Jang Kyeong Eun, Kim Geonwoo, Shin Mi Hee, Cho Jung Gun, Jeong Jae Hoon, Lee Seul Ki, Kang Dongyoung, Kim Jin Gook
Division of Applied Life Science, Graduate School of Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea.
Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea.
Plants (Basel). 2022 Sep 5;11(17):2327. doi: 10.3390/plants11172327.
Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of 'Madoka' peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of 'Madoka' samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the 'Madoka' samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in 'Madoka' peach. Consequently, the a* values and SSC content shows statistically significant R values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in 'Madoka' peaches.
针对“圆香”桃的理化性质，如色度（a*）、可溶性固形物含量（SSC）、硬度和可滴定酸度（TA）含量，已经开展了大量关于田间无损评估（NDE）的研究。为实现这一目标，在可见光和近红外（Vis/NIR）区域采用了基于快照的高光谱成像（HSI）方法用于田间应用。采集了“圆香”样本的高光谱图像，并与商业HSI分析软件相结合，进而预测“圆香”样本的理化性质。为验证基于田间的HSI应用的性能，还开展了基于实验室的HSI应用，并比较了它们的决定系数值（R）。最后，生成了基于像素的化学图像，以解释“圆香”桃理化性质的动态变化。结果表明，a值和SSC含量显示出具有统计学意义的R值（0.84）。另一方面，硬度和TA含量的预测精度相对较低（R = 0.6至0.7）。然后，创建了a值和SSC含量的化学图像，并能用灰度等级表示它们的不同水平。这表明，本研究中使用的集成HSI软件的HSI系统作为分析“圆香”桃理化性质的田间无损评估方法具有广阔的应用前景。
