nose MGD-1 to discriminate hard cheeses based on either ripening age, origin and variety. Discrimination of Emmental cheese samples by the MGD-1 were also demonstrated using cheese samples artificially spiked with butyric acid at different concentrations. 2. Materials and methods 2.1. Cheese samples Twenty-four Emmental cheese samples manufactured by the Ingman Foods Ltd. (Finland) were purchased from local markets in Finland. The ripening ages of 3, 6 and 9 months were chosen based on the market status of cheese samples. These Emmental cheese samples were used in the discrimination study based on ripening age. Nine different Emmental cheese samples from different countries (Germany, France, the Netherlands, Switzerland and Finland) were used to evaluate the potential use of MGD-1 to discriminate cheese samples based on geographical origin. Seven different cheese samples were used to discriminate cheese samples based on variety. These samples were extra-hard cheese samples [Parmigiano Reggiano (Grifone Azzurro, Italy), Pecorino Romano (Unigrana, Italy), Grana (Valio, Finland), and Grana Padano (Zanetti, Italy)] and hard cheese samples [Old Master (Frico, Denmark), Emmental (Ingman, Finland), and Gruyere (Entremont, France)]. Cheese samples were obtained from various markets in Finland, France or Belgium.
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during electronic nose analysis were optimized before actual measurements. Outer layers of vacuum packed cheese blocks (1 cm) were discarded. Cheese samples were stored at 4 °C until analysis. Before analysis, each cheese block was kept at room temperature for at least an hour. Then, cylindrical cuts in triplicates were bored at different places in each block to minimize any variation due to gradients within the blocks. Each cheese sample (2 g) from each cut was weighed and placed into an Erlenmeyer flask (250 mL). Flasks were incubated at 55 °C in a water bath for 20 min prior to headspace analyses. Humidity of sample air was decreased by placing about 50 g of calcium chloride (CaCl2) granules into the bottom of flasks. Airflow was adjusted to 2 L/min, and IMCELLTM temperature was set to 35 °C. During measurements, the sample flask was exposed to the airflow for 150 s. Prior to analyses, the original joint stopper was rapidly removed and replaced with a glass measuring cap connected to the nose, and the baseline of the MGD1 was stabilized by ambient air. MGD-1 was also decontaminated between sample series by heating the IMCELLTM up to 80 °C for 10 min. 2.3. Data analysis A response per second was collected from each of the seven electrodes (channels) of the MGD-1. Total duration for each measurement was 150 s, and the data matrix for each measurement contained 7 150 numeric values. Triplicates of the experiments were run, and the normalized data from the sensor array of the electronic nose were analyzed by PCA (MATLABÒ, The MathWorks Inc., USA).
3. Results 3.1. Optimization of MGD-1 measurements Optimization of measurement conditions was the first step in the discrimination of cheese samples with the electronic nose. Different flow rates (1.0, 1.5 and 2.0 L/min), IMCELLTM temperatures (35 and 40 °C), sample amounts (1.0, 1.5, 2.0 and 5.0 g), sample incubation temperatures (20, 30 and 55 °C), relative humidity (from 13% to 40–55%) and absolute humidity levels (from 5 to 18 g/m3) were used to obtain optimum measurement conditions. As expected all these parameters had a significant effect on the responses of electronic nose. Elevated incubation temperatures increased the concentration of the volatile compounds in the headspace. However, extremely high temperatures could induce undesirable chemical reactions and interference in the sample matrix. Gas flow rate had a significant effect on sensor responses. At high flow rates, the aroma compounds passed over the sensor chamber quite quickly. However, at low flow rates volatile compounds were degraded in the tubing, and arrived at the IMCELLTM chamber over a long period of time. Sensor responses were flow rate dependent. Optimum sensory response intensities were found when flow rate was 2 L/min, IMCELLTM temperature 35 °C, incubation temperature 55 °C, incubation time 20 min, low humidity values (13–25% of relative humidity, 4–7 g/m3 absolute humidity), and sample amount 2.0 g. Fig. 2 shows a sensor response profile of an Emmental cheese sample obtained from the MGD-1 channels.
2.2. Electronic nose measurements 3.2. Discrimination of cheeses by variety Aroma compounds were measured by the MGD-1. A portable PC (Toshiba Satellite 100 CS/528 Model No. PAI217E YV, Toshiba Europe GmbH, Germany) equipped with a specific MGD User Interface Program (Version 1.0.1.26, MGD Software Version v3.0.0.G) was used to control the operation of the device and to collect data. Sample amount, flow rate, temperature and measurement conditions
The aroma patterns obtained from the electronic nose sensors are complicated, and they need to be analyzed by appropriate pattern recognition techniques such as principal component analysis (PCA), partial least squares (PLS), functional discriminant analysis (FDA) or other type of multivariate data analysis techniques. PCA
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hard cheeses. This could be due to several factors including ripening time and conditions, milk treatment, and the type of milk used in production. While Emmental cheese sample was distinguished from Old Master and Gruyere, the latter two cheese varieties overlapped. 3.3. Discrimination of Emmental cheeses by ripening age
Fig. 2. Channel responses for headspace of an Emmental cheese sample. SCCEL is the semiconductor metal oxide sensor.
