Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by e.g. high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters fat and protein content were estimated using partial least squares regression (PLSR). Performance of PLSR models from RS was compared with near infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross validation (R2CV) from 0.73 to 0.96 and root mean square error of cross validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R2CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). Performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability for RS to analyse bulk composition in heterogeneous food matrices, and paves way for future applications of RS in routine food analyses.