In this paper, 317 literature in the Web of Science (WoS) related to research on apple by near‐infrared spectroscopy (NIRS) were drawn on the knowledge map of the number of literature, the co‐occurrence network of authors and institutions, the co‐occurrence and clustering of keywords based on CiteSpace. And a related analysis was carried out. Combined with the results of visual analysis and related literature, the research hotspots were sorted out and discussed. This paper provides a certain reference for relevant researchers to study in this field and provides a new method for macroscopically grasping the current status of apple quality detection research, which helps new researchers to quickly integrate into this field and obtain more valuable scientific information.

To explore a new method for the detection of soluble solids content (SSC) in apples, the reflectance spectra of apples with different SSC were obtained based on hyperspectral imaging technology in this paper, and fractal measurement of the reflection spectrum curve was carried out based on fractal theory, for using fractal dimension to quantitatively reflect its SSC. The results show that the samples with different SSC have little difference in fractal dimension, and are not sensitive to the change of apple SSC, which cannot show an obvious linear relationship. It can be considered to conduct a study on discrimination with obvious differences between spectral curves, such as variety discrimination and damage discrimination.

The bar fine-cropping, the first and one of the most important metal-forming procedure, separates the work-piece into accurate products and billets. The aim of this article is to investigate the influence of different main motor rotational frequencies on a new fine-cropping system. Five different rotational frequencies are applied to both the numerical simulations and the fine-cropping experiments. The numerical and experimental results show that good agreement is achieved by combining the average stress triaxiality under different stress states: the equivalent plastic strain, the fatigue-crack propagation path analysis and the micrograph fractography observation. Additionally, the cross-section quality of cropped billets and the final cropping time is also investigated. The results show that the cross-section quality and the final cropping time of the new fine-cropping are greatly influenced by the main motor rotational frequency, especially the frequency close to 33 Hz.

The difficulty of feature extraction and the small sample size are two challenges in the field of mechanical fault diagnosis for a long time. Here we propose an intelligent mechanical fault diagnosis method for scenario with small sample datasets. This method can not only diagnose bearing faults but also gear faults, and has strong generalization performance. We use convolutional neural network to realize automatic feature extraction. Through sliding window scanning, one sample set is expanded to three sub-sample sets with different scales to meet the needs of deep learning training. Three convolutional networks are used to extract the features of the subsets respectively to ensure that their useful features are fully extracted. After feature extraction, the feature is reconstructed through feature splicing. Because of the unique advantages of SVM in dealing with small sample sets, we use SVM to classify the reconstructed features. We use the bearing data set collected by Case Western Reserve University in the United States, the bearing fault data set collected by Xi'an Jiaotong University in China, and the gearbox fault data collected by the University of Connecticut in the United States to conduct experiments. The experimental results show that the accuracy of training, validation and testing of the proposed method on the three data sets all reach 100%. This proves that our method can not only tackle the two challenges, but also has high fault diagnosis accuracy and strong generalization performance. It is hoped that our proposed method can contribute to the development of mechanical fault diagnosis.

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