is an orthogonal linear transformation that transforms the data set containing a large number of interrelated variables into a new coordinate system in a way that the greatest variance by any projection of the data comes to lie on the first coordinate (first principle component), the second greatest variance on the second coordinate, and so forth (Jolliffe, 2002). PCA reduces the dimensionality of a data set while retaining the variation in the original data set (Piggott and Sharman, 1986; Deisingh et al., 2004). PCA allows easy visualization of the information in a data set (Hai and Wang, 2006). Seven different cheese varieties (Parmigiano Reggiano, Pecorino Romano, Grana, Grana Padano, Old Master, Emmental, and Gruyere) were tested, and PCA results of the sum of channels of the MGD-1 are presented in Fig. 3. In this score plot the first principle component (PC1) explains a high variance, an indicator for the significance of this component. Results indicated five clusters of seven cheese varieties, where a clear discrimination was obtained for cheese samples of Parmigiano Reggiano (number 1) and Pecorino Romano (number 4). Pecorino Romano is a hardcurd, cooked cheese made only with whole ewe’s fresh milk. Other hard cheeses like Parmigiano Reggiano, Grana and Grana Padano are produced from cow milk. There are also some differences concerning to production and maturation time of these hard cheeses (Simon, 1965; Fox et al., 2000). Parmigiano Reggiano and Pecorino Romano were also discriminated from each other. Using a different kind of electronic nose, Benedetti et al. (2004) also found similar results for these cheese varieties. Processing technology for Grana and Grana Padano cheeses have similarities although the former is produced in Finland and the latter in Italy. Our results showed that these cheese varieties (numbers 2 and 3, respectively) were of similar characteristics (Fig. 3). The results of the PCA analysis of data from the sensors of MGD-1 indicated that this type of electronic nose could be utilized for separation of hard cheeses from extra-
The signals of channel 1 for Emmental cheese samples with ripening age of 3, 6 and 9 months are summarized in a PCA score plot (Fig. 4). The most ripened Emmental cheeses were clearly discriminated from the other Emmental cheeses with 3 or 6 month ripening time. One sample was mislocated in the clusters of PCA analysis (arrows in Fig. 4). Several factors could have an effect on the mislocation of cheese samples such as the history of sample, mislabeling, variations on storage and marketing conditions. Results indicated that the discrimination of 3 and 6 month ripened cheese samples was unresolved. One reason could be that samples in these two clusters were indeed very similar in terms of volatile compounds. 3.4. Discrimination of Emmental cheeses by geographical origin Food authenticity and traceability of origin have become subjects of great interest in recent years. Emmental cheese is produced in almost all developed countries. Its value and quality depends on the technology used. In particular regions of central Europe such as Switzerland, Austria, Eastern France, and Southern Germany, Emmental cheese is still manufactured by traditional technology using raw milk and copper vats (Pillonel et al., 2005). Swiss Emmental cheese is regarded as the most expensive (Bosset, 2001; Pillonel et al., 2005), and consequently there is a higher risk for adulteration. Therefore, dairy industry has been searching for rapid, low-cost and simple novel methods like electronic nose technology to check the authenticity of Emmental cheeses. In this present study, nine Emmental cheese samples (produced in winter season) with a ripening age of 2–4 months from different countries (Finland, France, Germany, the Netherlands and Switzerland) were analyzed, and measurements were repeated three times for each sample. In this preliminary study, the results of principle component analyses indicated that the MGD-1 electronic nose can be potentially used for the discrimination of cheeses with different geographical origins (Fig. 5). Although one cheese sample of Swiss origin was included in the preliminary study, this cheese was discriminated from other cheese samples of different geographical origin. Emmental cheese with Swiss origin is traditionally produced and has the highest price among this type of cheese samples. As expected, discrimination with limited number of samples and origins is most likely to enhance the quality of dis-
Fig. 3. Score plot obtained by applying PCA to MGD-1 e-nose data for different cheese varieties. 1; Parmigiano Reggiano, 2; Grana, 3; Grana Padano, 4; Pecorino Romano, 5; Old Master, 6; Emmental, and 7; Gruyere. Results are from sum of the channel responses. Each cheese has been analyzed three times and the mean of these data has been used for PCA.
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Fig. 4. Score plot obtained by applying PCA to MGD-1 e-nose data for 24 Emmental cheeses at three different ripening age categories. Results are from channel 1. d; 9 months ripened cheese, h; 6 months ripened cheese, and 4; 3 months ripened cheese.
