基于近红外的柚子品种判别和糖度检测通用模型

李雄; 刘燕德; 欧阳爱国; 孙旭东; 胡军; 姜小刚; 欧阳玉平

doi:doi:10.3788/fgxb20194006.0808

发光学报, 2019, 40 (6): 808, 网络出版: 2019-09-03

基于近红外的柚子品种判别和糖度检测通用模型

A General Model for Judging and Brix Detection of Grapefruit Variety Based on Near Infrared

论文大纲

李雄刘燕德欧阳爱国孙旭东胡军姜小刚欧阳玉平

作者单位

华东交通大学机电与车辆工程学院, 江西南昌 330013

近红外光谱在线检测模型可溶性固形物品种判别 near-infrared spectroscopy on-line detection universal model soluble solids variety discrimination

摘要

针对厚皮果透光性差、不同柚子品种糖度在线检测要单独建模等问题, 本文以柚子为研究对象, 采集有效光谱325条, 对比分析不同柚子品种在710 nm和800 nm附近的两个吸收峰光谱响应特性。550～970 nm全波段范围内的光谱采用SPA无信息消除后建立柚子偏最小二乘判别模型误判率为1.25%; 偏最小二乘法在550～970 nm全波段范围和去差异化后750～930 nm波段范围的预测相关系数分别为0.58和0.86, 预测均方根误差RMSEP分别为0.84和0.55。实验结果表明, 连续投影法结合偏最小二乘判别模型可以实现不同柚子品种的定性判别, 变异系数法对光谱去差异化后结合最小二乘模型对厚皮果柚子可溶性固形物的定量检测效果最佳, 该研究为不同品种的厚皮果在线分选技术提供了参考和理论依据。

Abstract

In view of the poor light transmission of thick skin fruit and the independent modeling of the sugar content of different grapefruit varieties, the grapefruit was the research object, and 325 effective spectra were collected to compare and analyze the spectral responses of two absorption peaks of different grapefruit varieties near 710 nm and 800 nm. In the 550-970 nm full-band spectrum, the false positive rate of the grapefruit partial least squares discriminant model is 1.25% after SPA no information elimination; the partial least squares method is in the 550-970 nm full-band range and the de-differential 750-930 nm band. The prediction correlation coefficients of the range are 0.58 and 0.86, respectively, and the predicted root mean square error RMSEP is 0.84 and 0.55, respectively. Experiments show that the continuous projection method combined with the partial least squares discriminant model can realize the qualitative discrimination of different grapefruit varieties. The coefficient of variation method is the best to quantify the soluble solids of the thick fruit grapefruit with the least squares model. This study provides a reference and theoretical basis for the online sorting technology of different varieties of thick fruit.

参考文献

[1] 刘勇,周群,刘德春,等. 中国柚类生态分布多样性研究［J］. 江西农业大学学报, 2006,28(3):332-335.

LIU Y,ZHOU Q,LIU D C,et al.. A study on the ecological distribution of pummelo in China by means of AFLP ［J］. Acta Agric. Univ. Jiangxiensis, 2006,28(3):332-335. (in Chinese)

[2] 杨宁. 柚子全果综合利用及生物活性研究进展［J］. 广州化工, 2015,43(5):9-11.

YANG N. Research progress of comprehensive utilization and biological activity of grapefruit fruit ［J］. Guangzhou Chem. Ind., 2015,43(5):9-11. (in Chinese)

[3] KRINTIRAS G A,DIAZ J G,VAN DER GOOT A J,et al.. On the use of the Couette Cell technology for large scale production of textured soy-based meat replacers ［J］. J. Food Eng., 2016,169:205-213.

[4] 宋彩英,覃志豪. 吉安市井冈蜜柚产业发展研究［J］. 中国农业资源与区划, 2014,35(5):113-116.

SONG C Y,QIN Z H. Study on the development of Jinggang candied pomelo industry of Jian city ［J］. Chin. J. Agric. Resour. Reg. Plan., 2014,35(5):113-116. (in Chinese)

[5] 郭樱花,李艳梅. 梅州金柚品牌建设的经验与启示［J］. 现代农业科技, 2013(6):293-294.

