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Near infrared spectroscopy (NIRS) is widely used for quantitative analysis of oilseeds in a non-destructive manner. Canola is a popular oilseed crop that is used for food and biofuel markets. Due to limited seed availability in plant breeding programs, single plant analysis is often preferred. The objective of this work was to i) evaluate NIRS commercial calibration model to predict 3 g canola seeds, and ii) to develop an NIRS calibration model to predict moisture content, oil content, and saturated fatty acid (palmitic and stearic acid) content, and oleic content for 3 g canola (Brassica napus) seeds utilizing the Micro Mirror Module by Perten Instruments. NIR spectra from 100 canola samples (3 g portions) were collected on a DA7200 NIR (Perten Instruments) spectrometer. Calibration models were developed by applying different pre-processing techniques (mean center, derivatives, variates) and models (PLS, PCR) using GRAMS software. The resulting models were evaluated for coefficient of determination (R2), standard error of prediction (SEP), and ratio performance deviation (RPD). The selected models were compiled into one calibration model, which was validated against 30 samples on a DA7200 NIR spectrometer. The results showed that the developed calibration model was able to accurately analyze canola constituents.