phenotyping texture and aroma wp5 training session sensory laboratoru eugenio aprea

Apple activities20 May 2015

The multisensory spaceof apple quality

Eugenio Aprea Sensory Quality Research Group Food Quality and Nutrition Department


WHAT: to study the sensory quality of apples

• the physiological and psychological effects induced by the product to the individual thanks to the 5 senses

HOW: by means of combined investigation approaches

The multisensory space of apple quality

CRI SCIENCE DAYS11-12 December 2013

SENSORY QUALITY is the result of the INDIVIDUAL interacting with the PRODUCT.

Why “COMBINED”?

In order to study APPLES in a complete way it is necessary

1) to consider BOTH• SUBJECTIVE aspects related

to the CONSUMER• OBJECTIVE aspects related

to APPLES

2) to apply BOTH• SENSORY methods• INSTRUMENTAL methods


Alimentazione

Adding value to the food chain FOOD QUALITY AND NUTRITION DEPARTMENT

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pc(corr)[1], t(corr)[1]

pc(corr)[2] (X)pc(corr)[2] (Y)t(corr)[2]

Golden

Granny

Fuji

Renetta

SIMCA-P+ 12.0.1 - 2012-03-15 09:31:42 (UTC+1)

PC1 – 58%, 41%

PC2 – 38%, 41%

Juiciness

Grainess

Crunchiness

Flourness

Sweetness

Bitterness

AcidityAstringency

CrispnessFiberness

Hardness

Odour

Flavour

Tool: CONSUMER tests for liking and preference + additional information for group segmentation.

Measures from the CONSUMERS: preference

Endrizzi et al. A conjoint study on apple acceptability: Sensory characteristics and nutritional information. Food Quality and Preference 40 (2015) 39–48

SUBJECTIVE measures: “How much do you like it?”

PREFERENCE


Information about high ANTIOXYDANTS content increases apple liking in CONSUMERS with 20+ years of age: more informed about the meaning of antioxydants.

Information about high FIBRES content increases apple liking in CONSUMERS who use FOOD AS A REWARD: typical of who is on a diet to avoid gaining weight.

Apple PREFERENCE depends also on CONSUMERS’ COGNITION: motivation, expectations, emotions, etc.

CONSUMERS’ preference: cognitive side

Endrizzi et al. A conjoint study on apple acceptability: Sensory characteristics and nutritional information. Food Quality and Preference 40 (2015) 39–48

NO

NO

YES

YES

ANTYOXIDANTS INFO

FIBRES INFO


The QDA uses a language close to that of CONSUMERS, thus it helps to understand which are the sensory attributes important to acceptance.

OBJECTIVE measures : “How do we define this sensation? How intense is it?”

Tool: TRAINED PANEL with a shared vocabulary which measures the intensity of the relevant sensory properties by using a Quantitative Descriptive Method (QDA).

Measures from the PRODUCT: sensory profile

IDENTIFICATION

DISCRIMINATION

PERCEPTION

SmellTaste

Touch

Hearing

Vision


SENSORY PROPERTIES relevant in different COMMERCIAL apples

PRODUCT properties: sensory profile

Corollaro et al. (2013). Postharvest Biology and Technology, 77, pp. 111-120.


SENSORY PROPERTIES differences across different STORAGE TIMES or …. between FEM selections and PARENTALS.

PRODUCT properties: sensory variations

Corollaro et al. (2013). Postharvest Biology and Technology, 77, pp. 111-120.


Time consuming

Expensive

Limited number of samples

LIMITS of the SENSORY approach


Tool: correlation between SENSORY and INSTRUMENTAL data, that is……. using instruments to predict human perception.

SENSORY & INSTRUMENTAL

CONSUMERS and PANELISTS allow to DIRECTLY measure SUBJECTIVE and OBJECTIVE properties of apples.

INSTRUMENTAL ANALYSES allow to INDIRECTLY measure chemical or phisycal parameters (related to sensory properties) :

• a way to overcome temporal and economic issues.


INSTRUMENTAL ANALYSES

Gross composition: • tritrable acids• °brix• pH• Dry matter• Acids & sugars• ….

Maturation index: • Chlorophylle amount

• Colour of the flesh

• Shape and colour of the fruit • Volatile compounds

• Texture


• Stripes grading in FUJI and GALA apples (scale: 1-9)

• Class of «red» in GALA clones

• Intensity of «maroon» in FUJI clones

IMAGE ANALYZER (E-EYE)

For the analyses of shape and colour of the objects


SENSORY & INSTRUMENTAL: correlations

CrunchinessColor of the flesh

Costa et al. (2011). Postharvest Biology and Technology 61, pp. 21–28. Corollaro et al. (2014).Postharvest Biology and Technology 96, pp. 135–144.


