CN117054371A - 一种手持柑桔检测仪光谱信号校正方法 - Google Patents
一种手持柑桔检测仪光谱信号校正方法 Download PDFInfo
- Publication number
- CN117054371A CN117054371A CN202310674736.6A CN202310674736A CN117054371A CN 117054371 A CN117054371 A CN 117054371A CN 202310674736 A CN202310674736 A CN 202310674736A CN 117054371 A CN117054371 A CN 117054371A
- Authority
- CN
- China
- Prior art keywords
- detector
- matrix
- citrus
- near infrared
- infrared spectrum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 235000020971 citrus fruits Nutrition 0.000 title claims abstract description 33
- 241000207199 Citrus Species 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000003595 spectral effect Effects 0.000 title description 7
- 239000011159 matrix material Substances 0.000 claims abstract description 35
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 24
- 238000001228 spectrum Methods 0.000 claims abstract description 12
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 4
- 239000007787 solid Substances 0.000 claims description 10
- 238000013178 mathematical model Methods 0.000 claims description 5
- 239000013598 vector Substances 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 8
- 230000008859 change Effects 0.000 abstract description 5
- 235000013399 edible fruits Nutrition 0.000 abstract description 5
- 239000002420 orchard Substances 0.000 abstract description 5
- 238000011065 in-situ storage Methods 0.000 abstract description 3
- 238000004364 calculation method Methods 0.000 abstract 1
- 238000012630 chemometric algorithm Methods 0.000 abstract 1
- 238000010238 partial least squares regression Methods 0.000 description 3
- 230000002452 interceptive effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/127—Calibration; base line adjustment; drift compensation
- G01N2201/12746—Calibration values determination
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
本发明提供一种手持柑桔检测仪光谱信号校正方法,包括差谱矩阵生成、奇异值分解、校正系数计算、原建模近红外光谱数据校正、重新建模和模型应用。由于手持柑桔检测仪在果园原位检测中,日照强度随时间变化,而且阳光能穿透柑桔果实组织进入手持检测仪的检测器,引起果实近红外光谱数据的浮动变化,导致在实验室环境建立的近红外光谱检测模型,在果园环境中的检测精度下降,甚至不适用于果园环境。本发明从化学计量学算法的角度,提出光谱信号的校正方法,无需改变手持检测仪的软硬件,只要校正原建模用光谱数据,重新建模即可应用,方法简单实用。
Description
技术领域
本发明涉及水果品质无损检测领域,尤其涉及一种手持柑桔检测仪光谱信号校正方法。
背景技术
基于近红外光谱的手持检测仪,具有快速、无损、小巧、灵活等优点,为树上柑桔品质检测提供了原位检测手段。但在树上柑桔果实品质原位检测过程中,日照强度随时间动态变化,并且穿透果实组织进入手持检测仪的检测器,与柑桔的近红外光谱叠加在一起,引起柑桔的近红外光谱信号动态浮动,导致在实验室环境建立的数学模型,预测树上果实品质时,预测误差变大,最大可达2%以上。需要对果园环境中的手持柑桔检测仪近红外光谱信号动态校正,减少日照变化的影响,提高检测精度。在日照变化对手持检测仪近红外光谱信息影响方面进行光谱信号动态校正,目前未见相关报道。
