Haghbin Najmeh, Bakhshipour Adel, Zareiforoush Hemad, Mousanejad Sedigheh
Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
Department of Plant Protection, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
Plant Methods. 2023 Jun 2;19(1):53. doi: 10.1186/s13007-023-01032-y.
Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1 derivative, and Savitzky-Golay 2 derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits' firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky-Golay 1 derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.
研究了高光谱成像（HSI）和数据分析算法在灰霉病菌感染早期无损检测中的应用。在不同的时间间隔，从实验室受污染和未受污染的水果中采集高光谱图像。对450纳米至900纳米的光谱波长应用移动窗口平滑（MWS）、标准正态变量变换（SNV）、多元散射校正（MSC）、Savitzky-Golay一阶导数和Savitzky-Golay二阶导数算法进行预处理。此外，对光谱执行了三种不同的波长选择算法，即竞争自适应重加权采样（CARS）、无信息变量消除（UVE）和连续投影算法（SPA），以找出最具信息的波长。用经SNV滤波的光谱数据开发的线性判别分析（LDA）是区分受污染和未受污染猕猴桃最准确的分类器，在交叉验证和评估阶段的准确率分别为96.67%和96.00%。该系统能够在疾病症状出现之前检测出受感染的样本。结果还表明，灰霉病感染显著影响猕猴桃的硬度、可溶性固形物含量（SSC）和可滴定酸度（TA）属性。此外，Savitzky-Golay一阶导数-CARS-偏最小二乘回归（PLSR）模型在校准阶段对猕猴桃硬度、SSC和TA的预测率最高，决定系数（R）值分别为0.9879、0.9644、0.9797。硬度、SSC和TA相应的交叉验证R值分别为0.9722、0.9317、0.9500。高光谱成像和化学计量分析表明，在储存期间对真菌感染的猕猴桃进行快速无损评估具有很大潜力。
