近红外光谱结合SIMCA对不同产地枸杞溯源模型的优化

Optimization of Wolfberry (Lycium barbarum L.) Traceability Model for Different Geographic Origins Using Near Infrared Spectroscopy Combined with SIMCA

采用近红外光谱结合簇类独立软模式法(SIMCA)建立枸杞溯源模型,比较不同光谱预处理方法建立的溯源模型的优劣。结果表明,在950～1650nm波长范围内,原始光谱经二阶导数、五点平滑以及矢量归一化处理后,在波长1135、1175、1235、1335、1395、1535nm处,均有明显的特征吸收峰;新疆、中宁、甘肃、青海、南梁、惠农、固原和内蒙的8个产地模型的主成分数分别为3时,采用SIMCA法建立的枸杞溯源模型最好;在10%显著水平下,建立的8个产地模型中,固原模型最好,惠农模型最差。

In this study, traceability models for wolfberry from different geographic origins were established by using near infrared spectroscopy combined with cluster class independent soft model method （SIMCA）. Different spectral preprocessing methods were compared for modeling. The results showed that, in the wavelength range of 950 to 1650 nm, obvious characteristic absorption peaks at 1135, 1175, 1235, 1335, 1415, 1395 nm and 1535 nm were observed after second order derivative processing, five-point smoothing and SNV processing of the original spectra. When the number of principal components for each of the 8 growing areas, Xinjiang, Zhongning, Gansu, Qinghai, Nanliang, Huinong, Guyuan and Inner Mongolia was 3, the traceability models established by the SIMCA method were the best. At the significance level of 10%, 95%, 85%, 95%, 95%, 80%, 80%, 95% and 95% of unknown samples from these growing areas were recognized and only 2.86%, 14.28%, 2.86%, 0%, 5.72%, 17.13%, 0% and 2.86% were incorrectly judged. The model for samples from Guyuan was the best, whereas that for Huinong was the worst.