Jiang Mengwei, Li Yiting, Song Jin, Wang Zhenjie, Zhang Li, Song Lijun, Bai Bingyao, Tu Kang, Lan Weijie, Pan Leiqing
College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China.
Foods. 2023 Jan 17;12(3):435. doi: 10.3390/foods12030435.
In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.
在这项工作中,研究了高光谱成像(HSI)系统在检测采后贮藏期间感染[具体病菌未给出]的冬枣上黑斑病的潜力。使用两个系统在可见光和近红外(Vis-NIR,400 - 1000 nm)以及短波红外(SWIR,1000 - 2000 nm)光谱区域采集HSI图像。同时,测量了冬枣在致病过程中物理参数(果皮颜色、失重)、化学参数(可溶性固形物含量、叶绿素)的变化以及微观结构。结果表明,在感染期间,冬枣在Vis-NIR和SWIR波长范围内的光谱反射率总体呈下降趋势。基于冬枣Vis-NIR和SWIR区域HSI光谱的偏最小二乘判别模型(PLS-DA)在病害检测中均给出了令人满意的判别准确率,分类率分别超过92.31%和91.03%。对冬枣的HSI图像进行主成分分析(PCA),以可视化其在致病过程中的感染区域。Vis-NIR区域HSI光谱的第一主成分能够突出感染冬枣的病害区域。因此,Vis-NIR HSI和NIR HSI技术有潜力检测采后贮藏期间冬枣上的黑斑病,并且Vis-NIR HSI光谱信息能够在致病过程中可视化冬枣的病害区域。