Modeling Fragrance Components Release from a Simplified Matrix

Subscriber access provided by NATIONAL UNIV OF SINGAPORE

Article

Modelling fragrance components release from a simplified matrix used in toiletries and household products Patrícia Costa, Miguel André Teixeira, Yohan Lievre, José Miguel Loureiro, and Alirio Egidio Rodrigues Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b03852 • Publication Date (Web): 05 Nov 2015 Downloaded from http://pubs.acs.org on November 10, 2015

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 31

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

Industrial & Engineering Chemistry Research

Modelling fragrance components release from a simplified matrix used in toiletries and household products Patrícia Costa*, Miguel A. Teixeira, Yohan Lievre†, José Miguel Loureiro, Alírio E. Rodrigues Laboratório Associado LSRE-LCM, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal. †Present address: Mane, France. *E-mail: [email protected]. Tel.: +351 22 041 3688. Fax: +351 22 508 1674.

ABSTRACT A new methodology based on Henry’s Law is proposed for modelling the release of fragrances from a simplified matrix commonly used in consumer products’ formulations. For that purpose, different mixtures were formulated containing one, two, three or four fragrance ingredients diluted in dipropylene glycol (simplified matrix). Headspace concentrations were measured to estimate Henry’s constants (H) for each fragrance component in all mixtures. The individual Henry’s constants for multicomponent fragrance mixtures were also predicted from the ones measured for each single compound diluted in the matrix. Furthermore, we used a model that combines the UNIFAC group-contribution method with the modified Raoult’s Law and the psychophysicals Stevens’ Power Law and Strongest Component model to predict the perceived odor intensity and character, respectively. Results showed a strong linear relationship between experimental H for single fragrances and experimental H for binary (r2=0.998), ternary (r2=0.997) and quaternary (r2=0.996) fragrance mixtures. This new approach can bring a relevant advantage to the preformulation process by reducing time and cost associated to trial-and-error experiments.

1

ACS Paragon Plus Environment


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

Page 2 of 31

Keywords Fragrance release, headspace concentration, odor intensity, modelling, vapor-liquid equilibria, Henry’s Law

INTRODUCTION Among the wide range of components (e.g. solvents, stabilizers, colorants) used in the formulation of consumer products, fragrances are among the most important ones for customers’ appeal.1 Moreover, fragrances can be one of the most complex ingredients present in the formulation not only due to their large range of volatilities and varied chemical nature but also because their interaction with other components can undergo different chemical reactions, thus being responsible for changing the properties of the end product (viscosity, color, pH, etc.). For this reason, the knowledge about these interactions can remarkably enhance the probability of success on the market by creating optimal formulations.2,3 Fragranced product formulations comprise a mixture of substances with different chemistries (volatility, log P, solubility and molecular structure), functionalities (emulsifiers, solvents, fragrances, humectants) and odors. In what concerns to the fragrance, until the perception of the scent released from a fragranced product, several intermediate steps occur starting from the moment these molecules evaporate from the product or substrate into the gas phase, then binding to human olfactory receptors and, finally, upon olfactory perception by our brain. The complexity of understanding the performance of a fragrance in a consumer product is even higher when we consider the different consumer end-points: a fragrance smelled directly from out of the bottle of a shampoo, may have a different olfactory fingerprint as, for example, from that one perceived when washing our head due to different interactions between the fragrance and product matrix or between the fragrance and product application.3-7 Despite the relevance of this phenomenon for the industry, literature data on the behavior of fragrance chemicals when incorporated into different matrices, in terms of their evaporation and diffusion performance is still scarce. Friberg and co-workers have been developing extensive research on the release of fragrance chemicals from different emulsion systems combining phase diagrams with algebraic methods using physicochemical properties. Using their methodology it is possible to predict the evaporation path or the variation in the vapor pressure during the evaporation process of fragrance components from an oil/water emulsion.8-10 In what 2


Page 3 of 31

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


concerns fragrance-skin interactions, the evaporation of fragrance chemicals from the skin has been also measured using analytical measurements by the dynamic headspace technique4,11,12 and then the odor intensity calculated over time using, for example, the Odor Value (OV) concept.4,11 However, literature is still scarce when dealing with modelling multicomponent mixtures release from different types of product matrices. A theoretical model to assess the performance (release and diffusion) of simple and multicomponent perfume mixtures from a solvent (e.g. ethanol or water) was previously developed and validated by our group using group-contribution methods coupled with Raoult’s Law, Fickian diffusion and psychophysical models.7 However, when we transpose the application of fragrances from perfumes to other consumer products it becomes more complex from a modelling viewpoint, since the latter comprise different matrix compositions (surfactant, pH, or solids level), formula types (oil-in-water, waterin-oil), rheology (viscosity level) and types of applications (rinse off or deposition). Consequently, in such a system, excess Gibbs energy models based on the groupcontribution approach, equations of state (which often need critical properties) or molecular modelling tools, may be either likely to weakly correlate with real vapor composition data or extremely difficult to apply to this particular problem. Fragrance interactions with a typical solvent in complex systems like emulsions,13-15 standard fabric softener and detergent solutions5,16 or encapsulation systems17 have been addressed in the past by other authors. In this work, we started to design our approach by considering that fragrances are typically applied at low concentrations (ca. 0.25% to 5% (w/w)) in toiletries and household product (THP) formulations to avoid allergic reactions, destabilization of the formulation, and/or due to cost constrains.18 Consequently, we assumed that the evaporation of each fragrance component present in a multicomponent fragrance mixture dissolved in such a matrix could be calculated from its corresponding Henry’s Law constant (H) determined when present alone in the matrix for low concentrations. By doing so, we are assuming that, when in a multicomponent fragrance mixture at low concentration levels, molecular interactions between fragrance components (solute-solute interaction) can be negligible versus their molecular interactions with the matrix (solute-solvent interaction). Following this line of thought, it is intended to predict the olfactory perception of a multicomponent mixture using the abovementioned approach. It does not describe the phenomenon of olfactory perception occurring at the olfactory receptor level (combinatorial coding) or the neuronal interpretation of odorants, rather it uses a combination of physical 3



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

Page 4 of 31

descriptors combined with psychophysical models for perceptual continua, being one of the few so far proposed in literature and industry to describe the perception of fragrances. Henry’s Law states that the concentration of a chemical dissolved in a liquid is directly proportional to its corresponding partial pressure at the surface of the liquid for diluted concentrations and under equilibrium conditions.19 It is defined as the ratio of the equilibrium concentration between gas and liquid phases at a specific temperature (eq. 1): 20,21 𝑔𝑎𝑠

𝐶𝑖

= 𝐻 × 𝐶𝑖𝑙𝑖𝑞𝑢𝑖𝑑

(1)

𝑔𝑎𝑠

where H is the Henry’s Law constant for component i, 𝐶𝑖

and 𝐶𝑖𝑙𝑖𝑞𝑢𝑖𝑑 are the

concentrations of the fragrance component i in the gas and liquid phases (g/L), respectively. Therefore, the purpose of this study is to assess if Henry’s Law can be applied to multicomponent mixtures similar to those typically found in THPs and to predict the fragrance release in different fragrance systems. For that purpose, different fragrance mixtures were formulated and their vapor phase concentrations evaluated by headspace gas chromatography (HS-GC). These compositions were compared with those predicted using the Henry’s Law from single fragrance mixtures and those predicted by the UNIFAC group-contribution method. The olfactory perception of a fragrance mixture can be calculated from a combination of several variables, including the odorants’ gas concentration, their chemical structure, odor thresholds, and neuronal signals within the transduction process. In this study, we have simplified this approach to predict the perceived odor intensity and character of each fragrance mixture from each component’s vapor concentrations using a psychophysical model known as the Steven’s Power Law22 and the Strongest Component model.23

MATERIALS AND METHODS Chemicals R-(+)-Limonene (CAS no. 5989-27-5, purity ≥98%) was obtained from Sigma-Aldrich. (±)-Linalool (CAS no. 78-70-6, purity >97% GC), (-)-α-pinene (CAS no. 7785-26-4, purity >98%) and dipropylene glycol (CAS no. 25265-71-8, purity ≥99% GC) were 4


Page 5 of 31

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


obtained from Fluka. Ethyl acetate (CAS no. 141-78-6, purity >99.5%) and benzyl acetate (CAS no. 140-11-4, purity >99.5%) were purchased from Merck. Linalyl acetate (CAS no. 115-95-7, purity >97%, FCC) was supplied by Aldrich. p-Cymene (CAS no. 99-87-6, purity >97%) was obtained from Alfa Aesar. All reagents were used as received without further purification.

