Advertisement

Euphytica

, Volume 189, Issue 3, pp 365–378 | Cite as

Bringing the voice of consumers into plant breeding with Bayesian modelling

  • Lebeyesus Mesfin Tesfaye
  • Marco C. A. M. Bink
  • Ivo A. van der Lans
  • Bart Gremmen
  • Hans C. M. van Trijp
Article

Abstract

Improving flavour quality traits in fruit breeding calls for innovative consumer-oriented product development. This paper explores the potential of marker-assisted breeding from genomics and consumer-based quality-improvement models from marketing, and exploits the progresses at both sides as technology push and market pull. An integrative and cross-disciplinary quality-improvement model is proposed based on Bayesian modelling. This Bayesian modelling allows for the integration of elicited knowledge of breeders and flavour researchers concerning the degree of causal associations of metabolites and flavour quality traits of fruits in the model. We also present the flavour quality improvement challenge as a multi-criteria optimization process and show the potential and current limitations of the proposed model. Insights gained from the model would help flavour researchers determine the optimum concentration of flavour-affecting metabolites which could be used for further DNA marker development. These ideas and concepts will help translate consumer-desired product features into genomic information, ultimately resulting in successful new cultivars.

Keywords

Consumer-oriented breeding Flavour quality Bayesian statistics Quality Guidance Model Genomics Marker-assisted breeding Structural equation modelling Expert knowledge elicitation 

Notes

Acknowledgments

This project was financed by the Centre for Bio-Systems Genomics (CBSG) and Centre for Society and Genomics (CSG) in the Netherlands. The contribution of M.C.A.M. Bink was carried out as part of the EU-FruitBreedomics project funded by the Commission of the European Communities (Contract FP7-KBBE-2010-265582). We also extend our gratitude to the reviewers of this paper for their important comments to improve it.

