Flavor EngineeringâA Methodology To Predict Sensory Qualities of

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Flavor Engineering – a methodology to predict sensory qualities of flavored products Ana Monteiro, Patrícia Costa, José Miguel Loureiro, and Alirio Egidio Rodrigues Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b00527 • Publication Date (Web): 11 May 2018 Downloaded from http://pubs.acs.org on May 15, 2018

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Industrial & Engineering Chemistry Research

Flavor Engineering – a methodology to predict sensory qualities of flavored products Ana Monteiro§, Patrícia Costa*§, José Miguel Loureiro, Alírio E. Rodrigues Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials (LSRE-LCM), Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.

*E-mail: [email protected]. Tel.: +351 22 041 3688. Fax: +351 22 508 1674. §

Authors contributed equally to this work.

Abstract A simple methodology able to predict the sensory quality of flavored products based on their gas phase composition together with psychophysical models and olfactory descriptors is proposed. Fruit juices (lemon, peach, pineapple, apple and mango) were studied as an example of flavored products. The gas phase composition of each pure fruit juice was assessed using headspace and chromatographic techniques. Results revealed that the proposed methodology can be applied for the evaluation of the dominant olfactive families of pure fruit juices, as well as for binary and ternary fruit juices mixtures. The validation of this technique was performed through a sensorial evaluation (consumers) and a good agreement was achieved when compared their findings with those of the theoretical data.

Keywords: Fruit juices, headspace composition, sensory analysis, odor profile, flavor profile

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Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Introduction The food industry is interested in maintaining the sensorial quality of flavored products1 and in developing delightful flavors once the acceptance of a food product is influenced by the sensations (e.g. appearance, texture, odor and flavor) that consumers experiment during its intake. With this in mind, several novel flavored products are launched on the market aiming to provide new sensorial feelings to consumers. But the exploration for new odors and flavors is a complex task requiring a high number of formulations to attain the desired odor and flavor thus entailing high costs. This is because the release of aromas from the matrix and, ultimately, the sensory perception of a flavored product, is dependent on, for example, the volatility of each aroma compound, affinity with the matrix and concentration of aroma components. Besides that, the sensory quality of these formulations is evaluated by a panel, which means that the obtained results have a subjective character.2,3 Therefore, it would be of great relevance to create or adjust existent methodologies for predicting the final odor and flavor profiles (citrus, fresh, etc.) of mixtures containing different fruits just with reduced number of formulations and subjectivity. In the perfumery context, Teixeira et al.3 proposed a theoretical model for the classification of perfumes in olfactory families (citrus, fruity, floral, green, herbaceous, musk, oriental, woody) based on physicochemical models and odor descriptors called the Perfumery Radar (PR) methodology. The PR has shown to correctly predict the primary olfactory families of different commercial perfumes, giving important guidelines when designing perfumed products. Thus, based on the PR approach, and with the necessary adjustments, the present study proposes a methodology for predicting the odor (volatiles orthonasal perception) and flavor (volatiles retronasal perception) profiles of flavored products. Basically, this model intends to predict the final odor and flavor of flavored products from their gas phase composition together with a psychophysical model and sensory descriptors. In a first attempt, we decided to apply our methodology to fruit juices as a simple example of flavored products, once the liquid matrix usually offers a minimal resistance to volatiles passage in comparison with solid matrices.

4


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However, we believe that the methodology can be extended to other flavored products. For that, the headspace (i.e. the gas phase above the liquid mixture) of five commercial pure fruit juices (lemon, peach, pineapple, apple and mango) was analyzed using dynamic headspace and gas chromatography coupled to mass spectrometry (DHS/GC-MS). Then, based on the gas concentrations of each component present in the fruit juice together with the odor and flavor thresholds we calculated the odor and flavor intensities. Thereafter, the family odor intensity model where relative weights were attributed based on the odor and flavor descriptors. Then, using the data of single samples, it was also possible to predict the odor and flavor of three binary and one ternary fruit juices mixtures. The theoretical (i.e. data from DHS/GC-MS analysis, psychophysical model and sensory descriptors) and experimental (sensory analysis) radars obtained for pure, binary and ternary fruit juices mixtures were compared.

