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使用近红外反射光谱法(NIRS)预测卷心菜和抱子甘蓝叶片组织中的葡萄糖芸苔素浓度。

Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue.

作者信息

Renner Ilse E, Fritz Vincent A

机构信息

Department of Horticultural Science, University of Minnesota, Saint Paul, MN 55108 USA.

Southern Research and Outreach Center, University of Minnesota, Waseca, MN 56093 USA.

出版信息

Plant Methods. 2020 Oct 12;16:136. doi: 10.1186/s13007-020-00681-7. eCollection 2020.

Abstract

BACKGROUND

Glucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method.

RESULTS

Here we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R = 0.75 with RPD = 2.3 for predicting µmol GBS·100 g fresh weight and R = 0.80 with RPD = 2.4 for predicting µmol GBS·g dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines.

CONCLUSIONS

A calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

摘要

背景

硫代葡萄糖苷(GBS)及其水解产物吲哚 - 3 - 甲醇是参与癌症化学预防的重要营养成分。食用富含GBS的蔬菜来源,如卷心菜和抱子甘蓝,与肿瘤抑制、致癌物排泄及癌症风险降低有关。高效液相色谱法(HPLC)是目前鉴定GBS的标准方法,其定量基于与已知标准品比较的紫外光吸收或通过质谱法。这些分析技术需要昂贵的设备、训练有素的实验室人员、危险化学品,且劳动强度大。需要一种快速、无损、廉价的定量方法来加速GBS强化生产系统的应用。这样一种分析方法将使生产者能够量化其产品质量,并为植物育种者提供一种高通量表型分析工具,以扩大其高GBS积累品种的育种计划规模。近红外反射光谱法(NIRS)与偏最小二乘回归(PLSR)相结合可能是开发这种方法的有用工具。

结果

在此我们证明,使用950至1650 nm波长产生的NIRS数据通过偏最小二乘回归可以预测来自多种卷心菜和抱子甘蓝的冻干组织中的GBS浓度。交叉验证模型在预测μmol GBS·100 g鲜重时R = 0.75,RPD = 2.3;在预测μmol GBS·g干重时R = 0.80,RPD = 2.4。对方程载荷的检查表明,建模中使用的分子关联可能是由于胺的O - H拉伸和/或N - H拉伸的一次泛音。

结论

可以为卷心菜和抱子甘蓝创建一个适用于使用NIRS生成的数据与PLSR配对筛选冻干叶片组织中GBS浓度的校准模型。校准的最佳NIRS波长范围仍是一个未解决的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/865e/7552462/8c575bea0913/13007_2020_681_Fig1_HTML.jpg

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