Flavour Engineering

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FLAVOUR ENGINEERING

ANA ISABEL MOTA MONTEIRO

DISSERTAÇÃO DE MESTRADO APRESENTADA

À FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO EM ENGENHARIA QUÍMICA

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Mestrado Integrado em Engenharia Química

Flavour Engineering

Dissertação de Mestrado

de

Ana Isabel Mota Monteiro

Desenvolvida no âmbito da unidade curricular de Dissertação

realizado em

Laboratório Associado Laboratório de Processos de Separação e Reação – Laboratório de

Catálise de Materiais

Orientador: Doutora Patrícia Costa Co-orientadores: Professor Alírio Rodrigues

Professor José Miguel Loureiro

Departamento de Engenharia Química

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“Recomeça.... Se puderes Sem angústia E sem pressa. E os passos que deres, Nesse caminho duro Do futuro Dá-os em liberdade. Enquanto não alcances Não descanses. De nenhum fruto queiras só metade.”

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Agradecimentos

Agora que me encontro a terminar uma das etapas mais marcantes da minha vida, a concluir um dos meus primeiros grandes objetivos, percebo o quão importante são as pessoas que nos rodeiam. As que escolhemos, as que nos escolhem e aquelas que, por um ou outro motivo, cruzam a sua vida com a nossa a fim de nos ajudarem naquele momento e que partem sem explicação, os “anjos da guarda”.

Em primeiro lugar, quero agradecer à Doutora Patrícia Costa. Quero agradecer-lhe a paciência, a dedicação, a forma como esteve sempre lá quando foi preciso. A Patrícia, como pediu para ser chamada, aproximou-se como família, aconaproximou-selhou-me, deu-me a mão e guiou-me. A Patrícia confiou em mim e ajudou-me a chegar ao fim sem medo.

Ao Professor Alírio, quero agradecer a oportunidade de poder trabalhar naquilo que mais gosto. Quero agradecer-lhe também a sua disponibilidade constante para me receber e ajudar. Foi, sem dúvida, fundamental a sua presença e apoio.

Ao Professor José Miguel Loureiro, quero agradecer não só a oportunidade que me deu, mas também a sua paciência, carinho, amizade e apoio ao longo do meu percurso. Obrigada pela sua entrega e disponibilidade ao longo destes 5 anos. Será, para sempre, o professor que tinha solução para tudo (ou quase), o professor das mil histórias, que tanto aliviaram as aulas teóricas, o professor conversador, disponível e generoso. Devo agradecer, também, às minhas duas chatas companheiras de laboratório. A Joana e a Sofia, estiveram ao meu lado todos os dias, ouviram os meus devaneios, apararam as minhas lágrimas e sofrimento, tantas e tantas vezes, fizeram-me rir quando me apetecia chorar e fizeram-me chorar de tanto rir. Elas deram-me força, ajudaram-me e, sem elas, não teria sido tão divertido. Sem elas, eu não teria tantas histórias para contar.

A ti, meu avô António, obrigada. Obrigada pelo teu orgulho imensurável, que não disfarças em mostrar. Obrigada por todos os dias nos levares e trazeres da creche, da escola, do secundário, da faculdade. Obrigada por me vires buscar à faculdade no final de um dia de aulas porque “já está escuro”, “é tarde” ou “estava a chover tanto e assim não te molhas no caminho”.

Ao meu pão com queijo, as horas de gargalhadas que faziam esquecer as preocupações próprias de quem faz uma tese. A amizade, o companheirismo e o carinho que sempre me deste. A ti, que estiveste lá todos os dias, o meu profundo obrigada.

Aos meus pais, Ângela e Carlos, quero agradecer a dedicação diária. Obrigada por todo o esforço que fazem para trabalhar tantas e tantas horas por dia e por me terem dado a oportunidade de ter ido para a escola e ter conseguido entrar na faculdade. Vocês são, sem dúvida, a minha pedra angular. Obrigada pela paciência, apoio e amor ao longo de toda a minha vida. Obrigada pelos valores que me passaram e pela força que todos os dias vejo em vós e que me inspira a ser cada vez melhor.

Obrigada a todos! Com carinho,

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Flavour Engineering

Este trabalho foi financiado por: Projeto POCI-01-0145-FEDER-006984 - Laboratório Associado LSRE-LCM - financiado pelo Fundo Europeu de Desenvolvimento Regional (FEDER), através do COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI) e por fundos nacionais através da Fundação para a Ciência e a Tecnologia I.P.

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Abstract

The motivation for the purchase of a product is generated by different sensations induced in the consumer, like pleasure or memories. Considering the impact of these sensations in the consumer, the food industry has been working on the development of odours and flavours that provoke positive feelings in the customer with the aim of influencing the purchase of a flavoured product. Research and development departments of food area have several challenges regarding the creation of products with new odours and flavours. It is a complex task that includes several pre-formulations until reach the desired odour and flavour as well as a lot of time spending and expensive costs, it also includes the use of a panel to evaluate the several formulations. This evaluation relies on the sensory properties of this kind of products and it is usually done by trained (panel) or untrained (consumers) people, that evaluate their characteristics based on previously defined descriptors. However, the result of these evaluations has always a high level of subjectivity, since it is strongly influenced by evaluator’s anatomy, gender, age, eating habits, etc. Therefore, there is an interest in developing a methodology to reduce the empirical level of this kind of evaluations, giving a more scientific character, and to decrease the amount of pre-formulations, resulting in a significant decrease of time and costs. In this sense, this study proposes a methodology able to predict the odour and flavour profiles of non-alcoholic beverages based on their volatiles composition together with using psychophysical models. Thus, five fruit juices (lemon, peach, pineapple, apple and mango) were characterized regarding their gas phase composition above de liquid (i.e. headspace). The identification of the volatiles presents in the samples was made with a dynamic headspace sampler (DHS) connected to a gas chromatograph (GC) coupled to a mass spectrum detector (MS). After components’ identification, relative weights were attributed based on their odour and flavour descriptors, and the odour and flavour radars were constructed for each analysed juice. Based on the headspace compositions of each studied fruit juice, it was possible to predict the odour and flavour profiles of three binary fruit juices mixtures and a ternary one. The validation of the proposed methodology was made by consumers through a sensorial evaluation.

In conclusion, the obtained results reveal that the proposed methodology is efficient in the evaluation of the odour and flavour profiles of the studied juices, as well as of the binary and ternary fruit juices mixtures considered. The sensorial evaluation results showed a good agreement with those obtained using the developed methodology.

