Wang Ya-Qin, Liu Guang-Min, Hu Li-Ping, Zhao Xue-Zhi, Zhang De-Shuang, He Hong-Ju
Institute of Agri-Food Processing and Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China.
Foods. 2023 May 8;12(9):1922. doi: 10.3390/foods12091922.
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.
紫甘蓝（PCC）因其诱人的颜色和高营养价值而成为一种新的育种趋势，因为它含有丰富的花青素。为了快速评估紫甘蓝花青素含量并筛选育种材料，利用近红外光谱（NIR）建立了一种快速定量检测紫甘蓝中花青素的方法。用近红外光谱对紫甘蓝样品进行扫描，并结合高效液相色谱法获得的花青素含量化学计量学结果对光谱数据进行处理，建立预测模型。紫甘蓝中矢车菊素含量在93.5mg/kg至12802.4mg/kg之间，而其他花青素含量则低得多。基于无散射模式预处理和一阶导数的偏最小二乘回归建立的近红外预测模型显示出最佳预测性能：对于矢车菊素，校正集的外部相关系数（RSQ）和交叉验证标准误差（SECV）分别为0.965和693.004；对于总花青素，校正集的RSQ和SECV分别为0.966和685.994。所建立的模型是有效的，这种近红外方法具有省时、方便的优点，可应用于紫色蔬菜育种实践。
