Establishment of Detection Model of Soybean Quality Traits by Near Infrared Spectroscopy

LEGUME RESEARCH - AN INTERNATIONAL JOURNAL Pub Date : 2024-01-04 DOI:10.18805/lrf-760

Weiran Gao, Ronghan Ma, A. Jiang, Jiaqi Liu, Pingting Tan, Fang Liu, Jian Zhang

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Abstract

Background: Rapid prediction with near infrared (NIR) spectroscopy on quality traits is pretty popular recently, for the convenience and simple operation. But to make good use of this technology, precise and suitable calibration equations are very important to get dependable result. In this study, we mostly refer to the building of the equation and how the pretreatment effect them. Methods: In this paper, near infrared (NIR) spectroscopy was used to simultaneously predict the quality traits of soybean, including oil content, protein content, oleic acid content, linoleic acid content, stearic acid content. Near infrared spectral data of a total of 112 samples is collected from given materials in Chongqing. Samples were scanned from 1000 nm to 2500 nm using a monochromator instrument (SuperNIR-2700). Calibration equations were developed from NIR data using partial least squares (PLS) regression with internal cross validation. In addition, in this study, we also cover the affection of different pre-treatments to the different calibration equations predicting different quality traits. And measure the effect with three indicators including R, SECV and RPD. Result: Eventually we find the most suitable combination of pre-treatments for each calibration equation predicting a certain trait soybean. The present study would lay the foundations of rapid detection of quality traits in soybean.

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利用近红外光谱建立大豆品质性状检测模型

背景：利用近红外光谱对质量特性进行快速预测，因其操作方便、简单而近来颇为流行。但要充分利用这项技术，精确、合适的校准方程对获得可靠的结果非常重要。在本研究中，我们主要讨论方程的建立以及预处理对方程的影响。方法：本文使用近红外光谱同时预测大豆的质量性状，包括含油量、蛋白质含量、油酸含量、亚油酸含量、硬脂酸含量。从重庆的给定材料中收集了 112 个样品的近红外光谱数据。使用单色仪（SuperNIR-2700）从 1000 纳米到 2500 纳米对样品进行扫描。利用偏最小二乘法 (PLS) 回归和内部交叉验证，根据近红外数据建立了校准方程。此外，在这项研究中，我们还研究了不同的预处理对预测不同质量性状的不同校准方程的影响。并用三个指标（包括 R、SECV 和 RPD）来衡量其效果。结果最终，我们为每个预测大豆某一性状的校准方程找到了最合适的预处理组合。本研究将为大豆品质性状的快速检测奠定基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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LEGUME RESEARCH - AN INTERNATIONAL JOURNAL

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