Assessment of Influence Detective Position Variability on Precision of Near Infrared Models for Soluble Solid Content of Watermelon

文献类型： 外文期刊

第一作者： Qian Man

作者： Qian Man;Fan Shu-xiang;Chen Li-ping;Qian Man;Huang Wen-qian;Wang Qing-yan;Fan Shu-xiang;Zhang Bao-hua;Chen Li-ping;Qian Man;Huang Wen-qian;Wang Qing-yan;Fan Shu-xiang;Zhang Bao-hua;Chen Li-ping;Qian Man;Huang Wen-qian;Wang Qing-yan;Fan Shu-xiang;Zhang Bao-hua;Chen Li-ping

作者机构：

关键词： Mini watermelon;Detective position;Near-infrared spectroscopy;Soluble solids content

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2016 年 36 卷 6 期

页码：

收录情况： SCI

摘要： Non-destructive detection for soluble solids content (SSC) is important to improve watermelon's internal quality, which attracts more and more attention from consumers. In order to realize the precise detection for SSC of mini watermelon's whole surface by using Near-infrared (NIR) spectroscopy and reduce the influence of detective position variability on the accuracy of NIR prediction model for SSC, the diffused transmission spectra and soluble solids content were collected from three different detective positions of 'jingxiu' watermelon, including the equator, calyx and stem. The prediction models of single detective position and mixed three detective positions for SSC were established with Partial least square (PLS). Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were adopted to select effective variables of NIR spectroscopy for SSC of watermelon as well. The results showed that the prediction model of mixed three detective positions was better than the model of single detective position. Meanwhile, 42 characteristic variables of NIR spectroscopy selected with CARS were used to establish PLS prediction model for SSC. The prediction model was simplified significantly and the prediction accuracy for SSC was improved greatly. The correlation coefficient of prediction (RP) and root mean square error of prediction (RMSEP) by CARS-PT.S were 0. 892, 0. 684 degrees Brix for the equator, 0. 905, 0. 621 degrees Brix for the calyx, 0. 899, 0. 721 degrees Brix for the stem, respectively. However, the prediction result of SPA-PLS established by 19 characteristic wavelength variables of NIR spectroscopy was bad for the equator, calyx and stem detective positions. The correlation coefficient of prediction (RP) is less than 0. 752 and root mean square error of prediction (RMSEP) is relatively high. It was proposed that the PLS prediction model established by mixed three different detective positions with effective characteristic wavelength variables selected by CARS can improve the prediction accuracy for SSC. And the CARS-PLS prediction model can achieve fast and precise detection for SSC of mini watermelon's whole surface. The influence of detective position variability on the accuracy of NIR prediction model could be reduced simultaneously. Thispaper could provide theoretical basis for calibrating NIR prediction model for SSC of mini watermelon. It also could provide reference for developing the portable and non-destructive detection equipment for soluble solids content of mini watermelon's whole surface.

分类号：

  • 相关文献

[1]Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging. Huang Wen-qian,Li Jiang-bo,Chen Li-ping,Guo Zhi-ming. 2013

[2]Comparative analysis of models for robust and accurate evaluation of soluble solids content in 'Pinggu' peaches by hyperspectral imaging. Chen, Liping. 2017

[3]Temperature Compensation for Portable Vis/NIR Spectrometer Measurement of Apple Fruit Soluble Solids Contents. Li Yong-yu,Wang Jia-hua,Qi Shu-ye,Tang Zhi-hui,Jia Shou-xing. 2012

[4]Characteristic Wavelengths Selection of Soluble Solids Content of Pear Based on NIR Spectral and LS-SVM. Fan Shu-xiang,Zhao Chun-jiang,Fan Shu-xiang,Huang Wen-qian,Li Jiang-bo,Zhao Chun-jiang,Zhang Bao-hua. 2014

[5]Near-Infrared Spectra Combining with CARS and SPA Algorithms to Screen the Variables and Samples for Quantitatively Determining the Soluble Solids Content in Strawberry. Li Jiang-bo,Guo Zhi-ming,Huang Wen-qian,Zhang Bao-hua,Zhao Chun-jiang. 2015

[6]Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi.

