Near-infrared spectroscopy has been employed for determination of biochemicals in agricultural products. In this study, a prototype near infrared instrument is reported capable of simultaneously determining primary and secondary biochemical compounds in agricultural products using supervised modeling method. Maize flour was used as the material for model development. Calibration models were developed for four primary components (moisture, protein, oil, carbohydrate) and eight secondary compounds (amylose, amylopectin, oleic acid, linoleic acid, tocopherol, carotenoid, lysine, and tryptophan). Support vector machine, which is a supervised modeling method, was utilized to develop models. The graphical user interface of the prototype was developed using the R platform, which provides the user with flexible options for analysis and data management. The results obtained from the models generated with the prototype were compared those obtained from a bench-top near infrared reflectance instrument and reference analyses. The developed prototype was able to make measurements between 900 and 2100 nm with 6 nm resolution and completed each measurement in 60 s. The prototype demonstrated adequate performance in terms of repeatability and yielded comparable results with the bench-top instrument. The best calibration model for the prototype was obtained for carotenoids. This prototype is suitable for the determination of biochemicals in additional products after appropriate prediction models are prepared.