We will contribute to more sustainable food production by developing biotechnological processes, smart sensors and data analytical tools.  

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The project will develop enabling technology and knowledge, which will improve and modernize the existing land-based food industry. The novel biotechnological methodology to be developed will in long term potentially be the foundation of a new kind of Norwegian food industry that will produce tomorrow’s food in a smart, sustainable and innovative way.


The global food production is facing enormous challenges in terms of sustainability and food security for a growing population. 

We need to

  • reduce food losses
  • fully exploit raw materials and
  • produce food more sustainably 

To achieve this, we need precision food production, a food industry that produces exactly the products and qualities that the market needs and at the same time minimizes food waste.


This research program will contribute to such an industry by the development of three enabling technologies:

  1. Novel biotechnological processes which may provide targeted production of crucial food components, by exploiting rest raw materials from food processing. We will develop and study new processes based on combinations of enzymatic protein hydrolysis, precision fermentation and culturing of meat.
  2. Smart sensors for rapid assessment of food quality, which will enable monitoring and control of production processes to minimise food loss and optimise yield and end quality. We will develop spectroscopic sensors for in-line quality control and chemical characterisation, and study how these can be part of larger solutions that facilitate product differentiation, consumer satisfaction and personalized nutrition.
  3. Data analytical tools that transform large and complex data into relevant, reliable and useful information. Such tools are strictly needed to release the full potential of the technologies 1 and 2, as well as of other scientific disciplines and modern food industry. We will address challenges related to prediction and interpretation by combining methods from statistics, chemometrics and machine learning.

Results so far

Novel biotechnological processes

Within industrial biotechnology, lactic acid fermentation of selected hydrolysates to improve sensory properties has been conducted, and results are studied in combination with chromatography, FTIR spectroscopy and microbiology. In addition, we have performed fractionation of hydrolysates of egg-white and pig blood plasma and investigated potential bioactive effects in skeletal muscle cell cultures.

Bacterial cellulose (BC) samples have been produced using various bacterial strains under different cultivation strategies, and we examined the structural properties using scanning electron microscopy. We added eggshell membrane powder to the bacterial culture to improve functional properties as ingredients in foods. We have also seen that muscle cells grow well on this BC. By modifying bacteria E. coli, we have produced a growth factor useful for meat cultivation. Its bioactivity was measured using fibroblast cells. A literature study has been performed to investigate the potential for using the yeast Pichia for collagen production.

We have grown skeletal muscle cells in a lab-bench bioreactor and compared various processing conditions. We have conducted kinetics experiments, focusing on the effect of temperature and pH. We have investigated spectroscopic methods for analysing the cell growth medium or cells/microcarriers in a bioreactor. Preliminary data demonstrate promising potential for non-invasive monitoring of the cell culturing medium.

Smart sensors

A long-term goal for the smart sensor activities is to develop sensors that can be used for inline industrial food characterisation. With near-infrared spectroscopy we have recently shown how the optical geometry affects the depth of measurement and thereby also the accuracy of estimated food components like dry-matter in potatoes and fat in meat. We have shown that Raman spectroscopy can be used to predict bone contents in chicken carcasses or fatty acid composition in muscle foods moving at high speed on a conveyor belt.   

The use of smart spectroscopic sensors for characterisation of detailed chemistry of foods is important. Raman spectroscopy has been successfully used to measure individual sugars in intact apples, and promising results have been obtained for prediction of sugars and acids in strawberries. Infrared spectroscopy has been successfully used to measure milk fatty acids as indicators for negative energy balance and potential metabolic diseases in cows early after calving. In collaboration with WP1, we have used sensors to successfully follow changes in growth media during agricultural cellular production.  

Smart sensors can be a vital part of solutions that facilitate consumer satisfaction and personalized nutrition. We have promising results linking NMR spectra of protein hydrolysates to sensory perceived bitterness.  We have also shown that FTIR spectra of protein hydrolysates from chicken and milk correlate well with antioxidant activities measured in vitro. Models based on such correlation can serve as a rapid screening tool for health promoting effects of food ingredients.

Data analytical tools

Within Multivariate data analysis, we develop statistical methods for transforming complex data to new knowledge. A book that covers the latest developments in multiblock methods is soon ready for publication, as a collaboration between the Univ. of Amsterdam and NMBU. We are presently exploring the multi-block method “INDSCAL”, and a new multi-block method for distance matrices.

On the topic ​​robust calibration, we work closely with SFI DigiFoods. Large spectroscopic data sets from the industry are collected through DigiFoods, and these are further used for method development in Precision. We have started a work on calibration transfer methods, which is an important element for effective and affordable implementation of smart sensors in industry.

Within cluster analysis and segmentation, we have seen how binary data can be used to cluster consumers based on their preferences. In collaboration with the SUSEAT project, we have also tested various segmentation methods on consumer data from “Norsk Monitor”.

Within multivariate ANOVA, we have published a new strategy (“ER modeling”) to interpret effects in -omics data, and we are working on new applications of this methodology in diabetes studies. We have also compared statistical methods for analyzing the effects of diet on gut microbiota. This work was recently presented at an international conference.


Gesine Schmidt og Silje Johansen gjør forsøk i metabolomics-plattformen.

Strategic research program

This is one of four strategic research programs funded by the The Agricultural and Food Industry Research Funds (FFL/JA) in the period 2021-2025. In these programs, we set ourselves ambitious goals to bring Norwegian food production a good step forward with regard to health, taste, sustainability, security and quality.

The programs are:
FoodForFuture | FutureFoodControl | SusHealth | Precision