Line Elgård Nielsen

PhD Student, PhD Fellow

Line Elgård Nielsen



Title: Correlating unique compounds in beer to sensory perception - A novel model for artificial taste perception

University: Aarhus University

Department: Department of Food science, Science Team Food Quality, Perception and Society

Supervisor: Derek V. Byrne, Professor and Science Team Leader

Co-supervisor: Line Mielby, Post Doc

Project term: 01-11-2016 to 31-10-2019

Master’s degree: Food Science and Technology with specialization in sensory science from the University of Copenhagen.


Background for the project:

My PhD project is part of the DNA-shapes project. Food and beverages are extremely complex mixtures of substances that upon interaction with thousands of taste receptors on our tongue send a cascade of signals to our brain to create our perception of taste. We will, in the DNA-SHAPES project, endeavor to mimic and go some way towards understanding this mechanism by mixing the food with billions of nano-sized biosensors, which simultaneously can probe essentially all substances in the food and transform these associations into a digital output in the form of a large set of DNA sequences.

The full project description can be found here:



Using new high speed DNA sequencing methods and computer analysis, we hope to be able to develop ‘artificial tasting machines’ that, not only can help determine the sensory characteristics, but also enable assessment of quality, authenticity, and contamination in food in a rapid manner for use in industrial production. We imagine that this DNA sensor-based characterization of food constituents will help us to understand the complexity and specificity of human taste perception and how to mimic it.


Research outline:

I will cover the sensory part of the DNA-shapes project, and the main methods used will be Generalized Descriptive Analysis. For the purpose of this project, I will make an ultrasensitive modification of this method. The project will start with a focus on how to optimize all factors revolving the Generalized Descriptive Analysis method. The optimization include panel training, type of panel, sample presentation, product factors, type of scale used and length of the scale. When the method is optimized the focus will be on making ultraprecise taste profiles of different types of beer and linking these taste profiles to DNA profiles of the beers. The project will also include recruitment, screening and training of a new panel. The volatile compounds in the beers will be analyzed using CG-MS and SPME. The data will be analyzed using univariate and multivariate data analysis (ANOVA, PCA, PLS etc.).


Collaboration partners:

  • Interdisciplinary Nanoscience Center, Aarhus University

  • Carlsberg


ID: 104070030