有机与非有机大豆的营养成分分析及近红外鉴别

Nutrients analysis and near infrared identification of organic and non-organic soybeans

中文关键词:  有机大豆  非有机大豆  营养成分  近红外光谱  鉴别

英文关键词:organic soybeans  non-organic soybeans  nutrients  near infrared spectra  identification

中文摘要:

      目的 分析有机与非有机大豆的营养成分差异, 建立有机与非有机大豆的快速鉴别方法。方法 测定40份有机与非有机大豆的蛋白质、脂肪、水分、大豆苷和染料木苷含量并进行差异性分析; 采集大豆的近红外光谱, 基于主要营养成分以及近红外光谱结合不同预处理采用偏最小二乘法判别分析(partial least squares discriminant analysis, PLS-DA)建立有机与非有机大豆的快速鉴别模型; 通过PLS-DA模型筛选对于区分有机与非有机大豆有重要贡献的近红外光谱特征波段以建立最佳的快速鉴别模型。结果 有机与非有机大豆在蛋白质、水分和染料木苷含量上不存在显著性差异, 但有机大豆的脂肪和大豆苷含量显著高于非有机大豆。仅基于主要营养成分建立的鉴别模型显示出较低的模型参数和预测正确率, 而基于近红外光谱及其与主要营养成分结合建立的模型具有较高的模型参数和预测正确率。对于区分有机与非有机大豆有重要贡献的近红外光谱特征波段是5974~5372 cm?1以及5064~4000 cm?1, 这两个特征波段的近红外光谱吸收峰与脂肪、黄酮类物质以及蛋白质官能团的振动有关。基于近红外光谱特征波长结合Smoothing-SG预处理的模型显示出最优的模型参数(R2X为0.996, R2Y为0.735, Q2为0.677), 能够有效鉴别有机与非有机大豆。结论 由于种植模式不同, 有机与非有机大豆的营养成分存在差异, 有机大豆的营养价值高于非有机大豆。脂肪和大豆苷含量可以作为区分有机与非有机大豆的重要质量指标。近红外光谱技术在鉴别有机与非有机大豆上具有可行性, 为大豆的质量控制提供了技术基础。

英文摘要:

      Objective To analyze the differences of nutrients between organic and non-organic soybeans and establish a rapid method for identifying organic and non-organic soybeans. Methods The protein, fat, moisture, daidzin and genistin content of forty organic and non-organic soybeans were determined and analyzed for the differences. The near infrared spectra of soybeans were collected, and a rapid discrimination model between organic and non-organic soybeans was established using partial least squares discriminant analysis (PLS-DA) based on the main nutrients and near infrared spectra combined with different pretreatments. The characteristic wavelengths of near infrared spectra that contributed significantly to the distinction between organic and non-organic soybeans were screened by the PLS-DA model to establish the best rapid identification model. Results There was no significant difference between organic and non-organic soybeans in terms of protein, moisture and genistin content, but the fat and daidzin content of organic soybeans were significantly higher than those of non-organic soybeans. The identification model based only on major nutrients showed low model parameters and correct prediction, while models based on near infrared spectra and their combination with major nutrients had high model parameters and correct predictions. The characteristic wavelengths of near infrared spectra that contributed significantly to the distinction between organic and non-organic soybeans were 5974?5372 cm?1 and 5064?4000 cm?1. It was noted that the absorption peaks at these two characteristic wavelengths were related to the vibrations of functional groups of fat, flavonoids and protein. The model based on the characteristic wavelengths of near infrared spectra combined with Smoothing-SG pre-processing showed optimal model parameters (R2X of 0.996, R2Y of 0.735 and Q2 of 0.677) to effectively identify organic and non-organic soybeans. Conclusion Due to different cultivation patterns, the nutrients of organic and non-organic soybeans are different, and the nutritional value of organic soybeans is higher than that of non-organic soybeans. The fat and daidzin content can be used as important quality indicators to distinguish between organic and non-organic soybeans. Near infrared spectra technology is practicable in identifying organic and non-organic soybeans, which provides a technical basis for the quality control of soybeans.