Sample preparation First, liquid mixtures containing one or more fragrance ingredients and dipropylene glycol (DPG) (Table 1, Figure S1) (see Supporting Information) at different concentrations (Table S1) were prepared gravimetrically in 4 mL vials using an Adam Equipment balance model AAA250L with a precision of ±0.2 mg. The homogeneity of samples was ensured using a vortex. Different combinations (Table S1) were prepared in order to have each fragrance component at least one time in each binary, ternary and quaternary fragrance mixture. Dipropylene glycol (DPG) was chosen for its commercial relevance since it is very useful in toiletries formulations acting, for example, as moisturizer, emulsion stabilizer and as solvent of polar substances insoluble in oils24. In some formulations, DPG could be one of the major ingredients of the total formulation, for example, of deodorants sprays24-26. Limonene appears in all the combinations because it is usually applied in citrus formulations of TPHs. After the preparation of the fragrance mixtures, two replicates of each mixture (1 mL) were placed in 20 mL closed-cap headspace vials and allowed to equilibrate for at least 24 h, at a controlled room temperature (23 ± 1 ºC). The required time for equilibration proved to be enough in preliminary experiments considering the same experimental conditions used in the present study. The schematic representation of the experimental procedure is illustrated in Figure S2.

5



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

Page 6 of 31

Table 1. Properties of the fragrance components and simplified matrix: molecular formula, molecular weight (MW), vapor pressure (Psat), odor detection threshold (ODT), and olfactory Power Law exponent (n). Compound

Limonene α-Pinene

Psat (Pa)

ODTd

ne

Molecular

MW

formulaa

(g/mol)a

C10H16

136.23

205.4 b

0.619

0.37

136.23

513.4

b

0.240

0.49

c

0.061

0.35

§ §

C10H16

(mg/m3)

Linalyl acetate

C12H20O2

196.29

14.8

Linalool

C10H18O

154.25

22.1 b

0.009

0.35

Ethyl acetate

C4H8O2

88.11

12425.6 a,b,c

20.400

0.51

0.332

0.38

0.392

0.35

Benzyl acetate

C9H10O2

150.18

21.9

b

p-Cymene

C10H14

134.22

200.0

Dipropylene glycol

C6H14O3

134.17

3.5b

a,b

odorless

§

-

a

From PubMed.gov, US National Library of Medicine National Institutes of Health.27 From DIPPR 801 Database.28 c From Chemspider Database of Chemical Structures and Property Predictions, Royal Society of Chemistry.29 d From van Gemert (2003).30 e From Devos et al. (2002).31 § The median Power Law exponent value in the compilation of data from Devos et al. (2002).31 b

Activity coefficients for vapor-liquid equilibria The evaporation of fragrances can be determined from the molecular interactions between the solute(s) and the solvent, together with physicochemical descriptors like the vapor pressure, molecular structure, among others. There are several thermodynamic models to calculate and/or predict vapor-liquid equilibria (VLE) of mixtures. Our group has successfully used in the past an approach by calculating activity coefficients by group-contribution methods. These comprise correlations such as the NRTL,32 UNIQUAC,33 ASOG,34 COSMO-RS35 and UNIFAC.36,37 The UNIFAC (UNIversal Functional Activity Coefficient) method uses the UNIQUAC equation (which contains per se the summation of the entropic and enthalpic contributions) for the activity coefficient of the functional groups. From this point, combining group-contribution methods with some basic Thermodynamics we can predict the concentration of the 𝑔𝑎𝑠

odorants in the headspace (𝐶𝑖

) using the modified Raoult’s Law for describing the

VLE38 (eq. 2) according to eq. 3:7,37 𝑦𝑖 𝑃 = 𝑥𝑖 𝛾𝑖 𝑃𝑖𝑠𝑎𝑡

(2)

6


Page 7 of 31

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


𝑔𝑎𝑠

𝐶𝑖

=

𝑦𝑖 𝑀𝑖 𝑃 𝑀𝑖 𝑃𝑖𝑠𝑎𝑡 = 𝑥𝑖 𝛾𝑖 𝑅𝑇 𝑅𝑇

(3)

where 𝑥𝑖 and 𝑦𝑖 represent the liquid and gas mole fraction of component i, respectively, 𝑀𝑖 is its molecular mass, 𝛾𝑖 is the activity coefficient in the liquid phase, 𝑃𝑖𝑠𝑎𝑡 is the vapor pressure of pure component i, R is the universal gas constant, and T is the absolute temperature. The activity coefficient, 𝛾𝑖 , is a parameter that measures the deviations of the liquid phase from ideal behavior. For the particular case of a fragrance mixture, the activity coefficient can be assumed as a measure of the tendency of a molecule to “stay in” the liquid solution (γi < 1) or to be ‘‘pushed out’’ (γi > 1) into the headspace.7,36,38 In this work, we have calculated the activity coefficients using a VLE flash calculation with the UNIFAC method (Table S2). From eq. 3 one can calculate the theoretical Henry’s Law constant (𝐻 𝑇ℎ𝑒𝑜𝑟 ) as:

𝐻

𝑇ℎ𝑒𝑜𝑟

𝛾𝑖 𝑃𝑖𝑠𝑎𝑡 = 𝑅𝑇𝐶𝑇

where, 𝐶𝑇 is the total liquid concentration in

(4)

𝑚𝑜𝑙 𝐿

(𝐶𝑇 = ∑

𝐶 𝑔

𝑖( ) 𝐿 𝑔 𝑀𝑖 ( ) 𝑚𝑜𝑙

). The 𝐻 𝑇ℎ𝑒𝑜𝑟 for

single fragrance mixtures are listed in Table S3.

Odor intensity The perceived odor intensity of the studied fragrance mixtures was calculated from the measured vapor concentrations using the Stevens’ Power Law.22 This psychophysical model was derived from sensory experiments in different perceptual continua dealing with the relationship between the stimulus magnitude applied to subjects and their corresponding perceived sensations. It considers a non-linear relationship between these two, and for olfaction it can be expressed as assuming that the perceived sensation (ψ) is 𝑔𝑎𝑠

proportional to the stimulus magnitude (𝐶𝑖

) raised to an exponent (ni) (eq. 5): 𝑔𝑎𝑠

𝐶 𝜓𝑖 = ( 𝑖 ) 𝑂𝐷𝑇𝑖

𝑛𝑖

(5)

7



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

𝑔

where 𝐶𝑖

is the concentration of the odorant in the gas phase, ODTi

Page 8 of 31

is its

corresponding odor detection threshold measured in air (both in units of mass or mole per volume), and the parameter 𝑛𝑖 is defined as the Power Law exponent for each specific odorant. The Power Law results mostly from single component measurements and so it does not consider possible perceptual multicomponent interactions occurring at nose level for mixtures of odorants which can affect the odor intensity and quality of the mixture.39 The ODT corresponds to “the concentration of an odorous compound at which the physiological effect elicits a response 50% of the time” for a panel of subjects.40 In this work, ODTs were compiled and geometrically averaged (since it is a recommended procedure, commonly used in sensory analyses41) from the literature database.30 It is important to highlight that the available compilations report average values or ranges of thresholds without discussing important aspects which strongly influence the accuracy of the olfactory data like the used measurement techniques, individual physiological differences or psychological factors, just to mention a few.42-44 To account for the odor character of fragrance mixtures, the Strongest Component model23 can be applied. This model states that within a mixture of N odorants, the one having the highest odor intensity is the most strongly perceived, thus dominating the odor character: 𝜓𝑚𝑖𝑥 = 𝑚𝑎𝑥(𝜓𝑖 ), ∀ 𝑖 = 1, … , 𝑁

(6)

Statistical analysis The results obtained in this work for H were processed by one-way analysis of variance (ANOVA) and differences were considered significant at p < 0.05. Statistical analysis was performed using the SPSS statistical package for Windows, release 18.0 (SPSS Inc., Chicago, IL).

8


Page 9 of 31

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


RESULTS AND DISCUSSION Theoretical Henry’s Law constant for single fragrance mixtures HTheor for single fragrances diluted in DPG were calculated using eq. 4 and the results are shown in Table S3. The activity coefficients of all components involved in the liquid phase are listed in Tables S6-S9. HTheor vary over a wide range of magnitudes from (3.64 ± 0.23)×10-6 to (1.37± 0.04)×10-3 due to the significant differences in volatility of the studied fragrance components (Table S3). Overall, it seems that the order of magnitude of HTheor is associated with the molecular weight and vapor pressure of the fragrance components: limonene, α-pinene and p-cymene have similar molecular weights and vapor pressures, presenting HTheor within the same order of magnitude (10-4); the same is observed for linalool and benzyl acetate (order of magnitude 10-6) (Tables 1 and S3). Ethyl acetate presented the highest 𝐻 𝑇ℎ𝑒𝑜𝑟 value as result of its high vapor pressure and low molecular weight.

