基于光源发射接收一体化探头的糖度分选线改造研究

刘燕德; 饶宇; 孙旭东; 肖怀春; 姜小刚; 徐海; 李雄; 徐佳; 王观田

doi:10.37188/CO.2019-0165

Volume 13 Issue 4

Aug. 2020

Turn off MathJax

Article Contents

Chinese Optics > 2020 > 13(4): 795-804.

LIU Yan-de, RAO Yu, SUN Xu-dong, XIAO Huai-chun, JIANG Xiao-gang, XU Hai, LI Xiong, XU Jia, WANG Guan-tian. Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe[J]. Chinese Optics, 2020, 13(4): 795-804. doi: 10.37188/CO.2019-0165

Citation:

LIU Yan-de, RAO Yu, SUN Xu-dong, XIAO Huai-chun, JIANG Xiao-gang, XU Hai, LI Xiong, XU Jia, WANG Guan-tian. Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe[J]. Chinese Optics, 2020, 13(4): 795-804. doi: 10.37188/CO.2019-0165

Citation:

LIU Yan-de, RAO Yu, SUN Xu-dong, XIAO Huai-chun, JIANG Xiao-gang, XU Hai, LI Xiong, XU Jia, WANG Guan-tian. Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe[J]. Chinese Optics, 2020, 13(4): 795-804. doi: 10.37188/CO.2019-0165

PDF( 6550 KB)

Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe

doi: 10.37188/CO.2019-0165

Institute of Opto-Electronics Technology and Application, East China Jiaotong University, Nanchang 331300, China

Funds: Supported by National Natural Science Foundation of China (No.31760344); Subsidize Scheme for Outstanding Youth Talents of Jiangxi Province (No. 20171BCB23060); Photoelectric Detection Technology of Fruits’ Capacity Improvement Project (No. S2016-90)

More Information

Corresponding author: caeser-rao108@foxmail.com
Received Date: 12 Aug 2019
Rev Recd Date: 29 Sep 2019
Publish Date: 01 Aug 2020

Abstract

Abstract

Traditional quality sorting methods have been unable to meet people's increasing demands for fruit flavour and quality. Producers must therefore develop their traditional quality sorting methods to achieve sugar content sorting and ensure favourable flavour and quality. To address this, the near-infrared reflection spectra of navel oranges were collected separately through two different detection methods. The spectral energy of their ring transmission and diffuse reflection had to be stronger than that of the multi-point transmission and diffuse reflections. The positions of their peaks and troughs had to be approximately the same. The near-infrared diffuse reflectance spectra were preprocessed using baseline correction, multivariate scattering correction, first and second derivatives to reduce the influence of stray light and noise. A Partial Least Squares (PLS) model for the sugar content information that was collected through the two different reflection detection methods was established for their comparison and analysis. The experimental results show that the baseline correction preprocessing method produced the best results between the two methods. Its predicted correlation coefficient of sugar under ring transmission and diffuse reflection detection was 0.81 and its root mean square error was 0.46° Brix. The estimated correlation coefficient of the sugar content model using the multi-point transmission and diffuse reflection detection method was 0.76 and its root mean square error was 0.53° Brix. This research shows that it is feasible to use PLS modeling and near-infrared diffuse reflectance spectrum to upgrade the sugar content sorting methodology used on production lines.
- near-infrared diffuse reflectance spectroscopy,
- ring emission and reception,
- soluble solids content sorting,
- standard deviation,
- baseline correction

FullText(HTML)

References(21)