Fig. 5. Score plot obtained by applying PCA to MGD-1 e-nose data for cheeses from different countries. Results are from sum of channel responses of the MGD-1 e-nose (FIN; Finland, FR; France, G; Germany, SW; Switzerland, and NL; the Netherlands).
crimination especially for the Emmental cheeses from Switzerland, Germany, and the Netherlands. Four French Emmental cheese samples were expected to fall into a single cluster on the PCA. However, they constituted two different clusters (Fig. 5). The difference between Finnish cheeses (FIN-1 and FIN-2) was much larger than the difference of FIN-2 from either FR-2 or FR-1. However, this system should be tested with the higher number of samples and origins in order to reach any conclusive result.
4. Discussion Electronic nose systems have got a lot of interest since they appeared on the market. The large number of studies has been
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devoted into the development of electronic nose systems for detection and measurement of odor compounds over the past 15 years. A number of sensor systems have been developed for different applications including food industry. Studies on the utilization of ion mobility based sensors for food applications is rather limited (Kotiaho et al., 1995; Miettinen et al., 2002a,b; Voysey, 2004; Vestergaard et al., 2007). These limited number of studies with ion mobility based electronic noses were quite promising for food quality control applications. For instance, Vestergaard et al. (2007) have recently used the MGD-1 system for future at- or on-line implementation in quality control of pork meat pizza topping product. To the best of our knowledge, this present paper, not a classification study, is the first on the assessment of the ion mobility based electronic nose MGD-1 for the discrimination of cheeses. Most of the studies regarding the applications of electronic nose systems on foodstuffs have been focused on the beneficial effects while disadvantages and failures of electronic nose systems are intentionally or unintentionally disregarded. Thus, a person, who is unfamiliar with e-nose technology, could easily overrate the reputation of the system. This argument was also mentioned by Schaller (2000) almost a decade ago. A major issue with different gas sensors including MGD-1 is their sensitivity to humidity. It is well-documented that water vapor affects measurements by gas sensors, and manufacturers of these devices have been forced to implement specific operating procedures (Deisingh et al., 2004). In this present study, the relative humidity of the sample air was decreased by a humidity trap to minimize the effect of water vapor on the sensor responses. After this procedure, better sensor responses were achieved. Results of this study showed that the electronic nose system MGD-1, even with limited number of sensors (channels), was adequate to discriminate cheese samples. Poor sensitivity or insensitivity of the semiconductor metal oxide sensor (SCCEL) was encountered in many cases. Weak acids and cheese volatile fatty acids (C-1 to C-6) present in the headspace could induce this kind of problem due to their detrimental effects on SCCEL (Schaller et al., 1999). In general, responses from the channels 1, 4, and 5 and the sum of all seven channels were most effective on the discrimination data for cheese samples. For the discrimination of cheeses based on variety and geographical origin, responses from the sum of all seven channels were effective. However, responses from the channel 1 were responsible for the separation of cheeses based on ripening age. Data from the channels 1, 4, and 5 and the sum of the date from all seven channels were basically enough to differentiate cheese samples based on volatile compounds, which means that MGD-1 was equipped with some non-selective sensors for cheese volatiles. An advanced model of the e-nose could be superior in terms of better detection of cheese volatiles if these insensitive sensors are replaced with new selective sensors. The results were promising for the potential use of the electronic nose as a screening tool for cheese discrimination (Figs. 3– 5). Results for Emmental cheese samples indicated that ripening stage could be successfully monitored by the MGD-1 system. This system was able to discriminate 9 month ripened Emmental cheese samples from 3 or 6 month ripened cheeses (Fig. 4). Nine month ripened Emmental cheese has a superior quality; thus its economical value is higher than others with lesser ripening ages in the market. This type of cheese sample is very susceptible to food adulteration. Electronic nose systems like MGD-1 could have a potential to be used for cheese inspection purposes on the market. The electronic nose system is a promising tool to discriminate different cheese varieties and origins (Figs. 3 and 5). The determination of the geographical origin for food products is a hard task, especially for cheese samples that are biochemically and microbi-
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ologically dynamic and which undergo changes during ripening. To discriminate the origin of Emmental cheeses from different European countries (France, Switzerland, Germany, Finland and Austria), more than 20 analytical methods including electronic nose systems were used by the Federal Dairy Research Institute (Bern, Switzerland) (Pillonel et al., 2002, 2003a,b,c). Twenty Emmental cheese samples in total (6 Swiss cheese, 14 from Finland, Germany, France, and Austria) were analyzed using a mass spectrometrybased electronic nose, and a good classification as Swiss (91% correct classification) and non-Swiss cheeses (91% correct classification) was achieved. In this present study, the Emmental cheeses especially from Switzerland and Germany were discriminated from other cheese samples (Fig. 5). There were also distinct discrimination for cheese samples from Finland and the Netherlands. The most common microbial defects of cheese are early and late gas formation. It is due to fermentation of lactate to butyrate and production of copious amounts of H2 by Clostridium tyrobutyricum and C. butyricum (Fox et al., 2000). Consequently large holes are generally produced. Butyrate is responsible for flavor defects in cheese. Late gas formation can be particularly prevalent in Emmental cheese. MGD-1 system has a potential application for the discrimination of butyric acid-spoiled Emmental cheese from unspoiled Emmental cheese. To detect the discrimination ability of the electronic nose MGD-1, Emmental cheese samples were spiked with increasing concentrations of butyric acid (0, 37 and 367 ppm), and these samples were tested. Sensor responses increased with an increase in butyric acid concentration and peaked at the highest concentration. With the results of this preliminary study, an actual spoiled Emmental cheese sample containing 300 ppm butyric acid (Valio Ltd., Finland) was analyzed, and the sensor responses were compared with those from an unspoiled cheese sample (data not shown). Results indicated that the MGD1 could be successfully used to detect butyric acid defects in Emmental cheese.
