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NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2023

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Saccharomyces cerevisiae is commonly used for the production of alcoholic beverages, including cider. In this study, we examined indigenous S. cerevisiae and S. uvarum strains, both species commonly found in cider from Hardanger (Norway), for their strain-specific abilities to produce volatile and non-volatile compounds. Small-scale fermentation of apple juice with 20 Saccharomyces strains was performed to evaluate their aroma-producing potential as a function of amino acids (AAs) and other physicochemical parameters under the same experimental conditions. After fermentation, sugars, organic acids, AAs, and biogenic amines (BAs) were quantified using the HPLC–UV/RI system. A new analytical method was developed for the simultaneous determination of nineteen AAs and four BAs in a single run using HPLC–UV with prior sample derivatization. Volatile compounds were determined using HS-SPME-GC-MS. Based on 54 parameters and after the removal of outliers, the nineteen strains were classified into four groups. In addition, we used PLS regression to establish a relationship between aroma compounds and predictor variables (AAs, BAs, organic acids, sugars, hydrogen sulfide (H2S) production, CO2 release) of all 19 strains tested. The results of the VIP show that the main predictor variables affecting the aroma compounds produced by the selected yeasts are 16, belonging mainly to AAs.

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Nutrient uptake and transport depend on the root system of a tree. Various apple rootstock genotypes may interact fruit tree nutrition. In 2017, two multi-location apple rootstock trials were established at 16 sites in 12 European countries. The evaluations are performed by members of the EUFRIN (European Fruit Research Institute Network) Apple & Pear Variety & Rootstock Testing Working Group. Following rootstocks are included in the tests: G.11, G.41, G.202 and G.935 (US), EM_01, EM_02, EM_03, EM_04, EM_05 and EM_06 (UK), 62-396-B10® (Russia), P 67 (Poland), NZ-A, NZ-B, NZ-C and NZ-D (New Zealand) and Cepiland-Pajam®2 as control. The effect of rootstocks on the mineral content of leaf and fruit was studied at the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry in 2019-2020. The leaf and fruit mineral concentration of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and leaf mineral content of copper (Cu), zinc (Zn), iron (Fe), manganese (Mn) and boron (B) were measured. Significant rootstock effect was established on leaf P, Mg, Zn, Mn, B, and fruit Ca and Mg content. Rootstocks EM_01 and G.41 were the most efficient in leaf mineral uptake, while G.935 had the lowest content of all leaf macro nutrients. Rootstocks EM_06 and P 67 were the most efficient in fruit mineral uptake, while EM_02 had the lowest content of three nutrients. Current research reveals differences among rootstocks and their capacity to absorb separate minerals and enables creation of rootstock specific nutrition management.

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Exploring key factors has important guidance for understanding complex anaerobic digestion (AD) systems. This study proposed a multi-layer automated machine learning framework to understand the complex interactions in AD systems and explore key factors at the environmental factor, microorganisms and system levels. The first layer of the framework identified hydraulic residence time (HRT) as the most important environmental factor, with an optimal range of 33–45 d. In the second layer of the framework, Methanocelleus (optimal relative abundance (ORA) = 3.0%) and Candidatus_Caldatribacterium (ORA = 1.7%) were found to be the key archaea and bacteria, respectively. Furthermore, the prediction of key microorganisms based on environmental factors and remaining microbial data showed the essential roles of Methanothermobacter and Acetomicrobium. The third layer for finding the optimal combination of data variables for predicting biogas production demonstrated that combined Archaea genera and environmental factors should be achieved for the most accurate prediction (root mean square error (RMSE) = 84.21). GBM had the best model performance and prediction accuracy among all the built-in models. Based on the optimal GBM model, the analysis at the system level showed that HRT was the most important variable. However the most important microorganism, Methanocelleus, within the appropriate survival range is also essential to achieve optimal biogas production. This research explores key parameters at various levels through automated machine learning techniques, which are expected to provide guidance in understanding the complex architecture of industrial and laboratory AD systems.

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Aquaculture of the lumpfish (Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for “cleaner fish” to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype–environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our ‘global’ sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.

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This study aimed to evaluate the rheological properties of doughs with 50% brewers’ spent grain (BSG) derived from a rye-based (RBSG) and barley-based (BBSG) beer added, and the textural profile of the related baked products. Simple model systems using BSG flour mixed with water were studied. Two bakery products, focaccia and cookies, were made as food systems using BSG in a 1:1 ratio with wheat flour (WF). Their rheological properties and texture after baking were characterized. BSG-added dough exhibited viscoelastic properties with a solid gel-like behavior. The addition of BSG increased G′ > G″ and decreased the dough flexibility. BSG addition in baked RBSG focaccia increased the hardness, gumminess, and chewiness by 10%, 9%, and 12%, respectively. BBSG cookies had a 20% increase in fracturability. A positive correlation was found between the rheological metrics of the dough and the textural parameters of BBSG-added cookies. PCA analysis revealed that complex viscosity, G′, G″, and cohesiveness separated BBSG focaccia from RBSG focaccia and the control. Therefore, the rheological properties of BSG dough will have industrial relevance for 3D-printed customized food products with fiber. Adding RBSG and BBSG to selected foods will increase the up-cycling potential by combining techno-functional properties.