Experimental Henry’s Law constant for single fragrance mixtures Henry’s Law constants (H) were first calculated for each single fragrance, diluted in DPG, by plotting the experimental gas concentration (measured by HS-GC) of each fragrance ingredient as a function of its liquid concentration. The experimental points used to calculate the H values were selected based on the best statistical parameters (coefficient of determination and F value). The results are presented in Figure 1 and Table S4. As expected, for all single fragrance mixtures studied here, it was observed a linear relationship between liquid and gas concentrations defined by the corresponding H value till the concentration upper-limit value for the Henry’s Law validity. Furthermore, it was seen that H for the different fragrance ingredients tested in this matrix, increased with their corresponding vapor pressure (Tables 1 and S4). The relative deviations between the experimental and theoretical H (δH=100|HExpHTheor|/HExp) for each single fragrance mixture are presented in Table S4. The experimental H values of limonene and ethyl acetate were close to those predicted, with relative deviations of 19.3 and 21.4%, respectively. The highest relative deviation was observed for linalyl acetate (264.0%), though it should be mentioned that the experimental gas concentration of linalyl acetate was the most difficult one to be quantified by HS-GC. This result highlights the limitation of headspace techniques in 9



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

Page 10 of 31

the quantification of less volatile fragrances (like middle to base notes) and can explain the obtained relative deviations.

10


Page 11 of 31

140 120

20

Cgas -Pinene (mg/L)

Cgas Limonene (mg/L)

25

15 10 5

100 80 60 40 20

0

0 0

20

40

60

80

100

120

0

20

40

Cliquid Limonene (g/L)

100

120

100

120

100

120

0.6

Cgas Linalool (mg/L)

Cgas Linalyl acetate (mg/L)

80

0.7

0.3

0.2

0.1

0.5 0.4 0.3 0.2 0.1

0.0

0.0 0

20

40

60

80

100

120

0

20

40

Cliquid Linalyl acetate (g/L)

60

80

Cliquid Linalool (g/L)

140

0.4

120

Cgas Benzyl acetate (mg/L)

Cgas Ethyl acetate (mg/L)

60

Cliquid -Pinene (g/L)

0.4

100 80 60 40 20 0

0.3

0.2

0.1

0.0 0

20

40

60

80

100

120

Cliquid Ethyl acetate (g/L)

0

20

40

60

80

Cliquid Benzyl acetate (g/L)

5

Cgas p-Cymene (mg/L)

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


4 3 2 1 0 0

20

40

60

80

100

120

Cliquid p-Cymene (g/L)

Figure 1. Gas phase concentration of fragrance component as a function of its liquid phase concentration in DPG. 11



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

Page 12 of 31

Binary, ternary and quaternary fragrance mixtures The experimental H together with the relative deviation between experimental and predicted H for the binary, ternary and quaternary fragrance mixtures are presented in Table S5. Despite some differences between the experimental H for single fragrance mixtures and those obtained for the binary, ternary and quaternary fragrance mixtures, the order of magnitude was maintained for all the studied fragrance components. Comparing the experimental and theoretical H some deviations were detected. The highest difference was found for linalyl acetate in the binary (δH=271.0%), ternary (δH=320.1%) and quaternary (δH=184.0%) fragrance mixtures. The relative deviations for limonene are acceptable for the majority of fragrance mixtures (δH=3.3 - 18.2%) except in those containing benzyl acetate and/or ethyl acetate (δH=28.0 - 35.9%). Another interesting point is the absence of any correlation between the maximum liquid concentration of each fragrance component where Henry’s Law is valid (Cliquid,max) with the vapor pressure, molecular formula (Table 1) or measured H values (Tables S4 and S5). It was observed that the Cliquid,max of each fragrance ingredient in the binary, ternary and quaternary fragrance mixtures can be different depending on the studied fragrance mixture. In some fragrance mixtures, the total Cliquid,max is above the Cliquid,max of one or more fragrance components present in the mixture. It seems that the lowest Cliquid,max among the fragrance components present in the mixture is the limit of the validity domain for the Henry’s Law. Above this Cliquid,max, fragrance component interactions can no longer be neglected. As an example, we can analyze the fragrance mixture limonene+α-pinene (Figures 2 and 3A; Table S5): the Cliquid,max of limonene and αpinene are 43.77 ± 0.04 and 21.06 ± 0.07 g/L, respectively. So, the total Cliquid,max for this fragrance mixture is 64.83 g/L, i.e., above that found in the single fragrance mixture of α-pinene (38.93 ± 0.03 g/L). Another case is the performance of p-cymene (Figure 3F and Table S5): the total Cliquid,max in the binary (88.86 g/L), ternary (176.55 g/L) and quaternary (161.23 g/L, 121.60 g/L and 142.14 g/L) fragrance mixtures are much higher than that found in the single fragrance mixture (37.69 ± 0.03 g/L). The validity domain for the Henry’s Law of linalyl acetate was also affected by the number of components in the solution: in comparison with the single mixture (97.25 ± 0.02 g/L), the Cliquid,max decreased in the binary (46.11 ± 0.04 g/L) and ternary (20.9 ± 0.1 g/L) fragrance mixtures, but increased when a third molecule (α-pinene) was added to the solution (71.30 ± 0.03 g/L). Such behaviors will induce positive or negative deviations to the

12


Page 13 of 31

Henry’s Law. In addition, errors associated with the experimental procedure can also account to these discrepancies.

Cgas (mg/L)

-Pinene -Pinene+Linalyl acetate -Pinene+p-Cymene+Linalyl acetate -Pinene+p-Cymene+Linalool

Limonene Limonene+Linalyl acetate Limonene+ -Pinene+Linalyl acetate Limonene+ -Pinene+p-Cymene+Linalyl acetate

30

Cgas (mg/L)

Limonene Limonene+ Limonene+ Limonene+ Limonene+

30

20

10

20

10

0

0 0

20

40

60

80

100

120

0

20

Cliquid (g/L)

60

80

100

120

Limonene Limonene+Ethyl acetate Limonene+Ethyl acetate+Benzyl acetate Limonene+p-Cymene+Ethyl acetate+Benzyl acetate

30

Cgas (mg/L)

Cgas (mg/L)

40

Cliquid (g/L)

Limonene Limonene+Linalool Limonene+p-Cymene+Linalool Limonene+ -Pinene+p-Cymene+Linalool

30

20

10

20

10

0

0 0

20

40

60

80

100

120

0

20

Cliquid (g/L)

40

60

80

100

120

Cliquid (g/L)

Limonene Limonene+Benzyl acetate Limonene+Ethyl acetate+Benzyl acetate Limonene+p-Cymene+Ethyl acetate+Benzyl acetate

20

Limonene Limonene+p-Cymene Limonene+p-Cymene+Linalool Limonene+ -Pinene+p-Cymene+Linalyl acetate Limonene+ -Pinene+p-Cymene+Linalool Limonene+p-Cymene+Ethyl acetate+Benzyl acetate

30

Cgas (mg/L)

30

Cgas (mg/L)

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


20

10

10

0

0 0

20

40

60

80

100

120

0

20

Cliquid (g/L)

40

60

80

100

120

Cliquid (g/L)

Figure 2. Gas phase concentration of limonene as a function of its liquid phase concentration in all the studied mixtures in DPG.

13



-Pinene Limonene+ Limonene+ Limonene+ Limonene+

Cgas (mg/L)

150

0.6 A -Pinene -Pinene+Linalyl acetate -Pinene+p-Cymene+Linalyl acetate -Pinene+p-Cymene+Linalool

Linalyl acetate B Limonene+Linalyl acetate Limonene+ -Pinene+Linalyl acetate Limonene+ -Pinene+p-Cymene+Linalyl acetate

0.5 Cgas (mg/L)

200

100

0.4 0.3 0.2

50 0.1 0

0.0 0

20

40

60

80

100

0

120

20

40

1.0

Linalool

Linalool Limonene+Linalool Limonene+Linalool Limonene+p-Cymene+Linalool Limonene+p-Cymene+Linalool Limonene+ -Pinene+p-Cymene+Linalool Limonene+a-Pinene+p-Cymene+Linalool

80

100

120

Ethyl acetate D Limonene+Ethyl acetate Limonene+Ethyl acetate+Benzyl acetate Limonene+p-Cymene+Ethyl acetate+Benzyl acetate

200

C

Cgas (mg/L)

Cgas (mg/L)

0.8

60

Cliquid (g/L)

Cliquid (g/L)

0.6 0.4

150 100 50

0.2 0.0

0 0

20

40

60

80

100

120

0

20

40

Cliquid (g/L) 0.8

60

80

100

120

Cliquid (g/L)

Benzyl acetate E Limonene+Benzyl acetate Limonene+Ethyl acetate+Benzyl acetate Limonene+p-Cymene+Ethyl acetate+Benzyl acetate

0.4

0.2

p-Cymene F Limonene+p-Cymene Limonene+p-Cymene+Linalool Limonene+ -Pinene+p-Cymene+Linalyl acetate Limonene+ -Pinene+p-Cymene+Linalool Limonene+p-Cymene+Ethyl acetate+Benzyl acetate

8 Cgas (mg/L)

0.6 Cgas (mg/L)

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

Page 14 of 31

6 4 2 0

0.0 0

20

40

60

80

100

120

0

20

Cliquid (g/L)

40

60

80

100

120

Cliquid (g/L)

Figure 3. Gas phase concentrations of α-pinene (A), linalyl acetate (B), linalool (C), ethyl acetate (D), benzyl acetate (E), and p-cymene (F) as a function of their liquid phase concentrations in DPG for all the mixtures studied.