5. Conclusion MGD-1 electronic nose system can be used to discriminate cheese samples based on variety, ripening age or geographical origin. However, some improvements like replacing insensitive sensors are needed to increase the discrimination performance of the equipment used in this study. Discrimination of 9 month ripened Emmental cheese was successfully achieved while there was overlap in clusters of 3 and 6 month cheese samples. Although the number of samples used for the discrimination of cheese samples based on geographical origin is moderate, the MGD-1 system was able to discriminate cheese samples from different origins. This system has also a potential to be used to determine butyric acid defects in Emmental cheese samples. In conclusion, ion mobility based electronic nose system MGD-1 may have a great potential for the application of cheese discrimination once the limitations regarding the instrumentation are minimized or eliminated. Acknowledgements O. Gursoy was supported by the Centre for International Mobility (CIMO), Finland and Dairy Technology Research Group, Department of Food Technology, University of Helsinki, Finland. We wish to express our thanks to Mr. Ismo Loukoila (Environics Ltd., Mikkeli, Finland), Dr. Kirsi Jouppila (Department of Food Technology, University of Helsinki), Dr. Sanna-Maija Miettinen (Academy of Finland) and Mr. Antti Alavuotunki (Department of Food Technology, University of Helsinki) for their kind help. We would like to thank Dr. Yusuf Yilmaz (Pamukkale University, Turkey) for his suggestions and comments.
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Contents lists available at ScienceDirect
Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng
Preliminary study of ion mobility based electronic nose MGD-1 for discrimination of hard cheeses Oguz Gursoy a,*, Panu Somervuo b, Tapani Alatossava c a
Department of Food Engineering, Faculty of Engineering, Pamukkale University, Kinikli, TR-20070 Denizli, Turkey Department of Applied Biology, University of Helsinki, FIN-00014 Helsinki, Finland c Department of Food Technology, University of Helsinki, FIN-00014 Helsinki, Finland b
a r t i c l e
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Article history: Received 14 April 2008 Received in revised form 28 October 2008 Accepted 1 November 2008 Available online 12 November 2008 Keywords: Gas sensor Electronic nose Cheese Quality Control
a b s t r a c t Electronic nose presents a rapid method to determine quality characteristics of foodstuffs. Ion mobility spectrometry allows rapid and simple on-site determination of gaseous compounds. Ion mobility based electronic nose system ‘‘MGD-1” was used to determine the separation of various hard and extra-hard cheese samples via headspace analysis. The verification of the electronic nose system was performed for different applications such as the discrimination of Emmental cheeses based on either age or geographical origin, and the discrimination of different cheeses based on variety. Nine month ripened Emmental cheeses were successfully discriminated from Emmental cheeses with 3 or 6 month ripening time. The results obtained from Emmental cheeses and other hard and extra-hard cheese varieties showed that the MGD-1 system is of a significant potential for discrimination of the cheese samples. Ó 2008 Published by Elsevier Ltd.
