Recently, we have developed a quantitative shotgun proteomics strategy called MAPA (mass accuracy precursor alignment). The MAPA algorithm uses high mass accuracy to bin mass-to-charge ratios (m/z) of precursor ions from LC-MS analyses, determine their intensities and to extract a quantitative sample versus m/z-ratio data alignment matrix from a multitude of samples. Here, we introduce a novel feature of this algorithm by allowing the extraction and alignment of proteotypic peptide precursor ions or any other target peptide from complex shotgun proteomics data for accurate quantification of unique proteins. This strategy circumvents the problem of confusing the quantification of proteins due to undistinguishable protein isoforms by a typical shotgun proteomics approach. We applied this strategy to a comparison of control and heat-treated tomato pollen grains at two developmental stages (post meiotic and mature). Pollen is a temperature-sensitive tissue involved in the reproductive cycle of plants and plays a major role for fruit setting and yield. By LC-MS based shotgun proteomics we identified more than 2000 proteins in total for all different tissues. By applying the targeted MAPA data processing strategy, 51 unique proteins were identified which present heat treatment responsive protein candidates. The potential function of the identified candidates in a specific developmental stage is discussed.