Recently the world population with diabetes has increased significantly, and the market demand for noninvasive blood glucose monitoring has increased accordingly. Our previous study demonstrated the capability to detect glucose through the direct observation of glucose Raman fingerprint peaks from in vivo skin but using a benchtop device. From the perspective of commercialization, miniaturized devices are expected to make more impact on the market than bulky benchtop devices. In this study, as an effort for commercialization of noninvasive glucose sensing technology, we investigate the relationship between Raman spectrometer specification, especially collection efficiency, and glucose prediction performance. Raman spectra were synthesized at given spectrometer collection efficiencies in computer simulation, in which spectra are designed to contain glucose signal at specific concentrations. Then, we estimated glucose concentrations back using regression analysis and evaluated prediction performances. Finally, the relationship was analyzed between the collection efficiencies and glucose prediction performances. In order to mimic actual conditions with skin tissue, Monte-Carlo simulations were conducted to count the number of Raman photons escaping from the skin surface in a multi-layered skin model. As the collection efficiency decreased from 3.2 % to 0.2 %, the correlation coefficient between the actual and predicted glucose concentrations dropped from 0.91 to 0.35. The glucose Raman peaks at 1125 cm-1 was identified as the most important wavelength for glucose sensing. This study may help identify optimal Raman spectrometer specifications for transcutaneous blood glucose sensing in miniaturized devices and commercialize noninvasive blood glucose sensors in Raman spectroscopy.