1. 江南大学机械工程学院
2. 江苏省食品先进制造装备技术重点实验室
纸质出版:2017
移动端阅览
[1]李剑,李臻峰,宋飞虎,步正延.基于近红外光谱的水蜜桃采摘期的鉴别方法[J].传感器与微系统,2017,36(10):48-50+56.
LI Jian, LI Zhen-feng, SONG Fei-hu, et al. Identification method of picking period of juicy peaches based on near infrared spectroscopy[J]. 2017, 36(10): 48-50+56.
[1]李剑,李臻峰,宋飞虎,步正延.基于近红外光谱的水蜜桃采摘期的鉴别方法[J].传感器与微系统,2017,36(10):48-50+56. DOI: 10.13873/J.1000-9787(2017)10-0048-03.
LI Jian, LI Zhen-feng, SONG Fei-hu, et al. Identification method of picking period of juicy peaches based on near infrared spectroscopy[J]. 2017, 36(10): 48-50+56. DOI: 10.13873/J.1000-9787(2017)10-0048-03.
提出了一种利用近红外漫反射光谱技术结合光纤传感技术建立水蜜桃采摘期的鉴别方法。从无锡阳山镇的某大棚采摘了距最佳采摘期天数为3
2
1以及处于最佳采摘期的水蜜桃各48个
用近红外光谱仪对样品进行了光谱采集。对原始光谱进行平滑、一阶微分和多元散射校正预处理
采用主成分分析(PCA)结合偏最小二乘(PLS)法建立了水蜜桃采摘期的鉴别模型。研究显示:一阶微分和平滑组合预处理后的鉴别模型效果最好
校正集模型和预测集模型的决定系数分别为0.927 9和0.913 8;模型的内部交叉验证均方差(RMSECV)和预测均方根偏差(RMSEP)分别为0.300 3和0.334 9;水蜜桃样品校正集和预测集的鉴别正确率分别为95.13%和93.75%。结果表明:利用近红外漫反射光谱技术对水蜜桃采摘期的鉴别具有很好的应用前景。
A near infrared diffuse spectroscopy technology combined with optical fiber sensing technology is used to explore nondestructive testing methods to identify the picking period of juicy peaches. 48 each of juicy peaches of 3
2 and 1 days from the best picking period and the day in the best picking period from a greenhouse in Yangshan town of Wuxi. The samples are collected by near infrared spectrometer. Three pre-processing methods i. e. smoothing
first derivative and mutiplicative scatter correction are used. Identification models of picking period are developed by principal component analysis( PCA) and partial least square( PLS) regression. The results show that first derivative and smoothing combination preprocessing construct the best predicted model. The correlation coefficient of calibration and validation model are 0. 927 9 and 0. 913 8 respectively. The root mean square error of cross validation( RMSECV) and the root mean square error of prediction( RMSEP) are 0. 3003 and 0. 3349 respectively. The recognition rates in the calibration set and prediction set of juicy peaches are 95. 13 % and93. 75 % respectively. Results shows that the method of using the near infrared spectroscopy technology to identify the picking period of juicy peaches has a very good application prospect.
基于近红外与高光谱技术的小麦种子多指标检测方法 [J]. 吴静珠,刘倩,陈岩,孙丽娟,张泽宇. 传感器与微系统 . 2016(07)
水蜜桃货架期内糖度的近红外光谱检测 [J]. 潘磊庆,刘明,韩东海,陈卓,屠康. 南京农业大学学报 . 2013(04)
基于ARM9的近红外山茶油无损检测仪研究 [J]. 刘莹,胡云龙. 传感器与微系统 . 2013(04)
近红外和高光谱技术用于小麦籽粒蛋白含量预测探索 [J]. 吴静珠,吴胜男,刘翠玲,陈兴海,高峰. 传感器与微系统 . 2013(02)
砂梨糖度近红外光谱波段遗传算法优化 [J]. 潘璐,王加华,李鹏飞,孙谦,张勇,韩东海. 光谱学与光谱分析 . 2009(05)
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苹果有效酸度的近红外漫反射无损检测 [J]. 应义斌,刘燕德,傅霞萍. 农业机械学报 . 2004(06)
近红外反射光谱快速评定玉米和小麦营养价值的研究 [D]. 李军涛. 中国农业大学 2014
近红外光谱分析基础与应用[M]. 中国轻工业出版社 , 严衍禄主编, 2005
Nondestructive determination of total and soluble solids in fresh prune using near infrared spectroscopy [J] . D.C. Slaughter,J.F. Thompson,E.S. Tan. Postharvest Biology and Technology . 2002 (3)
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