References

[1]	陈亚平, 杨保华. 食品动态检重秤控制器设计[J]. 衡器,2018,47(1):44-46. doi: 10.3969/j.issn.1003-5729.2018.01.012 CHEN Y P, YANG B H. Food dynamic check weighing scale controller design[J]. Weighing Instrument, 2018, 47(1): 44-46. (in Chinese) doi: 10.3969/j.issn.1003-5729.2018.01.012
[2]	陈孝照. 柑桔重量式分选机自动控制系统设计[J]. 福建农机,2015(3):9-12. doi: 10.3969/j.issn.1004-3969.2015.03.003 CHEN X ZH. Design of automatic control system for citrus weight sorting machine[J]. Fujian Agricultural Machinery, 2015(3): 9-12. (in Chinese) doi: 10.3969/j.issn.1004-3969.2015.03.003
[3]	张剑一. 动态称重数据处理算法及其在禽蛋和类球形水果分选中的应用研究[D]. 杭州: 浙江大学, 2017. ZHANG J Y. Dynamic weighing data processing algorithm and its application to poultry egg and spherical fruits sorting[D]. Hangzhou: Zhejiang University, 2017. (in Chinese)
[4]	高升, 王巧华, 李庆旭, 等. 基于近红外光谱的红提维生素C含量、糖度及总酸含量无损检测方法[J]. 分析化学,2019,47(6):941-949. GAO SH, WANG Q H, LI Q Y, et al. Non-destructive detection of vitamin C, sugar content and total acidity of red globe grape based on near-infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2019, 47(6): 941-949. (in Chinese)
[5]	李龙, 彭彦昆, 李永玉. 苹果内外品质在线无损检测分级系统设计与试验[J]. 农业工程学报,2018,34(9):267-275. doi: 10.11975/j.issn.1002-6819.2018.09.033 LI L, PENG Y K, LI Y Y. Design and experiment on grading system for online non-destructive detection of internal and external quality of apple[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(9): 267-275. (in Chinese) doi: 10.11975/j.issn.1002-6819.2018.09.033
[6]	赵环, 宦克为, 石晓光, 等. 基于自加权变量组合集群分析法的近红外光谱变量选择方法研究[J]. 分析化学,2018,46(1):136-142. ZHAO H, HUAN K W, SHI X G, et al. A variable selection method of near-infrared spectroscopy based on automatic weighting variable combination population analysis[J]. Chinese Journal of Analytical Chemistry, 2018, 46(1): 136-142. (in Chinese)
[7]	谢越, 李飞跃, 范行军, 等. 基于近红外光谱技术的生物炭组分分析[J]. 分析化学,2018,46(4):609-615. XIE Y, LI F Y, FAN X J, et al. Component analysis of biochar based on near infrared spectroscopy technology[J]. Chinese Journal of Analytical Chemistry, 2018, 46(4): 609-615. (in Chinese)
[8]	赵娟, 全朋坤, 马敏娟, 等. 富士苹果采收成熟度光谱无损预测模型对比分析[J]. 农业机械学报,2018,49(12):347-354. doi: 10.6041/j.issn.1000-1298.2018.12.041 ZHAO J, QUAN P K, MA M J, et al. Comparative analysis of harvest maturity model for Fuji apple based on visible/near spectral nondestructive detection[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(12): 347-354. (in Chinese) doi: 10.6041/j.issn.1000-1298.2018.12.041
[9]	胡士成, 白凯伦, 毛丽婷, 等. 苹果糖度的光谱模型温度补偿设计[J]. 食品安全质量检测学报,2018,9(11):2716-2721. doi: 10.3969/j.issn.2095-0381.2018.11.028 HU SH CH, BAI K L, MAO L T, et al. Design of spectral model for temperature compensation in measurement of sugar content in apples[J]. Journal of Food Safety &Quality, 2018, 9(11): 2716-2721. (in Chinese) doi: 10.3969/j.issn.2095-0381.2018.11.028
[10]	杨卫梅, 刘刚, 欧全宏, 等. 红外光谱结合曲线拟合对自然老化豆类种子的研究[J]. 分析化学,2019,47(12):2004-2011. YANG W M, LIU G, OU Q H, et al.. Study on natural aging legume seeds by infrared spectroscopy combined with curve fitting[J]. Chinese Journal of Analytical Chemistry, 2019, 47(12): 2004-2011. (in Chinese)