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Resumo

A motivação para a compra de um qualquer produto é gerada pelas diferentes sensações que o mesmo desperta no consumidor, como o prazer e a memória. Deste modo, tendo em conta o impacto destas sensações no consumidor, a indústria alimentar tem trabalhado no desenvolvimento de odores e sabores que despertem sensações positivas no cliente, com o objetivo de influenciar a compra de um produto aromatizado. Os departamentos de investigação e desenvolvimento ligados à área alimentar encontram inúmeros desafios como a criação de produtos com novos odores e sabores. Trata-se, assim, de uma tarefa complexa que inclui inúmeras pré-formulações, até que se atinja o odor e o sabor desejados, além disso leva a grandes gastos de tempo e acarreta elevados custos. Esta avaliação recai sobre as propriedades sensoriais de um produto deste tipo e é, habitualmente, feita por pessoas treinadas (painel) ou não-treinadas (consumidor) que avaliam as suas características, com base em descritores previamente definidos. No entanto, no resultado destas mesmas avaliações, há sempre um nível elevado de subjetividade, uma vez que esta é fortemente influenciada pela anatomia do avaliador, género, idade, hábitos alimentares, etc. Assim, existe um grande interesse na criação de uma metodologia capaz de reduzir o nível empírico deste tipo de avaliação, conferindo um caráter mais científico e objetivo, bem como diminuir a quantidade de pré-formulações, resultando numa redução significativa de tempo e custos. Nesse sentido, no presente estudo é proposta uma metodologia capaz de prever os perfis de odor e sabor de bebidas não alcoólicas, com base na composição dos seus voláteis e em modelos psicofísicos. Deste modo, caracterizaram-se cinco sumos de fruta (limão, pêssego, ananás, maçã e manga) a partir da sua composição gasosa acima do líquido (headspace). A identificação e quantificação dos voláteis presentes nas amostras de sumo foi realizada recorrendo a um dynamic headspace sampler (DHS) conectado a um cromatógrafo gasoso (GC) por sua vez acoplado a um detetor de espectroscopia de massa (MS). Após a identificação dos componentes, atribuíram-se pesos relativos baseado nas suas descrições de odor e sabor, e construíram-se os respetivos radares de odor e sabor, para cada uma das bebidas não-alcoólicas analisadas. Além disso, baseado na composição do headspace de cada um dos sumos de fruta estudados, foi possível prever um perfil de odor e um de sabor de três misturas binárias e uma ternária. A validação da metodologia proposta foi conseguida recorrendo a uma avaliação sensorial realizada por consumidores.

Concluindo, os resultados obtidos revelaram que a metodologia apresentada é eficaz na avaliação dos perfis de odor e sabor, dos sumos puros estudados, assim como das misturas binárias e ternária consideradas. A avaliação sensorial realizada revelou, por isso, resultados bastante concordantes com aqueles que foi possível obter através da metodologia utilizada.

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Declaração

Declaro, sob compromisso de honra, que este trabalho é original e que todas as contribuições não originais foram devidamente referenciadas com identificação da fonte.

Ana Isabel Mota Monteiro julho 2017

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List of Figures

Figure 1 Schematic representation of orthonasal and retronasal pathways ([14,18]). ... 3

Figure 2 Wine aroma wheel [54]. ... 8

Figure 3 Beer flavour wheel [55]. ... 8

Figure 4 Rooibos sensory wheel [56]. ... 9

Figure 5 PR obtained for a fragrance developed by the methodology authors [57]. ... 10

Figure 6 Commercial juices studied (lemon, peach, pineapple, apple and mango). ... 11

Figure 7 Equipment arrangement (DHS connected to a GC-MS). ... 12

Figure 8 Meeting room used for sensory analysis (A). Panellists during the sensory evaluation (B). ... 17

Figure 9 Lemon juice headspace chromatogram. ... 18

Figure 10 Peach nectar headspace chromatogram. ... 20

Figure 11 Pineapple nectar headspace chromatogram. ... 22

Figure 12 Apple nectar headspace chromatogram. ... 24

Figure 13 Mango nectar headspace chromatogram. ... 26

Figure 14 Odour and flavour radars obtained for lemon juice. ... 28

Figure 15 Odour and flavour radars obtained for peach juice. ... 29

Figure 16 Odour and flavour radars obtained for pineapple juice. ... 29

Figure 17 Odour and flavour radars obtained for apple juice. ... 30

Figure 18 Odour and flavour radars obtained for mango juice. ... 30

Figure 19 Odour and flavour radars obtained for the mixture composed by apple and peach. ... 31

Figure 20 Odour and flavour radars obtained for the mixture composed by pineapple and peach. ... 31

Figure 21 Odour and flavour radars obtained for the mixture composed by pineapple and mango. ... 32

Figure 22 Odour and flavour radars obtained for mixture 1 (peach, mango and pineapple). ... 33

Figure 23 Odour and flavour radars obtained for lemon juice (▬ experimental radar; ▬ theoretical radar). ... 34

Figure 24 Odour and flavour radars obtained for peach juice (▬ experimental radar; ▬ theoretical radar). ... 34

Figure 25 Odour and flavour radars obtained for pineapple juice (▬ experimental radar; ▬ theoretical radar). .. 34

Figure 26 Odour and flavour radars obtained for apple juice (▬ experimental radar; ▬ theoretical radar). ... 35

Figure 27 Odour and flavour radars obtained for mango juice (▬ experimental radar; ▬ theoretical radar). ... 35

Figure 28 Odour and flavour radars obtained for the mixture composed by apple and peach (▬ experimental radar; ▬ theoretical radar). ... 37

Figure 29 Odour and flavour radars obtained for the mixture composed by pineapple and peach (▬ experimental radar; ▬ theoretical radar)... 37

Figure 30 Odour and flavour radars obtained for the mixture composed by pineapple and mango (▬ experimental radar; ▬ theoretical radar)... 38

Figure 31 Odour and flavour radars obtained for the ternary mixture composed by peach, mango and pineapple (▬ experimental radar; ▬ theoretical radar). ... 38

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List of Tables

Table 1 Dynamic headspace working parameters. ... 12

Table 2 Odour and Flavour descriptions of each family. ... 14

Table 3 Weights attribution for each olfactive family [37]. ... 15

Table 4 Samples used in each season and respective identification. ... 17

Table 5 Volatile compounds identified in the headspace of lemon juice. ... 19

Table 6 Volatile compounds identified in the headspace of peach juice. ... 21

Table 7 Volatile compounds identified in the headspace of pineapple juice. ... 23

Table 8 Volatile compounds identified in the headspace of apple juice... 25

Table 9 Volatile compounds identified in the headspace of mango juice... 27

Table 10 ODT and odour description of each component. ... 51

Table 11 Relative weights attributed to each component and its respective OVj. ... 51

Table 12 FDT and flavour description of each component. ... 52

Table 13 Relative weights attributed to each component and its respective FVj. ... 52