Link between volatile compounds (VOC) measured by GC and the labels used by PANEL to describe the perceived odours.

SENSORY & INSTRUMENTAL: volatile compounds

Aprea et al. (2012). Food Research International, 49 (2), pp. 677-686.


SENSORY & INSTRUMENTAL: models for odours

Aprea et al. (2012). Food Research International, 49 (2), pp. 677-686.


Static in-VITRO

&

Dynamic in-VIVO

Granny Smith Fuji Jonagold Red Delicious Morgen Dallago Golden Delicious

m/z 21.0 m/z 34.0 m/z 47.0 m/z 60.0 m/z 73.0 m/z 86.0 m/z 99.0 m/z 112.0 m/z 125.0 m/z 138.0 m/z 151.0 m/z 164.0 m/z 177.0 m/z 190.0 m/z 203.01.00E+00

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Example of a full apple spectrum

m/z

cps (

Log)

HEADspace of sample

Measure of the relationship of static and dynamic VOC release (by PTR-MS of the CV Facility) and texture (Texture analyzer) in different cultivars

PERCEPTION IS NOT STATIC!

Ting et al. (2012). Journal of Food Science, 77 (11), pp. C1226-C1233.

NOSEspace of

sample

VOC RELEASE ... & TEXTURE


High VOC concentration

Low VOC concentration

Soft texture

Firm texture

Short Tmax (s)

Long Tmax (s)

Morgen Dallago Golden Delicious

Jonagold Red Delicious

Fuji Granny Smith

DYNAMIC process: VOC concentration and texture

Ting et al. (2012). Journal of Food Science, 77 (11), pp. C1226-C1233.

Hard apples have a lower VOC release and longer lasting emission period. Softer apples have a higher VOC release but shorter emission period.


DYNAMISM of perceptions and interactions(TDS: Temporal Dominance of Sensations)

Measure the evolution of in-mouth dominant sensations and study dynamically sensory interactions

Temporal Dominance of Sensations

Golden Delicious

+ +

“A dominant attribute is associated to the sensation catching the attention at

a given time”

Attribute SWEETAttribute SOURAttribute CRUNCHYAttribute MELTY/FONDANTAttribute HARDAttribute JUICYAttribute FL.VEGETALAttribute FL.FRUITY

Time Intensity TDS File: AnalisiTDSMele.frsPanel Dominance

Product: STD

Standardized time (%)10095908580757065605550454035302520151050

Domi

nanc

e rate (%

)

60

50

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20

10

0

Trained Panel

=Crunchy

Hard Juicy

Sweet

Fruity Sour

Fruity or Vegetal AromaSweet or Sour Taste+

Research questions• How the perception is changing during

consumption time?• Does taste and/or aroma influence the dynamic

description of apples? If yes, how?• Are there perceptual interactions between

taste-aroma-texture? Are these interactions dynamic?

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38

Charles et al. (2015). Food Research International, 69, pp. 9-20. (example on coffee)


The senses do not work in isolation: constant INTERACTION that shapes our perception.

Tool: cognitive-behavioural studies.

INTERACTION between the SENSES

Demattè et al., Effects of the sound of the bite on apple perceived crispness and hardness. Food Quality and Preference 38 (2014) 58–64.


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Microphone off -24 dB 0 dB

Sound

Mea

n in

tens

ity

Crispness

Hardness

INTERACTION: sound on texture evaluation

Sound reduction affects both CRISPNESS and HARDNESS evaluation at FIRST bite.

Demattè et al., Effects of the sound of the bite on apple perceived crispness and hardness. Food Quality and Preference 38 (2014) 58–64.

30

35

40

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50

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60

-24 dB 0 dB +12 dB

Sound

Mea

n in

tens

ity

Crunchiness

Hardness

UNFILTERD REAL SOUND

UNFILTERD REAL SOUND

Sound reduction affects only CRUNCHINESS evaluation during MASTICATION.


INDIVIDUAL DIFFERENCES

Romano et al. (2014). International Journal of Mass Spectrometry 365–366, pp. 20-27.


