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基于高光谱技术的枇杷总酚含量无损检测
发表时间：2024-06-28 浏览量：60 下载量：13
| 全部作者： | 伍柯翰，徐丹，任丹，吴习宇 |
| 作者单位： | 西南大学食品科学学院；西南大学食品贮藏与物流研究中心 |
| 摘 要： | 为评估高光谱成像技术（hyperspectral imaging，HSI）用于冷藏过程中枇杷总酚含量（totally phenolic content，TPC）预测的可能性，在可见光/近红外波段（363～1 026 nm）采集115个枇杷样本高光谱信息，通过福林酚法对样本TPC进行测定。再采用蒙特卡洛算法对异常样本进行剔除，竞争性自适应加权算法（competitive adaptive reweighted sampling，CARS）对特征波长进行提取，分别建立枇杷TPC的非线性迭代偏最小二乘法（nonlinear iterative partial least square，NIPALS）预测模型和简单偏最小二乘法（simple partial least square，SIMPLS）预测模型并进行验证。结果显示，CARS共提取到特征波段47条，占波长总数7.62%；在最佳主成分数时，CARS-NIPALS与CARS-SIMPLS的建模集决定系数R2c分别为0.922 1、0.916 0，预测集决定系数R2p分别为0.816 6、0.812 8，NIPALS与SIMPLS两种算法表现相似，HSI能对冷藏过程中的枇杷TPC进行有效预测。 |
| 关 键 词： | 食品包装与储藏；非线性迭代偏最小二乘法（NIPALS）；简单偏最小二乘法（SIMPLS）；枇杷；总酚；高光谱成像技术（HSI） |
| Title： | Nondestructive detection of total phenolic content of loquat based on hyperspectral imaging |
| Author： | WU Kehan, XU Dan, REN Dan, WU Xiyu |
| Organization： | College of Food Science, Southwest University; Food Storage and Logistics Research Center, Southwest University |
| Abstract： | To evaluate the feasibility of using hyperspectral imaging (HSI) to predict the total phenol content (TPC) in loquat during postharvest cold storage, the present experiment was conducted to collect the spectral information of 115 loquat samples using HSI in the Vis-NIR wavelength band (363-1 026 nm), and then the TPC was determined by the forintol method. The anomalous samples were eliminated by the Monte Carlo algorithm and then the competitive adaptive reweighted sampling (CARS) algorithm was used to select the featured bands. Nonlinear iterative partial least square (NIPALS) and simple partial least square (SIMPLS) algorithms of TPC in loquat were established and made a validation respectively. As the result shows, 47 featured bands, accounting for 7.62% of the total number of wavelengths, are selected by CARS. CARS-NIPALS and CARS-SIMPLS reach high prediction accuracy, with the of the best principal component 0.922 1 and 0.916 0, and the R2p of the best principal component 0.816 6 and 0.812 8 respectively. NIPALS and SIMPLS perform similarly. Therefore, it can be shown that HSI can effectively predict the TPC of loquat during cold storage. |
| Key words： | food packaging and storage; nonlinear iterative partial least square (NIPALS); simple partial least square (SIMPLS); loquat; total phenolic; hyperspectral imaging (HSI) |
| 发表期数： | 2024年6月第2期 |
| 引用格式： | 伍柯翰，徐丹，任丹，等. 基于高光谱技术的枇杷总酚含量无损检测[J]. 中国科技论文在线精品论文，2024，17（2）：240-250. |
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