Near infrared reflectance spectroscopy was used to identify alternative seed meals proposed for food and feed formulations. Spectra were collected from cold pressed Camelina (Camelina sativa), Coriander (Coriandrum sativum), and Pennycress (Thlaspi arvense) meals. Additional spectra were collected from Dried Distillers Grains with Solubles (DDGS) which is an inexpensive co-product obtained from dry milling maize (Zea mays). Spectra were processed by multiplicative scatter correction and first derivative transform prior to principle component analysis (PCA). The PCA score plots showed separate groups for each material and identified potential groups for classification by linear discriminant analysis (LDA), soft independent modeling of class analogy (SIMCA), and support vector machine (SVM) methods. Results showed that LDA and SVM were both successful in classifying the type of source material while SIMCA was not able to correctly identify solvent extracted materials. The ability to rapidly and non-destructively confirm the identity and quality of components at the process line will promote the use of alternative seed meals to supplement commodity meals such as maize and soybean.