Spectral Detection for Quality and Freshness Index of Main Leaf Vegetables Based on Smart Cellphone
JIAN Xun1, 2, ZHANG Li-fu1, YANG Hang1*, SUN Xue-jian1, DAI Shuang-feng1, ZHANG Hong-ming1, LI Jing-yi1
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：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.
Key words：Vegetable; Smart cellphone spectral system; Chlorophyll; Water content; Nondestructive detection in real-time
简 讯，张立福，杨 杭，孙雪剑，代双凤，张红明，李晶宜. 智能手机的主要叶类蔬菜品质和新鲜度指标的光谱检测[J]. 光谱学与光谱分析, 2019, 39(05): 1524-1529.
JIAN Xun, ZHANG Li-fu, YANG Hang, SUN Xue-jian, DAI Shuang-feng, ZHANG Hong-ming, LI Jing-yi. Spectral Detection for Quality and Freshness Index of Main Leaf Vegetables Based on Smart Cellphone. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1524-1529.
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