1. Introduction An electronic nose is an instrument comprising an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of comparing and recognizing single or complex volatile samples (Gardner and Bartlett, 1994). Electronic noses attracted great deal of interest throughout the last decade. A key search for ‘‘electronic nose”, ‘‘artificial nose” or ‘‘sensor arrays” in the Web of ScienceÒ database reveals more than 2000 papers during the last 10 years (from 1998 to 2008). Electronic noses offer a cheap, quick, and reliable alternative for determination of the quality of foods to other expensive methods such as gas chromatography–mass spectrometry, infrared spectrometry or even a sensory panel (Bargon et al., 2003). Sensory evaluation methods are generally time-consuming and labor-intensive for routine quality control applications and require panelist training, or high numbers of consumer panelists. An electronic nose combines the response profiles of the various sensors that react to different types of volatiles in foodstuffs (Ramajaki et al., 2006). Since the early developments in electronic noses, food analysis has been considered as one of its most useful applications (Di Natale et al., 1997). Electronic noses have been used for different applications in dairy industry including the determination of microbial quality of milk (Marsili, 1999; Haugen et al., 2006), the differentiation of ice cream samples (Miettinen * Corresponding author. Tel.: +90 258 296 3120; fax: +90 258 296 3262. E-mail address: [email protected] (O. Gursoy). 0260-8774/$ - see front matter Ó 2008 Published by Elsevier Ltd. doi:10.1016/j.jfoodeng.2008.11.002
et al., 2002; Jiamyangyuen and Harper, 2004), the on-line monitoring of yogurt fermentation (Navratil et al., 2004), the characterization of Cheddar cheese flavor (O’Riordan and Delahunty, 2003), the ripening process monitoring of Danish blue cheese (Trihaas, 2004; Trihaas and Nielsen, 2005; Trihaas et al., 2005), the determination of shelf life of Crescenza cheese (Benedetti et al., 2005), the pattern recognition of Swiss cheese aroma compounds (Jou and Harper, 1998), quality control of cheeses (Schaller, 2000), the detection of rind taste in Swiss Emmental cheese (Schaller et al., 2000), and the determination of the geographical origin of Emmental cheese (Pillonel et al., 2003a). Although the results of these studies are challenging, results are usually limited to the number of samples studied. High number of products should be evaluated before an electronic nose system is introduced as a routine tool to determine cheese quality in dairy industry. Ion mobility spectrometry (IMS) permits rapid and simple onsite determination of gaseous compounds by field-able and handheld devices. MGD-1 is a fast, versatile and commercially available IMS-based gas detector manufactured by Environics Ltd. (Mikkeli, Finland). The detection is based on the ionization of gas molecules in an open-type ionization cell, IMCELLTM. Schematic diagram of the IMCELLTM sensor part of ion mobility based spectrometry MGD-1 gas detector is shown in Fig. 1. Operating principle of MGD-1 is similar to the military model gas sensor M90 described by Kotiaho et al. (1995). Briefly, IMCELLTM contains a radioactive source 160 lC of 241Americium, which is responsible for the ionization of the incoming sample gas molecules in the ionization chamber. The ion clusters formed are brought into different electrical fields
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Fig. 1. Schematic diagram of the IMCELLTM sensor part of ion mobility based spectrometry MGD-1 gas detector. There are three positive and negative channels detecting the particles, of which only the positive channels are shown in the figure (modified from Kolehmainen et al. (2003)).
perpendicular to the sample flow, and collected to the six separate electrodes forming a specific identifiable signal pattern for each gas. The cell of the MGD-1 sensor has six detection channels, three for positive and three for negative ions (Anonymous, 2000; Miettinen et al., 2002b). A background signal level was obtained by passing ambient air or control sample through the MGD-1 sensor. Ion mobility distribution changes in the presence of the volatile compounds present in a sample gas, and the responses of the channels are collected (Tuovinen et al., 2000). The MGD-1 has another semiconductor metal oxide sensor, which is mainly used for further confirmation of the responses of electrodes. The objective of this preliminary study is to investigate the potential use of an IMS-based electronic nose MGD-1 to discriminate hard cheeses based on either ripening age, origin and variety. Discrimination of Emmental cheese samples by the MGD-1 were also demonstrated using cheese samples artificially spiked with butyric acid at different concentrations. 2. Materials and methods 2.1. Cheese samples Twenty-four Emmental cheese samples manufactured by the Ingman Foods Ltd. (Finland) were purchased from local markets in Finland. The ripening ages of 3, 6 and 9 months were chosen based on the market status of cheese samples. These Emmental cheese samples were used in the discrimination study based on ripening age. Nine different Emmental cheese samples from different countries (Germany, France, the Netherlands, Switzerland and Finland) were used to evaluate the potential use of MGD-1 to discriminate cheese samples based on geographical origin. Seven different cheese samples were used to discriminate cheese samples based on variety. These samples were extra-hard cheese samples [Parmigiano Reggiano (Grifone Azzurro, Italy), Pecorino Romano (Unigrana, Italy), Grana (Valio, Finland), and Grana Padano (Zanetti, Italy)] and hard cheese samples [Old Master (Frico, Denmark), Emmental (Ingman, Finland), and Gruyere (Entremont, France)]. Cheese samples were obtained from various markets in Finland, France or Belgium.
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during electronic nose analysis were optimized before actual measurements. Outer layers of vacuum packed cheese blocks (1 cm) were discarded. Cheese samples were stored at 4 °C until analysis. Before analysis, each cheese block was kept at room temperature for at least an hour. Then, cylindrical cuts in triplicates were bored at different places in each block to minimize any variation due to gradients within the blocks. Each cheese sample (2 g) from each cut was weighed and placed into an Erlenmeyer flask (250 mL). Flasks were incubated at 55 °C in a water bath for 20 min prior to headspace analyses. Humidity of sample air was decreased by placing about 50 g of calcium chloride (CaCl2) granules into the bottom of flasks. Airflow was adjusted to 2 L/min, and IMCELLTM temperature was set to 35 °C. During measurements, the sample flask was exposed to the airflow for 150 s. Prior to analyses, the original joint stopper was rapidly removed and replaced with a glass measuring cap connected to the nose, and the baseline of the MGD1 was stabilized by ambient air. MGD-1 was also decontaminated between sample series by heating the IMCELLTM up to 80 °C for 10 min. 2.3. Data analysis A response per second was collected from each of the seven electrodes (channels) of the MGD-1. Total duration for each measurement was 150 s, and the data matrix for each measurement contained 7 150 numeric values. Triplicates of the experiments were run, and the normalized data from the sensor array of the electronic nose were analyzed by PCA (MATLABÒ, The MathWorks Inc., USA).
