School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
Food Chem. 2022 May 30;377:131981. doi: 10.1016/j.foodchem.2021.131981. Epub 2021 Dec 30.
This study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity. Isolated forest (IF) algorithm and principal component analysis (PCA) were used to remove the abnormal data of sorghum grains. Competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA) were combined and used to extract the characteristic wavelengths. Gray-level co-occurrence matrix (GLCM) was used to extract the textural features. DF models were established based on the different types of data. Specifically, the DF models established using the characteristic spectra produced the best recognition results: the average correct recognition rate (CRR) of the models was greater than 91%. In addition, the average CRR of validation set Ⅰ was 88.89%. These results show that a combination of HSI and DF could be used for the rapid and nondestructive determination of sorghum purity.
本研究结合高光谱成像（HSI）和深度森林（DF），开发了一种可靠的模型，用于快速、无损地测定高粱纯度。孤立森林（IF）算法和主成分分析（PCA）用于去除高粱籽粒的异常数据。竞争自适应重加权抽样（CARS）算法和连续投影算法（SPA）相结合，用于提取特征波长。灰度共生矩阵（GLCM）用于提取纹理特征。基于不同类型的数据建立了 DF 模型。具体来说，基于特征谱建立的 DF 模型得到了最佳的识别结果：模型的平均正确识别率（CRR）大于 91%。此外，验证集Ⅰ的平均 CRR 为 88.89%。这些结果表明，HSI 和 DF 的结合可用于快速、无损地测定高粱纯度。
