Zhang Yijun, Wu Jianguo, Zhang Hao, Shen Zhiwei, Bai Xiaoyu, Gao Xu, Zhu Qiyun, Huang Yunshuai, Hong Seung-Beom, Zhu Zhujun, He Daogen, Zang Yunxiang
Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs; Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China.
Daishan County Agriculture Village Bureau, Daishan 316200, Zhejiang, China.
Food Chem X. 2025 May 10;28:102528. doi: 10.1016/j.fochx.2025.102528. eCollection 2025 May.
Broccoli's pigments enhance its nutritional value by affecting color and antioxidant properties. Traditional methods like high-performance liquid chromatography (HPLC) and spectrophotometry are accurate but destructive, labor-intensive, and unsuitable for high-throughput screening. This study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli. The optimal models for total chlorophyll (Chl), Chl a, and Chl b were established with the use of SNV / 2nd derivative / PLS, which yielded an R of 0.992, RMSEC of 0.478 mg g DW, and RPD of 6.476. For carotenoids (CAR), the SNV / 1st derivative / PLS model provided the best results, with an R of 0.976, RMSEC of 0.098 mg g DW, and RPD of 4.455. However, the ACN model based on SNV / 1st derivative / PLS exhibited relative lower accuracy, with an R of 0.790, RMSEC of 1.777 units g DW, RPD of 1.267, suggesting the necessity for preliminary analysis. This study fills a critical gap in NIRS applications for plant pigment analysis, presenting a rapid, non-destructive, and high-throughput approach for quality assessment and breeding selection.
西兰花的色素通过影响颜色和抗氧化特性来提高其营养价值。高效液相色谱法（HPLC）和分光光度法等传统方法准确但具有破坏性、劳动强度大且不适合高通量筛选。本研究基于近红外光谱（NIRS）技术构建了非破坏性模型，以预测西兰花中的色素化合物。使用SNV / 二阶导数 / PLS建立了总叶绿素（Chl）、叶绿素a和叶绿素b的最佳模型，其R值为0.992，RMSEC为0.478 mg g DW，RPD为6.476。对于类胡萝卜素（CAR），SNV / 一阶导数 / PLS模型提供了最佳结果，R值为0.976，RMSEC为0.098 mg g DW，RPD为4.455。然而，基于SNV / 一阶导数 / PLS的ACN模型表现出相对较低的准确性，R值为0.790，RMSEC为1.777单位g DW，RPD为1.267，这表明有必要进行初步分析。本研究填补了NIRS在植物色素分析应用中的关键空白，为质量评估和育种选择提供了一种快速、无损且高通量的方法。