3. Results 3.1. Optimization of MGD-1 measurements Optimization of measurement conditions was the first step in the discrimination of cheese samples with the electronic nose. Different flow rates (1.0, 1.5 and 2.0 L/min), IMCELLTM temperatures (35 and 40 °C), sample amounts (1.0, 1.5, 2.0 and 5.0 g), sample incubation temperatures (20, 30 and 55 °C), relative humidity (from 13% to 40–55%) and absolute humidity levels (from 5 to 18 g/m3) were used to obtain optimum measurement conditions. As expected all these parameters had a significant effect on the responses of electronic nose. Elevated incubation temperatures increased the concentration of the volatile compounds in the headspace. However, extremely high temperatures could induce undesirable chemical reactions and interference in the sample matrix. Gas flow rate had a significant effect on sensor responses. At high flow rates, the aroma compounds passed over the sensor chamber quite quickly. However, at low flow rates volatile compounds were degraded in the tubing, and arrived at the IMCELLTM chamber over a long period of time. Sensor responses were flow rate dependent. Optimum sensory response intensities were found when flow rate was 2 L/min, IMCELLTM temperature 35 °C, incubation temperature 55 °C, incubation time 20 min, low humidity values (13–25% of relative humidity, 4–7 g/m3 absolute humidity), and sample amount 2.0 g. Fig. 2 shows a sensor response profile of an Emmental cheese sample obtained from the MGD-1 channels.
2.2. Electronic nose measurements 3.2. Discrimination of cheeses by variety Aroma compounds were measured by the MGD-1. A portable PC (Toshiba Satellite 100 CS/528 Model No. PAI217E YV, Toshiba Europe GmbH, Germany) equipped with a specific MGD User Interface Program (Version 1.0.1.26, MGD Software Version v3.0.0.G) was used to control the operation of the device and to collect data. Sample amount, flow rate, temperature and measurement conditions
The aroma patterns obtained from the electronic nose sensors are complicated, and they need to be analyzed by appropriate pattern recognition techniques such as principal component analysis (PCA), partial least squares (PLS), functional discriminant analysis (FDA) or other type of multivariate data analysis techniques. PCA
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hard cheeses. This could be due to several factors including ripening time and conditions, milk treatment, and the type of milk used in production. While Emmental cheese sample was distinguished from Old Master and Gruyere, the latter two cheese varieties overlapped. 3.3. Discrimination of Emmental cheeses by ripening age
Fig. 2. Channel responses for headspace of an Emmental cheese sample. SCCEL is the semiconductor metal oxide sensor.
is an orthogonal linear transformation that transforms the data set containing a large number of interrelated variables into a new coordinate system in a way that the greatest variance by any projection of the data comes to lie on the first coordinate (first principle component), the second greatest variance on the second coordinate, and so forth (Jolliffe, 2002). PCA reduces the dimensionality of a data set while retaining the variation in the original data set (Piggott and Sharman, 1986; Deisingh et al., 2004). PCA allows easy visualization of the information in a data set (Hai and Wang, 2006). Seven different cheese varieties (Parmigiano Reggiano, Pecorino Romano, Grana, Grana Padano, Old Master, Emmental, and Gruyere) were tested, and PCA results of the sum of channels of the MGD-1 are presented in Fig. 3. In this score plot the first principle component (PC1) explains a high variance, an indicator for the significance of this component. Results indicated five clusters of seven cheese varieties, where a clear discrimination was obtained for cheese samples of Parmigiano Reggiano (number 1) and Pecorino Romano (number 4). Pecorino Romano is a hardcurd, cooked cheese made only with whole ewe’s fresh milk. Other hard cheeses like Parmigiano Reggiano, Grana and Grana Padano are produced from cow milk. There are also some differences concerning to production and maturation time of these hard cheeses (Simon, 1965; Fox et al., 2000). Parmigiano Reggiano and Pecorino Romano were also discriminated from each other. Using a different kind of electronic nose, Benedetti et al. (2004) also found similar results for these cheese varieties. Processing technology for Grana and Grana Padano cheeses have similarities although the former is produced in Finland and the latter in Italy. Our results showed that these cheese varieties (numbers 2 and 3, respectively) were of similar characteristics (Fig. 3). The results of the PCA analysis of data from the sensors of MGD-1 indicated that this type of electronic nose could be utilized for separation of hard cheeses from extra-
