College of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China.
College of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China; State Grid Jiangxi Extra High Voltage Company, Nanchang 330013, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Oct 15;279:121412. doi: 10.1016/j.saa.2022.121412. Epub 2022 May 23.
Citrus fruit is susceptible to postharvest rot by fungal infection. The detection of early rot is difficult due to similar skin characteristics to sound area, which limits the ability of the grading system to evaluate the comprehensive quality of citrus. In this study, the visible and near infrared hyperspectral imaging system with the wavelength range of 325-1000 nm was used to collect hyperspectral images of oranges. Hyperspectral data of three types of tissues including sound tissue from 80 samples, rotten tissue infected by Penicillium digitatum from 100 samples and rotten tissue infected by Penicillium italicum from 100 samples were extracted. The bootstrapping soft shrinkage (BOSS) and BOSS-SPA (BOSS-Successive Projections Algorithm) combination algorithm were separately used to optimize spectrum variables. The partial least squares discriminant analysis (PLS-DA) model for classifying three types of tissues and PLS-DA model for classifying two types of tissues (sound tissue and rotten tissue) were constructed based on full-spectrum and the selected informative variables. Model comparisonshowed that the BOSS-PLS-DA model can effectively identify three types of tissues with the classification accuracy of 97.1%, while the BOSS-SPA-PLS-DA model was more effective for the binary classification of sound and rotten citrus tissues with the accuracy of 100%. Furthermore, the wavelength images corresponding to the nine informative variables extracted by BOSS-SPA were performed the principal component analysis (PCA), and four feature wavelength images (508, 568, 578 and 614 nm) were obtained by analyzing the weighting coefficients of each single-wavelength images constituting the optimal principal component (PC) image. Finally, a fast multispectral image processing algorithm combined with the global threshold theory was proposed for the rotten orange detection based on the extracted four wavelength images. A total of 280 samples including 80 sound and 200 rotten samples were used to evaluate the classification ability, which showed the proposed multispectral image detection algorithm can successfully differentiate between sound and rotten oranges with an overall classification accuracy of 98.6%.
柑橘果实易受真菌感染的采后腐烂。由于与健康区域的皮肤特征相似，早期腐烂的检测较为困难，这限制了分级系统评估柑橘综合品质的能力。在这项研究中，使用波长范围为 325-1000nm 的可见近红外高光谱成像系统采集了橙子的高光谱图像。从 80 个样本的健康组织、100 个样本感染青霉的腐烂组织和 100 个样本感染意大利青霉的腐烂组织中提取了高光谱数据。分别使用引导软收缩（BOSS）和 BOSS-SPA（BOSS-连续投影算法）组合算法对光谱变量进行优化。基于全谱和选择的信息变量，构建了用于分类三种组织的偏最小二乘判别分析（PLS-DA）模型和用于分类两种组织（健康组织和腐烂组织）的 PLS-DA 模型。模型比较表明，BOSS-PLS-DA 模型可以有效地识别三种类型的组织，分类准确率为 97.1%，而 BOSS-SPA-PLS-DA 模型对于健康和腐烂柑橘组织的二元分类更为有效，准确率为 100%。此外，对 BOSS-SPA 提取的九个信息变量的波长图像进行主成分分析（PCA），通过分析构成最优主成分（PC）图像的每个单波长图像的加权系数，得到了四个特征波长图像（508、568、578 和 614nm）。最后，提出了一种基于提取的四个波长图像的快速多光谱图像处理算法，结合全局阈值理论，用于腐烂橙子的检测。使用了包括 80 个健康样本和 200 个腐烂样本的 280 个样本来评估分类能力，结果表明，所提出的多光谱图像检测算法能够成功地区分健康和腐烂的橙子，总体分类准确率为 98.6%。
