Abstract: In a first aspect the present invention relates to a pellet mill (10) comprising a die (30) having a first surface (34) and a second surface (36), the die further comprising press holes (38) extending from the first surface to the second surface, at least one roller (50) configured to roll over the first surface for forcing a pelletizable material, forming a feed layer (2) on the first surface, through the press holes to the second surface, and a nozzle (82) configured to provide an additive fluid (4) directly into the nip (86) between the at least one roller and the first surface. In a second aspect the present invention relates to a method of producing pellets, and in a third aspect the present invention related to method of retrofitting a pellet mill.

Abstract: In a first aspect the present invention relates to a pellet mill (10) comprising a die (30) having a first surface (34) and a second surface (36), the die further comprising press holes (38) extending from the first surface to the second surface, at least one roller (50) configured to roll over the first surface for forcing a pelletizable material, forming a feed layer (2) on the first surface, through the press holes to the second surface, and a nozzle (82) configured to provide an additive fluid (4) directly into the nip (86) between the at least one roller and the first surface. In a second aspect the present invention relates to a method of producing pellets, and in a third aspect the present invention related to method of retrofitting a pellet mill.

Abstract: A method to determine viability or non-viability of seeds is disclosed, wherein individual seeds or seed populations are analysed by a spectrometric method to provide spectral data; and said spectral data are compared with reference spectral data obtained for a reference seed or reference seed population by means of said spectrometric method and calibrated with reference to viability or non-viability of said reference seed or seed population by means of a calibrating method based on pattern recognition. Suitable calibrating methods are based on multivariate or megavariate analysis, neural network systems and regression analysis.