In order to validate our hypothesis that the H of a fragrance solute dissolved in a simplified matrix could be used to model and, ultimately, predict multicomponent fragrances release from the same matrix we have plotted this relationship for binary, ternary and quaternary fragrance mixtures in Figure 4. It is observed a high coefficient 14


Page 15 of 31

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


of determination, showing a strong linear relationship between the experimental H for single fragrance mixtures and the experimental H for binary (r2=0.998), ternary (r2=0.997) and quaternary (r2=0.996) fragrance mixtures (Figure 4). No statistically significant differences (p < 0.05) were found for the correlations between the experimental H measured for pure fragrance in DPG and for binary, ternary, and quaternary fragrance mixtures, demonstrating a close correspondence between the H for single and multicomponent mixtures as shown in Figure 4. The presence of a linear relationship suggests that our hypothesis for using the measured Henry’s Law of one solute dissolved in a solvent can be transposed to predict the gas phase of multicomponent mixtures dissolved in the same solvent.

15



H binary fragrance mixtures

0.0025

H binary fragrance mixtures=1.261 × H single fragrance mixtures 2 r =0.998 b

0.0020

e

0.0015 6.6x10-5

A

5.5x10-5

0.0010

g

4.4x10-5 3.3x10-5 2.2x10-5

0.0005

1.1x10-5

a

c

0.0000

fd

0 1.3x10-5 2.6x10-5 3.9x10-5 5.2x10-5

0

dg 0.0000 cf

0.0005

0.0010

0.0015

0.0020

H single fragrance mixtures

H ternary fragrance mixtures

0.0025

H ternary fragrance mixtures=1.323 × H single fragrance mixtures 2 b r =0.997

e

0.0020 0.0015 6.6x10-5 5.5x10-5

0.0010

g

B

4.4x10-5 3.3x10-5 2.2x10-5

0.0005

1.1x10-5 cf d 0 0 1.4x10-5 2.8x10-5 4.2x10-5 5.6x10-5

a fd

g

0.0000 c 0.0000

0.0005

0.0010

0.0015

0.0020

H single fragrance mixtures 0.0025

H quaternary fragrance mixtures

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

Page 16 of 31

0.0020

H quaternary fragrance mixtures=1.312 × H single fragrance mixtures e 2 r =0.996 b

0.0015 6.6x10-5

g

C

5.5x10-5

0.0010

4.4x10-5 3.3x10-5 2.2x10-5

0.0005

c fd

1.1x10-5

a

0 0

dg

0.0000 fc 0.0000

0.0005

1.4x10-5 2.8x10-5 4.2x10-5 5.6x10-5

0.0010

0.0015

0.0020

H single fragrance mixtures

Figure 4. Correlation between the experimental Henry’s Law constant for single fragrance mixtures containing one fragrance component and the matrix (DPG) and the experimental Henry’s Law constant for binary (A), ternary (B) and quaternary (C) fragrance mixtures. Each lowercase letter corresponds to each studied fragrance component: a-Limonene, b-α-Pinene, c-Linalyl acetate, d-Linalool, e-Ethyl acetate, fBenzyl acetate, g-p-Cymene. A-C: B: Zoom-in of the lower four points. 16


Page 17 of 31

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


Odor intensity The previous discussions are referred to the relationship between the liquid and headspace compositions. However, such analysis does not reflect the magnitude of the perceived odor since this typically follows a non-linear relationship with headspace data.22,37 From the measured headpsace compostions it is possible to calculate the perceived odor intensity of each fragrance component using the aforementioned Stevens’ Power Law.22 It is important to mention that the Stevens’ Power Law does not account for olfactory physiological interactions of odorants that may occur in multicomponent mixtures like synergy or suppression phenomena.45,46 In fact, interactions at the level of olfactory receptors and during neural processing of olfactory information can impact the perceived scent of a mixture.46 However, the psychophysical Power Law can still be of value for modelling fragrance perception, as shown by our research group for several complex fragrance mixtures evaluated through the same methodology proposed here with non-trained37 and non-trained plus trained panellists.47,48 In these studies we found a good agreement between odor character evaluations performed by sensorial analysis and the odor character predicted from the headspace concentrations measured by gas chromatography or even from those obtained using purely predictive methods from the liquid mixture composition. Figure 5 represents the gas phase concentrations and odor intensities for each odorant as a function of its liquid composition in single fragrance mixtures (i.e. containing only one fragrance ingredient diluted in DPG). For each case, the odor intensity was predicted using eq. 5. From the obtained results on odor intensity, it is possible to observe that α-pinene and ethyl acetate are the strongest odors. This is somewhat expectable because these components have high volatility, high psychophysical exponents n and low molecular weight in the particular case of ethyl acetate.

17


60

Cgas -

300

Cliquid vs Cgas Cliquid vs

20

40

60

80

100

20

0 120

0 0

20

Cliquid Limonene (g/L)

8 4

20 0.3 15

40 40 20 30 30 15

0.2 10

20 20 10

0.1 5

0 0 0.0 0 0 0

liquid vs Cgas 10 Cgas 10 5 gas liquid Cliquid Cgas vs CCliquid vs CCvs liquid liquid C vs liquid liquid liquid vs Cvs C vsC C vs

20 2020

gas (mg/L) C gas Limonene acetate C (mg/L) Cgas Ethyl Linalyl acetate (mg/L)

20 40 80

12 80 0.2

15 30 60

8 60

10 20 40 liquid gas 5 10 20 vs vs C CCCliquid vs CC liquid vsliquid liquid vs vs C Cliquid vsC C C vs Cgas C gas liquid gas liquid vs liquid

20

Cgas Limonene (mg/L) Cgas Ethyl acetate (mg/L) Cgas p-Cymene (mg/L)

100 50 30

20 30 60

12 80 3

15 20 40 10 liquid vs gas10 20 liquid gas gas liquid C C liquid gas CCC vs vs CCvs C 5 liquid liquid vsliquid Cliquid vs Cvs Cliquid vsC C 0 00 60 8080 100 100 100120 120 60 60 80

60 8 2 40 4 1 20 20 2020

0 120

000.0 00 0

40 40 40 CCliquid Ethyl acetate (g/L) liquid Cliquid Limonene (g/L) p-Cymene (g/L)

20 2020

700

16 4

40

25

100

600

12 3

30

8 2

20

4 1 0 0 0 0

liquid vs Cgas Cgas10 vs CCliquid vs CCgasliquid liquid vs Cvsliquid Cliquid vsC Cliquid

2020

40 40

60 60

10 5

0 0 8080 100 100120

Cliquid p-Cymene (g/L) Cliquid Limonene (g/L)

Cgas -Pinene (mg/L)

120

15

400 300 200 Cliquid vs Cgas Cliquid vs

0 0

20

40

60

80

100 0 100


Figure 5. Gas phase concentrations and odor intensity (using the Stevens’ Power Law as odor intensity model) for the single fragrance mixtures in DPG as a function of their liquid phase concentrations. 18


20

100 80 60 40 20

0

60

20

20

0

500

40

40

20

Cliqu

50 30

80

60

120

40 40 40 liquid Benzyl acetate (g/L) liquid liquid CCC Limonene -Pinene (g/L) (g/L)

20 5

20

80

Cliqu

30015 20 20010 liquid gas gas 10 liquid C vs C gas liquid CC vs Cliquidvs vsCC Cgas 1005 liquid liquid Cliquid vs Cliquid vsCCliquid vs Cvs 00 0 60 60 60 8080 80 100100 100120

0.1

20

100

0

60030 40 50025 30 20 400

4 20

20

0

50 70035

600.2

40

120

40 40 40 40 liquid C Limonene Linalool (g/L) liquid CCliquid (g/L) Cliquid Benzyl -Pineneacetate (g/L) (g/L)