Table 18 ODT and flavour description of each component. ... 55

Table 20 FDT and taste description of each component. ... 56

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Notation

Cgas _{Concentration in the gas phase} _counts

FDT Flavour Detection Threshold mg kg-1

FV Flavour Value

FV’ Normalized Flavour Value

ODT Odour Detection Threshold g m-3

OV Odour Value

OV’ Normalized Odour Value

w Relative weight

Indexes

d Detection

exp Experimentally obtained

i Component

j Family

lit Obtained from available literature

N Number of components

r Recognization

List of Acronyms

DA Descriptive Analysis

DHS Dynamic Headspace Sampler

EI Electron impact

FEUP Faculdade de Engenharia da Universidade do Porto

FFNSC2 Flavors and Fragrances of Natural and Synthetic Compounds 2

GC Gas Chromatography

GC/O Gas-Chromatography Olfactometry

LA LSRE-LCM Associate Laboratory, Laboratory of Separation and Reaction Engineering and Laboratory of Catalysis and Materials

LRI Linear Retention Index

MS Mass Spectrometry

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NIST National Institute of Standards and Technology

O.D. Outer Diameter

QDA Quantitative Descriptive Analysis SDA Spectrum Descriptive Analysis

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Introduction 1

1 Introduction

1.1 Motivation and Relevance

The acceptance of a food product is influenced by the sensations that consumers experiment during their consumption. These sensations are generated not only by their appearance or texture, but also by their odour and flavour. In the case of fruity products, flavour is the most attractive characteristic because it is understood as a quality representative [1]. Food market deals everyday with the challenge of producing rigorously the same odour and flavour among productions [2]. However, they are also very interested in developing new odour and flavours in order to create new products or improve the performance of those already available. The creation of new and innovative flavoured products is a complex task. It requires a high number of formulations to attain the desired odour and flavour that result in excessive costs due to the required time for the pre-formulations, as well as, raw materials and generated waste. Besides that, the sensory quality of these formulations is evaluated by a panel, which means that the obtained results have a subjective and arbitrary character [3-4].

On the market, there are several food products available whose food matrices can be classified into three different types: liquid (e.g. juices), semi-liquid (e.g. yogurts) and solid (e.g. cookies) [2]. The type of matrix influences the volatiles release [2,5] diffusion, and consequently, their perception by human receptors cells. The release is strongly influenced by the level of affinity that the volatiles have with the matrix; more affinity between the molecules and the matrix means more difficulty to escape to the gas phase [6]. For instance, in beverages, the liquid matrix usually offers a minimal resistance to volatiles passage [6]. Instead, in the solid ones, the volatiles release is strongly dependent on the chewing process, once it breaks the food matrix [6].

In the perfumery context, Teixeira et at. (2010) proposed a theoretical model able to predict the olfactory families of perfumes based on their liquid concentration and odour descriptors – the Perfumery Radar (PR) methodology. This model was presented as a way to deal with the usual empirical evaluation of perfumes, becoming more objective and with less arbitrariness [6], problems also found in the food industry. Thus, based on the PR approach, and making the necessary adjustments, in the present study a new methodology, able to predict the odour (i.e., volatiles perception by nose, via orthonasal) and flavour [(composed by aroma (i.e., volatiles perception by nose, via retronasal pathway) and taste (i.e. perceived at the tongue level - sweet, sour, bitter, salty and umami)] of non-alcoholic beverages, was developed. Being the approach presented in this work new in the food area, it was decided to start with non-alcoholic beverages, due to the less complexity of the matrix. This methodology intends to reduce the high costs associated with the number of formulations needed to reach the desired odour and flavour, raw materials, generated waste, and to deal with the subjectivity linked with the use of sensory panels [3-4]. It starts with the analysis of the headspace (i.e. the gas phase above the liquid mixture) of five commercial fruit juices (lemon, peach, pineapple, apple and mango) using dynamic headspace and gas chromatography techniques coupled with mass spectrometry

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Introduction 2

(DHS/GC-MS). After that, based on the odour and flavour descriptors of the identified components relative weights were attributed, allowing the creation of the odour and flavour radars of each analysed juice. Then, using the data of single samples, it was also possible to predict the odour and flavour of three binary fruit juices mixtures and a ternary one. All the samples (single juices, binary and ternary mixtures) were validated by consumers.

1.2 Thesis objectives and Layout

The main goal of the present work was to develop a model able to predict the odour and flavour of simple and complex non-alcoholic beverages, from the analysis of their volatile composition, and taking into consideration the odour and flavour thresholds and pure components descriptors.

The layout of the present document is detailed below.

First, it is presented a general view of what already exists in the food area (chapter 2: Context and State of the Art). It is made a brief reference of what sensory perception is and it is given a short definition of odour, aroma, taste and flavour (subchapter 2.1: Sensory Perception). The techniques available to make the identification of the odour-active components are presented in the section 2.2, whose title is “Identification of the odour-active compounds”. Then, in subchapter 2.3: “Psychophysics of Flavour” are clarified the parameters and its use to determine the odour and flavour intensities. In section 2.4: “Odour and flavour sensory descriptions”, are described the most frequently used ways to describe a product; namely, lexicons and sensory wheels. It is also mentioned a work done in the perfume industry, the perfume radar, that served as an inspiration for the present work. In chapter 3: “Materials and Methods”, are mentioned the samples and techniques used (subchapters 3.1 and 3.2). The sections 3.3 and 3.4 have the odour and flavour intensity references and the mathematical procedure behind the radars construction. In the point 3.5, the details of the sensory analysis are displayed.

The results, and respective discussion, are presented in chapter 4 “Results and Discussion”. There, it is possible to consult the obtained chromatograms, odour and flavour descriptions, thresholds, intensities and theoretical and experimental radars. Finally, the main conclusions are exhibited in section 5: “Conclusion”. In addition, a work evaluation can be consulted in section 6, where the limitations found are mentioned, as well as suggestions for a future work.

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Context and State of the Art 3

2 Context and State of the Art

2.1 Sensory perception

Humans are constantly exposed to several and different stimuli every day. The perception of sensations like odour, aroma, taste and flavour are part of these stimuli which are mainly felt during food consumption. The extent of these sensations depends on each individual because they are influenced, for example, by nasal anatomy, saliva flow rate and temperature, just to mention a few [7-11].

The human nose, or tongue, receives a stimulus through their receptor cells. These cells send a signal to the brain, which is the responsible organ for its decoding (transduction mechanism). This mechanism allows the detection and recognition of a sensation, for example, odour or flavour. People who are trained to do sensory analysis, show a higher ability to distinguish different components, being able to classify them into families (e.g. fruity, citrus, green) [12].

2.1.1 Odour, Aroma, Taste and Flavour

Great controversy exists among scientists in what concerns to odour, aroma, taste and flavour definitions and the unappropriated use of these terms has generated some confusion. So, to avoid any misperception, the definition considered in this work for each sensation is presented below.

Odour and aroma are both associated to the olfactory sense but by distinct ways [5]. Thus, while odour is linked to what is perceived by the orthonasal pathway [5,13] and associated to the sniffing process [14], aroma is detected by the retronasal pathway [5,13,15] and occurs after swallowing [14]. In this last case, the volatile aroma molecules do not get directly the nasal cavity (orthonasal smell) but, after ingestion, they are released from the food matrix and reach the nasal cavity through the pharynx [14,16] (Figure 1). According to some authors, the retronasal smell can be eliminated by closing the nose during food consumption or using a pure air flow directly into the nose, which will stop the aroma molecules uprise [15,17].

