He Hong-Ju, Wang Yuling, Wang Yangyang, Al-Maqtari Qais Ali, Liu Hongjie, Zhang Mian, Ou Xingqi
School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore.
School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China.
Int J Biol Macromol. 2023 Jul 1;242(Pt 1):124748. doi: 10.1016/j.ijbiomac.2023.124748. Epub 2023 May 9.
This study aimed to achieve the rapid quantification and visualization of the starch content in sweet potato via near-infrared (NIR) spectral and image data fusion. The hyperspectral images of the sweet potato samples containing 900-1700 nm spectral information within every pixel were collected. The spectra were preprocessed, analyzed and the 18 informative wavelengths were finally extracted to relate to the measured starch content using the multiple linear regression (MLR) algorithm, producing a good quantitative prediction accuracy with a correlation coefficient of prediction (r) of 0.970 and a root-mean-square error of prediction (RMSE) of 0.874 g/100 g by an external validation using a set of dependent samples. The MLR model was further verified in terms of soundness and predictive validity via F-test and t-test, and then transferred to each pixel of the original two dimensional images with the help of a developed algorithm, generating color distribution maps to achieve the vivid visualization of the starch distribution. The study demonstrated that the fusion of the NIR spectral and image data provided a good strategy for the rapidly and nondestructively monitoring the starch content of sweet potato. This technique can be applied to industrial use in the future.
本研究旨在通过近红外（NIR）光谱和图像数据融合实现甘薯淀粉含量的快速定量和可视化。采集了每个像素内包含900 - 1700 nm光谱信息的甘薯样品的高光谱图像。对光谱进行预处理、分析，最终提取出18个信息波长，使用多元线性回归（MLR）算法将其与测得的淀粉含量相关联，通过一组相关样本进行外部验证，预测相关系数（r）为0.970，预测均方根误差（RMSE）为0.874 g/100 g，产生了良好的定量预测精度。通过F检验和t检验进一步验证了MLR模型的稳健性和预测有效性，然后借助开发的算法将其转移到原始二维图像的每个像素上，生成颜色分布图以实现淀粉分布的生动可视化。研究表明，近红外光谱和图像数据的融合为快速、无损监测甘薯淀粉含量提供了一个良好的策略。该技术未来可应用于工业用途。