Materials and Methods Samples Five commercial pure fruit juices from Sumol+Compal (lemon, peach, pineapple, apple and mango) were purchased at a local supermarket (Table 1).

Table 1. Composition and nutritional value of the studied juices. Juice

Composition

Peach

Peach fruit juice and pulp, water, sugar and ascorbic acid (vitamin C). Apple fruit juice: made from concentrate and lemon concentrate. Pineapple fruit juice: made from concentrate, water, sugar, acidity regulator: citric acid and antioxidant: ascorbic acid. 50% of juice content (minimum). Water, mango fruit juice and pulp: made from concentrate, sugar, acidity regulator: citric acid, antioxidant: ascorbic acid.

Apple

Pineapple

Mango

Nutritional value (per 100 mL) Energy: 187 kJ / 44 kcal, carbohydrates 11 g where 7.9 g are sugars, vitamin C: 6 mg. Energy: 184 kJ / 43 kcal; Carbohydrates: 11 g where 9.5 g are sugars. Energy: 201 kJ /4 7 kcal; Carbohydrates: 12 g where 9 g are sugars.

Energy: 226 kJ/53 kcal; Carbohydrates where 9.3 g are starch, 13 g are polyols; Proteins 0.7 g. 3

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Lemon

50% of juice content (minimum). Water, lemon fruit juice (10%): made from concentrate, sugar, citrus fruit pulp (0.7%), aromas, antioxidant: ascorbic acid and sweeteners: sucralose and acesulfame K. 10% of juice content (minimum).

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Energy: 53 kJ/12 kcal; Carbohydrates 3 g where 1.5 g are sugar; Proteins 0.3 g; Salt: 0.08 g.

Dynamic Headspace and Gas Chromatography Coupled to Mass Spectrometry Analysis The headspace compositions of the studied fruit juices, i.e., the vapor phase above the liquid mixtures, were assessed using the Master Dynamic Headspace (DHS) (DANI Instruments S.p.A., Milan, Italy) coupled to a gas chromatograph (GC) (Varian CP-3800) and a Varian Saturn 2000MS ion-trap mass spectrometer (MS), controlled by Varian MS Workstation 6.9 software and equipped with Rxi®-5Sil MS (30 m × 0.25 mm, 0.25 µm film thickness) and CPWAX 52CB (50 m × 0.25 mm, 0.2 µm film thickness) columns. The DHS analysis occurred in five steps: incubation, stripping, dry, injection and baking. In incubation step, the vial containing the sample was preheated inside the DHS oven and, after that, it was pierced by a double needle and flushed with the auxiliary gas (nitrogen). Then, in stripping step, the volatiles present in the fruit juices samples were concentrated in a cooled trap (90 mm long quartz tube with 4 mm O.D.) filled with Tenax GR 60–80 mesh for approximately 4 cm. The dry step was the stage after; here, the humidity from the trap was removed prior to GC injection and, afterwards, the switching valve rotated and the trap was disabled from the auxiliary gas circuit. The carrier gas (He N60) was passed through the trap, heated to the set temperature, and then passed through the dew stop device kept at 0 °C to remove humidity. The trap desorption phase started and the sample was injected in the GC-MS (injection step). Finally, the baking step occurred where the system was flushed with the auxiliary gas to condition the trap and to remove the possible traces of condensed water or sample analytes. The DHS working parameters are presented in Table 2.


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Table 2. DHS parameters. Incubation step Sample volume (µL) Time (min) Oven temperature (ºC) Shaking Stripping step Time (min) Flow (mL/min) Trap temperature (ºC) Trap dry step Time (min) Dry step flow (mL/min) Trap temperature (ºC) Injection Time Time (min) Dew stop temperature (ºC) Trap temperature (ºC) Baking step Time (min) Flow (mL/min) Trap temperature (ºC) Dew stop temperature (ºC) Other Transfer line temperature (ºC) Switching valve temperature (ºC) Trap maximum temperature (ºC)