GUOY H,LI Y M. Experience and enlightenment of brand construction of Meizhou golden pomelo ［J］. Mod. Agric. Sci. Technol., 2013(6):293-294. (in Chinese)

[6] 耿一曼. X射线无损检测技术在柚子品质检测中的应用研究［D］. 福州:福建农林大学, 2012.

GENG Y M. Application Study of X-ray NDT in Grapefruit Quality Testing ［D］. Fuzhou:Fujian Agriculture and Forestry University, 2012. (in Chinese)

[7] 颜静,唐成,梁亚雄,等. 柚子原产地溯源鉴定技术［J］. 食品科学, 2014,35(4):248-252.

YAN J,TANG C,LIANG Y X,et al.. Tracing the geographical origin of grapefruit ［J］. Food Sci., 2014,35(4):248-252. (in Chinese)

[8] 陈兆杰,黄慧俐,韦婕,等. 色谱-质谱联用技术在我国3种柚子组分鉴定及指纹图谱建立中的应用［J］. 色谱, 2016,34(6):558-566.

CHEN Z J,HUANG H L,WEI J,et al.. Application of chromatography-mass spectrometry technology in the component identification and fingerprint establishment of the three kinds of pomelo in China ［J］. Chin. J. Chromatogr., 2016,34(6):558-566. (in Chinese)

[9] 刘燕德,马奎荣,孙旭东,等. 梨和苹果糖度在线检测通用数学模型研究［J］. 光谱学与光谱分析, 2017,37(7):2177-2183.

LIU Y D,MA K R,SUN X D,et al.. The fruits soluble solids content detection online using universal mathematical model ［J］. Spectrosc. Spect. Anal., 2017,37(7):2177-2183. (in Chinese)

[10] 陆辉山,陈鹏强,闫宏伟,等. 基于近红外光谱漫透射技术的苹果可溶性固形物含量在线检测［J］. 食品与机械, 2015,31(3):40-42.

LU H S,CHEN P Q,YAN H W,et al.. On-line detection research of apples soluble solids content of diffuse transmission technology based on near infrared spectroscopy ［J］. Food Mach., 2015,31(3):40-42. (in Chinese)

[11] 刘燕德,陈兴苗,欧阳爱国. 可见/近红外光谱法无损检测赣南脐橙可溶性固形物［J］. 光学学报, 2008,28(3):478-481.

LIU Y D,CHEN X M,OUYANG A G. Non-destructive measurement of soluble solid content in Gannan navel oranges by visible/near-infrared spectroscopy ［J］. Acta Opt. Sinica, 2008,28(3):478-481. (in Chinese)

[12] 刘燕德,朱丹宁,吴明明,等. 玉露香梨可溶性固形物近红外漫透射光谱在线检测［J］. 食品与机械, 2016,32(10):115-119.

LIU Y D,ZHU D N,WU M M,et al.. Online detection of soluble solids contents for “Yuluxiang” pear by visible-near infrared diffuse transmission spectroscopy ［J］. Food Mach., 2016,32(10):115-119. (in Chinese)

[13] 王加华,韩东海. 便携式近红外水果糖度分析模型简化研究［J］. 食品安全质量检测技术, 2009,1(1):32-38.

WANG J H,HAN D H. Simplification of NIR model for determination of SSC in fruits by portable Vis/NIR instrument ［J］. Food Saf. Qual. Detect. Technol., 2009,1(1):32-38. (in Chinese)

李雄, 刘燕德, 欧阳爱国, 孙旭东, 胡军, 姜小刚, 欧阳玉平. 基于近红外的柚子品种判别和糖度检测通用模型[J]. 发光学报, 2019, 40(6): 808. LI Xiong, LIU Yan-de, OUYANG Ai-guo, SUN Xu-dong, HU Jun, JIANG Xiao-gang, OUYANG Yu-ping. A General Model for Judging and Brix Detection of Grapefruit Variety Based on Near Infrared[J]. Chinese Journal of Luminescence, 2019, 40(6): 808.

基于近红外的柚子品种判别和糖度检测通用模型

关于本站 Cookie 的使用提示

全站搜索

基于近红外的柚子品种判别和糖度检测通用模型

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索