基于可见/近红外透射光谱技术的红提糖度和含水率无损检测

高升; 王巧华

doi:10.37188/CO.2020-0085

基于可见/近红外透射光谱技术的红提糖度和含水率无损检测

doi: 10.37188/CO.2020-0085

高升^{1, 2,},
王巧华^{1, 2, ,}

1.
华中农业大学工学院，湖北武汉 430070
2.
农业部长江中下游农业装备重点实验室，湖北武汉 430070

基金项目: 国家自然科学基金资助项目（No. 31871863）；湖北省自然科学基金资助项目（No. 2012FKB02910）；湖北省研究与开发计划项目（No. 2011BHB016）

详细信息

作者简介:
高　升（1988—），男，山东临朐人，博士，2017年于青岛农业大学获得硕士学位，主要从事农产品无损智能检测、机电一体化技术及装备。Email：401116575@qq.com

王巧华（1970—），女，湖北黄梅人，博士，教授，2009年于华中农业大学获得博士学位，主要从事农畜禽产品无损智能检测、机电一体化技术及装备。E-mail：wqh@mail.hzau.edu.cn

中图分类号: O657
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出版历程
- 收稿日期: 2020-05-08
- 修回日期: 2020-06-15
- 网络出版日期: 2021-04-28
- 刊出日期: 2021-05-14

Non-destructive testing of red globe grape sugar content and moisture content based on visible/near infrared spectroscopy transmission technology

GAO Sheng^{1, 2
,},
WANG Qiao-hua^{1, 2
, ,}

1.
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2.
Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River; Ministry of Agriculture, Wuhan 430070, China

Funds: Supported by National Natural Science Foundation of China (No. 31871863); Natural Science Foundation of Hubei Province (No. 2012FKB02910); Research and Development Plan Project of Hubei Province(No. 2011BHB016)

More Information

Corresponding author: wqh@mail.hzau.edu.cn

摘要

摘要: 本文研究基于可见/近红外透射光谱技术的红提糖度和含水率的无损检测方法。采集360个红提样本，并分别利用标准正态变量变换（Standard Normal Variable transformation，SNV）、SavitZky-Golay卷积平滑处理法（SavitZky-Golay，S_G）等光谱预处理方法处理后的数据建立PLSR模型，分别采用一次降维（GA、SPA、CARS、UVE）和二次降维组合（CARS-SPA、UVE-SPA、GA-SPA）7种数据降维方法对光谱进行特征变量提取，分别建立红提糖度和含水率的偏最小二乘回归算法(Partial Least Squares Regression，PLSR)和最小二乘支持向量机（Least Squares Support Vector Machine，LSSVM）含量检测模型并对比分析模型的优劣。结果表明：红提糖度和含水率的最优PLSR模型波长提取方法为GA-SPA-PLSR，最优模型的预测集相关系数分别为0.958、0.938；红提糖度和含水率的最优LSSVM模型波长提取方法分别为CARS-SPA-LSSVM、UVE-SPA-LSSVM，最优模型的预测集相关系数分别为0.969、0.942；LSSVM所建模型的效果好于PLSR所建模型，但模型的运算时间较长。研究结果表明：基于可见/近红外技术无损检测红提糖度和含水率的方法可行，两种最优检测模型的预测精度均较高，都能满足检测要求。在不同应用下，可酌情选择不同模型，PLSR所建最优模型的运算时间较短，适合在线快速检测；LSSVM的检测性能最佳，可更加准确地检测红提糖度和含水率。
- 红提 /
- 糖度 /
- 含水率 /
- 可见/近红外技术 /
- 无损检测
Abstract: In this paper, a non-destructive detection method for the sugar and moisture content of red globe grapes based on visible/near-infrared spectroscopy transmission technology is studied. The PLSR model is established by collecting 360 red globe grape samples by using spectral data processed by spectral preprocessing methods such as Standard Normal Variable transformation (SNV), SavitZky-Golay(S_G) and other spectral preprocessing methods respectively to determine the best spectral preprocessing method. Seven data dimensionality reduction methods of primary dimensionality reduction (GA, SPA, CARS, UVE) and secondary dimensionality reduction combinations (CARS-SPA, UVE-SPA, GA-SPA) are used to identify characteristic variables of spectra. PLSR and LSSVM detection models of sugar content and moisture content of red globe grape are established respectively, and the advantages and disadvantages of each model are compared and analyzed. The results show that the optimal PLSR model wavelength extraction method for red globe grape sugar content and moisture content is GA-SPA-PLSR, and the correlation coefficients of the optimal model are 0.958 and 0.938, respectively. The optimal LSSVM model wavelength extraction methods for red globe grape sugar and moisture content are CARS-SPA-LSSVM and UVE-SPA-LSSVM, respectively. The correlation coefficients of the optimal model are 0.969 and 0.942, respectively. The model built using LSSVM is better than that built using PLSR, but its operation time is longer. The results also show that the non-destructive detection method of red globe grape sugar and moisture content based on visible/near-infrared technology is feasible, and the detection accuracy of both two optimal detection models is high, which can meet detection requirements. Different models can be selected for different applications. The optimal model built by PLSR has shorter computation time and is suitable for online rapid detection. LSSVM has the best detection performance and can accurately detect red globe grape sugar and moisture content.
- red globe grape /
- sugar content /
- moisture content /
- visible/near-infrared technology /
- non-destructive testing