80 12

60

Cliqu

30015 20 20 20010 liquid gas 10 10 Cliquidgas vs Cgas liquid gas liquid liquid gas CC vs C vs C Cliquid vsCC Cvs 5 liquid 100 Cliquid vs liquid vs vs C Cliquid vsCCliquid C vs vs 0 00 0 60 80 100 120 60 60 60 8080 80 100100 100120

100 16 0.3

80

0

30 60050 40 25 50040 30 20 40030

20 20 2020

100

0

60 50 70035

20 0.4 120

8 40

120

40 40 40 40 liquid C liquid -Pinene (g/L) liquid C Limonene Linalool (g/L) CCliquid (g/L) -Pinene (g/L)

Cgas p-Cymene (mg/L)

0 00 0 00

000.0 0.0 00 00

0 00 40 60 8080 100 100 100120 120 40 60 40 60 80 liquid Linalyl acetate (g/L) CCliquid Limonene (g/L)(g/L) Cliquid Ethyl acetate

80 40 25

20 20 2020

100 16 0.6 0.3 80 12 0.4 60 0.2 8 40 0.2 0.1 4 20

20 2020

20140 5 120 16 4 100

100

20 0.8 0.4 120

25 50 100

120 16 0.3 100

0 0.0 0 0 00

0 00 0.0 0 00 0

40 60 40 60 40 60 liquid C Limonene (g/L) liquid liquidLimonene CC (g/L) (g/L) Linalyl acetate

40 0.1

80

700 50 70060 600 600 40 50 500 50040 30 400 400 30 300 300 20 20 200 200 liquid vs Cgas C 10 liquid gas gas liquid Cliquid vs Cgas 100 10 vs CC vs Cliquid vsCC Cgas 100 liquid Cliquid vs liquid vs vs Cliquid vsCCliquid vs C vs Cliquid 0 00 0 60 80 100 120 60 80 100 120 60 80 100 60 80 100

60 0.4 60 8 40 40 0.2 4 20 20

0 0 0 80 100 120 8080 100100120

0.4 20140

4

140 20 0.8 120 120 100 16 100 0.6 80 12 80

-Pinene (mg/L) Cgas Limonene (mg/L) gas -Pinene (mg/L) (mg/L) CCgas Linalool

12

50 50 25

gas -Pinene (mg/L) C (mg/L) Cgas Limonene gas Linalool (mg/L) gas acetate (mg/L) C CBenzyl

16

60

100


gas -Pinene (mg/L) C (mg/L) Cgas Limonene gas C Benzyl acetate (mg/L)

Cgas Limonene (mg/L) gas Limonene Linalyl acetate(mg/L) C Cgas (mg/L)

25 20 0.4

40

Page 18 of 31

200 Cliquid vs Cgas Cliquid vs

Cgas -Pinene (mg/L)

0

10

Cgas -Pinene (mg/L)

5

Cgas -Pinene (mg/L)

Cgas Li

20

Industrial & Engineering Chemistry Research 40 0

Cgas Limonene (mg/L)

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

10

Page 19 of 31

The calculated odor intensities for binary fragrance mixtures are showed in Figures 6 S3-S7. Limonene was strongly perceived dominating the odor space of the mixtures containing linalyl acetate (Figure S5) and benzyl acetate (Figure 6); this can be a) Limonene+Linalool 20 20 explained by their high volatility 50 and50 lower chemical affinities with the polar 50 solvent 20 0.7 120 70060

0.5

500 40 80 dominated the perceived odor of this mixture because of its significantly lower ODT (2 12 12 30 30 12 0.4 30 400 30 60 orders of magnitude lower). In the20 mixture limonene+ethyl acetate, it is seen that 300 the 20 8 8 8 0.3 20 20 40 0.2 200 This odor is dominated by ethyl acetate for the five highest concentrations (Figure S6). 10 4 4 10 liquid liquid 4 10 gasCvs gas gas liquid liquid liquid gas vs Cgas CCliquid vs CCgas Cliquid Cvs C vs 20 0.1 Cvs Cgas 10010 C vs C C liquid liquid liquid liquid liquid Cvs vs Cvs C vs Cliquid vs Cvs Cliquid vsC is due to the factCliquid thatvsCliquid ethyl acetate has a steepest dose response curve (higher 0 0 0 0 00 0.0 00 0 0 0 20 20 40 40 60 60 80 80 100100 00 0 20 20 20 40 40 40 60 60 60 80 80 80 100 100100 psychophysical exponent).

Cliquid Limonene Cliquid Limonene (g/L)(g/L)

Cgas -Pinene (mg/L)

gas -Pinene (mg/L) Cgas Limonene (mg/L) C Cgas Linalool (mg/L)

Cgas Limonene (mg/L) Cgas Limonene (mg/L)

120

0.6

60050 DPG limonene+linalool (Figure S3) linalool 16 16 (Table 1). Conversely, in the 40 40mixture 100 16 40

100 80 60 40 20 0 0

20

liquid Cliquid Linalool (g/L) CCliquid Limonene -Pinene (g/L)

Cl

b) Limonene+Benzyl acetate

16 20

40 40

12 15

30 30

8 10

20 20

4 5

10 10

0 0 0 0

liquid

gas

gas gas liquid C vs vs C CCliquid vs CC liquidliquid liquid vs vs C Cliquid vsC C vs

20 20

40 40

60 60

80 80

0 0 100100

20 0.5 120

50 70020

120

100 16 0.4

60016 40

100

80 12 0.3

500 30 12 400

60 8 0.2 40 4 0.1 20

000.0 00 0

Cliquid Limonene (g/L) Cliquid Limonene (g/L)

Cgas -Pinene (mg/L)

50 50

gas Limonene -Pinene (mg/L) CCgas (mg/L) Cgas Benzyl acetate (mg/L)

20 25


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


3008 20 gas gas liquid Cliquid vs Cgas liquid CCliquid vs vs CCgas liquid liquid liquid C vs vs vsC liquid

C

Cliquid

C

vs C vs vs C

200 10 4 100

00 0 20 40 60 80 100 20 40 60 80 100 20 40 60 80 100 liquid liquid C Benzyl acetate (g/L) liquid CC Limonene (g/L) -Pinene (g/L)

60 40 20 0 0

20

Cli

Figure 6. Gas phase concentrations and odor intensities as a function of their liquid phase concentrations for the binary fragrance mixture limonene + benzyl acetate diluted in DPG.

Analyzing the ternary fragrance mixtures (Figure S8), it can be observed that in the mixture limonene+p-cymene+linalool (Figure S8b), in analogy with the binary fragrance mixture limonene+linalool (Figure S3), the dominant perceived fragrance changed with the liquid concentration: the mixture started to smell more strongly to limonene but with increasing concentrations of fragrance components it changes to linalool. To the mixture limonene+ethyl acetate+benzyl acetate (Figure 7) ethyl acetate was the most strongly perceived. This trend is coherent with that detected in the binary fragrance mixture limonene+ethyl acetate (Figure S6).

19


80


c) Limonene+Ethyl acetate+Benzyl acetate


14 16 12

40 40

12 10

30 30

8

8

20 20

6 4

4

2 0

0 0

0

2020

40 40

10 10 gas gas liquid Cliquid vs Cgas vs CCliquid vs CC liquid liquid liquid liquid vs vs C C vsC C vs 0 0 60 8080 100100 120 60

20140 120

50 100 700

120

120 100 16 100 80 12 80 60 8 60 40 40

600 40 80

100

-Pinene (mg/L) Cgas Cgas Limonene (mg/L) Cgas Ethyl acetate (mg/L)

50 50

500 30 60 400 300 20 40

4 20 20 00 0 00 0

2020 20

Cliquid Limonene Cliquid Limonene (g/L)(g/L) 20 0.5

40 40 40

200 10 20 liquid gas gas liquid liquid liquid gas vsCCCvs Cvs Cgas CC vs C liquid liquid liquid 100 liquid liquid C Cvs vs Cvs vs C vsC 00 0 60 8080 60 80 100100 100120 60

16 0.4

40 16

100

600

12 0.3

30 12

0 0.0 0 0

10 4

liquid gas gas vs liquidgas CCliquid CCvs Cvs C liquid liquid liquid vs Cvs Cliquid vsC Cvs

2020

40 40

60 60

0 0 8080 100100 120

Cliquid (g/L) (g/L) CliquidLimonene Benzyl acetate

Cgas -Pinene (mg/L)

700

4 0.1

400 60 300 40

200

20

gas

C vs C Cliquid vs

0 0

20

40

60

80

100 0 100


Figure 7. Gas phase concentrations and odor intensities (using the Stevens’ Power Law as odor intensity model) as a function of their liquid phase concentrations for the ternary fragrance mixture limonene + ethyl acetate + benzyl acetate diluted in DPG.