Figure 1 Schematic representation of orthonasal and retronasal pathways ([14,18]).

Regarding to taste, it can also be denominated as taste primary or basic. It is a sensation that occurs at the tongue level and can be defined as the perception of the basic tastes: sweet, bitter, salty, sour and umami.

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Recent discovers are making some authors consider fat as the sixth basic [19]. Finally, flavour is considered a more complex sensation, once it is a combination of aroma and taste perceptions, during the consumption of food products [2,20-21].

2.2 Identification of odour-active compounds

Odour-active components in food matrices have been identified by gas chromatography-olfactometry (GC/O). This technique combines the gas chromatographic analysis with sensory detection to identify odour-active compounds. Here the individual compounds present in the GC effluent are identified by a trained human assessor or a team (detector) [22]. This technique is recognized as a valid way to study the volatiles from complex mixtures [23], allowing the selection of the odour-active components [24]. However, it also presents some disadvantages. One of them is the discomfort usually provoked by the elevated temperature and dry during sniffing process. In addition, is also difficult to evaluate the sensory significance of the volatiles in just one GC/O run [24].

Dynamic Headspace Analysis (DHS) or Static Headspace Analysis (SH) together with gas chromatography are also methodologies used to study complex mixtures of odorants and to identify odour-active compounds. The main advantage of these techniques, mainly of the DHS, is the ability to concentrate components which can be found in lower amounts in the food matrix. Furthermore, these techniques discard the use of organic solvents to extract volatiles from the matrix. In terms of optimization, for both DHS and SH, it is necessary to take into consideration the sample temperature and the volatility of the components present in the study sample [25]. The principal difference between them is mainly related to the equilibrium between the sample matrix (liquid or solid phases) and the headspace (gas phase). So, the SH is used when the two phases present in the vial are in equilibrium. Here, an aliquot of the gas phase above the liquid or solid phases is analysed. In the DHS, the sample is placed into a vial and volatiles are draw by a carrier gas that takes the volatiles. This is a dynamic process where the volatiles are concentrated before the injection. The Static Headspace is also very applied in sensory studies. For example, Van Ruth et al. (2001) used this technique to study the influence of saliva in volatile release of twenty aroma compounds in model systems [25].

Dynamic headspace with gas chromatography is very used in fruit aroma analysis [26-27] as, for example, to identify the volatile components present in peach fruit [1,28] and apple fruit [25]. The interest in this technique is because it does not involve sample procedures that can change the sample at qualitative or quantitate levels (e.g. there is no solvent) [29-28]; it avoids that non-volatiles residues pass to the column [28]; and besides that, this procedure is considered fast [29-28]. Moshonas and Show (1992) applied both techniques for the quantification of sixteen fresh orange juice volatiles [28]. Other authors also used both techniques to citrus juice analysis [30-31].

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2.3 Psychophysics of Flavour

2.3.1 Odour and flavour intensities

A common way to convert gas concentrations in odour intensity is using psychophysics methods. Psychophysics is a scientific area that study the integration between the physical stimuli and the psychological interpretation of it having a mathematical interpretation as a result [32]. The odour value (OV) is an example of a psychophysical method to evaluate the odour intensity of a product. For its determination, it is necessary to know the odour threshold and gas concentration of the components in study [4].

An odour or flavour threshold is understood as the less concentration of a compound in a medium which makes panellists detect a difference between the test solution and the blanc one – detection threshold (Thrd_)–

or that one who allows to recognize and identify the component in solution – recognition threshold (Thrr_).

Both are defined as absolute thresholds [21]. They are determined using a specialized panel which sniff (orthonasal olfaction) the fluid until the detection or recognition of the component happen. During the process, they sniff air fluxes with a successive incensement of the compound concentration. In the flavour case, it is done at the same mode but by ingestion [21]. The information related to components thresholds can be obtained in databases available in the literature like van Gemert [21,33]. The databases compile a large amount of information from several studies. The odour thresholds are frequently evaluated in air or water, while the flavour thresholds are most commonly determined in water.

2.4 Odour and flavour sensory descriptors

A technique used to identify, categorize and determine the sensory characteristics in food products is known as Descriptive Analysis (DA) [22,34] or sensory profile [35]. This methodology includes three phases: descriptive generation, assessor training and evaluation of samples [36]. Descriptive Analysis is a rigorous and precise methodology, providing solid results [37]. It is performed by a group of trained panellists, between 8 and 12 members, responsible for assigning the sensory characteristics of a certain product, namely the appearance, aroma, flavour and texture [34-35].

An adequate usage of DA includes a training program. It has the objective of standardize the evaluation practices among the panellists [37].

2.4.1 Lexicon

Lexicon is a group of words, scientifically accepted, that describe a certain product or commodity [22]. According to Drake and Civille (2002) a flavour lexicon is a group of words that can describe the product flavour [22], aroma and/or mouthfeel [35]. This standardized vocabulary exists in order to facilitate the communication among several audiences [38]. In short, a lexicon works like a technical dictionary where it is possible to find a list of characteristics of a certain product [22]. This vocabulary is useful to understand the differences among products of the same category.

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The creation of a product’s lexicon is a complex task that begun with Arthur D. Little during 1940s [38]. He started with the Flavour Profile Method [39] that consisted in a tool for flavour characterization of complex products using a group of trained panellists (panel), replacing the use of a single taster expert [38]. This technique was later improved and replaced by the Texture Profile Method [40] which is used in order to evaluate the levels of “mechanical, geometric, fat and moisture characteristics of a food” [38]. Next, these two gave place to the Spectrum Descriptive Analyses (SDA) which involves the creation of absolute intensity references and provides more discrimination points [37]. Note that, the use of an absolute scale conducted to a solid evaluation among panels. The Tragon Quantitative Descriptive Analyses method (Tragon QDA) uses a language based on the consumers usage which means that does not use standardized terms between panels. So, each panel creates its own language [38]. For this reason, this methodology causes some disagreements when a high level of training is needed [41]. In addition, it does not usually use intensity references, as well as SDA does, but both use qualitative references [38].

Lexicon has several applications in a product development [22], maintenance, quality control and shelf-life studies [42]. In addition, its development is advantageous in product variability quantification [43] as well as for studies regarding the potential competitive products [44]. During lexicon formation, there are some relevant aspects to consider [22,38]. According to The American Society for Testing and Materials, the lexicon is made taking into account five fundamental steps which includes: 1) the establishment of references, 2) descriptor terms generation and development, 3) the use of references to orientate terms and definitions perception, 4) examples to increase the panel perception and 5) the creation of a list of features that will be part of the lexicon created [45]. However, for Lawless and Civille (2013) the lexicon construction could be resumed just in two central steps: the preparation and the development. In short, the first includes the cautiously selection of the panellists, samples selection and the development of protocols which panellists should follow during the task. On the other hand, the development part contains the protocols review which must be done before evaluations, generation terms or definitions that characterize the products, selection of references and the review of examples. Finally, it also includes the lexicon finalization [38].