The signals of channel 1 for Emmental cheese samples with ripening age of 3, 6 and 9 months are summarized in a PCA score plot (Fig. 4). The most ripened Emmental cheeses were clearly discriminated from the other Emmental cheeses with 3 or 6 month ripening time. One sample was mislocated in the clusters of PCA analysis (arrows in Fig. 4). Several factors could have an effect on the mislocation of cheese samples such as the history of sample, mislabeling, variations on storage and marketing conditions. Results indicated that the discrimination of 3 and 6 month ripened cheese samples was unresolved. One reason could be that samples in these two clusters were indeed very similar in terms of volatile compounds. 3.4. Discrimination of Emmental cheeses by geographical origin Food authenticity and traceability of origin have become subjects of great interest in recent years. Emmental cheese is produced in almost all developed countries. Its value and quality depends on the technology used. In particular regions of central Europe such as Switzerland, Austria, Eastern France, and Southern Germany, Emmental cheese is still manufactured by traditional technology using raw milk and copper vats (Pillonel et al., 2005). Swiss Emmental cheese is regarded as the most expensive (Bosset, 2001; Pillonel et al., 2005), and consequently there is a higher risk for adulteration. Therefore, dairy industry has been searching for rapid, low-cost and simple novel methods like electronic nose technology to check the authenticity of Emmental cheeses. In this present study, nine Emmental cheese samples (produced in winter season) with a ripening age of 2–4 months from different countries (Finland, France, Germany, the Netherlands and Switzerland) were analyzed, and measurements were repeated three times for each sample. In this preliminary study, the results of principle component analyses indicated that the MGD-1 electronic nose can be potentially used for the discrimination of cheeses with different geographical origins (Fig. 5). Although one cheese sample of Swiss origin was included in the preliminary study, this cheese was discriminated from other cheese samples of different geographical origin. Emmental cheese with Swiss origin is traditionally produced and has the highest price among this type of cheese samples. As expected, discrimination with limited number of samples and origins is most likely to enhance the quality of dis-
Fig. 3. Score plot obtained by applying PCA to MGD-1 e-nose data for different cheese varieties. 1; Parmigiano Reggiano, 2; Grana, 3; Grana Padano, 4; Pecorino Romano, 5; Old Master, 6; Emmental, and 7; Gruyere. Results are from sum of the channel responses. Each cheese has been analyzed three times and the mean of these data has been used for PCA.
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Fig. 4. Score plot obtained by applying PCA to MGD-1 e-nose data for 24 Emmental cheeses at three different ripening age categories. Results are from channel 1. d; 9 months ripened cheese, h; 6 months ripened cheese, and 4; 3 months ripened cheese.
Fig. 5. Score plot obtained by applying PCA to MGD-1 e-nose data for cheeses from different countries. Results are from sum of channel responses of the MGD-1 e-nose (FIN; Finland, FR; France, G; Germany, SW; Switzerland, and NL; the Netherlands).
crimination especially for the Emmental cheeses from Switzerland, Germany, and the Netherlands. Four French Emmental cheese samples were expected to fall into a single cluster on the PCA. However, they constituted two different clusters (Fig. 5). The difference between Finnish cheeses (FIN-1 and FIN-2) was much larger than the difference of FIN-2 from either FR-2 or FR-1. However, this system should be tested with the higher number of samples and origins in order to reach any conclusive result.
4. Discussion Electronic nose systems have got a lot of interest since they appeared on the market. The large number of studies has been
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devoted into the development of electronic nose systems for detection and measurement of odor compounds over the past 15 years. A number of sensor systems have been developed for different applications including food industry. Studies on the utilization of ion mobility based sensors for food applications is rather limited (Kotiaho et al., 1995; Miettinen et al., 2002a,b; Voysey, 2004; Vestergaard et al., 2007). These limited number of studies with ion mobility based electronic noses were quite promising for food quality control applications. For instance, Vestergaard et al. (2007) have recently used the MGD-1 system for future at- or on-line implementation in quality control of pork meat pizza topping product. To the best of our knowledge, this present paper, not a classification study, is the first on the assessment of the ion mobility based electronic nose MGD-1 for the discrimination of cheeses. Most of the studies regarding the applications of electronic nose systems on foodstuffs have been focused on the beneficial effects while disadvantages and failures of electronic nose systems are intentionally or unintentionally disregarded. Thus, a person, who is unfamiliar with e-nose technology, could easily overrate the reputation of the system. This argument was also mentioned by Schaller (2000) almost a decade ago. A major issue with different gas sensors including MGD-1 is their sensitivity to humidity. It is well-documented that water vapor affects measurements by gas sensors, and manufacturers of these devices have been forced to implement specific operating procedures (Deisingh et al., 2004). In this present study, the relative humidity of the sample air was decreased by a humidity trap to minimize the effect of water vapor on the sensor responses. After this procedure, better sensor responses were achieved. Results of this study showed that the electronic nose system MGD-1, even with limited number of sensors (channels), was adequate to discriminate cheese samples. Poor sensitivity or insensitivity of the semiconductor metal oxide sensor (SCCEL) was encountered in many cases. Weak acids and cheese volatile fatty acids (C-1 to C-6) present in the headspace could induce this kind of problem due to their detrimental effects on SCCEL (Schaller et al., 