500, 1000 5, 1 40 Fast mode 5, 2 500 40 1 30 0

1 0 250 15 80 300 120 250 210 320

Regarding to GC-MS analysis, the injector was programmed at 240 ºC and the samples were injected with a split ratio of 2:1 and 20:1. The used carrier gas was helium (He N60) at a constant flow rate of 1.0 mL/min. The oven temperature was set at 65 ºC for 5 min, followed by an increase of 4 ºC/min up to 250 ºC and held isothermal during 5 min. For the CP-WAX 52CB column (used for the chemical analysis of lemon juice), the temperature was set at 65 ºC for 5 min increasing to 200 ºC at a rate of 3 ºC/min and held isothermally for 20 min. All mass spectra were acquired in the electron impact (EI) mode. The transfer line, manifold and trap temperatures were 171, 83 and 150 °C, respectively. The mass ranged from 18 to 500 m/z, the emission current was 10 µA, and the maximum ionization time was 0.025 s. The components were identified taking into account their retention indices relative to C8-C20 n-alkanes and mass spectra. It was also used the NIST98 Spectral Library, the mass spectral database of Flavors and Fragrances of Natural and Synthetic Compounds 2 (FFNSC2) from Wiley, an in-house library (with more than 200 pure reference chemicals) and literature data.5-31 5 ACS Paragon Plus Environment


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Odor and Flavor Radars For the classification of fruit juices the following steps were followed: (1) Identification of components present in the headspace of the studied fruit juices. Pease note that this methodology assumes that all components identified in the headspace contribute to the overall odor and flavor of the mixture; (2) Calculation of the odor and flavor intensities using psychophysical models. Here, the gas phase concentrations were converted into olfactory perceptions through the psychophysical model Odor Value (OV). In this study, the gas concentration of each component was directly obtained from gas chromatographic peak areas without any correction factor and considering a linear response factor for all species. The is the ratio between the gas phase concentration of

a component (

) and the odour detection threshold ( ) (eq 1), i.e., the minimum gas

concentration at which an odorant is perceived by the human nose.32,33 As recommended, the used ODTs were obtained from a compilation of values from the literature database and geometrically averaged.34,35

=

(1)

As previously mentioned, volatile molecules can reach the olfactory epithelium through orthonasal (sniff) or retronasal (mouth) airways. Once the OV model only accounts for the orthonasal perception, it was necessary to find a similar approach in order to evaluate the sensory intensity of aromas that reach the nose by retronasal way. For that, we proposed the Flavor Value (FV) which, in analogy to the OV, considers the concentration of a component in the gas phase

(

) but applies the flavor detection threshold ( ) – instead the – which corresponds

to the lowest liquid concentration of a component at which it is detected by the retronasal route (eq 2):

=

(2)


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FDT values were obtained from a compilation of values from the literature database and geometrically averaged.36 In this database,36 flavor is defined as “all perceptions based on the senses of odor and/or taste and/or the common chemical sense - or other “chemical” senses -, when a food or a drink is put into the mouth. Thus, odor can again be perceived, but this time via the retronasal route”. (3) Classification of pure components in terms of odor and flavor descriptors, i.e., olfactory families that describe the odor or flavor of a certain component (Table 3). Here, nine olfactory families were selected (fruity, sweet, green, woody, fresh, spicy, citrus, fatty and ripe) based on the most frequent terms for the classification of pure components. For the particular case of fruity and sweet families, they were differentiated in fruity peach, fruity pineapple, fruity apple, fruity mango, sweet peach, sweet pineapple, sweet apple and sweet mango, in order to distinguish the dominant fruit in binary and ternary fruit juices mixtures. Table 3. Odor and flavor descriptions of each selected family. Family Fruity

Odor Description Mixture of fruits.

Flavor Description

3,37

Sense from natural fruits. It evokes a variety of fruits.38,39

Sweet

Sweet

taste

(e.g.

ripe

frequently smell “sweet”). Green

fruit

- Basic

40,38

taste

associated

to

a

sucrose

40,39,41

solution.