Regarding the quaternary fragrance mixtures (Figures 8 and S9) α-pinene was the dominant odor in both limonene+α-pinene+p-cymene+linalyl acetate (Figure S9a) and limonene+α-pinene+p-cymene+linalool (Figure S9b) systems as indeed it has been observed for all the mixtures where it is present. And for the mixture limonene+pcymene+ethyl acetate+benzyl acetate (Figure 8), ethyl acetate dominated the odor of the mixture.

20


40 20

0

500

80

liquid

60

20

Cliq

120

20 8

80

0

liquid Cliquid Ethyl acetate C Limonene (g/L)(g/L) Cliquid -Pinene (g/L) 50 20

8 0.2

Cgas -Pinene (mg/L)

20 16

Cgas Limonene (mg/L) gas C Benzyl acetate (mg/L)

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

Page 20 of 31

Page 21 of 31

c) Limonene+p-Cymene+Ethyl acetate+Benzyl acetate

40

4

100 100 16

20

12 8

25

80 80 12 60 60 8 40 40

30

3

15 20

2

4 1 0 0 0 0

liquid gas gas liquid Cliquid vs Cgas

CC vs vs CC liquid liquid Cliquid vsC C vs Cvsliquid

20

20 40

40 60

8060

10

10 5

00

C

Cliquid vs C

0

20 20

Cliquid Limonene (g/L) Cliquid Limonene (g/L) 50 16

16 0.4

40

12 0.3

30 10 8 20 6

8 0.2

0 0.0 0 0

gas gas liquid C vs vs C CCliquid vs CC liquidliquid liquid vs vs C Cliquid vsC C vs

20

20 40

60 40

8060

2040 40

200 10 20 100

vs C vs vs C

40 60 60

80 8060

00 0 100 100 80

80 60 40 20 0 0

gas

10

12

50 25 700

120

100 16 1.6

600 40 20 500 30 15 400

100

80 12 1.2 60 8 0.8 40

4

300 20 10

4 0.4 20

2

0 0 100 80

00 0.0 20 00 0 20

liquidLimonene CCliquid (g/L) (g/L) Benzyl acetate

2040 40

200 10 5 liquid gas liquidgas gas vs liquid liquid vsCCCvs Cvs Cgas100 CCC liquid liquid liquid vs Cvs Cliquid vsCCliquidCvs 00 0 40 60 8060 100 100 80 60 80

60 40 20

0

20

Cl

Figure 8. Gas phase concentrations and odor intensities as a function of their liquid phase concentrations for the quaternary fragrance mixture limonene + p-cymene + ethyl acetate + benzyl acetate diluted in DPG.

Predicted gas concentrations and odor intensity The vapor concentrations of each odorant in each fragrance mixture were also predicted from a VLE flash calculation using the UNIFAC method to estimate activity coefficients by applying eq. 3. Finally, each component’s odor intensity was also calculated from the predicted vapor compositions. Towards a quantitative comparison, the average relative deviations (ARDUNIFAC, %) of the predicted gas phase concentrations using the UNIFAC method were calculated following eq. 7,

𝑁𝑃

(7)

1

where NP corresponds to the number of experimental data points, 𝐴𝑖 to the gas phase concentration, or Ψ of component i, and superscripts exp and pred to experimental and 21


80

0

Cliquid p-Cymene Cliquid Limonene (g/L)(g/L) -Pinene (g/L)

|𝐴𝑒𝑥𝑝 − 𝐴𝑝𝑟𝑒𝑑 | 100 𝑖 𝐴𝑅𝐷𝑈𝑁𝐼𝐹𝐴𝐶 (%) = ∑ 𝑖 𝑒𝑥𝑝 𝑁𝑃 𝐴𝑖

20

Cliq

20 2.0 120 Cgas -Pinene (mg/L) Cgas Limonene (mg/L) Cgas p-Cymene (mg/L)

14

liquid

100

liquid Cliquid Ethyl acetate CCliquid Limonene (g/L) (g/L) -Pinene (g/L)

20 0.5

4 0.1

600 40 80 500 30 60 400

gasCliquidgas liquid liquid vs Cgas CCliquid vs CCgas vs liquid liquid liquidC Cliquid vs vs

0

00

120

30040 20

4 20 20

0 0 100 80

50 700100 Cgas -Pinene (mg/L)

5

20 120 120

Cgas -Pinene (mg/L)

16

50 30

gas -Pinene (mg/L) C (mg/L) Cgas Limonene gas Ethyl acetate (mg/L) C


20 6

Cgas Limonene (mg/L) gas C Benzyl acetate (mg/L)

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



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

Page 22 of 31

predicted gas phase concentrations and odor intensities, respectively. Furthermore, the average relative deviations (ARDH, %) between the experimental and calculated gas concentrations obtained from the experimental H for single fragrance mixtures were evaluated following eq. 8,

𝑁𝑃

𝑒𝑥𝑝

|𝐴𝑖 100 𝐴𝑅𝐷𝐻 (%) = ∑ 𝑁𝑃

𝑐𝑎𝑙𝑐 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝐻 𝑒𝑥𝑝 𝑓𝑜𝑟 𝑠𝑖𝑛𝑔𝑙𝑒 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠

− 𝐴𝑖

1

𝐴𝑒𝑥𝑝 𝑖

|

(8)

where superscripts 𝑐𝑎𝑙𝑐 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝐻 𝑒𝑥𝑝 𝑓𝑜𝑟 𝑠𝑖𝑛𝑔𝑙𝑒 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠 corresponds to the calculated gas phase concentrations and odor intensities from the experimental H for single fragrance mixtures, respectively. Table 2 summarizes the ARD values between the experimental and predictive gas phase concentrations and odor intensities from the UNIFAC method and between the experimental and calculated gas concentrations and odor intensities obtained from the experimental H for single fragrance mixtures. The averages of Cgas and ψ are reported as a geometric mean indicating the central tendency of the obtained data. It can be considered that the ARD values for the predictive gas concentrations and odor intensities obtained from single H are acceptable for the majority of the studied odorants in different systems. The ARD values for the gas concentration for each fragrance ingredient varied in accordance with the mixture composition; for the particular case of limonene relative errors ranged from 6.2 to 36.4% in the binary fragrance mixtures, in the ternary from 6.0 to 31.3%, and in the quaternary fragrance mixture the ARD values ranged from 4.1 to 48.5%. In respect to the predicted odor intensity, the results were also very positive with average deviations of 6.4%. When the UNIFAC method was applied, it was observed that, although the predicted gas concentrations fits well with the experimentally determined counterparts, the average ARD value is higher as compared with those obtained using Henry’s Law (40.5% versus 17.2%). The major deviations were found to linalyl acetate (114.8 - 366.0%), and α-pinene (77.8 - 83.2%) (Table 2). The UNIFAC method together with other group-contribution methods (AUNIFAC, UNIFAC Dortmund and ASOG) were previously applied to predict the gas concentration of fragranced systems dissolved or not in ethanol.37 UNIFAC was the best method for prediction of vapor-liquid equilibrium, performing better for systems 22


Page 23 of 31

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


without ethanol (ARD of 24% and 13%, in the presence and absence of ethanol, respectively). These results emphasized some limitation of the UNIFAC groupcontribution method to accurately predict the gas headspace concentrations of fragrance ingredients when in the presence of specific associative interactions, something that commonly happens in THP products. Nevertheless, in terms of the final product’s hedonics it is important to evaluate our predictions at the light of the perceived odor. In this way, in a previous study, Cain49 investigated the capacity of human subjects to detect olfactory differences in the fragrance intensity of four odorants (n-butyl alcohol, ethyl n-butyl alcohol, ethyl n-butyrate, and n-amyl alcohol) by looking at the relative difference of their concentration in the headspace. The author reported that relative headspace concentration differences of 5% to 16% were sufficient to detect a difference in perceived odor intensity, for the components with the lowest (n-butyl alcohol) and highest (n-amyl alcohol) deviation, respectively. Other studies reported that a detectable change in the sensorial intensity (known as the “just noticeable difference”) of an odorant is only attained by changing its headspace concentration of about 25 to 33%,50 depending on the odorant molecules. More recently, Le Berre and co-workers51 confirmed that a change on the sensorial quality of a fragrance mixture is only induced by a significant alteration in the components’ concentrations, being this difference characteristic of each molecule. Consequently, if we look at our approach using Henry’s Law for predicting the headspace of fragrance components, we can observe that our average ARD value of 17.2% (Table 2) is in line with the abovementioned concentration-sensory experiments. This means that the deviations we observe in the prediction of the headspace concentrations are within the range where olfactory (intensity) discrimination is not observed by the human nose. This happens because, despite the fact that both fragrance concentration and olfactory intensity are extensive properties, they are not linearly related (the relationship is a Power law). Thus, predicting the behavior of multicomponent fragrance mixtures in DPG from the pure fragrance chemical diluted in this simplified matrix using our approach, results in a very good prediction for the odor intensity. This highlights our proposed model as a simple and efficient predictive tool for the odor intensity and performance of multicomponent mixtures in this kind of products.