In the literature, can be found different lexicons depending on the product category. Meilgaard et al. (1982) made a list of chemical references for the beer flavour lexicon [46]. Stampanoni (1994) identified food or chemical references for flavour lexicons for strawberry yogurts, caramel milk drinks and cheese in order to show the standardization of the sensory procedures and conventions [47]. In addition, there is a book written by Civille and Lyon (1996) with a compilation of standard definitions and references for a vast range of flavour descriptors [48].

2.4.1.1 Sensory panel

There are two types of panellists: trained and untrained. Trained panellists should be minutely selected and extremely trained in order to facilitate the lexicon selection [38]. For this reason, the selection of panellists is based on three stages: recruitment, selection and preliminary training [35]. For Muñoz and Civille (1998),

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the most important training component is the establishment of qualitative references and due to its importance, it is usually the first thing that is learned during the training. This training allows having a calibrated set of panellists and it is as better as extensive the programme training is [49]. Consumers (untrained panellists) usually use their own words to characterize a product (qualitative) and use their own criteria and intensity reference points to rare strengths [49].

According to the International Organization for Standardization (ISO) document 8586 these individuals should have high acuity, thinking skills well defined and a positive attitude [38]. These skills are usually tested with interviews, tests or questionnaires [38] and it also allows evaluating personal aspects (e.g. smoker/non-smoker) [35]. The panel should be self-confident in their perceptual abilities although they need to be open mind in order to accept terminology and opinions from other panellists [38]. During the lexicon creation, the panel should evaluate several products, not less than 25 (25 to 100) [22].

It is important to mention that, despite all training process, the obtained results are even considered subjective and dependent on the individual characteristics and sensory abilities. Taking the description of ethyl propanoate as an example of this subjectivity, we can found its aroma defined as “sweet and fermented” in a study conducted by Xu et al. (2007) [50] and, in the Fenaroli’s handbook of flavour ingredients (2010), it appears as having “an odour reminiscent of rum and pineapple” and a “sharp, fermented, rummy and fruity” flavour [51].

2.4.1.2 Samples

A set of samples that represents all product categories should be collected [22], which dimension will depend on the product category [38]. An important request is to have brands and varieties enough to represent the sensory space. After collecting all samples, there is a kind of “tracking” where the number of them is reduced and just those considered as representative are used in the lexicon generation. Thus, the number of samples, the criteria used in their choice and the strategy used in the selection part have influence in the lexicon creation [38]. The samples could be collected where they are available (e.g. local markets) [38].

2.4.1.3 Protocols

Protocols have a key role in a lexicon generation, once they are responsible to keep the process consistent and homogeneous. The choice, preparation and presentation of the samples, as well as the evaluation process follow, thus, a standard procedure. Tasks like a term definition or a process evaluation have restrictive rules that preserve the preciseness for the panellist assess or a product usage [38]. Protocol development includes comparisons with other protocols that were already published for others identical products [38,52]. An understandable protocol must have complete instructions for evaluation [38].

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2.4.2 Sensory Lexicon Wheel

A sensory lexicon wheel is a normal lexicon, as described above in the section 2.4.1, but hierarchically arranged in. Its creation and organization is an equally qualitative process [38]. According to Monteiro et al. (2013), sensory wheels is different from lexicons (presented in section 2.4.1) due to its visual aspect, helping the understanding [35]. The usual procedure to create a sensory lexicon wheel starts with the preliminary terms selection and, after that, the panel’s leader chooses a group of experts to discuss the similarities among a set of attributes. Similar categories are grouped near to each other on a wheel graph which results in a visual representation of the attributes relationship [38]. Sensory analysts can make use of wheels to explain, for example, descriptive analyses to costumers during business presentations [53]. In the literature, we can found different flavour wheels depending on the product category (e.g. wine, beer, coffee, whiskey, etc). Some examples of sensory wheels are present below (Figures 2 to 4):

Figure 2 Wine aroma wheel [54].

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Figure 4 Rooibos sensory wheel [56].

It is easy to find some points in common among these wheels. It is usual to find fruity, floral, green or vegetative and earthy attributes. Particularities are identified in the attributes position on the wheel and in their sub-attributes. Some wheels also contain basic tastes and feelings factors [38]. According to Lawless et al. (2012), these feeling factors and basic tastes are sometimes putting together to establish that these attributes are perceived in the mouth rather than in the nasal cavity [53]. It is important to mention that the construction of wheels is adapted to product categories and sensations.

2.4.3 Scientific methodology to classify fragranced products into olfactory families

The most commun methodologies to classify the odour or flavour of a product are, as mentionated above: lexicons or sensory wheels. However, they are made using panellists and, although trained and following a rigorous protocol, it has always an associated subjective character. This empirical classification are now more scientific for odours with the use of the Perfumery Radar (PR) methodology. It introduces a scientific classification for perfumes in opposition to the subjectivity of a panel. This model can predict the olfactive families of different perfumes and, consequently, reduces the amount of experiences during a perfume formulation, time and costs.

The classification of an odorant into olfactory families is considered an arduous work which requires a high sense of olfactory perception. For this reason, it is usually done by trained people that just use their olfactory evaluation - Perfumers. The odorants can be classified into one family only or with subfamilies or nuances. In the PR, it was used a group of databases that include a combination of two different approaches: empirical classifications and statistical descriptors supported by odour profiles and semantic descriptors [12]. The PR methodology starts with the classification of pure fragrances present in the perfume in their respective olfactory families. If a substance belongs to several olfactory families, it should be chosen until the third one more important. Each pure fragrance has an odour description which could be obtained from scientific databases. For the predictive radars authors chosen, based on the most common terms used for

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pure fragrances classification, eight representative olfactory families: citrus, fruity, floral, green, herbaceous, musk, oriental and woody. The second step involves the evaluation of the gas phase composition, which could be predicted or calculated experimentally, and the calculation of the odour intensity of each component present in the mixture [12]. Finally, a radar is generated, Figure 5.

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Materials and Methods 11

3 Materials and Methods

3.1 Samples

In this work, non-alcoholic beverages were selected due to their matrix simplicity in comparison with other matrices (e.g. yogurts). It usually offers a minimal resistance to aroma release from the matrix to the gas phase. In addition, this kind of drink is also very consumed by a large amount of people [58]. Commercial fruit juices of lemon, peach, pineapple, apple and mango nectars from Sumol+Compal were studied (Figure 6).

Figure 6 Commercial juices studied (lemon, peach, pineapple, apple and mango).

