1. Department of Mathematics and Artificial Intelligence, Lyuliang University, Lvliang 033001, China
2. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
3. College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, China
Abstract：Yuluxiang pear is loved by consumers because of its crisp flesh and sweet juice, but it is prone to mechanical damage during transportation. Once it does not occur in time, it will lead to internal decay of the fruit, which will affect the whole batch of fruits and cause economic losses. It is difficult to detect early damage quickly and accurately depending on the human eye. Modern optical technology has been widely used in fruit tree quality inspection because of its non-contact and rapid advantages. Raman spectroscopy has emerged in fruit and vegetable quality detection due to its fast molecular fingerprint characteristics and non-sensitivity to water. To solve the problem of Yuluxiang pear being vulnerable to mechanical damage during picking and transportation, this study proposes a method based on surface-enhanced Raman scattering (SERS) technology combined with a deep learning method. The change law of Raman optical characteristics of Yuluxiang pear in the early stage of mechanical damage was explored, and the coupling relationship between the early damage stage and the spectrum was mined through the deep learning algorithm to detect the early mechanical damage of Yuluxiang pear. Specific research contents: (1) The SERS spectrum data of Yuluxiang pear surface at different damage stages were obtained by constructing a highly sensitive SERS silver sol nano substrate combined with Raman spectrometer; (2) The S-G smoothing algorithm and the iterative adaptive weighted penalized least square method are used to preprocess the original spectrum to eliminate the fluorescence noise and baseline drift. (3) Data enhancement technology was used to expand the training data, a fast Fourier transform was used to extract features, and a one-dimensional Convolutional neural network (1D-CNN) model was constructed to detect the mechanical damage of Yuluxiang pear. The results showed that the model achieved ideal accuracy, precision, recall, and F1 score, especially when the injury was only 4 hours old. At the same time, the Raman characteristic peak of the protein in the injured part of pome fruit shifted from 1 607 to 1 589 cm-1. The study shows that SERS combined with deep learning has a strong discrimination ability in detecting mechanical damage of Yuluxiang pear early. This study provides a new research idea for the early detection of fruit damage and data support for developing high-sensitivity fruit quality detection sensors.
Key words：Surface-enhanced Raman scattering; Yuluxiang pear; Deep learning; Early damage detection
许德芳，关洪浦，赵华民，张淑娟，赵艳茹. 融合SERS和深度学习的玉露香梨机械损伤早期检测方法[J]. 光谱学与光谱分析, 2025, 45(06): 1712-1718.
XU De-fang, GUAN Hong-pu, ZHAO Hua-min, ZHANG Shu-juan, ZHAO Yan-ru. Early Detection Method of Mechanical Damage of Yuluxiang Pear Based on SERS and Deep Learning. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(06): 1712-1718.
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