Chemometrics, Dynametrics and Big Data Cybernetics. We live at the beginning of a disruptive data explosion. Combine Internet of Things with small hyperspectral cameras and other low-cost, high-speed, multichannel instruments, and we make chaos unless we learn to handle all these data.We must develop a non-reductionist science and technology culture that combines prior theoretical expectation and practical experience with the unexpected surprises hidden in all these measurements.  To put it a bit bluntly:We need to use a lot more mathematical modelling, otherwise we cannot summarize, combine and utilize the rapid increase in scientific insight, and describe the dynamics of reality. But we must avoid falling into the mental trap of “Macho mathematics” – focusing on stale proofs and needless exactness, limiting ourselves to oversimplified causality and fearing to be proven wrong. We need a lot more statistical design and statistical validity assessments to ensure informative measurements and reliable conclusions. But given the widespread math and statistics fear in society, we must avoid falling for the academic temptation of “Gucci statistics” – sprinkling in meaningless p-values just to look good. We must try to understand our statistics and focus on the useful signal instead of the useless noise. We need a lot more machine learning to extract all the useful information from our instruments, even the unexpected. But “Blind machine learning” can be dangerous, if it leaves us alienated and irresponsible. The local user of a trained neural net classifier does not need to know its details, as long as someone knows – but where is that person today?How can the valuable information content from Quantitative Big Data, e.g. multichannel analyzers or hyperspectral video cameras, be discovered, extracted, stored, recalled, quantified and interpreted? A new method for On-The-Fly processing of “ever-lasting” high-dimensional data streams, e.g. from modern bio-production, will be demonstrated.

Cell-surface polysaccharides are produced by several bacteria, including Lactic Acid Bacteria (LAB). These polysaccharides may be covalently linked to the cell wall (peptidoglycan) – capsular polysaccharides (CPS) – or loosely attached/secreted as slime – exopolysaccharides (EPS). In the production of fermented milks and some cheese types (e.g. fresh cheese, pasta filata) polysaccharide producing LAB are of great importance, mainly because the polysaccharides contribute to the texture, mouth-feel, perception and stability of the final product and therefore provide functional alternatives to additives like pectin or starch. Despite the market potential of polysaccharide-producing LAB, the understanding of polysaccharide production and its regulation in LAB is surprisingly limited. In contrast, the genetics of EPS production are far better understood and genes involved in EPS production are typically clustered in the chromosome or plasmids of LAB.The development of novel polysaccharide-producing LAB with superior functionality is of importance for the industry and here we present classical strain improvement and high throughput screening strategies to identify and improve polysaccharide producing LAB.

Extended lactation achieved by delayed insemination resulting in longer calving intervals can be a beneficial strategy for high-yielding dairy cows due to reduced feed use per kg milk produced and reduced risk periods around calving. However, there are still concerns whether extended lactation for more than the traditional 12 months impairs milk quality. Previous studies indicate a possible decline in the epithelial integrity when lactation is prolonged and a salty taste in milk has been reported.The aim of the present study was to ascertain effects on milk quality from cows managed for extended lactation.The experimental set-up covered milk from two different experiments. In experiment 1, milk was obtained at 3 occasions from 47 Holstein-Friesian cows managed for 18-month carving interval. For experiment 2, milk from 28 cross-bred cows managed for 16 or 18 months calving interval was compared. This design ensured that the effects of both gestation and days in milking could be explored. The result from experiment 1 showed that lactation period was the main factor affecting milk composition, and fat, protein and casein content increased with lactation stage. This was reflected in significant improvements in the rennet coagulation properties and curd yields. Milk indicators for udder integrity remained unchanged through the lactation period. Milk from late lactation did not score higher in negative sensory attributes than milk from normal lactating cows. Additionally, the data from experiment 2 designed to evaluate in more detail the protein composition of the milk over the lactation periods indicated lactational changes in both individual milk proteins, as well as in some of their post-translational modifications. Our results indicate that the quality of milk from healthy, high-yielding cows in extended lactation should not be a concern for cheese processing.