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图 1 红提可见/近红外光谱采集系统图

Figure 1. Schematic of visible / near-infrared spectrum acquisition system for red globe grape

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图 2 红提样本的原始光谱

Figure 2. Original spectra of red globe grape samples

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图 3 红提糖度的GA特征波长选取图

Figure 3. GA characteristic wavelength selection map of sugar content of red globe grape

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图 4 红提糖度的SPA特征波长选取图

Figure 4. SPA characteristic wavelength selection map of red globe grape′s sugar content

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图 5 红提糖度的CARS特征波长选取图

Figure 5. CARS characteristic wavelength selection map of red globe grape′s sugar content

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图 6 红提糖度的UVE特征波长选取图

Figure 6. UVE characteristic wavelength selection map of red globe grape′s sugar content

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图 7 基于GA-SPA-PLSR红提糖度最优PLSR模型

Figure 7. Optimal PLSR model based on GA-SPA-PLSR for red globe grape′s sugar content

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图 8 基于GA-SPA-PLSR红提含水率最优PLSR模型

Figure 8. Optimal PLSR model based on GA-SPA-PLSR red globe grape′s moisture content

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图 9 基于CARS-SPA-LSSVM红提糖度最优LSSVM模型

Figure 9. Optimal LSSVM model based on CARS-SPA-LSSVM for red globe grape′s sugar content

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图 10 基于CARS-SPA-LSSVM红提含水率最优LSSVM模型

Figure 10. Optimal LSSVM model based on CARS-SPA-LSSVM for red globe grape′s moisture content

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表 1 原始光谱及采用不同预处理方法后建立的的全波长PLSR检测模型

Table 1. Original spectra and full-wavelength PLSR detection model established by different pretreatment methods

指标	预处理	LVs主因子数	校正集		预测集
指标	预处理	LVs主因子数	R_c	RMSEC	R_p	RMSEP
糖度	原始光谱	15	0.927	0.498	0.933	0.493
	SNV	19	0.954	0.412	0.907	0.468
	S_G	14	0.793	0.809	0.808	0.759
	Nor	20	0.957	0.401	0.878	0.516
含水率	原始光谱	15	0.901	0.549	0.868	0.780
	SNV	15	0.892	0.583	0.842	0.731
	Nor	15	0.893	0.603	0.832	0.719

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表 2 利用KS算法划分样本集的数据统计

Table 2. Data statistics of sample sets partitioned by KS algorithm

样本数量	指标	最小值	最大值	平均值	标准差S.D	变异系数C.V
校正集（270个）	糖度	16.8（°Brix）	24.0（°Brix）	20.2（°Brix）	1.334	6.595%
校正集（270个）	含水率	76.689%	84.327%	80.746%	1.268	1.570%
预测集（90个）	糖度	17.8（°Brix）	22.7（°Brix）	20.2（°Brix）	1.275	6.293%
预测集（90个）	含水率	76.635%	83.621%	80.582%	1.450	1.780%

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表 3 不同放置模式的全波长PLSR检测模型

Table 3. Full-wavelength PLSR detection models of samples with different placement modes

放置模式	指标	LVs主因子数	校正集		预测集
放置模式	指标	LVs主因子数	R_c	RMSEC	R_p	RMSEP
竖放	糖度	16	0.922	0.503	0.907	0.589
竖放	含水率	15	0.897	0.567	0.861	0.812
横放	糖度	15	0.908	0.576	0.890	0.614
横放	含水率	15	0.884	0.629	0.811	0.921
平均光谱	糖度	15	0.927	0.498	0.933	0.493
平均光谱	含水率	15	0.901	0.549	0.868	0.780

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表 4 基于特征波长建立的红提糖度和含水率PLSR检测模型

Table 4. PLSR detection models of red globe grape′s sugar and moisture content based on wavelength characteristics

