  Singapore has been striving to improve the provision of healthcare services
to her people. In this course, the government has taken note of the deficiency
in regulating and supervising people's nutrient intake, which is identified as
a contributing factor to the development of chronic diseases. Consequently,
this issue has garnered significant attention. In this paper, we share our
experience in addressing this issue and attaining medical-grade nutrient intake
information to benefit Singaporeans in different aspects. To this end, we
develop the FoodSG platform to incubate diverse healthcare-oriented
applications as a service in Singapore, taking into account their shared
requirements. We further identify the profound meaning of localized food
datasets and systematically clean and curate a localized Singaporean food
dataset FoodSG-233. To overcome the hurdle in recognition performance brought
by Singaporean multifarious food dishes, we propose to integrate supervised
contrastive learning into our food recognition model FoodSG-SCL for the
intrinsic capability to mine hard positive/negative samples and therefore boost
the accuracy. Through a comprehensive evaluation, we present performance
results of the proposed model and insights on food-related healthcare
applications. The FoodSG-233 dataset has been released in
https://foodlg.comp.nus.edu.sg/.
