基于近红外光谱技术建立橡胶草根部菊糖和总糖含量快速检测方法

doi:10.6048/j.issn.1001-4330.2025.05.007

新疆农业科学 ›› 2025, Vol. 62 ›› Issue (5): 1102-1110.DOI: 10.6048/j.issn.1001-4330.2025.05.007

• 作物遗传育种·耕作栽培·生理生化 • 上一篇下一篇

基于近红外光谱技术建立橡胶草根部菊糖和总糖含量快速检测方法

陈润峰¹^,²(), 高强¹(), 严青青¹, 徐麟¹, 吐汗姑丽·托合提¹, 张龑¹(), 郑立鹏¹, 任海龙³, 聂秋海⁴

1.新疆维吾尔自治区农业科学院作物研究所/国家中亚特色作物种质资源中期库(乌鲁木齐),乌鲁木齐 830091
2.新疆农业大学农学院,乌鲁木齐 830052
3.广东省农业科学院作物科学研究所,广州 510335
4.北京玲珑蒲公英科技发展有限公司,北京 101102

收稿日期:2024-10-20 出版日期:2025-05-20 发布日期:2025-07-09
通信作者: 高强(1988-),男,四川资阳人,副研究员,硕士,研究方向为种质资源收集、评价与创新利用,(E-mail)blueskysmallfish@163.com;
张龑(1981-),男,新疆石河子人,高级农艺师,硕士,研究方向为种质资源收集、评价与创新利用,(E-mail)251717640@qq.com
作者简介:陈润峰(1998-),男,河南郑州人,硕士研究生,研究方向为种质资源收集与创新, (E-mail)1063554708@qq.com
基金资助:
“天山英才”培养计划项目“橡胶草种质资源挖掘与创新利用研究”(2023TSYCLJ0010);新疆农业科学院青年科技骨干创新能力培养项目“栽培条件下不同生态型橡胶草橡胶积累规律研究”(xjnkq-2023016);新疆维吾尔自治区自然科学基金青年基金“基于近红外光谱技术建立蒲公英橡胶草橡胶检测模型的研究”(2022D01B33);新疆农业科学院科技创新稳定支持项目“耐盐橡胶草种质资源筛选及优异基因挖掘”(xjnkywdzc-2023001-28);新疆农业科学院自主培育项目“新疆农作物种质资源优异特性挖掘与利用”(nkyzzkj-008);新疆农业科学院自主培育项目“农作物种质资源繁殖与利用团队”(nkyzztd-004)

A rapid determination method for inulin and total sugar content in araxacum kok-saghyz Rodin based on near-infrared spectroscopy

CHEN Runfeng¹^,²(), GAO Qiang¹(), YAN Qingqing¹, XU Lin¹, Tuhanguli Touheti¹, ZHANG Yan¹(), ZHENG Lipeng¹, REN Hailong³, NIE Qiuhai⁴

1. Institute of Crop Xinjiang Uyghur Asuronomous Region Academy of Agricultural Sciences,National Central Asian Characteristic Crop Germplasm Resources Medium-term Gene Bank (Urumqi), Urumqi 830091, China
2. College of Agronomy, Xinjiang Agricultural University, Urumqi 830091, China
3. Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510308, China
4. Beijing Linglong Tyre Company Limited, Beijing 101102, China

Received:2024-10-20 Published:2025-05-20 Online:2025-07-09
Supported by:
“Exploration and Innovative Utilization of Germplasm Resources in Taraxacum kok-saghyz Rodin”(2023TSYCLJ0010);“Study on the Accumulation Regularity of Rubber in Different Ecotypes of Taraxacum kok-saghyz Rodin under Cultivation Conditions”(xjnkq-2023016);“Establishment of a Rubber Detection Model for Taraxacum kok-saghyz Rodin based on Near-Infrared Spectroscopy”(2022D01B33);“Screening of Salt-Tolerant Germplasm Resources in Taraxacum kok-saghyz Rodin and Excavation of Superior Genes”(xjnkywdzc-2023001-28);“Exploration and Utilization of Superior Traits in Xinjiang Crop Germplasm Resources”(nkyzzkj-008);Independent Cultivation Project of Xinjiang Academy of Agricultural Sciences "Crop Germplasm Resources Propagation and Utilization Team"(nkyzztd-004)

摘要/Abstract

摘要：

【目的】基于近红外光谱技术建立橡胶草根部菊糖和总糖含量绿色、低成本的快速检测技术。【方法】以103个橡胶草干根粉末样品作为研究对象,使用福斯公司的FOSS NIRS^TM DS2500F SR近红外光谱仪(850~2500nm)采集样品光谱,使用酶标仪法检测样品根部的总糖、还原糖含量,并使用总糖含量减去还原糖含量计算出菊糖含量,采用SPXY算法划分训练集和验证集。用移动窗口平滑(MWS)、标准化(SNV)、多元散射校正(MSC)和一阶导数(FD)算法对原始光谱进行预处理,使用竞争性自适应重加权采样法(CARS)筛选波长,建立橡胶草根部菊糖和总糖含量的PLS预测模型。【结果】检测样品集总糖、还原糖和菊糖含量的区间分别为9.79%~51.85%、3.11%~8.98%和6.41%~45.35%,变异系数分别为34.18%、32.61%和39.46%。使用SPXY算法将校正集和验证集按照4∶1的比例划分,总糖和菊糖的校正集、验证集的个数均为82和21个。使用划分后的样品集进行模型建立,最优的菊糖含量PLS预测模型使用的光谱预处理方法为MWS-SNV-CARS,其验证集相关系数R_v达到了0.942,验证集均方根误差RMSEv为2.515,验证集相对分析误差RPDv为2.977;最优的总糖含量PLS预测模型使用的光谱预处理方为MWS-SNV-CARS,其R_v达到了0.949,RMSEv为2.490,RPDv为3.175。2个模型的RPDv均大于2.5,性能较佳。【结论】使用近红外光谱技术能够实现对橡胶草根部菊糖和总糖的绿色、低成本的快速定量检测。

关键词: 橡胶草; 近红外光谱; 菊糖; 总糖; 快速检测; PLS

Abstract:

【Objective】 This study aims to develop a green, low-cost, rapid detection technique for inulin and total sugar content in the roots of Taraxacum kok-saghyz Rodin(TKS) using near-infrared (NIR) spectroscopy, thereby advancing research in cultivation and breeding of TKS.【Methods】 This study involved 103 dry root powder samples of TKS (Rubber Grass). Spectra were collected using the FOSS NIRSTM DS2500F SR near-infrared spectrometer (wavelength range 850-2500 nm) by Foss Company. Enzyme labeling method was employed to measure the total sugar and reducing sugar content in the root samples and the inulin content was calculated by subtracting the reducing sugar content from the total sugar content. The SPXY algorithm was utilized to divide the samples into training and validation sets. Pretreatments on the raw spectral data included Moving Window Smoothing (MWS), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and First Derivative (FD). Competitive Adaptive Reweighted Sampling (CARS) was applied for wavelength selection, followed by establishing Partial Least Squares (PLS) prediction models for inulin and total sugar content in TKS roots.【Results】 The content range of total sugar, reducing sugar, and inulin in the sample set was 9.79%-51.85%, 3.11%-8.98%, and 6.41%-45.35%, respectively, with variation coefficients of 34.18%, 32.61%, and 39.46%. The samples were divided into calibration and validation sets at a ratio of 4∶1 using the SPXY algorithm, with each set containing 82 and 21 samples for both total sugar and inulin. The optimal PLS prediction model for inulin content utilized MWS-SNV-CARS preprocessing, achieving a validation set correlation coefficient (Rv) of 0.942, a root mean square error (RMSEv) of 2.515, and a relative performance deviation (RPDv) of 2.977. For total sugar, the best PLS model also employed MWS-SNV-CARS preprocessing, reaching an Rv of 0.949, RMSEv of 2.490, and RPDv of 3.175. Both models exhibited RPDv values exceeding 2.5, indicating good performance. 【Conclusion】 NIR spectroscopy proves to be an effective method for the green, low-cost, and rapid quantitative determination of inulin and total sugar contents in the roots of TKS.

Key words: Taraxacum kok-saghyz Rodin; near-infrared spectroscopy; inulin; total sugar; rapid detection; partial least squares

中图分类号:

陈润峰, 高强, 严青青, 徐麟, 吐汗姑丽·托合提, 张龑, 郑立鹏, 任海龙, 聂秋海. 基于近红外光谱技术建立橡胶草根部菊糖和总糖含量快速检测方法[J]. 新疆农业科学, 2025, 62(5): 1102-1110.

CHEN Runfeng, GAO Qiang, YAN Qingqing, XU Lin, Tuhanguli Touheti, ZHANG Yan, ZHENG Lipeng, REN Hailong, NIE Qiuhai. A rapid determination method for inulin and total sugar content in araxacum kok-saghyz Rodin based on near-infrared spectroscopy[J]. Xinjiang Agricultural Sciences, 2025, 62(5): 1102-1110.

图/表 8

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组分 Composition	样品数 Number of samples (个)	最大值 Max (%)	最小值 Minimum (%)	平均值 Mean (%)	标准偏差 Standard deviation	变异系数 CV(%)
总糖 Total Sugar	103	51.85	9.79	24.66	8.43	34.18
还原糖 Reducing Sugar	103	8.98	3.11	4.63	1.51	32.61
菊糖 Inulin	103	45.35	6.41	19.99	7.89	39.46

组分 Composition	样品数 Number of samples (个)	最大值 Max (%)	最小值 Minimum (%)	平均值 Mean (%)	标准偏差 Standard deviation	变异系数 CV(%)
总糖 Total Sugar	103	51.85	9.79	24.66	8.43	34.18
还原糖 Reducing Sugar	103	8.98	3.11	4.63	1.51	32.61
菊糖 Inulin	103	45.35	6.41	19.99	7.89	39.46

组分 Composition	样品集 Sample set	样品数 Number of samples (个)	最大值 Max (%)	最小值 Minimum (%)	平均值 Mean (%)	标准偏差 Standard deviation	变异系数 CV(%)
菊糖 Inulin	校正集	82	45.35	6.41	19.50	7.86	40.32
菊糖 Inulin	验证集	21	41.80	8.28	21.90	7.88	36.00
总糖 Total Sugar	校正集	82	51.85	9.79	24.27	8.51	35.08
总糖 Total Sugar	验证集	21	45.83	11.59	26.17	8.10	30.95

组分 Composition	样品集 Sample set	样品数 Number of samples (个)	最大值 Max (%)	最小值 Minimum (%)	平均值 Mean (%)	标准偏差 Standard deviation	变异系数 CV(%)
菊糖 Inulin	校正集	82	45.35	6.41	19.50	7.86	40.32
菊糖 Inulin	验证集	21	41.80	8.28	21.90	7.88	36.00
总糖 Total Sugar	校正集	82	51.85	9.79	24.27	8.51	35.08
总糖 Total Sugar	验证集	21	45.83	11.59	26.17	8.10	30.95

组分 Composition	光谱处理方法 Pretreatment method	主成分数 PCs	校正集 Calibration		验证集 Validation		RPDv
组分 Composition	光谱处理方法 Pretreatment method	主成分数 PCs	Rc	RMSEc	Rv	RMSEv	RPDv
菊糖 Inulin	无	10	0.858	4.005	0.888	3.545	2.170
	MWS	10	0.858	4.006	0.888	3.543	2.171
	MWS-SNV	10	0.862	3.995	0.925	2.841	2.635
	MWS-MSC	10	0.856	4.045	0.901	3.314	2.322
	MWS-FD	10	0.879	3.719	0.873	3.752	2.050
	MWS-SNV-FD	10	0.874	3.798	0.854	4.003	1.922
	MWS-MSC-FD	10	0.874	3.798	0.853	4.022	1.913
	MWS-SNV-MSC--FD	10	0.874	3.798	0.853	4.021	1.913
总糖 Total Sugar	无	10	0.867	4.218	0.905	3.367	2.348
	MWS	10	0.867	4.219	0.905	3.368	2.348
	MWS-SNV	10	0.868	4.210	0.939	2.720	2.902
	MWS-MSC	10	0.857	4.355	0.919	3.113	2.540
	MWS-FD	10	0.884	3.960	0.892	3.576	2.211
	MWS-SNV-FD	10	0.878	4.053	0.876	3.819	2.070
	MWS-MSC-FD	10	0.878	4.052	0.873	3.848	2.054
	MWS-SNV-MSC--FD	10	0.878	4.052	0.873	3.848	2.054

基于近红外光谱技术建立橡胶草根部菊糖和总糖含量快速检测方法

A rapid determination method for inulin and total sugar content in araxacum kok-saghyz Rodin based on near-infrared spectroscopy

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参考文献 27

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Metrics

组分 Composition	光谱处理方法 Pretreatment method	主成分数 PCs	波长筛选方法 Spectral wavelength selection method	校正集 Calibration		验证集 Validation		RPD_v
组分 Composition	光谱处理方法 Pretreatment method	主成分数 PCs	波长筛选方法 Spectral wavelength selection method	R_c	RMSEc	R_v	RMSEv	RPD_v
菊糖 Inulin	MWS-SNV	10	全波段	0.862	3.995	0.925	2.841	2.635
	MWS-SNV	10	CARS	0.844	4.226	0.942	2.515	2.977
	MWS-MSC	10	全波段	0.856	4.045	0.901	3.314	2.322
	MWS-MSC	10	CARS	0.838	4.304	0.922	2.898	2.583
	MWS-FD	10	全波段	0.879	3.719	0.873	3.752	2.050
	MWS-FD	10	CARS	0.873	3.851	0.933	2.684	2.789
总糖 Total Sugar	MWS-SNV	10	全波段	0.868	4.206	0.939	2.724	2.902
	MWS-SNV	10	CARS	0.853	4.419	0.947	2.542	3.109
	MWS-MSC	10	全波段	0.857	4.355	0.919	3.113	2.540
	MWS-MSC	10	CARS	0.838	4.611	0.942	2.655	2.978
	MWS-FD	10	全波段	0.884	3.960	0.892	3.576	2.211
	MWS-FD	10	CARS	0.887	3.918	0.949	2.490	3.175

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