发明内容
本发明在于提供一种手持柑桔检测仪光谱信号校正方法,减少阳光变化对手持检测仪的干扰。
本发明的技术方案是:一种手持柑桔检测仪光谱信号校正方法,包括以下步骤:
S1:采用手持检测仪,分别采集柑桔在树上和实验室内的近外光谱数据;在实验室内将柑桔破损、榨汁和过滤,采用阿贝折光仪测定可溶性固形物含量。将柑桔在树上的近红外光谱数据和实验室内的近红外光谱数据做差,构建差谱矩阵D;
S2:对差谱矩阵D及其转秩矩阵DT的乘积,进行奇异值分解(SVD),[U,S,V]=SVD(DDT);其中,U和V分别为特征向量和特征值,S为正交矩阵;
S3:计算校正系数矩阵P=I-VVT,I为单位矩阵,VT为V的转秩矩阵;
S4:校正原建模近红外光谱数据矩阵X,X*=XP,X*为校正后的近红外光谱矩阵;
S5:采用校正后的建模用近红外光谱矩阵X*和可溶性固形物真实值Y,进行偏最小二乘法建模;以交互验证均方根误差最小为准则,确定最佳主成分因子数和外部正交参数校正参数。
S6:将校正后的数学模型导入手持检测仪,预测树上柑桔的可溶性固形物含量;与阿贝折光仪分析结果对比验证。
与现有技术相比,本发明具备以下有益效果:
本发明从化学计量学软件算法角度,校正日光对近红外光谱信号的影响,不改动手持式仪器的软硬件系统,只需要重新加载校正后的数据模型即可,简单实用。
附图说明
图1为本发明实施例的一种手持柑桔检测仪光谱信号校正方法的工作流程图;
图2为柑桔在树上和实验室内的手持式柑桔检测仪近红外光谱信号曲线图及其差谱;
图3为校正前和校正后的偏最小二乘回归模型回归系数曲线;
图4为校正前和校正后的树上柑桔手持检测仪的预测结果对比。
具体实施方式
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。
实施例1:
如图1所示,本发明的技术方案是:一种手持柑桔检测仪光谱信号校正方法,步骤如下:
采用手持检测仪,在柑桔果树上选择20枚代表性果实,分别采集柑桔果实在树上(A20×151)和实验室内(B20×151)的近外光谱数据,其中,A20×151为树上柑桔的近红外光谱数据,20为样品数,151为手持柑桔检测仪的波长数;B20×151为采摘送至实验室采集的柑桔近红外光谱数据,20为样品数,151为手持柑桔检测仪的波长数。在实验室内将柑桔破损、榨汁和过滤,采用阿贝折光仪测定可溶性固形物含量Y20×1,其中20代表样品数,1代表可溶性固形物含量的维度。柑桔在树上和实验室内的近红外光谱数据做差(D20×151=A20×151-B20×151),生成差谱矩阵D20×151,如图2所示。
对差谱矩阵D20×151及其转秩矩阵DT 151×20的乘积,进行奇异值分解(SVD),[U151×151,S151×151,V151×151]=SVD(DTD151×151)。其中,U151×151和V151×151分别为特征向量和特征值,S151×151为正交矩阵。
取V151×g的前g个特征值,g的取值范围在1-20之间,g为本校正方法的参数,以1为步长进行循环。然后,计算校正系数矩阵P151×151=I151×151-VVT 151×151,I151×151为单位矩阵,VT g×151为V151×g的转秩矩阵。校正原建模近红外光谱数据矩阵X500×151,500为原建模集样品数量,151为手持仪波长数,X* 500×151=X500×151P151×151。X* 500×151为校正后的近红外光谱矩阵,500为原建模集的样品数,151为手持仪波长数,Y500×1为原建模集样品的可溶性固形物真实值,500为样品数,1为可溶性固形物数据的维度。在校正后的光谱数据X* 500×151和可溶性固形物真实值Y500×1之间,进行偏最小二乘回归,建立数学模型其中βi为偏最小二乘回归模型的回归系数,b为模型的截距,i为波长点数,Y为模型的预测值,其中校正前后的回归系数曲线,如图3所示:校正前模型的截距b为15.919,校正后模型的截距b为16.111。以留一法交互验证均方根误差最小为准则,确定最佳主成分因子数和外部正交参数校正参数g。
将校正后的数学模型导入手持检测仪,预测树上柑桔的可溶性固形物含量,未进行阳光影响校正的模型预测均方根误差为2.83%,校正后模型的预测均方根误差为0.50%,如图4所示。
本发明实施例通过校正方法,减少了外界阳光照射信号的影响,提高了手持检测仪在果园环境中的检测精度,不需改变手持式仪器软硬件。针对同款手持仪器用于树上柑桔品质检测,只需要用本例中校正系数矩阵,校正原建模集光谱矩阵重新建模即可。如其它型号手持检测仪,则需要重新生成差谱矩阵,进行校正。
值得注意的是,差谱矩阵生成过程中,代表性柑桔样品数量,可以根据实际情况灵活选择。
上述实例仅为本发明的较佳实例而已。对本领域的技术人员,在不脱离本发明的原理和精神的情况下,可以对实施例进行变化、替换、改进和变型。
Claims (1)
1.一种手持柑桔检测仪光谱信号校正方法,其特征在于,包括以下步骤:
S1:采用手持检测仪,分别采集柑桔在树上和实验室内的近外光谱数据;将柑桔在树上的近红外光谱数据和实验室内的近红外光谱数据做差,构建差谱矩阵D;
S2:对差谱矩阵D及其转秩矩阵DT的乘积,进行奇异值分解(SVD),[U,S,V]=SVD(DDT);其中,U和V分别为特征向量和特征值,S为正交矩阵;