3.2 Dynamic Headspace and Gas Chromatography Coupled to Mass

Spectrometry Analysis

The headspace composition, i.e. the vapour phase above the liquid mixture, of the studied samples was 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) (Figure 7), controlled by Varian MS Workstation 6.9 software and equipped with Rxi®-5Sil MS columns (30 m × 0.25 mm, 0.25 μm film thickness) and CP-WAX 52CB (50 m × 0.25 mm, 0.2 μm film thickness). The DHS analysis was made in five steps which are, in this order, incubation, stripping, dry step, 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). Next, in stripping step, the juice compounds were concentrated in a cooled trap (90 mm long quartz tube with a 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

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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 1.

Figure 7 Equipment arrangement (DHS connected to a GC-MS).

Table 1 Dynamic headspace working parameters.

Incubation step

Sample volume (µL) 500, 1000

Time (min) 5, 1

Oven temperature (ºC) 40

Shaking Fast mode

Stripping step

Time (min) 5, 2

Flow (mL min-1₎ ₅₀₀

Trap temperature 40

Trap dry step

Time (min) 1

Dry step flow (mL min-1₎ ₃₀

Trap temperature (ºC) 0

Injection Time

Time (min) 1

Dew stop temperature (ºC) 0

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Baking step

Time (min) 15

Flow (mL min-1₎ ₈₀

Trap temperature (ºC) 300

Dew stop temperature (ºC) 120

Others

Transfer line temperature (ºC) 250

Switching valve temperature (ºC) 210

Trap maximum temperature (ºC) 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 carrier gas used was helium (He N60) at a constant flow rate of 1.0 mL min-1_{. The used temperature programme for the Rxi®-5Sil MS column started at 65 ºC and this}

temperature was maintained during 5 min. After that, the temperature increased to 250 ºC at a rate of 4 ºC min-1_{, and held isothermal during 5 min. For the CP-WAX 52CB column, the programme used started}

at 65 ºC and it was maintained during 5 min. Then, the temperature increased to 200 ºC at a rate of 3 ºC min-1_{and held isothermal for 20 minutes. 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 [50,59-82].

The gas phase concentrations of each components present in the liquid phase identified in the studied fruit juices were converted into olfactory perceptions through the psychophysical model, odour value (OV). The odour value OV quantifies the odour intensity of each mixture component (OVi) and is defined as the ratio

between the gas phase concentration of a component i (Cigas) and its odour detection threshold (ODTi)

(Equation 3.1). The ODTi is defined as the minimum concentration, in the gas phase, at which an odorant

is perceived by the human nose [83].

3.3 Odour and Flavour Intensities

𝑂𝑉𝑖 =

𝐶_𝑖𝑔𝑎𝑠 𝑂𝐷𝑇𝑖

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In the literature, there is not a model able to evaluate the flavour intensity of a food product based on the gas phase composition of the components present in the mixture. However, considering that the volatiles that reach the olfactory receptors via retronasal during a food product consumption are those involved in the flavour perception, in the present study a new approach based on the OV was used for the quantification of the volatiles perceived via retronasal (Equation 3.2). Thus, the flavour value (FV) is the ratio between the concentration of a component i in the gas phase (Cigas)and the flavour detection threshold of a

component i (FDTi), which corresponds to the minimum concentration of a component i in water that allows

its detection by a human [21] (Equation 3.2).

𝐹𝑉𝑖 =

𝐶_𝑖𝑔𝑎𝑠 𝐹𝐷𝑇𝑖

(3.2)

It is important to mention that the proposed methodology assumes that all components identified in the headspace contribute to the overall odour and flavour, however, some authors mentioned that not all of them have a positive contribute to it [84].

3.4 Odour and Flavour Radars

For the classification of the studied fruit juices in olfactory families, the components identified in the studied samples were firstly classified in terms of odour and flavour descriptors (i.e. group olfactory families that describe an odour or flavour of a component). In Table 2, it is possible to find the odour and flavour descriptors. Then, based on the most frequent families found in the classification of pure components present in the studied samples, nine families were selected: fruity, sweet, green, woody, fresh, spicy, citrus, fatty and ripe. 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 the evaluation of the binary and ternary fruit juices mixtures.

Table 2 Odour and Flavour descriptions of each family.

Family Odour Description Flavour Description

Fruity Associated to a mixture of fruits [4,85].

Sense from natural fruits. It evokes a variety of fruits [86-87].

Sweet Associated to a sweet taste (e.g. ripe fruit - frequently smell “sweet”) [43,86].

A basic taste associated to a sucrose solution [43,87-88].

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Green Typical botanical notes with scent of fresh leaves or with reminiscent freshness [4,85].

Associated to green or under-ripe fruit [86-88].

Woody Linked to dry fresh cut wood [4,13]. Brown, musty aromatics related to plants with fibrous and bark [89].

Fresh It is clean, cool, refreshing, and new [90].

Associated with fresh fruit (e.g. pineapple). It is also linked with raw fruit [87].

Spicy Associated to spices. Understood as having a sweet brown*_with

musty nuance, aromatic reminiscent of cinnamon [88].

Citrus Freshness and lightness related to fruits with a citrus character (e.g. lime, lemon, orange) [4,85].

Associated with freshly cut citrus fruits and a blend of flavours related with citrus fruits [86-87,89].

Fatty Related to exudates fat [13]. Amount of fatty perceived by the tongue when moved over the surface of the mouth [43].

Ripe Related to a more intense fruit odour with a sweet nuance [13].

Sensation associated to over-ripe fruit [85].

*_{Brown: Defined as a sharp, caramel, almost burnt aromatic [86]; Brown spice: It is associated with a variety of}

brown spices (e.g. cinnamon, nutmeg, allspice) [86].

After the calculation of OVi and FVi, a weight factor was attributed for each component (i) in each family j

(

wij

),

according to the Table 3. Thus, it was possible to consider not only the main family in which each

component belongs but also their subfamilies (nuances).

Table 3 Weights attribution for each olfactive family [4].

Number of families

Family

Primary Secondary Tertiary

1 100% - -

2 70% 30% -

3 60% 30% 10%

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Materials and Methods 16 𝑂𝑉𝑗= ∑ 𝑤𝑖 𝑗 × 𝑂𝑉𝑖 𝑁 𝑖=1 (3.3) 𝐹𝑉𝑗= ∑ 𝑤𝑖 𝑗 × 𝐹𝑉𝑖 𝑁 𝑖=1 (3.4)

Odour and flavour families were normalized (OV’j and FV’j) following Equations 3.5 and 3.6:

𝑂𝑉_𝑗′= 𝑂𝑉𝑗 ∑𝐿𝑗=1𝑂𝑉𝑗 (3.5) 𝐹𝑉_𝑗′= 𝐹𝑉𝑗 ∑𝐿𝑗=1𝐹𝑉𝑗 (3.6)

The odour and flavour radars were finally plotted for the single fruit juices, and the results are displayed in Figures 14 to 18. The binary (Figures 19 to 21) and ternary (Figure 22) mixtures radars were constructed based on the DHS/GC-MS analysis of pure juices presented in the mixtures.

