The quality and freshness of vegetables not only affect the taste, but the nutrient content. The research on detection of chlorophyll and water content that are important reference indexes of vegetables quality and freshness has become more attention to the researchers both at home and abroad. With the quickness, high efficiency, non-destruction and non-contact features, the novel visible/near-infrared spectral analysis technology is more suitable for real-time detection of vegetables, comparing wtih traditional estimating methods by naked eyes. The relevant research is primarily focus on retrieval of growing vegetation chlorophyll and water content at present. There is little research aiming at ripe vegetables in market, or lacking of universality because of single species. Moreover, collecting of spectral data requires professional Field Spectrometer, wasting time and energy. There is a distance between research of physiological and biochemical index and practical application. In order to combining the research with the real life, this paper builds quickly, precise, universal models that can retrieve chlorophyll and water content in vegetables, based on Smart Cellphone Spectral System(SCSS). Simultaneously, SVC are used to validate the reliability of SCSS. Five kinds of common vegetables (spinach, rape, romaine, lettuce and baby cabbage) are selected as samples in experiment, and the ways of cold storage and normal temperature preservation are used to simulate the market and supermarket environment. Datas are collected per 24 hours. Then Band-Selecting and Wavelet-Transform preprocessing are adopted to improve the quality of spectral data. This paper constructs Vegetable Chlorophyll Retrieval Index (VCRI) and Vegetable Water Retrieval Index (VWRI), and extracts the correlation coefficients between the two indexes and measured values of chlorophyll and water content as weight coefficients. Finally, the chlorophyll and water content retrieval models are built. The result shows, SVC and SCSS have the same sensitive bands to chlorophyll and water content. The sensitive wavelength for chlorophyll retrieval is from 730 to 980 nm. The precision R2 are 0.863 and 0.8081, and standard deviation are 8.679 5 and 8.892 5 respectively. The sensitive wavelength for water content retrieval is from 950 to 1 000 nm. The precision R2 are 0.742 9 and 0.712 9, and standard deviation are 8.789 9% and 8.861 4% respectively. The result of SVC and SCSS is similar enough to prove the validation of new-style Smart Cellphone Spectral System. Furthermore, SCSS has the advantage of small size and low price. It can smartly detect the quality and freshness index of vegetables, with the features of internet cloud services and data feedback in real-time. This makes the spectral analysis technology applying to the people’s daily life.