The use of multivariate modelling of near infra-red spectra to predict the butter fat content of spreads.

In order to obtain a rapid method that can detect adulteration of butter fats with cheaper vegetable fats, the use of NIR spectroscopy and multivariate modelling was explored. For model building and validation, an extensive set of samples was collected, consisting of 152 butter samples, 42 oils and 200 blends thereof. Variations in butter fat composition are reflected in distinct NIR spectral regions. Principal components analysis and partial least square discriminant analysis was used to inspect the variation within the sample set. As reference values for training partial least squares models, butter fat levels as declared by suppliers were taken, as well as C4:0 fatty acid levels as measured directly by GC. All samples were used for training, except for 100 blends, which were used later for validation. Different pre-processing and PLS approaches were explored, resulting in models that had a RMSEPs for butter fat and C4:0 fatty acid level in the range of 4.3-8.2 and 0.33-0.38% (w/w), respectively. The performance of NIR in assessment of C4:0 fatty acid levels is lower as for GC, but this disadvantage is outweighed by shorter measurement times and the lower skill levels required. Furthermore NIR is able to assess overall levels of butter fat, in addition to the indirect indicator provided by the C4:0 fatty acid level.