3.5 Sensory Analysis

3.5.1 Consumers

Consumers are the main responsible for a product acceptance. They construct a judgment based on what they are perceiving, feeling or understanding. On the other side, panellists are people specially trained to be able to characterize a product based on it sensory attributes.

The sensory analysis was performed using a panel (consumers) composed of 7 persons (4 females and 3 males) from LA LSRE-LCM (FEUP), aged between 22 and 41 years. The sensory analysis was carried out in four different days. They were chosen based on their odour and flavour detection and recognition abilities. In the day before the sensory evaluation, the panellists were asked to avoid the use of strong odours (e.g. perfumes, creams, etc.) and to not eat or drink at least 30 minutes before the evaluation, as recommended for this type of analysis [91]. Sensory analysis occurred in a meeting vented room (Figure 8).

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Figure 8 Meeting room used for sensory analysis (A). Panellists during the sensory evaluation (B).

The four sessions were performed in the morning. All samples were previously prepared in equal PET cups (fifteen millilitres were added in each PET cup, as recommended by Watts et al. (1989) [91]) and put on the table together with the sensory questionnaire before the panel came in.

For odour evaluation, consumers smelled the sample with closed mouth and, for flavour, they closed the nose, drunk a small amount of juice and, just after that, they left the nose free.

In the first sensorial test, 8 samples were given to the consumers labelled from 1 to 8, according to Table 4. This session allowed to give some training to consumers and some clarifications about the procedure. It was also important to know the potential of each consumer to discriminate the odour and flavour of each sample. In the next sessions, the pure juices together with different mixtures were given. The difference between the sessions 2 and 3 (and 4) is related to the worksheet that is more detailed in the last case (see Appendix 1).

Regarding to consumers’ evaluation, data processing was made considering the punctuation that consumers attributed to each sample. In this process, they were invited to identify the three most perceptive families as described in Appendix 1.

Table 4 Samples used in each session and respective identification.

Sample Session 1 Session 2 Session 3 Session 4

Apple 1 Apple Apple 1

Pineapple 2 Pineapple Pineapple 2

Lemon 3 - - -

Peach 4 Peach Peach Peach

Mango 5 Mango Mango Mango

Apple + Peach 6 1 2 -

Pineapple + Peach 7 2 3 -

Pineapple + Mango 8 3 1 -

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Results and Discussion 18

4 Results and Discussion

4.1 Chemical Analysis

The individual chromatograms of each analysed juice (lemon, peach, pineapple, apple and mango) are displayed in Figures 9 to 13. It is also possible to find the respective components identification in Tables 5 to 9. It is important to mention that, due to the complexity of the nectar juices (beverage matrices), it was not possible to identify all components of each nectar juice. It is also important to mention that the columns used were not chiral. Limonene is a chiral molecule which means that it exists in two forms: (R)-(+)-limonene and (S)-(-)-(R)-(+)-limonene. The first one could be found in orange fruit while the second is found in lemon. These two isomers have distinct characteristics in terms of odour and flavour which results in different descriptions. However, as the columns used in the present study were not chiral, it was not possible to confirm which isomer was present and for that reason it was decided to use a general description for both sensations (odour and flavour).

4.1.1 Lemon juice

The lemon juice chromatogram is presented in Figure 9. The gas chromatography allowed the identification of six components in the headspace of the lemon juice (Table 5). As expected, limonene was identified as the major component.

Figure 9 Lemon juice headspace chromatogram.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 2 4 6 8 10 12 14 C o u n ts (× 10 5) Time (min) 1 2 3 4 5 7 6 8 9 10

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Table 5 Volatile compounds identified in the headspace of lemon juice.

Peak Component* _{Chemical formula} _C.A.S. _{MW (g mol}-1₎ _LRI

Exp LRILit Peak area (counts)

1 NI - - - 6.944×104 2 NI - - - 1.384×105 3 NI - - - 1.573×105 4 NI - - - 3.412×104 5 β-Pinene C10H16 127-91-3 136.23 1196 1110 4.858×104 6 α-Terpinene C10H16 99-86-5 136.23 1210 1178 1.918×104 7 Limonene C10H16 138-86-3 136.23 1224 1198 9.850×105 8 ϒ-Terpinene C10H16 99-85-4 136.23 1262 1245 2.018×104 9 ρ-Cymene C10H14 99-87-6 134.22 1284 1270 2.011×104 10 Terpinolene C10H16 586-62-9 136.23 1295 1282 1.262×104

*_{Compounds identified by comparison with 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 [59-61]. NI - Not Identified.

LRIExp - Linear Retention Indices obtained experimentally.

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4.1.2 Peach nectar

In peach juice analysis, it was possible the identification of six components (peaks no. 4, 5, 6, 8, 10 and 11) among the 11 peaks presented in the chromatogram (Figure 10 and Table 6). Isoamyl acetate is the most abundant component.

Figure 10 Peach nectar headspace chromatogram.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 2 4 6 8 10 12 14 C o u n ts (× 10 6) Time (min) 1 ₂ 3 4 5 6 7 8 9 10 11

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Table 6 Volatile compounds identified in the headspace of peach juice.

Peak Component* _{Chemical formula} _C.A.S. _{MW (g mol}-1₎ _LRI

1 NI - - - 3.178×105 2 NI - - - 1.411×105 3 NI - - - 1.359×106 4 Ethyl butyrate C6H12O2 105-54-4 116.16 840 808 1.887×106 5 Isoamyl acetate C7H14O2 123-92-2 130.18 904 876 3.161×106 6 Benzaldehyde C7H6O 100-52-7 106.12 977 968 6.754×105 7 NI - - - 1.014×106 8 Ethyl hexanoate C8H16O2 123-66-0 144.21 1010 1005 1.307×106 9 NI - - - 5.188×105 10 Limonene C10H16 138-86-3 136.23 1038 1033 2.598×105 11 Linalool C10H18O 78-70-6 154.25 1112 1110 1.170×106

*_{Compounds identified by comparison with 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 [62-68]. NI - Not Identified.

LRIExp - Liner Retention Indices obtained experimentally.

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4.1.3 Pineapple nectar

From the chromatogram of pineapple nectar (Figure 11) was possible the identification of the peaks 2 (methyl methylbutanoate), 4 (ethyl methylbutanoate) and 5 (limonene), respectively. The ethyl 2-methylbutanoate was the second major component identified (Table 7).

Figure 11 Pineapple nectar headspace chromatogram.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 2 4 6 8 10 12 14 C o u n ts (× 10 5) Time (min) 1 2 3 4 5

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Table 7 Volatile compounds identified in the headspace of pineapple juice.

1 NI - - - 9.675×105

2 Methyl 2-methylbutanoate C6H12O2 868-57-5 116.16 862 771 2.556×105

3 NI - - - 3.166×105

4 Ethyl 2-methylbutanoate C7H14O2 7452-79-1 130.18 909 850 9.370×105

5 Limonene C10H16 138-86-3 136.23 1046 1033 1.445×105

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4.1.4 Apple nectar

Figure 12 shows the apple juice chromatogram where the 2-methylbutyl butanoate appears as the predominant component identified (Table 8).