[7]Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging. Fan, Shuxiang,Huang, Wenqian,Guo, Zhiming,Zhang, Baohua,Zhao, Chunjiang,Fan, Shuxiang,Zhao, Chunjiang.

[8]A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. Li, Jiangbo,Huang, Wenqian,Zhao, Chunjiang,Zhang, Baohua.

[9]Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of 'Ya' Pear. Li, Jiangbo,Huang, Wenqian,Chen, Liping,Fan, Shuxiang,Zhang, Baohua,Guo, Zhiming,Zhao, Chunjiang,Li, Jiangbo.

[10]Feasibility of SSC Prediction for Navel Orange Based on Origin Recognition Using NIR Spectroscopy. Lyu, Qiang,Liao, Qiuhong,Liu, Yanli,Lyu, Qiang,Lan, Yubin. 2015

[11]Optimization of Informative Spectral Regions in FT-NIR Spectroscopy for Measuring the Soluble Solids Content of Apple. Wang, Jiahua,Liu, Haiying,Cheng, Jingjing,Cheng, Jingjing,Tang, Zhihui,Han, Donghai. 2015

[12]Application of Characteristic NIR Variables Selection in Portable Detection of Soluble Solids Content of Apple by Near Infrared Spectroscopy. Fan Shu-xiang,Zhao Chun-jiang,Fan Shu-xiang,Huang Wen-qian,Li Jiang-bo,Guo Zhi-ming,Zhao Chun-jiang. 2014

[13]Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple. Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng.

[14]Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple. Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang,Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang,Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang,Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang.

[15]The Characteristic Spectral Selection Method Based on Forward and Backward Interval Partial Least Squares. Qu Fang-fang,Ren Dong,Hou Jin-jian,Zhang Zhong,Wang Ji-hua,Hou Jin-jian,Lu An-xiang,Wang Ji-hua,Xu Hong-lei. 2016

[16]Simultaneous Determination of Instant Coffee, Plant Fat and Sugar Content in Liquid Coffee Beverage by Diffuse Reflectance Near-Infrared Spectroscopy. Wang Dong,Min Shun-geng,Duan Jia,Xiong Yan-mei,Li Qian-qian,Wang Dong,Wang Dong. 2012

[17]Preliminary Research on the Adaptability of NIR Quantitative Calibration Models for Metal Elements in Soil. Pan Li-gang. 2017

[18]Penetration Depth of Near-Infrared Light in Small, Thin-Skin Watermelon. Qian, Man,Chen, Liping,Fan, Shuxiang,Qian, Man,Wang, Qingyan,Chen, Liping,Huang, Wenqian,Fan, Shuxiang,Zhang, Baohua,Qian, Man,Wang, Qingyan,Chen, Liping,Huang, Wenqian,Fan, Shuxiang,Zhang, Baohua,Qian, Man,Wang, Qingyan,Chen, Liping,Huang, Wenqian,Fan, Shuxiang,Zhang, Baohua,Qian, Man,Wang, Qingyan,Chen, Liping,Huang, Wenqian,Fan, Shuxiang,Zhang, Baohua. 2016

[19]Near-infrared spectroscopy combined with equidistant combination partial least squares applied to multi-index analysis of corn. Lyu, Ning,Chen, Jiemei,Pan, Tao,Yao, Lijun,Han, Yun,Yu, Jing,Han, Yun,Yu, Jing. 2016

[20]Establishment and Application of Model for Determining Oil Content of Cottonseed Using Near Infrared Spectroscopy. Shang Lian-guang,Li Yu-hua,Wang Dan,Xiong Min,Hua Jin-ping,Shang Lian-guang,Li Yu-hua,Wang Dan,Xiong Min,Hua Jin-ping,Li Jun-hui,Wang Yu-mei. 2015

作者其他论文 更多>>

微信小程序