References

  1. Acquaah G (2007) Principles of plant genetics and breeding. Blackwell, OxfordGoogle Scholar
  2. Alston FH (1992) Flavour improvement in apples and pears through plant breeding In: Patterson RLS, Charlwood BV, MacLeod G (eds) Bioformation of flavours. The Royal Society of Chemistry, Cambridge, pp 33–41Google Scholar
  3. Auerswald H, Peters P, Bruckner B, Krumbein A, Kuchenbuch R (1999) Sensory analysis and instrumental measurements of short-term stored tomatoes (Lycopersicon esculentum Mill.). Postharvest Biol. Technol. 15(3):323–334CrossRefGoogle Scholar
  4. Bech AC, Juhl HJ, Hansen M, Martens M (2000) Quality of peas modelled by a structural equation system. Food Qual. Prefer. 11(4):275–281CrossRefGoogle Scholar
  5. Benner M, Linnemann AR, Jongen WMF, Folstar P (2003) Quality Function Deployment (QFD)—can it be used to develop food products? Food Qual. Prefer. 14(4):327–339CrossRefGoogle Scholar
  6. Benner M, Dekker M, Linnemann A (2007) Structured food product development based on quality function deployment. In: Linneman AR, Schroen C, Van Boekel M (eds) Food product design—an integrated approach. Wageningen Academic Publishers, WageningenGoogle Scholar
  7. Berna AZ, Lammertyn J, Buysens S, Natale SD, Nicolai BM (2005) Mapping consumer liking of tomatoes with fast aroma profiling techniques. Postharvest Biol. Technol. 38(2):115–127CrossRefGoogle Scholar
  8. Bertschinger L, Corelli-Grappadelli L, Derkx MPM, Hall S, Kockerols K, Sijtsema SJ, Steiner S, Van Der Lans IA, Van Schaik ACR, Zimmermann KL (2009) A search for a systematic method to bridge between pre-harvest, post-harvest, and consumer research aimed at increasing fruit consumption:The “Vasco da Gama” process. J. Horticult. Sci. Biotechnol. 84(6):2–6Google Scholar
  9. Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH, Trethewey RN, Lange BM, Wurtele ES, Sumner LW (2004) Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 9(9):418–425PubMedCrossRefGoogle Scholar
  10. Causse M, Buret M, Robini K, Verschave P (2003) Inheritance of nutritional and sensory quality traits in fresh market tomato and relation to consumer preferences. J. Food Sci. 68(7):2342–2350CrossRefGoogle Scholar
  11. Causse M, Friguet C, Coiret C, Lepicier M, Navez B, Lee M, Holthuysen N, Sinesio F, Moneta E, Grandillo S (2010) Consumer preferences for fresh tomato at the European scale: a common segmentation on taste and firmness. J. Food Sci. 75(9):S531–S541PubMedCrossRefGoogle Scholar
  12. Causse M, Stevens R, Amor BB, Faurobert M, Munos S (2011) Breeding for fruit quality in tomato. In: Jenks MA, Bebeli PJ (eds) Breeding for fruit quality. Wiley, HobokenGoogle Scholar
  13. Clemen RT, Fischer GW, Winkler RL (2000) Assessing dependence: some experimental results. Manage. Sci. 46(8):1100–1115CrossRefGoogle Scholar
  14. Collard BCY, Mackill DJ (2007) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society of LondonSeries B, Biological Sciences 363(1491):557–572CrossRefGoogle Scholar
  15. Collard BCY, Jahufer MZZ, Brouwer JB, Pang EC (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker assisted selection for crop improvement: the basic concepts. Eupyhtica 142(1–2):169–196CrossRefGoogle Scholar
  16. Congdon P (2006) Bayesian statistical modelling. Wiley, ChichesterCrossRefGoogle Scholar
  17. Corny D (2000) Designing food with Bayesian belief networks. In: ACDM 2000 fourth international conference on adaptive computing in design and manufacture, pp 83–94Google Scholar
  18. Craig A, Hart S (1992) Where to now in new product development research? Eur. J. Mark. 26(11):2–49CrossRefGoogle Scholar
  19. Dalliant-Spinnler B, MacFie HJH, Beyts PK, Hedderley D (1996) Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the southern hemisphere. Food Qual. Prefer. 7(2):113–126CrossRefGoogle Scholar
  20. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, West SussexGoogle Scholar
  21. Dekkers JCM, Hospital F (2001) The use of molecular genetics in the improvement of agricultural populations. Nature Reviews/Genetics 3(1):22–32CrossRefGoogle Scholar
  22. Dijkhuizen AA, Huirne RBM, Hardaker JB (1997) Scope and concepts of risky decision making. In: Dijkhuizen AA, Morris RS (eds) Animal health economics: principles and applications. Post Graduate Foundation in Veterinary Science, University of Sydney, SydneyGoogle Scholar
  23. Duarte CW, Klimentidis YC, Harris JJ, Cardel M, Fernandez JR (2012) A hybrid Bayesian network/structural equation (BN/SEM) modelling approach for detecting physiological networks for obesity-related genetic variants. IEEE Int Conf Bioinforma Biomed 696–702Google Scholar