Data INTEGRATION: models

DYNAMISM: static & dynamic measures

PRODUCT: sensory & instrumental profiles

MULTISENSORY interaction

CONSUMERS: perception & cognition

The multisensory space of apple quality

CONSUMERS: perception & cognition

Data INTEGRATION: models

MULTISENSORY interaction

PRODUCT: sensory & instrumental profiles

DYNAMISM: static & dynamic measures


Chemistry

Psychology Statistics

Food Science

A MULTI-SKILLED approach

Agronomy

Biotechnology

Genetics


CURRENT MEMBERSFlavia Gasperi, team leaderEugenio Aprea, researcherIsabella Endrizzi, technologistEmanuela Betta, technicianJessica Zambanini, technicianMathilde Charles, technologistMatteo Bergamaschi, PhD studentStefano Benetti, internship

PAST MEMBERSMaria Laura Corollaro, PhD student Luisa Demattè, postdoc

https://sites.google.com/a/fmach.it/sensorylab/home

FlaviaEugenio

MathildeEmanuela

Isabella

+ Volatile Compounds Facility!

A MULTI-SKILLED laboratory

Matteo

Maria Laura

Luisa

https://sites.google.com/a/fmach.it/sensorylab/people/flavia-gasperi



https://sites.google.com/a/fmach.it/sensorylab/people/eugenio-aprea

https://sites.google.com/a/fmach.it/sensorylab/people/eugenio-aprea

https://sites.google.com/a/fmach.it/sensorylab/people/isabella-endrizzi

https://sites.google.com/a/fmach.it/sensorylab/people/isabella-endrizzi

https://sites.google.com/a/fmach.it/sensorylab/people/emanuela-betta



https://sites.google.com/a/fmach.it/sensorylab/people/jessica-zambanini

https://sites.google.com/a/fmach.it/sensorylab/people/jessica-zambanini

https://sites.google.com/a/fmach.it/sensorylab/people/mathilde-charles



https://sites.google.com/a/fmach.it/sensorylab/people/matteo-bergamaschi




1. Endrizzi, I., Torri, L., Corollaro, M.L., Demattè, M.L., Aprea, E., Charles, M., Biasioli, F., Gasperi, F. (2015). A conjoint study on apple acceptability: Sensory characteristics and nutritional information. Food Quality and Preference 40, Part A, 39–48.

2. Corollaro, M.L., Manfrini, L., Endrizzi, I., Aprea, E., Demattè, M.L., Charles, M., Bergamaschi, M., Biasioli, F., Zimbordi, M., Corelli Grappadelli, L., Gasperi, F. (2015). The effect of two light management practices on the sensory quality of apple: fruit thinning by shading or photo-selective nets. Journal of Horticultural Science & Biotechnology, 90 (1), 99-107.

3. Corollaro, M.L., Gasperi, F., Corelli Grappadelli, L. (2014). An overview of sensory quality of apple fruit. Journal of the American Pomological Society 68(3): 6141-157.

4. Corollaro, M.L., Aprea, E., Endrizzi, I., Betta, E., Demattè, M.L., Charles, M., Bergamaschi, M., Costa, F., Biasioli, F., Corelli Grappadelli, L., Gasperi, F. (2014). A combined sensory-instrumental tool for apple quality evaluation. Postharvest Biology and Technology, 96, 135-144.

5. Demattè, M.L., Pojer, N., Endrizzi, I., Corollaro, M.L., Betta, E., Aprea, E., Charles, M., Biasioli, F., Zampini, M. & Gasperi, F. (2014). Effects of the sound of the bite on apple perceived crispness and hardness. Food Quality and Preferences, 38, 58-64.

6. Costa, F., Cappellin, L., Farneti, B., Tadiello, A., Romano, A. Soukoulis, C. Sansavini, S., Velasco, R. , Biasioli, F. (2014). Advances in QTL mapping for ethylene production in apple (Malus x domestica Borkh.). Postharvest Biology And Technology, 87, 127-132

7. Cappellin, L., Makhoul, S., Schuhfried, E., Romano, A., Sanchez del Pulgar, J., Aprea, E., Farneti, B., Costa, F., Gasperi, F., Biasioli, F. (2014). Ethylene: Absolute real-time high-sensitivity detection with PTR/SRI-MS. The example of fruits, leaves and bacteria. International Journal of Mass Spectrometry, 365-366, 33-41.

8. Ting, V., Silcock, P., Bremer, P.J., Biasioli, F., (2013). X-Ray Micro-Computer Tomographic Method to Visualize the Microstructure of Different Apple Cultivars. JOURNAL OF FOOD SCIENCE, 78 (1), pp. 1735-1742

9. Soukoulis, C., Cappellin, L., Aprea, E., Costa, F., Viola, R., Märk, T.D., Gasperi, F., Biasioli, F. (2013). PTR-ToF-MS, A Novel, Rapid, High Sensitivity and Non-Invasive Tool to Monitor Volatile Compound Release During Fruit Post-Harvest Storage: The Case Study of Apple Ripening. Food and Bioprocess Technology, 6 (10), pp. 2831-2843.