23



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

Page 24 of 31

Table 2. Average relative deviations (ARD, %) between experimental and predicted gas concentrations and odor intensities using the UNIFAC method and ARD (%) between experimental and calculated gas concentrations and odor intensities obtained from the experimental Henry’s Law from single fragrance mixtures. ARD UNIFAC Cgas ψ Single fragrance mixtures Limonene 37.3 16.9 α-Pinene 79.7 55.1 Linalyl acetate 114.8 34.2 Linalool 49.8 21.4 Benzyl acetate 64.8 20.8 Ethyl acetate 28.1 15.9 p-Cymene 67.5 19.7

ARD H Cgas ψ -

-

Binary fragrance mixtures Limonene α-Pinene

27.4 77.8

9.1 52.1

31.0 13.1

15.6 6.6

Limonene Linalyl acetate

7.0 157.0

3.3 41.6

31.1 13.6

11.2 5.4

Limonene Linalool

15.3 51.1

6.0 22.3

6.2 6.5

2.2 2.2

Limonene Benzyl acetate

58.2 42.5

33.8 13.5

32.8 16.4

18.1 6.0

Limonene p-Cymene

43.1 58.2

20.1 37.4

36.4 9.8

13.0 3.7

Limonene Ethyl acetate

11.0 63.5

4.4 19.5

12.6 35.1

5.5 21.2

Ternary fragrance mixtures Limonene α-Pinene Linalyl acetate

16.6 81.9 366.0

6.4 56.7 71.4

8.4 33.0 30.3

3.1 17.8 9.7

Limonene Ethyl acetate Benzyl acetate

4.2 42.7 9.7

1.6 24.6 3.7

31.3 25.7 11.0

10.5 14.1 4.4

Limonene 16.2 6.3 p-Cymene 45.5 13.9 Linalool 57.3 15.2 Quaternary fragrance mixtures

6.0 17.4 17.1

2.2 6.5 5.5

Limonene α-Pinene p-Cymene Linalyl acetate

9.7 79.4 53.1 264.1

3.0 53.9 15.7 54.0

20.3 22.7 6.5 22.3

7.1 11.8 2.3 6.9

Limonene

17.7

6.2

48.5

15.8 24


Page 25 of 31

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


p-Cymene Ethyl acetate Benzyl acetate

68.3 44.5 27.7

20.1 25.9 9.9

8.4 27.6 8.3

3.0 15.2 3.2

Limonene α-Pinene p-Cymene Linalool

23.8 83.2 25.0 42.7

10.3 58.3 15.3 17.6

4.1 35.6 25.5 33.2

1.5 19.5 10.2 10.6

Average

40.5

16.4

17.2

6.4

Values are presented as geometric averages.

CONCLUSIONS In this study, the gas phase concentrations of different fragrance mixtures (single, binary, ternary and quaternary fragrance mixtures diluted in DPG) were evaluated by HS-GC. Partitioning of multicomponent fragrance mixtures between liquid and vapor phases were predicted by each ingredient’s corresponding Henry’s constant measured in DPG only. These predicted values for fragrance release were compared with measured experimental data and with those predicted using vapor-liquid flash calculations with the UNIFAC method. The results highlighted that our approach based on single fragrance Henry’s Law allows good predictions of headspace compositions for multicomponent mixtures, performing better than the purely predictive group-contribution methods like UNIFAC for this type of mixtures. Nevertheless, some limitations should be considered since Henry’s Law requires experimental data to be measured and can only be applied over a limited concentration range depending on the fragrance mixture. In terms of the odor profile of the studied fragrance mixtures, slight differences were found in its prediction depending on the fragrance mixture and concentrations tested. Based on literature data, we can say that the predicted odor intensities (from calculated H values from pure fragrance in simplified matrix) calculated using the Stevens’ Power Law model were in very good agreement with the experimental measurements. From this point, it can be assumed that the presented methodology is a suitable approach for prediction of the gas concentration and perceived odor of multicomponent fragrance mixtures released from a simplified matrix. This approach may help perfumers to reduce trial-and-error experiments by understanding better the fragrance-matrix interactions while creating their formulations to, ultimately, predict fragrance performance.

25



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

Page 26 of 31

ACKNOWLEDGEMENTS This work was partially supported by Fundação para a Ciência e a Tecnologia (FCT) and FEDER under Programme COMPETE (Project UID/EQU/50020/2013) and by QREN, ON2 and FEDER (Projects NORTE-07-0162-FEDER-000050 and NORTE-070124-FEDER-0000012). P. Costa and M. A. Teixeira acknowledge their postdoctoral grants from the Fundação para a Ciência e a Tecnologia (SFRH/BPD/93108/2013 and SFRH/BPD/76645/2011, respectively).

SUPPORTING INFORMATION Figures on the molecular structure of the odorants and matrix, schematic representation of the experimental procedures and headspace gas chromatography analysis, gas phase concentrations and odor intensities as a function of their liquid phase concentrations for the binary, ternary and quaternary fragrance mixtures, tables on the studied fragrance mixtures and total fragrance concentration percentages, UNIFAC groups and subgroups, number of groups in molecule, the parameters of molecular van der Waals volumes and surface area, predicted activity coefficients, liquid phase and predicted molar composition of the vapor phase of the fragrance mixtures, experimental Henry’s Law constants together with the coefficient of determination, F value, maximum liquid concentrations, and relative deviation of Henry’s Law constants between experimental and theoretical Henry’s Law constants for the studied mixtures are provided. This information is available free of charge via the Internet at http://pubs.acs.org.

REFERENCES (1) Sell, C. S. On the Unpredictability of Odor. Angew. Chem. Int. Ed. 2006, 45, 6254. (2) Friberg, S. E. Vapour Pressure of Some Fragrance Ingredients in Emulsion and Microemulsion Formulations. Int. J. Cosmet. Sci. 1997, 19, 75. (3) Herman, S. Fragrance in Emulsion and Surfactant Systems. Cosmet. Toiletries 2006, 121, 59. (4) Baydar, A.; McGee, T.; Purzycki, K. Skin Odor Value Technology for Fragrance Performance Optimization. Perfum. Flavor. 1995, 20, 45. (5) Stora, T.; Escher, S.; Morris, A. The Physicochemical Basis of Perfume Performance in Consumer Products. Chimia 2001, 55, 406. (6) Street, C.; Woody, J.; Ardila, J.; Bagajewicz, M. Product Design: A Case Study of Slow-Release Carpet Deodorizers/Disinfectants. Ind. Eng. Chem. Res. 2008, 47, 1192. 26