S3:计算校正系数矩阵P=I-VVT,I为单位矩阵,VT为V的转秩矩阵;
S4:校正原建模近红外光谱数据矩阵X,X*=XP,X*为校正后的近红外光谱矩阵;
S5:采用校正后的建模用近红外光谱矩阵X*和可溶性固形物真实值Y,进行偏最小二乘法建模;
S6:将校正后的数学模型导入手持检测仪,预测树上柑桔的可溶性固形物含量。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310674736.6A CN117054371A (zh) | 2023-06-08 | 2023-06-08 | 一种手持柑桔检测仪光谱信号校正方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310674736.6A CN117054371A (zh) | 2023-06-08 | 2023-06-08 | 一种手持柑桔检测仪光谱信号校正方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
CN117054371A true CN117054371A (zh) | 2023-11-14 |
Family
ID=88665139
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310674736.6A Pending CN117054371A (zh) | 2023-06-08 | 2023-06-08 | 一种手持柑桔检测仪光谱信号校正方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN117054371A (zh) |
-
2023
- 2023-06-08 CN CN202310674736.6A patent/CN117054371A/zh active Pending
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cogdill et al. | Single-kernel maize analysis by near-infrared hyperspectral imaging | |
Xue et al. | Application of particle swarm optimization (PSO) algorithm to determine dichlorvos residue on the surface of navel orange with Vis-NIR spectroscopy | |
CN101915744A (zh) | 物质成分含量的近红外光谱无损检测方法及装置 | |
CN111678599B (zh) | 基于深度学习优化s-g滤波的激光光谱降噪方法及装置 | |
CN108152235B (zh) | 一种联合土壤室内外光谱的重金属含量反演方法 | |
CN111855608B (zh) | 基于融合特征波长选择算法的苹果酸度近红外无损检测方法 | |
CN116738153B (zh) | 基于光谱分析的有机肥利用效果评价方法 | |
CN109060760B (zh) | 分析模型建立方法、气体分析装置及方法 | |
CN110503156B (zh) | 一种基于最小相关系数的多变量校正特征波长选择方法 | |
CN103543132A (zh) | 一种基于小波变换的煤质特性测量方法 | |
CN109520941B (zh) | 在线光谱测量仪器的响应函数校正方法 | |
CN114611582A (zh) | 一种基于近红外光谱技术分析物质浓度的方法及系统 | |
CN114216877A (zh) | 茶叶近红外光谱分析中谱峰自动检测与重构方法及系统 | |
CN113447452A (zh) | 一种用于绝缘纸光谱的水分影响因素校正方法及系统 | |
CN108120694B (zh) | 用于晒红烟化学成分分析的多元校正方法及系统 | |
CN113030011A (zh) | 水果糖度快速无损检测方法及检测系统 | |
CN110186870B (zh) | 一种极限学习机光谱模型判别恩施玉露茶鲜叶产地的方法 | |
CN117054371A (zh) | 一种手持柑桔检测仪光谱信号校正方法 | |
Liu et al. | Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data. | |
CN110887798A (zh) | 基于极端随机树的非线性全光谱水体浊度定量分析方法 | |
CN115266583A (zh) | 环境光滤除方法、系统、计算机设备及计算机可读存储介质 | |
CN113795748A (zh) | 用于配置光谱测定装置的方法 | |
Esteban-Díez et al. | GA-ACE: Alternating conditional expectations regression with selection of significant predictors by genetic algorithms | |
CN117805024B (zh) | 一种酥梨糖精度检测方法、装置、云端设备及计算机装置 | |
CN116952893B (zh) | 一种近红外检测猪粪堆肥过程中的腐殖化程度的方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |