青花菜中硫代葡萄糖苷RAA和GBC的近红外光谱快速测定

doi:10.3864/j.issn.0578-1752.2020.21.017

摘要/Abstract

摘要：

【目的】青花菜是硫代葡萄糖苷含量非常高的十字花科蔬菜,大量医学和营养学研究表明,青花菜具有防癌抗癌的显著功效,其防癌抗癌特性主要与硫代葡萄糖苷的多种降解产物有关,尤其是4-甲基硫氧丁基硫苷（RAA）和3-甲基吲哚基硫苷（GBC）的降解产物。本研究拟建立青花菜抗癌硫代葡萄糖苷的近红外光谱快速测定方法。【方法】采用高效液相色谱法（HPLC）测定青花菜中硫代葡萄糖苷RAA和GBC的含量,将近红外光谱仪扫描样品所获得的光谱文件与化学分析结果在偏最小二乘回归法（partial least squares,PLS）分析的基础上,采用不同的散射处理方式（SNV、Detrend、SNV+Detrend）和导数处理方式（FD、SD）对光谱数据进行预处理,从而得到定标方程,再进一步对模型进行验证。【结果】 4-甲基硫氧丁基硫苷（RAA）和3-甲基吲哚基硫苷（GBC）是青花菜中存在的主要硫苷,占总含量的60%以上。90份青花菜的硫苷组分含量结果表明RAA平均含量最高,含量变化范围最大,平均含量为6.20 μmol?g^-1,变化范围为0.66—14.54 μmol?g^-1;GBC平均含量为4.43 μmol?g^-1,变化范围为0.25—10.79 μmol?g^-1。经过筛选,采用SNV+SD处理后RAA预测模型的校正集和预测集相关系数分别为0.867和0.912;采用SNV+SD处理后的GBC预测模型的校正集和预测集相关系数分别为0.918和0.960。【结论】建立了RAA和GBC的近红外光谱快速检测模型,为青花菜营养品质的快速检测、优异抗癌青花菜种质资源的快速筛选与利用奠定了基础。

关键词: 青花菜, 硫代葡萄糖苷, 4-甲基硫氧丁基硫苷, 3-甲基吲哚基硫苷, 近红外光谱, 快速测定

Abstract:

【Objective】Broccoli is a cruciferous vegetable with high glucosinolates content. A large number of medical and nutritional studies have shown that regular consumption of broccoli can effectively reduce the incidence of a variety of cancers. It has been confirmed that the anticancer characteristics of broccoli are mainly related to the degradation products of glucosinolates, especially the degradation products of 4-methylthiobutyl-thioglucoside (RAA) and 3-methylindolythioglucoside (GBC). The aim of this study was to establish a rapid method for the determination of anticancer glucosinolates in broccoli by near infrared spectroscopy.【Method】In this study, the content of RAA and GBC in broccoli was determined by high performance liquid chromatography (HPLC). Based on the partial least squares (PLS), the spectral files obtained by NIRS scanning and chemical analysis results were progressed by different scattering methods (SNV, detrend, and SNV + detrend) and different derivative treatment (FD and SD). After the spectral data were preprocessed, the calibration equation was obtained and the model was verified.【Result】RAA and GBC were the main glucosinolates in broccoli, accounting for more than 60% of the total content. The results of 90 broccoli samples showed that the average content of RAA was the highest, the range of change was the largest, the average content was 6.20 μmol?g^-1, and the range of change was 0.66-14.54 μmol?g^-1; the average content of GBC was 4.43 μmol?g^-1, and the range of change was 0.25-10.79 μmol?g^-1. After screening, the correlation coefficients of calibration set and prediction set of RAA prediction model with SNV + SD treatment were 0.867 and 0.912, respectively; the correlation coefficients of calibration set and prediction set of GBC prediction model using SNV + SD treatment were 0.918 and 0.960, respectively. 【Conclusion】In this study, the rapid detection model of RAA and GBC was established, which laid a foundation for the rapid detection of the nutritional quality of broccoli and the rapid detection and utilization of superior broccoli germplasm resources.

Key words: broccoli, glucosinolates, 4-methylsulfonylbutyl-glucosinolate, 3-indolylmethyl-glucosinolate, near infrared spectroscopy, rapid determination

刘倩男,黄伟,丁云花,王亚钦,胡丽萍,赵学志,何洪巨,刘光敏. 青花菜中硫代葡萄糖苷RAA和GBC的近红外光谱快速测定[J]. 中国农业科学, 2020, 53(21): 4497-4506.

LIU QianNan,HUANG Wei,DING YunHua,WANG YaQin,HU LiPing,ZHAO XueZhi,HE HongJu,LIU GuangMin. Rapid Determination of RAA and GBC in Broccoli by Near Infrared Spectroscopy[J]. Scientia Agricultura Sinica, 2020, 53(21): 4497-4506.

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硫代葡萄糖苷成分 Glucosinolates components	含量范围 Content range (μmol?g^-1)	平均值 Average (μmol?g^-1)
RAA	0.66—14.54	6.20
GBC	0.25—10.79	4.43
PRO	0.01—3.42	0.72
NAP	0—0.23	0.07
4OH	0.03—1.86	0.53
4ME	0.05—1.16	0.42
NEO	0.06—11.84	2.91
ERU	0.01—1.96	0.27

光谱预处理方法 Spectral treatment method	RAA
光谱预处理方法 Spectral treatment method	定标相关系数 RSQ	矫正标准偏差 SEC	内部交叉验证误差 SECV	内部交叉验证相关系数 1-VR
原始光谱+FD Original spectrum+FD	0.814	1.436	1.461	0.808
原始光谱+SD Original spectrum+SD	0.848	1.263	1.330	0.831
SNV+Detrend+FD	0.832	1.363	1.402	0.823
SNV+Detrend+SD	0.859	1.214	1.275	0.844
SNV+FD	0.834	1.359	1.401	0.823
SNV+SD	0.867	1.209	1.269	0.846
Detrend+SD	0.794	1.511	1.535	0.788
Detrend+FD	0.839	1.319	1.385	0.823

光谱预处理方法 Spectral treatment method	GBC
光谱预处理方法 Spectral treatment method	定标相关系数 RSQ	矫正标准偏差 SEC	内部交叉验证误差 SECV	内部交叉验证相关系数 1-VR
原始光谱+FD Original spectrum+FD	0.860	0.813	0.829	0.854
原始光谱+SD Original spectrum+SD	0.884	0.778	0.825	0.869
SNV+Detrend+FD	0.861	0.831	0.845	0.855
SNV+Detrend+SD	0.899	0.717	0.760	0.887
SNV+FD	0.862	0.846	0.867	0.854
SNV+SD	0.912	0.692	0.738	0.892
Detrend+FD	0.849	0.836	0.861	0.839
Detrend+SD	0.875	0.802	0.842	0.862

组分 Components	外部检验 External inspection
	验证集（个） Validation set (n)	平均值 Mean		外部检验相关系数 RSQ	预测标准差 SEP	检验偏差 Bias
	验证集（个） Validation set (n)	Lab	NIR	外部检验相关系数 RSQ	预测标准差 SEP	检验偏差 Bias
RAA	20	6.325	6.166	0.918	1.429	0.143
GBC	20	4.319	4.467	0.960	0.780	0.064