Page 27 of 31

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


(7) Teixeira, M. A.; Rodríguez, O.; Gomes, P.; Mata, V.; Rodrigues, A. E. Perfume Engineering: Design, Performance & Classification; Elsevier: Oxford, UK, 2013. (8) Friberg, S. E. Fragrance Compounds and Amphiphilic Association Structures. Adv. Colloid Interface Sci. 1998, 75, 181. (9) Friberg, S. E. Evaporation from Three Single‐Compound Phases in Equilibrium. J. Dispersion Sci. Technol. 2007, 28, 207. (10) Friberg, S. E.; Al-Bawab, A.; Odeh, F.; Bozeya, A.; Aikens, P. A. Emulsion Evaporation Path. A First Comparison of Experimental and Calculated Values. Colloids Surf., A 2009, 338, 102. (11) Baydar, A.; Carles, A.; Decazes, J. M.; McGee, T.; Purzycki, K. Behavior of Fragrances on Skin. Cosmetics & Toiletries 1996, 111, 49. (12) Vuilleumier, C.; Flament, I.; Sauvegrain, P. Headspace Analysis Study of Evaporation Rate of Perfume Ingredients Applied onto Skin. Int. J. Cosmet. Sci. 1995, 17, 61. (13) Behan, J. M.; Perring, K. D. Perfume Interactions with Sodium Dodecyl Sulphate Solutions. Int. J. Cosmet. Sci. 1987, 9, 261. (14) Brossard, C.; Rousseau, F.; Llamas, G.; Dumont, J. -P. Determination of Sensory Oil-Water Partition Coefficients of Single Aroma Compounds Combining Panelist Free Intensity Rating and Theoretical Modeling of Odor Intensity. J. Sens. Stud. 2002, 17, 445. (15) Friberg, S. Phase Diagram Approach to Evaporation From Emulsions with n Oil Compounds. J. Phys. Chem. B 2009, 113, 3894. (16) Escher, S.; Oliveros, E. A. Quantitative Study of Factors that Influence the Substantivity of Fragrance Chemicals on Laundered and Dried Fabrics. J. Am. Oil Chem. Soc. 1994, 71, 31. (17) Teixeira, M. A.; Rodríguez, O.; Rodrigues, S.; Martins, I.; Rodrigues, A. E. A Case Study of Product Engineering: Performance of Microencapsulated Perfumes on Textile Applications. AlChE J. 2012, 58, 1939. (18) Arribas, M. P.; Soro, P.; Silvestre, J. F. Allergic Contact Dermatitis to Fragrances: Part 2. Actas Dermo-Sifiliogr. 2013, 104, 29. (19) Faust, S.; Aly, O. Chemistry of Water and Treatment, 2nd ed.; CRC Press LLC: USA, 1997. (20) Robbins, G. A.; Wang, S.; Stuart, J. D. Using the Static Headspace Method to Determine Henry's Law Constants. Anal. Chem. 1993, 65, 3113. (21) Peng, J.; Wan, A. Measurement of Henry’s Constants of High-Volatility Organic Compounds using a Headspace Autosampler. Environ. Sci. Technol. 1997, 31, 2998. (22) Stevens, S. S. On the Psychophysical Law. Psychological Rev. 1957, 64, 153. (23) Cain, W. S.; Schiet, F. T.; Olsson, M. J.; de Wijk, R. A. Comparison of Models of Odor Interaction. Chem. Senses 1995, 20, 625. (24) Iwata, H.; Shimada, K. Formulas, Ingredients and Production of Cosmetics; Springer: USA, 2013. (25) Oh, Y. Antiperspirant Compositions. Procter & Gamble Company; EP0404532 A1, 1989. (26) SpecialChem. http://cosmetics.specialchem.com/ (Accessed June 2015). (27) PubMed.gov, US National Library of Medicine National Institutes of Health. http://pubchem.ncbi.nlm.nih.gov/ (Accessed May 2015).

27



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

Page 28 of 31

(28) DIPPR 801 Database. http://www.aiche.org/dippr/events-products/801database (Accessed May 2015). (29) Chemspider Database of Chemical Structures and Property Predictions. http://www.chemspider.com/Default.aspx (Accessed May 2015). (30) van Gemert, L. J. Compilations of Odour Threshold Values in Air, Water and Other Media, the Netherlands; Oliemans Punter & Partners BV: The Netherlands, 2003. (31) Devos, M.; Rouault, J.; Laffort, P. Standardized Olfactory Power Law Exponents; France: Editions Universitaires de Dijon, 2002. (32) Renon, H.; Prausnitz, J. M. Local Compositions in Thermodynamic Excess Functions for Liquid Mixtures. AlChE J. 1968, 14, 135. (33) Abrams, D. S.; Prausnitz, J. M. Statistical Thermodynamics of Liquid Mixtures: A New Expression for the Excess Gibbs Energy of Partly or Completely Miscible Systems. AlChE J. 1975, 21, 116. (34) Tochigi, K.; Yoshida, K.; Kurihara, K.; Ochi, K.; Murata, J.; Urata, S.; Otake, K. Determination of ASOG Parameters for Selecting Azeotropic Mixtures Containing Hydrofluoroethers. Fluid Phase Equilib. 2002, 194-197, 653. (35) Klamt, A. COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design; Elsevier: Amsterdam, 2005. (36) Poling, B.; Prausnitz, J.; O’Connell, J. The Properties of Gases and Liquids, 5th ed.; McGraw-Hill: New York, 2004. (37) Teixeira, M. A.; Rodríguez, O.; Mota, F. L.; Macedo, E. A.; Rodrigues, A. E. Evaluation of Group-Contribution Methods to Predict VLE and Odor Intensity of Fragrances. Ind. Eng. Chem. Res. 2011, 50, 9390. (38) Fredenslund, A.; Jones, R. L.; Prausnitz, J. M. Group-Contribution Estimation of Activity Coefficients in Nonideal Liquid Mixtures. AlChE J. 1975, 21, 1086. (39) Barkat, S.; Le Berre, E.; Coureaud, G.; Sicard, G.; Thomas-Danguin, T. Perceptual Blending in Odor Mixtures Depends on the Nature of Odorants and Human Olfactory Expertise. Chem. Senses 2012, 37, 159. (40) Mayer, V.; Fazio-Fluehr, P.; Arendt, S. ASTM Dictionary of Engineering Science and Technology; Mayfield , PA, ASTM International, 2005. (41) Teixeira, M. A.; Rodríguez, O.; Rodrigues, A. E. The Perception of Fragrance Mixtures: A Comparison of Odor Intensity Models. AlChE J. 2010, 56, 1090. (42) Chastrette, M. Data Management in Olfaction Studies. SAR QSAR Environ. Res. 1998, 8, 157. (43) Cain, W. S.; Schmidt, R. Can We Trust Odor Databases? Example of tand n-Butyl Acetate. Atmos. Environ. 2009, 43, 2591. (44) Teixeira, M. A.; Rodríguez, O.; Rodrigues, A. E. Perfumery Radar: A Predictive Tool for Perfume Family Classification. Ind. Eng. Chem. Res. 2010, 49, 11764. (45) Laing, D. G. Perceptual Odour Interactions and Objective Mixture Analysis. Food Qual. Prefer. 1994, 5, 75. (46) Jinks, A.; Laing, D. G. The Analysis of Odor Mixtures by Humans: Evidence for a Configurational Process. Physiol. Behav. 2001, 72, 51. (47) Teixeira, M. A.; Rodríguez, O.; Rodrigues, A. E.; Selway, R. L.; Riveroll, M.; Chieffi, A. Prediction Model for the Odor Intensity of Fragrance Mixtures: A Valuable Tool for Perfumed Product Design. Ind. Eng. Chem. Res. 2013, 52, 963.

28


Page 29 of 31

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


(48) Teixeira, M. A.; Barrault, L.; Rodríguez, O.; Carvalho, C. C.; Rodrigues, A. E. Perfumery Radar 2.0: A Step Toward Fragrance Design and Classification. Ind. Eng. Chem. Res. 2014, 53, 8890. (49) Cain, W. Differential Sensitivity for Smell: "Noise" at the Nose. Science 1977, 195, 796. (50) Gamble, E. Applicability of Weber's Law to Smell. Am. J. Psychol. 1898, 10, 82. (51) Le Berre, E.; Béno, N.; Ishii, A.; Chabanet, C.; Etiévant, P.; ThomasDanguin, T. Just Noticeable Differences in Component Concentrations Modify the Odor Quality of a Blending Mixture. Chem. Senses 2008, 33, 389.

FIGURE CAPTIONS Figure 1. Gas phase concentration of fragrance component as a function of its liquid phase concentration in DPG. Figure 2. Gas phase concentration of limonene as a function of its liquid phase concentration in all the studied mixtures in DPG. Figure 3. Gas phase concentrations of α-pinene (A), linalyl acetate (B), linalool (C), ethyl acetate (D), benzyl acetate (E), and p-cymene (F) as a function of their liquid phase concentrations in DPG for all the mixtures studied. Figure 4. Correlation between the experimental Henry’s Law constant for single fragrance mixtures containing one fragrance component and the matrix (DPG) and the experimental Henry’s Law constant for binary (A), ternary (B) and quaternary (C) fragrance mixtures. Each lowercase letter corresponds to each studied fragrance component: a-Limonene, b-α-Pinene, c-Linalyl acetate, d-Linalool, e-Ethyl acetate, fBenzyl acetate, g-p-Cymene. A-C: B: Zoom-in of the lower four points. Figure 5. Gas phase concentrations and odor intensity (using the Stevens’ Power Law as odor intensity model) for the single fragrance mixtures in DPG as a function of their liquid phase concentrations. Figure 6. Gas phase concentrations and odor intensities as a function of their liquid phase concentrations for the binary fragrance mixtures limonene + benzyl acetate diluted in DPG. Figure 7. Gas phase concentrations and odor intensities (using the Stevens’ Power Law as odor intensity model) as a function of their liquid phase concentrations for the ternary fragrance mixture limonene + ethyl acetate + benzyl acetate diluted in DPG. Figure 8. Gas phase concentrations and odor intensities as a function of their liquid phase concentrations for the quaternary fragrance mixture limonene + p-cymene + ethyl acetate + benzyl acetate diluted in DPG.

29



Page 30 of 31

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

30


Page 31 of 31

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


44x23mm (600 x 600 DPI)


Modeling Fragrance Components Release from a Simplified Matrix

Recommend Documents