This research strives to implement strong analytical chemistry technique to improve near infrared (NIR) predictive modeling of bovine milk. This is done with orthogonal sample set design as well as sturdy reference chemistry. A method for improving the accuracy of enzymatic assays for chemical reference testing methods for measurement of lactose and milk urea nitrogen (MUN) concentration in milk through measurement and certification of cuvette path length was developed using a confocal displacement sensor. This new method nondestructive method to measure cuvette path length eliminates the need for use of potassium chromate. Partial least square predictive models for homogenized and unhomogenized milks were created for measurement of the concentration of fat, protein, lactose, and total solids using a commercial NIR instrument. The external validation performance of the NIR prediction models developed in our study exceeded all previously published NIR prediction models for fat, protein, lactose and total solids. These methods will aid in possible implementation of NIR milk analysis and for rapid in-line milk analysis.