近红外光谱检测技术可反映样品内部含氢化学键伸缩振动与合频吸收信息, 具有分析速度快、 经济、 重现性好以及环境友好等优点, 常用于食品、 药品及材料领域的检测分析之中。 豌豆是世界上最重要的栽培作物之一, 种植、 分布广泛, 具有高淀粉、 高蛋白、 低脂质等营养特性, 长久以来受到消费者的喜爱。 为了明确不同产地豌豆的近红外光谱建模差异, 对不同产地豌豆进行建模分析。 研究采集了河南省南阳市不同地区42份豌豆样本, 首先测定了豌豆的营养成分(总淀粉、 蛋白质、 水分、 灰分及脂质), 再重点采用近红外光谱中的积分球漫反射技术, 在12 000~4 000 cm-1波段对不同豌豆样品进行光谱采集, 通过判别分析模型(DA)结合不同的预处理方法得到最优预处理数据, 结合主成分分析(PCA)、 偏最小二乘判别分析(PLS-DA)以及正交偏最小二乘判别分析(OPLS-DA)等方法, 对光谱特征差异进行分类分析, 从而构建并验证南阳豌豆的产地识别模型。 结果表明, 不同区域南阳豌豆的营养组分及含量总体差异较小(总淀粉36.30%~46.93%, 蛋白质16.37%~25.50%, 水分6.78%~9.16%, 灰分2.29%~3.38%, 脂质0.37%~1.43%)； 基于近红外光谱建立的判别模型表明, DA模型判别分析准确率可达92.4%, 并且PCA、 PLS-DA以及OPLS-DA得到的模型预测能力分别为96.7%, 85.1%和83.6%, 表明以上模型均可实现南阳豌豆鉴别模型的建立。 此外, 通过变量重要性投影值法提取(VIP>1.0)筛选出的不同产地差异波段显示, 4 710~4 000, 5 320~5 200以及7 200~6 220 cm-1可作为南阳豌豆产地鉴别的特异性检测波段。 该研究可为构建不同区域豌豆产地鉴别、 追溯信息库提供方法学依据。

Near-infrared spectroscopy can reflect the internal hydrogen-containing chemical bond stretching vibration and ensemble frequency absorption information of the sample, with the advantages of high-speed, economical, reproducible and environmentally friendly analysis, commonly used in food, pharmaceuticals and materials detection for analysis. Peas are one of the most important cultivated crops in the world, widely grown and distributed, with nutritional properties such as high starch, high protein as well as low lipid content, which consumers have long loved. In order to clarify the modeling differences in the NIR spectra of peas from different origins, modeling analysis was performed on peas from different origins. In this study, 42 pea samples collected from different regions of Nanyang city, Henan province, were investigated. Firstly, pea samoles’ nutritional components (total starch, protein, moisture, ash, and lipid) were determined. Then, with an emphasis on using the integrating sphere diffuse reflectance technology in Near-infrared spectroscopy, the spectra of different pea samples were collected in the wavelength range of 12 000~4 000 cm-1. By combining different pre-processing methods with discriminant analysis models (DA) to obtain the optimal pre-processed data and combining principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) along with orthogonal partial least squares discriminant analysis (OPLS-DA), the differences in the spectral characteristics were screened and analyzed, constructing and verifying the identification models of Nanyang peas.The results show that the overall difference in nutritional composition and content of Nanyang peas in different regions is slight (total starch 36.30%~46.93%, protein 16.37%~25.50%, moisture 6.78%~9.16%, ash 2.29%~3.38%, lipid 0.37%~1.43%), and the results of the discriminant model established based on Near-infrared spectroscopy showed that the accuracy of discriminant analysis for the DA model could reach 92.4%. The predictive abilities obtained from PCA, PLS-DA and OPLS-DA models were 96.7%, 85.1% and 83.6%, respectively, indicating that the above models can achieve accurate classification and identification of geographical origin for Nanyang peas. In addition, the results of the different bands among different geographical origins screened by the variable importance projection (VIP>1.0) method showed that 4 710~4 000, 5 320~5 200 and 7 200~6 220 cm-1 could be used as specific detection bands to identify geographical origins of Nanyang peas. Therefore, this study can provide a methodological basis to construct a database for the identification and traceability of pea’s origin in different regions.