  24. Duchene E, Butterlin G, Claudel P, Dumas V, Jaegli N, Merdinoglu D (2009) A grapevine (Vitis vinifera L.) deoxy-d-xylulose synthase gene colocates with a major quantitative trait loci for terpenol content. Theor. Appl. Genet. 118(3):541–552PubMedCrossRefGoogle Scholar
  25. Dunemann E, Ulrich D, Boudichevskaia A, Grafe C, Weber E (2009) QTL mapping of aroma compounds analysed by headspace solid-phase microextractiongas chromatography in the apple progeny. Mol. Breeding 23(3):501–521CrossRefGoogle Scholar
  26. Fernie AR, Schauer N (2008) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet. 25(1):39–48PubMedCrossRefGoogle Scholar
  27. Foolad MR (2007) Genome mapping and molecular breeding of tomato-review article. International Journal of Plant Genomics 2007:52Google Scholar
  28. Gatti E, Defilippi BG, Predieri S, Infante R (2009) Apricot (Prunus armeniaca L.) quality and breeding perspectives. J Food Agric Environ 7(3–4):573–580Google Scholar
  29. Georgelis N, Scott JW, Baldwin EA (2004) Relationship of tomato fruit sugar concentration with physical and chemical traits and linkage of RAPD markers. J Am Soc Hotric Sci 129(6):839–845Google Scholar
  30. Grunert KG, Baadsgaard A, Larsen HH, Madsen TK (1996) Market orientation in food and agriculture. Kluwer Academic Publishers, NorwellGoogle Scholar
  31. Gupta S, Kim HW (2007) Application of Bayesian modeling to management information systems: a latent scores approach. In: Mittal A, Kassim A, Tan T (eds) Bayesian network technologies: application and graphical models. IGI, New YorkGoogle Scholar
  32. Hair J, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice 19(2):139–151CrossRefGoogle Scholar
  33. Hampson CR, Quamme HA, Hall JW, MacDonald RA, King MC, Cliff MA (2000) Sensory evaluation as a selection tool in apple breeding. Euphytica 111(2):79–90Google Scholar
  34. Hanf C, Bocker A (2002) Is European consumers’ refusal of GM food a serious obstacle or a transient fashion? In: Santaniello S, Evenson RE, Zilberman D (eds) Market development for genetically modified foods. CABI Publisher, OxonGoogle Scholar
  35. Harker FR, Johnston JW (2008) Importance of texture in fruit and its interaction with flavour. In: Bruckner B (ed) Fruit and vegetable flavour. Woodhead Publishing Limited, CambridgeGoogle Scholar
  36. Harvey M, Quilley S, Benyon H (2002) Exploring the tomato: transformations of nature, society and economy. Edward Elgar, CheltenhamGoogle Scholar
  37. Herzog E, Frisch M (2011) Selection strategies for marker-assisted breeding backcrossing with high-throughput marker systems. Theor. Appl. Genet. 123:251–260PubMedCrossRefGoogle Scholar
  38. Jackman S (2009) Bayesian analysis for the social sciences. Wiley, ChichesterCrossRefGoogle Scholar
  39. Jaeger SR, Rossiter KL, Wismer WV, Harker FR (2003) Consumer-driven product development in the kiwifruit industry. Food Qual. Prefer. 14(3):187–198CrossRefGoogle Scholar
  40. Kader AA (2002) Postharvest technology of horticultural crops. University of california Agriculture and Natural Resources, OaklandGoogle Scholar
  41. Klee HJ (2010) Improving the flavor of fresh fruits: genomics, biochemistry, and biotechnology. New Phytol. 187(1):44–56PubMedCrossRefGoogle Scholar
  42. Klee HJ, Goff SA (2006) Plant volatile compounds: sensory cues for health and nutritional value. Science 311(5762):815–819PubMedCrossRefGoogle Scholar
  43. Korb KB, Nicholson AE (2011) Bayesian artificial intelligence. CRC Press, Boca RatonGoogle Scholar
  44. Langridge P, Fleury D (2011) Making the most of ‘omics’ for crop breeding. Trend Biotechnol. 29(1):33–40CrossRefGoogle Scholar
  45. Lee SY (2007) Structural equation modeling: a Bayesian approach. Wiley, ChichesterCrossRefGoogle Scholar
  46. Linnemann AR, Benner M, Verkerk R, Van Boekel MAJS (2006) Consumer-driven food product development. Trends Food Sci. Tech. 17(4):184–190CrossRefGoogle Scholar
  47. Lynch SM (2007) Introduction to applied Bayesian statistics and estimation for social scientists. Springer, New YorkCrossRefGoogle Scholar
  48. Martens M, Tenenhaus M, Vinzi VE, Martens H (2007) The use of partial least squares methods in new product development. In: Macfie H (ed) Consumer-led food product development. CRC Press, Boca RatonGoogle Scholar
  49. Otto KN, Wood KL (2003) Product design: techniques in reverse engineering and new product development. Pearson Education Asia Limited and Tsinghua University Press, New JerseyGoogle Scholar
  50. Paterson AH, Tanksley SD, Sorrells ME (1991) DNA markers in plant improvement. In: Sparks DL (ed) Advances in agronomy. Academic Press Inc, San DiegoGoogle Scholar