指标	特征波长提取方法	波点个数	LVs主因子数	校正集		预测集		RPD
指标	特征波长提取方法	波点个数	LVs主因子数	R_c	RMSEC	R_p	RMSEP	RPD
糖度	原始光谱	1150	15	0.927	0.498	0.933	0.493	2.586
	GA	85	13	0.941	0.450	0.929	0.499	2.555
	SPA	17	15	0.889	0.610	0.921	0.527	2.419
	CARS	27	15	0.915	0.537	0.926	0.502	2.540
糖度	UVE	437	13	0.912	0.547	0.925	0.510	2.500
	CARS-SPA	9	5	0.896	0.592	0.919	0.529	2.410
	UVE-SPA	15	10	0.898	0.585	0.917	0.531	2.401
	GA-SPA	15	10	0.957	0.390	0.958	0.375	3.400
含水率	原始光谱	1150	15	0.901	0.549	0.868	0.780	1.860
	GA	78	10	0.900	0.551	0.892	0.728	1.992
	SPA	12	11	0.840	0.687	0.838	0.833	1.741
	CARS	27	15	0.901	0.549	0.868	0.780	1.859
	UVE	615	13	0.891	0.574	0.874	0.758	1.913
	CARS-SPA	11	11	0.819	0.726	0.848	0.858	1.690
	UVE-SPA	19	14	0.882	0.597	0.867	0.770	1.884
	GA-SPA	13	7	0.934	0.454	0.938	0.512	2.832

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表 5 红提糖度和含水率PLSR检测模型的最优特征波点列表

Table 5. List of optimal wave point characteristics of the sugar and moister content of PLSR detection model for red globe grapes

指标	建模方法	波长/nm
糖度（15个）	GA-SPA-PLSR	722.35、774.15、802.39、813.39、867.92、882.13、904.45、910.44、929.67、943.31、950.11、954.36、968.36、975.57、1002.59
含水率（13个）	GA-SPA-PLSR	750.06、799.03、825.98、835.52、859.73、863.61、869.64、878.26、904.02、909.58、913.01、947.56、967.09

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表 6 基于特征波长建立的红提糖度和含水率LSSVM检测模型

Table 6. LSSVM detection models of sugar and moisture content for red globe grapes based on wavelength characteristics

指标	特征波长提取方法	波点个数	γ	σ²	校正集		预测集		RPD
指标	特征波长提取方法	波点个数	γ	σ²	R_c	RMSEC	R_p	RMSEP	RPD
糖度	原始光谱	1150	606877.813	27698.587	0.976	0.296	0.937	0.451	2.827
	GA	85	486007.978	1715.475	0.964	0.354	0.940	0.441	2.891
	SPA	17	255631.106	269.184	0.946	0.436	0.905	0.544	2.344
	CARS	27	352524.566	442.091	0.944	0.442	0.941	0.434	2.938
	UVE	437	709628.506	8410.785	0.968	0.338	0.942	0.431	2.958
	CARS-SPA	9	493958.299	187.240	0.967	0.340	0.969	0.322	3.960
	UVE-SPA	15	394145.82	282.089	0.935	0.472	0.925	0.485	2.629
	GA-SPA	15	347263.829	384.322	0.935	0.473	0.937	0.449	2.839
含水率	原始光谱	1150	54302.715	46313.338	0.949	0.405	0.888	0.711	2.040
含水率	GA	78	351395.906	2351.707	0.931	0.465	0.899	0.683	2.124
含水率	SPA	12	224241.827	258.227	0.873	0.620	0.844	0.806	1.800
	CARS	27	454665.452	23000.299	0.962	0.350	0.891	0.686	2.114
	UVE	615	647436.819	22685.185	0.947	0.412	0.896	0.684	2.120
	CARS-SPA	11	751032.167	865.070	0.883	0.595	0.843	0.820	1.769
	UVE-SPA	19	606836.672	365.462	0.945	0.451	0.942	0.475	3.053
	GA-SPA	13	3888528.517	496.001	0.908	0.531	0.889	0.728	1.992

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表 7 红提糖度和含水率LSSVM检测模型的最优特征波点列表

Table 7. List of optimal wave point characteristics of the sugar and moisture content of LSSVM detection model for red globe grape

指标	建模方法	波长/nm
糖度（9个）	CARS-SPA-LSSVM	826.41、874.38、880.84、904.45、910.44、915.15、944.16、950.96、974.30
含水率（19个）	UVE-SPA-LSSVM	644.83、647.94、711.77、726.76、768.90、781.14、803.82、815.56、825.98、863.61、876.53、888.14、909.16、914.72、959.03、965.40、995.85、997.96、1032.01

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