10. Corollaro, M.L., Endrizzi, I., Bertolini, A., Aprea, E., Demattè, M.L., Costa, F., Biasioli, F., Gasperi, F. (2013). Sensory profiling of apple: Methodological aspects, cultivar characterisation and postharvest changes. Postharvest Biology and Technology, 77, pp. 111-120.

11. Aprea, E., Corollaro, M.L., Betta, E., Endrizzi, I., Demattè, M.L., Biasioli, F., Gasperi, F. (2012). Sensory and instrumental profiling of 18 apple cultivars to investigate the relation between perceived quality and odour and flavour. Food Research International, 49 (2), pp. 677-686.

A partial list of our works on apples


12. J.L. Ting, V., Soukoulis, C., Silcock, P., Cappellin, L., Romano, A., Aprea, E., Bremer, P.J., Märk, T.D., Gasperi, F., Biasioli, F. (2012). In Vitro and In Vivo Flavor Release from Intact and Fresh-Cut Apple in Relation with Genetic, Textural, and Physicochemical Parameters. Journal of Food Science, 77 (11), pp. C1226-C1233.

13. Cappellin, L., Soukoulis, C., Aprea, E., Granitto, P., Dallabetta, N., Costa, F., Viola, R., Märk, T.D., Gasperi, F., Biasioli, F. (2012) . PTR-ToF-MS and data mining methods: A new tool for fruit metabolomics. Metabolomics, 8 (5), pp. 761-770.

14. Costa, F., Cappellin, L., Fontanari, M., Longhi, S., Guerra, W., Magnago, P., Gasperi, F., Biasioli, F. (2012). Texture dynamics during postharvest cold storage ripening in apple (Malus×domestica Borkh.). Postharvest Biology and Technology, 69, pp. 54-63.

15. Costa, F., Cappellin, L., Longhi, S., Guerra, W., Magnago, P., Porro, D., Soukoulis, C., Salvi, S., Velasco, R., Biasioli, F., Gasperi, F. (2011). Assessment of apple (Malus×domestica Borkh.) fruit texture by a combined acoustic-mechanical profiling strategy. Postharvest Biology and Technology, 61 (1), pp. 21-28.

16. Cappellin, L., Biasioli, F., Granitto, P.M., Schuhfried, E., Soukoulis, C., Costa, F., Märk, T.D., Gasperi, F. (2011). On data analysis in PTR-TOF-MS: From raw spectra to data mining. Sensors and Actuators, B: Chemical, 155 (1), pp. 183-190.

17. Aprea, E., Gika, H., Carlin, S., Theodoridis, G., Vrhovsek, U., Mattivi F. (2011). Metabolite profiling on apple volatile content based on solid phase microextraction and gas-chromatography time of flight mass spectrometry. Journal of Chromatography A, 1218 (2011) 4517–4524

18. Costa, F., Longhi, S., Magnago, P., Porro, D., Gasperi, F., Biasioli, F., Troggio, M., Velasco, R., Salvi, S. (2010). Novel possibilities for marker-assisted breeding exploiting the apple genome. (2010) Acta Horticulturae, 859, pp. 357-360.

19. Gasperi, F., Aprea, E., Biasioli, F., Carlin, S., Endrizzi, I., Pirretti, G., Spilimbergo, S. (2009). Effects of supercritical CO2 and N2O pasteurisation on the quality of fresh apple juice. Food Chemistry, 115 (1), pp. 129-136.

20. Zini, E., Biasioli, F., Araghipour, N., Kellerhals, M., Mott, D., Aprea, E., Gasperi, F., Märk, T.D., Komjanc, M., Gessler, C. (2009). Proton transfer reaction-mass spectrometry analysis is a valuable tool for the identification of genomic regions related to volatile organic compounds. Acta Horticulturae, 814, pp. 577-582.

21. Zini, E., Biasioli, F., Gasperi, F., Mott, D., Aprea, E., Märk, T.D., Patocchi, A., Gessler, C., Komjanc, M. (2005) . QTL mapping of volatile compounds in ripe apples detected by proton transfer reaction-mass spectrometry. Euphytica, 145 (3), pp. 269-279.


Thank youfor your attention!