  51. Peleman J, van der Voort JR (2003) Breeding by design Trends in Plant Science 8(7):330–334CrossRefGoogle Scholar
  52. Plaehn D, Lundahl DS (2006) An L-PLS preference cluster analysis on French consumer hedonics to fresh tomatoes. Food Qual Prefer 17(3–4):243–256Google Scholar
  53. Poulsen CS, Juhl HJ, Kristensen K, Bech AC, Engelund E (1996) Quality guidance and quality formation. Food Qual Prefer 7(2):127–135CrossRefGoogle Scholar
  54. Prohens J (2011) Plant breeding: a success story to be continued thanks to the advances in genomics. Frontiers Plant Sci 2(51)Google Scholar
  55. Rossi PE, Allenby GM (2003) Bayesian statistics and marketing. Marketing Science 22(3):304–328CrossRefGoogle Scholar
  56. Rossi PE, Allenby GM, McCulloch R (2005) Bayesian statistics and marketing. Wiley, ChickesterCrossRefGoogle Scholar
  57. Saliba-Colombani V, Causse M, Langlois D, Philouze J, Buret M (2001) Genetic analysis of organoleptic quality in fresh market tomato.1. Mapping QTLs for physical and chemical traits. Theor Appl Genet 102(2–3):259–272Google Scholar
  58. Scott JW (2002) A breeder’s perspective on the use of molecular techniques for improving fruit quality. HortScience 37(3):464–467Google Scholar
  59. Spetsidis NM, Schamel G (2002) A consumer-based approach towards new product development through biotechnology in the agro-food sector. In: Santaniello S, Evenson RE, Zilberman D (eds) Market development for genetically modified foods. CABI Publishing, OxonGoogle Scholar
  60. Statistiek CBvd (2011) Statistical yearbook 2011. Statistics Netherlands, The HagueGoogle Scholar
  61. Steenkamp JEBM, Van Trijp JCM (1996) Quality guidance: a consumer-based approach to food quality improvement using partial least squares. European Review of Agricultural Economics 23(2):195–215CrossRefGoogle Scholar
  62. Ulrich D, Olbricht K (2011) Fruit organoleptic properties and potential for their genetic improvement. In: Jenks MA, Bebeli PJ (eds) Breeding for fruit quality. Wiley, HobokenGoogle Scholar
  63. Ulrich D, Komes D, Olbricht K, Hobeg E (2007) Diversity of aroma patterns in wild and cultivated Fragaria accessions. Genet. Resour. Crop Evol. 54(6):1185–1196CrossRefGoogle Scholar
  64. Van Boekel MAJS (2005) Technological innovation in the food industry: product design. In: Jongen WMF, Meulenberg MTG (eds) Innovation in agri-food systems. Wageningen Academic Publishers, WageningenGoogle Scholar
  65. Van Den Heuvel T, Van Trijp HCM, Van Woerkum C, Jan Renes R, Gremmen B (2007) Linking product offering to consumer needs; inclusion of credence attributes and the influences of product features. Food Qual. Prefer. 18(2):296–304CrossRefGoogle Scholar
  66. Van Kleef E, Van Trijp HCM, Luning P (2005) Consumer research in the early stages of new product development: a critical review of methods and techniques. Food Qual. Prefer. 16(3):181–201CrossRefGoogle Scholar
  67. Van Trijp JCM, Steenkamp JEBM (2005) Consumer-oriented new product development: principles and practices. In: Jongen WMF, Meulenberg MTG (eds) Innovation in agri-food systems. Wageningen Academic Publishers, WageningenGoogle Scholar
  68. Varshney RK, Graner A, Sorrells ME (2005) Genomics-assisted breeding for crop improvement. Trends Plant Sci. 10(12):621–630PubMedCrossRefGoogle Scholar
  69. Watada AA, Aulenbach BB (1979) Chemical and sensory qualities of fresh market tomatoes. J. Food Sci. 44(4):1013–1016CrossRefGoogle Scholar
  70. Whitaker BD (2008) Postharvest flavor deployment and degradation in fruits and vegetables. In: Brückner B, Wyllie SG (eds) Fruit and vegetable flavour: recent advances and future prospects. Woodhead Publishing Limited, CambridgeGoogle Scholar
  71. Wolters CJ, Van Gemert LJ (1990) Towards an integrated model of sensory attributes, instrumental data and consumer perception of tomatoes. Part I. Relation between consumer perception and sensory attributes. Acta Hortic. 259:91–106Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Lebeyesus Mesfin Tesfaye
    • 1
  • Marco C. A. M. Bink
    • 2
  • Ivo A. van der Lans
    • 3
  • Bart Gremmen
    • 4
  • Hans C. M. van Trijp
    • 3
  1. 1.Centre for Methodical Ethics and Technology AssessmentWageningen UniversityWageningenThe Netherlands
  2. 2.BiometrisWageningen University and Research CentreWageningenThe Netherlands
  3. 3.Marketing and Consumer Behaviour GroupWageningen UniversityWageningenThe Netherlands
  4. 4.Centre for Methodical Ethics and Technology AssessmentWageningen UniversityWageningenThe Netherlands

Personalised recommendations