Typical botanical notes with scent of Green or under-ripe fruit.38,39,41 fresh leaves or with reminiscent freshness.3,37

Woody

Linked to dry fresh cut wood.3,42

Brown, musty aromatics related to plants with fibres and bark.43

Fresh

Clean, cool, refreshing, and new.44

Fresh fruit (e.g. pineapple); also linked with raw fruit.39

Spicy

Sweet brown* with musty nuance, aromatic

Spices.

reminiscent of cinnamon.41 Citrus

Freshness and lightness related to Freshly cut citrus fruits and a blend of fruits with a citrus character (e.g. flavors related with citrus fruits.38,39,43 lime, lemon, orange).3,37

Fatty

Related to exudates fat.42

Amount of fatty perceived by the tongue 7



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when moved over the surface of the mouth.40 Ripe

Intense fruit odor with a sweet Over-ripe fruit.37 nuance.42

*

Brown: Defined as a sharp, caramel, almost burnt aromatic;38

Once literature classifications consider one or more families/subfamilies to each fragrant component a weighting criterion was considered (family odor intensity model).3 Here, a weight

factor was attributed for each component () in each family ( ) (Table 4) and, from this point, the overall OV and FV of each olfactory family were calculated through the summation of the OVs and FVs, respectively, of all fragrant components belonging to a certain family (eqs 3 and 4). Table 4. Attribution of weights for each olfactory family.3 Family Number of families Primary

Secondary

1

100%

2

70%

30%

3

60%

30%

Tertiary

10%

Then and were calculated according to eqs 3 and 4 and represented in the odor and flavor radars.

(3)

(4)

= ×

= ×

Finally, the odor and flavor families were normalized (′ and ′ ) following eqs 5 and 6: 8 ACS Paragon Plus Environment

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′ =

∑

(5)

′ =

∑

(6)

Sensory Analyses The odor and flavor radars were validated through sensory analyses using a non-trained panel (consumers) composed of 7 persons (4 females and 3 males) between 22 to 41 years of age, from the Associate Laboratory LSRE-LCM (University of Porto). Consumers were selected based on having no allergic reactions. They underwent an orientation session where they were asked to avoid the use of strong odors (e.g. perfumes, scented creams, etc.) the day before and the day of the sensory evaluation, and to not eat or drink at least 30 minutes before the evaluation, as recommended for this type of analyses.45 The four test sessions were performed in different days, in a meeting vented room. All samples were previously prepared in equal PET cups (15 mL were added in each PET cup, as recommended by Watts et al.45 For binary fruit juices mixtures, 7.5 mL of each pure fruit juice were added in a PET cup and for the ternary fruit juices mixture, 5 mL of each pure fruit juice were mixed also in a PET cup. Following the family odor intensity model, consumers were asked to attribute a weight factor to the three most perceived olfactory families of each sample in a 3-point scale with 3 being the most perceived family and 1 the least perceived. For odor evaluation, consumers smelled the sample with the mouth closed, while for flavor consumers clamped their noses before and kept them clamped whilst drinking the sample, releasing it only after they set the PET cup on the table.



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Results Chemical Analysis The headspace chemical profile of each pure fruit juice (lemon, peach, pineapple, apple and mango) was obtained through GC-MS and the results are displayed in Tables S1-S5. Although the proposed methodology assumes that all components identified in the headspace contribute to the overall odor and flavor, it is important to highlight that some authors refer that not all of them have a positive contribute to the scent of the mixture.46 Another important point to clarify before analyzing the results is that the used GC columns are not chiral. This note is particularly important in the case of limonene, which is a chiral molecule with two isomers [(R)-(+)limonene and (S)-(-)-limonene] with distinct characteristics in terms of odor and flavor: the first one is found in orange fruit while the second one is found in lemon fruit. However, as the columns here used were not chiral, it was not possible to confirm which isomer was present in the samples and, for that reason, it was decided to use the general description citrus for both sensations (odor and flavor). The chemical analysis allowed the identification of limonene and isoamyl acetate as the major components found in the headspace of the lemon (Table S1) and peach (Table S2) juices, respectively. Pronounced amounts of ethyl 2-methylbutanoate were detected in the headspace of the pineapple juice (Table S3). 2-Methylbutyl butanoate appeared as the predominant component identified in the apple juice (Table S4), while the headspace composition

of

mango

juice

was

mainly

composed

of

3-carene

(Table

S5).


