Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple

文献类型: 外文期刊

第一作者: Fan, Shuxiang

作者: 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

作者机构:

关键词: Apple;Soluble solids content;Fruit orientation;Diffuse transmittance;Variable selection;Area change rate

期刊名称:FOOD ANALYTICAL METHODS ( 影响因子:3.366; 五年影响因子:3.07 )

ISSN:

年卷期:

页码:

收录情况: SCI

摘要: The objectives of this research were to compare the effect of different fruit orientations on the quality of acquired spectra and to provide a suitable calibration model for further online determination of soluble solids content (SSC) of "Fuji" apples using visible and near-infrared (Vis/NIR) diffuse transmittance. The diffuse transmittance spectra between 650 and 910 nm were collected with the designed spectrum measurement system in two fruit orientations: stem-calyx axis horizontal (T1) and stem-calyx axis vertical (T2). Area change rate (ACR) was used to evaluate the stability of spectra collected in two fruit orientations. Results showed that the fruit orientation T1 was better for our designed spectrum measurement system. Then, the performance of partial least squares (PLS) models based on spectral data after the pretreatment of several preprocessing methods was analyzed and compared. Finally, the modified competitive adaptive reweighted sampling (MCARS), successive projection algorithm (SPA), and their combination were investigated to select the effective variables for the determination of SSC. It concluded that the MCARS-SPA-PLS model based on the spectra after preprocessing of Savitzky-Golay (SG) smoothing achieved better results for SSC prediction. The correlation coefficients between measured and predicted SSC were 0.962 and 0.946, and the root mean square errors were 0.510 and 0.527A degrees Brix for calibration and prediction set, respectively. Moreover, the physicochemical properties of 27 variables selected by MCARS-SPA were discussed to obtain a better interpretation of the calibration model. The overall results indicated that the designed diffuse transmittance spectrum measurement system together with the PLS calibration model with 27 effective variables selected by MCARS-SPA method had a potential application for online SSC detection of apple.

分类号: TS2

  • 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]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

[3]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

[4]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.

[5]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.

[6]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

[7]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.

[8]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

[9]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.

[10]An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor. Qu, Fangfang,Ren, Dong,Wang, Jihua,Zhang, Zhong,Lu, Na,Meng, Lei,Wang, Jihua. 2016

[11]Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten. Lu, Chengxu,Zhang, Yinqiao,Wei, Chongfeng,Mao, Wenhua,Jiang, Xunpeng,Zhou, Xingfan,Zhang, Naiqian. 2017

[12]Near-Infrared Hyperspectral Imaging Combined with CARS Algorithm to Quantitatively Determine Soluble Solids Content in "Ya" Pear. Li Jiang-bo,Chen Li-ping,Huang Wen-qian,Peng Yan-kun. 2014

[13]Variable Selection Based Cotton Bollworm Odor Spectroscopic Detection. Lu, Chengxu,Gai, Shasha,Luo, Min,Zhao, Bo. 2016

[14]FOURIER TRANSFORM MID-INFRARED PHOTOACOUSTIC SPECTROSCOPY (FTIR-PAS) COUPLED WITH CHEMOMETRICS FOR NON-DESTRUCTIVE DETERMINATION OF OIL CONTENT IN RAPESEED. Lu, Y.,Du, C.,Yu, C.,Zhou, J..

[15]Study on Disease Level Classification of Rice Panicle Blast Based on Visible and Near Infrared Spectroscopy. Wu Di,Cao Fang,Sun Guang-ming,Feng Lei,He Yong,Zhang Hao. 2009

[16]Application of Long-Wave Near Infrared Hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear. Li, Jiangbo,Tian, Xi,Huang, Wenqian,Zhang, Baohua,Fan, Shuxiang,Li, Jiangbo,Tian, Xi,Huang, Wenqian,Zhang, Baohua,Fan, Shuxiang,Li, Jiangbo,Huang, Wenqian,Li, Jiangbo,Huang, Wenqian.

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

[18]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

[19]Assessment of Influence Detective Position Variability on Precision of Near Infrared Models for Soluble Solid Content of Watermelon. 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. 2016

[20]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.

作者其他论文 更多>>