Many model based approaches have been proposed for a personalized insulin therapy in type 1 diabetes (T1D). These approaches rely on patient-specific models of the glucose metabolism which typically need to be identified on high quality data. However, patient data recorded in an at-home setting most often do not meet this criterion, since these are based, among others, on diary entries, which are often erroneous and incomplete. The problem is especially pronounced for recordings of meal intakes which are often accidentally omitted or recorded with wrong time stamps. This paper presents two methods for meal detection based on retrospective analysis of recorded glucose traces. The first method uses the typical signal features of postprandial glucose traces and simple heuristics to detect meals, whereas the second approach relies on similarity measures of glucose traces as compared to postprandial reference profiles. Matching the meal detection results of the algorithms with the actual patient diaries, the methods presented here can be used to find complete, high quality segments in at-home data. Being able to easily distinguish between high and low quality segments in such dataset is expected to improve the reliability of identified patient models.