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Odor and Flavor Radars The odor and flavor radars were plotted for the pure fruit juices from their chemical composition assessed by DHS/GC-MS together with the OV and FV models, sensory analysis and the sensory descriptors47-48 (Tables S6-S43) and the results are displayed in Figures 1 to 5 (theoretical radars). Then, based on the data of the pure juices, the theoretical binary (Figures 6 to 8) and ternary (Figure 9) fruit juices mixtures radars were created. The theoretical (i.e. data from DHS/GC-MS analysis, OV and FV models and sensory descriptors) and experimental (sensory analysis) radars obtained for pure, binary and ternary fruit juices mixtures were compared.


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Pure fruit juices The theoretical and experimental radars obtained for the single juices are displayed in Figures 1 to 5. Inspecting these figures we conclude that the theoretical radars fit well with the sensory analysis (experimental radars) for the majority of the studied samples. The experimental and theoretical odor radars in Figure 1 show the expected citrus character, with a less intensity in the experimental one; for flavor, the sensory analysis confirmed fresh and citrus as the main families, however, with different levels of intensity: while citrus is more intense in the experimental radar, in the theoretical one the fresh family is the dominant. Regarding to peach juice (Figure 2), consumers identified the odor as fruity and sweet with a ripe note. In this case, although the obtained intensities for the experimental radar are minor than those observed for the theoretical radar, they appear at the same order, being fruity the more intense, followed by sweet and ripe. In terms of flavor, consumers defined it as fruity and sweet, being the behavior of both radars very similar. For pineapple juice (Figure 3) the experimental and theoretical radars revealed fruity as the dominant family. Apple juice (Figure 4) was classified as fruity by consumers being in agreement with the theoretical radar, but with some differences in terms of intensity and, in the case of the green family, it only appears in the experimental radar. The flavor is considered fruity, fresh and citrus for both represented radars. Finally, the sensory analysis for mango juice (Figure 5) shows that its odor is fruity, sweet and ripe. On the other hand, its flavor is theoretically classified as fruity and sweet with a fresh note; experimentally, the main families identified were sweet and fruity. Thus, it is possible to conclude that overall this methodology is able to efficiently elucidate the olfactory family dominating the overall odor and flavor of the studied single fruit juices.


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Odor Radar

Flavor Radar

Figure 1. Odor and flavor radars obtained for lemon juice (··· experimental radar; ▬ theoretical radar).

Odor Radar

Flavor Radar

Figure 2. Odor and flavor radars obtained for peach juice (··· experimental radar; ▬ theoretical radar).

Odor Radar

Flavor Radar

Figure 3. Odor and flavor radars obtained for pineapple juice (··· experimental radar; ▬ theoretical radar).


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Odor Radar

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Flavor Radar

Figure 4. Odor and flavor radars obtained for apple juice (··· experimental radar; ▬ theoretical radar).

Odor Radar

Flavor Radar

Figure 5. Odor and flavor radars obtained for mango juice (··· experimental radar; ▬ theoretical radar).

Binary fruit juices mixtures From Figure 6 to 8 are represented the experimental and theoretical results for both odor and flavor radars of binary fruit juices mixtures. For mixture 1, composed of apple and peach (Figure 6), the experimental odor radar matches the classification given by the theoretical one for the primary olfactory family (fruity peach). Consumers identified fruity peach and sweet peach as the principal sensory characteristics. In terms of flavor, some differences can be found: the experimental radar shows a tendency for peach characteristics (fruity and sweet) while the theoretical radar presents apple (fruity and citrus) as the dominant fruit. For mixture 2 composed


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of pineapple and peach (Figure 7), fruity peach, sweet peach and fruity pineapple were clearly identified in both odor radars. In terms of flavor, the visual aspect of the theoretical and experimental radars is similar despite differences in the intensities. Regarding to the odor profile of mixture 3 composed of pineapple and mango (Figure 8), both experimental and theoretical radars identified fruity mango and sweet mango as the dominant families. However, it is possible to observe that, while the theoretical radar identifies a ripe note, the experimental one shows a fresh one. In terms of flavor, fruity mango and sweet mango with a fresh nuance are the main families presenting just a slight deviation in terms of intensities. Odor Radar

Flavor Radar

Figure 6. Odor and flavor radars obtained for the mixture composed by apple and peach (··· experimental radar; ▬ theoretical radar).