[11]	李梦依, 仓世龙, 孔寒, 等. 近红外光谱分析技术在玉米品质分析中的应用[J]. 山东化工,2019,48(3):68-71. LI M Y, CANG SH L, KONG H, et al. The application of near infrared spectroscopy in corn quality analysis[J]. Shandong Chemical Industry, 2019, 48(3): 68-71. (in Chinese)
[12]	史云颖, 李敬岩, 褚小立. 多元校正模型传递方法的进展与应用[J]. 分析化学,2019,47(4):479-487. SHI Y Y, LI J Y, CHU X L. Progress and application of multivariate calibration model transfer method[J]. Chinese Journal of Analytical Chemistry, 2019, 47(4): 479-487. (in Chinese)
[13]	SHAO W H, LI Y J, DIAO S F, et al. Rapid classification of Chinese quince (Chaenomeles speciosa Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration[J]. Analytical and Bioanalytical Chemistry, 2017, 409(1): 115-120. doi: 10.1007/s00216-016-9944-7
[14]	崔丰娟, 闸建文. 近红外透射苹果运动速度模型适用性的研究[J]. 农机化研究,2010,32(11):170-173. doi: 10.3969/j.issn.1003-188X.2010.11.042 CUI F J, XIA J W. Study of the applicability of the apple speed model by near-infrared transmission[J]. Journal of Agricultural Mechanization Research, 2010, 32(11): 170-173. (in Chinese) doi: 10.3969/j.issn.1003-188X.2010.11.042
[15]	章海亮, 孙旭东, 郝勇, 等. 近红外漫反射无损检测赣南脐橙中可溶性固形物和总酸[J]. 食品科学,2011,32(6):151-154. ZHANG H L, SUN X D, HAO Y, et al. Determination of soluble solids and total acidity in Gannan navel orange by near infrared diffuse reflection spectroscopy[J]. Food Science, 2011, 32(6): 151-154. (in Chinese)
[16]	刘燕德, 吴明明, 李轶凡, 等. 苹果可溶性固形物和糖酸比可见/近红外漫反射与漫透射在线检测对比研究[J]. 光谱学与光谱分析,2017,37(8):2424-2429. LIU Y D, WU M M, LI Y F, et al. Comparison of reflection and diffuse transmission for detecting solid soluble contents and ratio of sugar and acid in apples by on-line Vis/NIR spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(8): 2424-2429. (in Chinese)
[17]	SARANWONG S I, SORNSRIVICHAI J, KAWANO S. Performance of a portable near infrared instrument for Brix value determination of intact mango fruit[J]. Journal of Near Infrared Spectroscopy, 2003, 11(3): 175-181. doi: 10.1255/jnirs.364
[18]	WALSH K B, GOLIC M, GREENSILL C V. Sorting of fruit using near infrared spectroscopy: application to a range of fruit and vegetables for soluble solids and dry matter content[J]. Journal of Near Infrared Spectroscopy, 2004, 12(1): 141-148.
[19]	GREENSILL C V, WALSH K B. A remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit[J]. Measurement Science and Technology, 2000, 11(12): 1674-1684. doi: 10.1088/0957-0233/11/12/304
[20]	TSUTA M, YOSHIMURA M, KASAI S, et al. Prediction of internal flesh browning of “Fuji” apple using Visible-Near Infrared spectra acquired by a fruit sorting machine[J]. Japan Journal of Food Engineering, 2019, 20(1): 7-14. doi: 10.11301/jsfe.18530
[21]	DASZYKOWSKI M, SERNEELS S, KACZMAREK K, et al. TOMCAT: A MATLAB toolbox for multivariate calibration techniques[J]. Chemometrics and Intelligent Laboratory Systems, 2007, 85(2): 269-277. doi: 10.1016/j.chemolab.2006.03.006