Figure 12 Apple nectar headspace chromatogram.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 2 4 6 8 10 12 14 C o un ts (× 10 6) Time (min) 1 2 ₃ 4

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Table 8 Volatile compounds identified in the headspace of apple juice.

Exp LRILit Peak area (counts)

1 Pentyl alcohol C5H12O 71-41-0 88.15 809 766 7.735×105

2 (E)-2-Hexanal C6H10O 6728-26-3 98.14 841 855 9.594×105

3 Ethyl 2-methylbutanoate C7H14O2 7452-79-1 130.18 882 850 8.482×105

4 2-Methylbutyl butanoate C9H18O2 51115-64-1 158.24 905 - 6.920×106

an in-house library (with more than 200 pure reference chemicals) and literature data [50,73-76]. NI - Not Identified.

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4.1.5 Mango nectar

The headspace composition of mango juice is mainly composed of 3-Carene, followed by ethyl butyrate (Figure 13 and Table 9).

Figure 13 Mango nectar headspace chromatogram.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 2 4 6 8 10 12 14 C o u n ts (× 10 6) Time (min) 1 2 3 4 5 6 7 8

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Table 9 Volatile compounds identified in the headspace of mango juice.

Peak Component* _{Chemical formula} _C.A.S. _{MW (g mol}-1₎ _LRI

1 NI - - - 3.650×106 2 NI - - - 1.105×106 3 Ethyl butyrate C6H12O2 105-54-4 116.16 874 808 1.020×107 4 NI - - - 1.380×106 5 α-Pinene C10H16 80-56-8 136.23 964 944 2.060×106 6 β-Pinene C10H16 127-91-3 136.23 1013 981 3.080×106 7 3-Carene C10H16 13466-78-9 136.23 1029 1020 2.300×107 8 Limonene C10H16 138-86-3 136.23 1047 1033 1.380×106

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4.2 Odour and Flavour Radars

In order to plot the odour and flavour radars, it was necessary to evaluate the odour and flavour intensities based on the gas phase concentrations above the liquid sample of each component identified in the fruit juices, and on the odour and flavour detection thresholds. The odour descriptors and detection threshold (in air), flavour descriptors and flavour detection threshold (in water) of each component present in each nectar studied, were also essential to plot the radars, they are presented in Tables 10, 12, 14, 16, 18, 20, 22, 24, 26 and 28 of Appendix 2. Regarding to the relative weights attributed to each component, based on odour and flavour descriptors, and the respective OVj and FVj, obtained for each

family, they are displayed in Tables 11, 13, 15, 17, 19, 21, 23, 25, 27 and 29 of Appendix 2.

4.2.1 Single fruit juices

Figures 14 to 18 show the obtained odour and flavour radars for lemon, peach, pineapple, apple and mango juices.

The odour radar of lemon juice presented in Figure 14 reveals a strong citrus character (0.96) as expected. In terms of flavour, the fresh family dominates the radar (0.53), followed by citrus (0.23) and green (0.15) notes.

Figure 14 Odour and flavour radars obtained for lemon juice.

The odour and flavour radars of peach nectar are illustrated in Figure 15. Fruity is the strongest character for both odour (0.59) and flavour (0.44) radars. However, some differences were found in their subfamilies: sweet (0.30) and ripe (0.10) were identified in the odour radar, while sweet (0.34), woody (0.12), fresh (0.07) and green (0.03) were found in the flavour radar.

0.0 0.2 0.4 0.6 0.8 1.0Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Odour radar 0.0 0.2 0.4 0.6 Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Flavour radar

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Figure 15 Odour and flavour radars obtained for peach juice.

Pineapple odour and flavour radars are showed in Figure 16. Analysing the odour radar, fruity was the strongest family (0.60), followed by ripe (0.28) and green (0.09) families. For flavour radar, the highest value was obtained for fruity family (0.60) followed by fresh (0.30) and citrus (0.07).

Figure 16 Odour and flavour radars obtained for pineapple juice.

Figure 17 shows the odour and flavour radars for apple juice. Its odour is described as fruity (0.59), with sweet (0.29) and fresh (0.10) as subfamilies. In turn, the flavour could be described as fruity (0.60), fresh (0.30) and citrus (0.10), in this order.

0.0 0.2 0.4 0.6 0.8Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Odour radar 0.0 0.2 0.4 0.6 Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Flavour radar 0.0 0.2 0.4 0.6 0.8Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Odour radar 0.0 0.2 0.4 0.6 0.8 Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Flavour radar

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Figure 17 Odour and flavour radars obtained for apple juice.

Finally, odour and flavour radars of mango juice (Figure 18) reveal that fruity is the strongest family in both (representing in both cases 0.60). For odour radar, sweet (0.30) and ripe (0.10) are the subfamilies and, for flavour radar, they are sweet (0.30) and fresh (0.10).

Figure 18 Odour and flavour radars obtained for mango juice.

0.0 0.2 0.4 0.6 0.8Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Odour radar 0.0 0.2 0.4 0.6 0.8 Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Flavour radar 0.0 0.2 0.4 0.6 0.8 Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Odour radar 0.0 0.2 0.4 0.6 0.8 Fruity Sweet Green Woody Fresh Spicy Citrus Fatty Ripe Flavour radar

Flavour Engineering

FLAVOUR ENGINEERING

Mestrado Integrado em Engenharia Química

Flavour Engineering

Dissertação de Mestrado

Ana Isabel Mota Monteiro

Laboratório Associado Laboratório de Processos de Separação e Reação – Laboratório de

Catálise de Materiais

Departamento de Engenharia Química

Agradecimentos

Abstract

Resumo

Declaração

Contents

List of Figures

List of Tables

Notation

1 Introduction

1.1 Motivation and Relevance

1.2 Thesis objectives and Layout

2 Context and State of the Art

2.1 Sensory perception

2.1.1 Odour, Aroma, Taste and Flavour

2.2 Identification of odour-active compounds

2.3 Psychophysics of Flavour

2.3.1 Odour and flavour intensities

2.4 Odour and flavour sensory descriptors

2.4.1 Lexicon

2.4.2 Sensory Lexicon Wheel

2.4.3 Scientific methodology to classify fragranced products into olfactory families

3 Materials and Methods

3.1 Samples

3.2 Dynamic Headspace and Gas Chromatography Coupled to Mass

Spectrometry Analysis

3.3 Odour and Flavour Intensities

3.4 Odour and Flavour Radars

(

),

3.5 Sensory Analysis

3.5.1 Consumers

4 Results and Discussion

4.1 Chemical Analysis

4.1.1 Lemon juice

4.1.2 Peach nectar

4.1.3 Pineapple nectar

4.1.4 Apple nectar

4.1.5 Mango nectar

4.2 Odour and Flavour Radars

4.2.1 Single fruit juices