This paper proposes a novel pre-processing method based on combining bandpass filtering with scatter correction techniques Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) to enhance the prediction capability of the linear regression models Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) in near infrared (NIR) spectroscopy. The method is implemented into a calibration model, evaluated and then validated for the prediction of the glucose concentration from NIR spectra of an aqueous mixture of human serum albumin and glucose in a solution of distilled water and phosphate buffer. The results obtained demonstrate improved prediction performance for both PCR and PLSR. Compared to the efficient feature weighting pre-processing (RRelief), the proposed method is shown to yield better prediction reducing the Root Mean Square Error Prediction RMSEP.