Odor Radar

Flavor Radar

Figure 7. Odor and flavor radars obtained for the mixture composed by pineapple and peach (··· experimental radar; ▬ theoretical radar).


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Odor Radar

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Flavor Radar

Figure 8. Odor and flavor radars obtained for the mixture composed by pineapple and mango (··· experimental radar; ▬ theoretical radar).

Ternary fruit juices mixture In Figure 9 are represented both experimental and theoretical radars for the ternary fruit juices mixture, composed of peach, mango and pineapple.

Odor Radar

Flavor Radar

Figure 9. Odor and flavor radars obtained for the ternary mixture composed by peach, mango and pineapple (··· experimental radar; ▬ theoretical radar).

As it can be observed, the predicted odor and flavor of this mixture is dominated by fruity mango and sweet mango families with a ripe nuance, whereas the flavor is described as fruity mango, sweet mango and fresh. It is important to mention that the theoretical radars predicted the same


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primary families identified by the consumers (experimental radars), in both odor and flavor radars. The proposed methodology has shown to efficiently predict the dominant odor and flavor families of both pure and combinations of fruit juices and provides valuable information about the dominant fruit of the mixture. However, for some cases the theoretical and experimental classifications did not agree. This is the case of the pure apple and pineapple juices and the binary mixture composed of peach and apple, particularly the flavor profile. Factors as the complexity of fruit juice compositions, limitations at the headspace analysis level - in this case the gas concentrations of some components are highly diluted in air which hampers their detection - can explain, at least in part, these incongruities. In addition, and in our opinion, the factor with more impact is probably linked with the use of consumers instead a trained panel. Although consumers are quite comfortable about describing basic odors and flavors as fruity and citrus, this cannot be true for other sensory descriptors as green or woody. In addition, the more complex the mixture to be analyzed the more difficult it is to verbalize its odor and flavor character. To confirm the influence of the panel in the sensory description of these samples, it would be interesting to repeat the sensory analysis using a trained panel. However, despite the limitations of this methodology it should be highlighted that it allows predicting the sensory description of pure beverages and respective mixtures in a simple way.

Conclusions In the present study, a novel approach is presented aiming the prediction of odor and flavor profiles of beverages based on the gas phase composition of pure samples (fruit juices) together with odor and flavor thresholds and descriptors of each component identified in the gas phase above the liquid mixtures. The results showed that the developed methodology describes well the dominant family of the odor and flavor of both pure and combinations of fruit juices and provides valuable information about the dominant fruit of the mixture. Furthermore, the


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predicted sensory description of pure fruit juices and respective mixtures matched with that given by consumers for the main olfactory family and for the majority of the studied cases. Based on our findings, we believe that this approach of characterizing the odor and flavor of beverages is a powerful tool for the food industry by reducing the time required for the development of new products and improve quality control of not only beverages but also other flavored products.

Supporting Information Headspace composition of each pure fruit juice, odor and flavor detection thresholds, odor and flavor descriptors, relative weights, OVj and FVj, and sensory analysis are presented.

Acknowledgments This work was financially supported by: Project POCI-01-0145-FEDER-006984 – Associate Laboratory LSRE-LCM funded by FEDER through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para a Ciência e a Tecnologia. P. Costa acknowledges her postdoctoral grant from the Fundação para a Ciência e a Tecnologia (SFRH/BPD/93108/2013).

References (1) Taylor, A. J. Release and Transport of Flavors In Vivo: Physicochemical, Physiological, and Perceptual Considerations. Compr. Rev. Food Sci. Food Saf. 2002, 1, 45-56. (2) Akitomi, H.; Tahara, Y.; Yasuura, M.; Kobayashi, Y.; Ikezaki, H.; Toko, K. Quantification of Tastes of Amino Acids using Taste Sensors. Sensors Actuators B: Chem. 2013, 179, 276-281. (3) Teixeira, M. A.; Rodríguez, O.; Rodrigues, A. E. Perfumery Radar: A Predictive Tool for Perfume Family Classification. Ind. Eng. Chem. Res. 2010, 49, 11764-11777. (4) Voilley, A.; Etiévant, P. Flavour in Food. Woodhead Publishing Limited: Cambridge, 2006.


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