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Protein i korn

En torkstudie utförd med etablerade analysmetoder på tre kornsorter


A study was performed to evaluate whether established methods of analysis of protein content in barley (Kjeldahl, Dumas, or NIT (short for Near Infrared Transmittance)) gives different results for wet and dried barley. This was carried out because there are concerns regarding the well-worn NIT prediction model giving different results for these conditions and that such an error causes significant price fluctuations on the market. By performing analyses of samples, both before and after drying, of the three different barley varieties Tipple, Prestige, and Quench, with all the techniques, data was obtained that could be analyzed statistically. The study showed that the NIT prediction model gives results for wet barley that is about 0.29 percentage points higher compared to dried barley. This difference was also statistically significant when a t-test was performed. The same difference was substantially smaller for Kjeldahl and Dumas and furthermore not statistically significant.When the difference between the different barley varieties is studied a deviation of 0.34 percentage points was obtained for Tipple, 0.18 percentage points for Prestige, and 0.30 percentage points for Quench. NIT, thereby, gave results with greatest deviation when wet Tipple was analyzed. When t-tests were performed for each barley variety, it was found that results for all varieties differ significantly between wet and dry barley.A study of the difference between the crude protein content determination by Kjeldahl and Dumas was also performed by calculating the mean differences between the various methods of determination. The average difference in crude protein determination was calculated to 0.07 percentage points.

Författare

Mikael Broberg

Lärosäte och institution

Linköpings universitet/Institutionen för fysik, kemi och biologi

Nivå:

"Kandidatuppsats". Självständigt arbete (examensarbete ) om minst 15 högskolepoäng utfört för att erhålla kandidatexamen.

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