Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (21): 4497-4506.doi: 10.3864/j.issn.0578-1752.2020.21.017

• HORTICULTURE • Previous Articles     Next Articles

Rapid Determination of RAA and GBC in Broccoli by Near Infrared Spectroscopy

LIU QianNan1,2(),HUANG Wei2,DING YunHua1,WANG YaQin1,HU LiPing1,ZHAO XueZhi1,HE HongJu1,LIU GuangMin1()   

  1. 1Beijing Vegetable Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097
    2College of Agricultural and Forestry Science and Technology, Hebei North University, Zhangjiakou 075000, Hebei
  • Received:2020-03-09 Accepted:2020-04-20 Online:2020-11-01 Published:2020-11-11
  • Contact: GuangMin LIU E-mail:1367194814@qq.com;liuguangmin@nercv.org

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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

Fig. 1

High performance liquid chromatography of the glucosinolates"

Table 1

Content of glucosinolates in broccoli samples"

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

Fig. 2

Original near-infrared spectrum of broccoli"

Fig. 3

Spectra treated by the SNV+Detrend+FD method"

Fig. 4

Spectra treated by the SNV+Detrend+SD method"

Table 2

Comparison of calibration equations of RAA in broccoli by different treatment methods"

光谱预处理方法
Spectral treatment method		 RAA
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

Table 3

Comparison of GBC in broccoli with different treatment methods"

光谱预处理方法
Spectral treatment method		 GBC
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

Fig. 5

RAA model result"

Fig. 6

GBC model result"

Table 4

Analysis of validation samples by calibration model"

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
Lab NIR
RAA 20 6.325 6.166 0.918 1.429 0.143
GBC 20 4.319 4.467 0.960 0.780 0.064
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