The author presents his techniques of applying first-order perturbation theory of quantum mechanics to predict and build a postprandial plasma glucose (PPG) waveform based on the “perturbation factor” of carbs/sugar intake amount. This is a part of his GH-Method: math-physical medicine research methodology. Initially, he applied segmentation pattern analysis to analyze his 1,825 meals with 23,725 PPG Sensor data collected during a period of 5/5/2018 - 12/13/2019. His two segments are based on both “first factor” of meal’s carbs/sugar intake amounts and “second factor” of post-meal walking steps. His low-carb meals occupy about 2/3 of the total meals (1,209 meals with 8.5 grams per meal) and high-carb meals occupy about 1/3 of the total meals (615 meals with 27.1 grams per meal). His post-meal walking steps are comparable (4,238 vs. 4,282 steps). A standard waveform (curves) contains 13 data points for each PPG curve and one input data for each 15- minute time segment. Glucose variance is an extremely complex biochemical and biophysical phenomenon. After a diabetes patient collects and establishes an initial waveform with an accurate input dataset, we can then predict the glucose behavior and then draw a new approximate PPG waveform according to one prominent perturbation factor, such as carbs/sugar intake or post-meal exercise. Therefore, a patient will have the ability to predict his PPG behavior before consuming his meal or initiates his post-meal exercise.