1999). In general, responses from the channels 1, 4, and 5 and the sum of all seven channels were most effective on the discrimination data for cheese samples. For the discrimination of cheeses based on variety and geographical origin, responses from the sum of all seven channels were effective. However, responses from the channel 1 were responsible for the separation of cheeses based on ripening age. Data from the channels 1, 4, and 5 and the sum of the date from all seven channels were basically enough to differentiate cheese samples based on volatile compounds, which means that MGD-1 was equipped with some non-selective sensors for cheese volatiles. An advanced model of the e-nose could be superior in terms of better detection of cheese volatiles if these insensitive sensors are replaced with new selective sensors. The results were promising for the potential use of the electronic nose as a screening tool for cheese discrimination (Figs. 3– 5). Results for Emmental cheese samples indicated that ripening stage could be successfully monitored by the MGD-1 system. This system was able to discriminate 9 month ripened Emmental cheese samples from 3 or 6 month ripened cheeses (Fig. 4). Nine month ripened Emmental cheese has a superior quality; thus its economical value is higher than others with lesser ripening ages in the market. This type of cheese sample is very susceptible to food adulteration. Electronic nose systems like MGD-1 could have a potential to be used for cheese inspection purposes on the market. The electronic nose system is a promising tool to discriminate different cheese varieties and origins (Figs. 3 and 5). The determination of the geographical origin for food products is a hard task, especially for cheese samples that are biochemically and microbi-
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ologically dynamic and which undergo changes during ripening. To discriminate the origin of Emmental cheeses from different European countries (France, Switzerland, Germany, Finland and Austria), more than 20 analytical methods including electronic nose systems were used by the Federal Dairy Research Institute (Bern, Switzerland) (Pillonel et al., 2002, 2003a,b,c). Twenty Emmental cheese samples in total (6 Swiss cheese, 14 from Finland, Germany, France, and Austria) were analyzed using a mass spectrometrybased electronic nose, and a good classification as Swiss (91% correct classification) and non-Swiss cheeses (91% correct classification) was achieved. In this present study, the Emmental cheeses especially from Switzerland and Germany were discriminated from other cheese samples (Fig. 5). There were also distinct discrimination for cheese samples from Finland and the Netherlands. The most common microbial defects of cheese are early and late gas formation. It is due to fermentation of lactate to butyrate and production of copious amounts of H2 by Clostridium tyrobutyricum and C. butyricum (Fox et al., 2000). Consequently large holes are generally produced. Butyrate is responsible for flavor defects in cheese. Late gas formation can be particularly prevalent in Emmental cheese. MGD-1 system has a potential application for the discrimination of butyric acid-spoiled Emmental cheese from unspoiled Emmental cheese. To detect the discrimination ability of the electronic nose MGD-1, Emmental cheese samples were spiked with increasing concentrations of butyric acid (0, 37 and 367 ppm), and these samples were tested. Sensor responses increased with an increase in butyric acid concentration and peaked at the highest concentration. With the results of this preliminary study, an actual spoiled Emmental cheese sample containing 300 ppm butyric acid (Valio Ltd., Finland) was analyzed, and the sensor responses were compared with those from an unspoiled cheese sample (data not shown). Results indicated that the MGD1 could be successfully used to detect butyric acid defects in Emmental cheese.
5. Conclusion MGD-1 electronic nose system can be used to discriminate cheese samples based on variety, ripening age or geographical origin. However, some improvements like replacing insensitive sensors are needed to increase the discrimination performance of the equipment used in this study. Discrimination of 9 month ripened Emmental cheese was successfully achieved while there was overlap in clusters of 3 and 6 month cheese samples. Although the number of samples used for the discrimination of cheese samples based on geographical origin is moderate, the MGD-1 system was able to discriminate cheese samples from different origins. This system has also a potential to be used to determine butyric acid defects in Emmental cheese samples. In conclusion, ion mobility based electronic nose system MGD-1 may have a great potential for the application of cheese discrimination once the limitations regarding the instrumentation are minimized or eliminated. Acknowledgements O. Gursoy was supported by the Centre for International Mobility (CIMO), Finland and Dairy Technology Research Group, Department of Food Technology, University of Helsinki, Finland. We wish to express our thanks to Mr. Ismo Loukoila (Environics Ltd., Mikkeli, Finland), Dr. Kirsi Jouppila (Department of Food Technology, University of Helsinki), Dr. Sanna-Maija Miettinen (Academy of Finland) and Mr. Antti Alavuotunki (Department of Food Technology, University of Helsinki) for their kind help. We would like to thank Dr. Yusuf Yilmaz (Pamukkale University, Turkey) for his suggestions and comments.
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