关键词: 蜜柑, 近红外光谱, 卷积神经网络, 可溶性固形物, 无损检测

Abstract: Aiming at the problems of destructive traditional fruit quality detection methods such as sampling assay, spectral information loss and incomplete feature extraction in the existing regression prediction models,a novel model and method based on near‑infrared spectroscopy analysis technology and a one‑dimensional Convolutional neural network (1D-CNN) topredict the soluble solids content of citrus fruit were proposed in this paper. A dataset has been established by gathering near‑infrared spectroscopy of citrus fruits and determining their soluble solid content. The network structure parameters have been optimized to enhance the performance of the model, including the depth of the network structure, the size and quantity of convolutional kernels, the presence or absence of batch normalization (BN) layers, pooling methods, the depth of fully connected layers, and Dropout values. The final 1D-CNN model comprises two convolutional layers, two BN layers, two maximum pooling layers, and two fully connected layers, with a Dropout value of 0.2. Traditional Partial least squares regression, principal component regression, and support vector machine regression prediction models have been established to compare their performance with the 1D-CNN model. The outcomes reveal that the 1D-CNN model exhibits significantly superior prediction accuracy and model stability compared to conventional algorithms. The Root‑mean‑square deviation on the verification set is 0.333 9, and the Coefficient of determination is 0.865 5. This demonstrates that the 1D-CNN model can carry out feature extraction of citrus near‑infrared spectral data, thereby permitting non‑destructive detection of soluble solid content in citrus. Consequently, this approach provides a better solution for non‑destructive detection of citrus based on near‑infrared spectral analysis technology.

Key words: citrus, near?infrared spectroscopy, convolutional neural network, soluble solids, non?destructive testing