Wu Jian, Liu Chenlin, Ouyang Aiguo, Li Bin, Chen Nan, Wang Jing, Liu Yande
Intelligent Mechanical and Electrical Equipment Innovation Research Institute, East China Jiaotong University, Nanchang 330013, China.
National and Local Joint Engineering Research Center of Intelligent Photoelectric Detection Technology and Equipment for Fruit, Nanchang 330013, China.
Foods. 2024 Nov 28;13(23):3843. doi: 10.3390/foods13233843.
Assessing the internal quality of fruits is crucial in food chemistry and quality control, and bruises on peaches can affect their edible value and storage life. However, the early detection of slight bruises in yellow peaches is a major challenge, as the symptoms of slight bruises are difficult to distinguish. Herein, this study aims to develop a more simple and efficient structured-illumination reflectance imaging system (SIRI) and algorithms for the early nondestructive detection of slight bruises in yellow peaches. Pattern images of samples were acquired at spatial frequencies of 0.05, 0.10, 0.15, and 0.20 cycle mm-1 and wavelengths of 700, 750, and 800 nm using a laboratory-built multispectral structured-illumination reflectance imaging system (M-SIRI), and the direct component (DC) and alternating component (AC) images were obtained by image demodulation. A spatial frequency of 0.10 cycle mm-1 and wavelength of 700 nm were determined to be optimal for acquiring pattern images based on the analysis of the pixel intensity curve of the AC image; then, the pattern images of all yellow peaches samples were obtained. The ratio image (RT) between the AC image and the DC image significantly enhances bruise features. An improved Otsu algorithm is proposed to improve the robustness and accuracy of the Otsu algorithm against dark spot noise in AC and RT images. As a comparison, the global thresholding method and the Otsu method were also applied to the segmentation of the bruised region in all samples. The results indicate that the I-Otsu algorithm has the best segmentation performance for RT images, with an overall detection accuracy of 96%. This study demonstrates that M-SIRI technology combined with the I-Otsu algorithms has considerable potential in non-destructive detection of early bruises in yellow peaches.
评估水果的内部品质在食品化学和质量控制中至关重要，桃子上的瘀伤会影响其食用价值和储存寿命。然而，黄桃轻微瘀伤的早期检测是一项重大挑战，因为轻微瘀伤的症状难以辨别。在此，本研究旨在开发一种更简单高效的结构光反射成像系统（SIRI）及算法，用于黄桃轻微瘀伤的早期无损检测。使用实验室构建的多光谱结构光反射成像系统（M-SIRI），在空间频率为0.05、0.10、0.15和0.20周期毫米-1以及波长为700、750和800纳米的条件下采集样品的图案图像，并通过图像解调获得直流分量（DC）图像和交流分量（AC）图像。基于对AC图像像素强度曲线的分析，确定空间频率为0.10周期毫米-1和波长为700纳米时最适合采集图案图像；然后，获取了所有黄桃样品的图案图像。AC图像与DC图像之间的比率图像（RT）显著增强了瘀伤特征。提出了一种改进的大津算法，以提高大津算法对AC图像和RT图像中暗点噪声的鲁棒性和准确性。作为比较，还将全局阈值法和大津法应用于所有样品瘀伤区域的分割。结果表明，I-Otsu算法对RT图像具有最佳的分割性能，总体检测准确率为96%。本研究表明，M-SIRI技术与I-Otsu算法相结合在黄桃早期瘀伤的无损检测中具有相当大的潜力。
