Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm, which is simple and efficient, is not necessary to specify the number of clusters in advance, and can find any nonspherical class clusters. However, DPC relies heavily on the calculation methods of the cutoff distance threshold and local density and cannot analyze complex manifold data, especially datasets with uneven density distribution and multiple peaks in the same cluster. To solve these problems, we propose an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC). First, we redefine the local density calculation method using the layered k-nearest neighbors. To adapt to datasets with different densities, the k-nearest neighbors are divided into multiple layers. Second, for the multiple peaks in the same cluster problem, we design a new mechanism to calculate the similarity of subclusters based on the idea of shared neighbors and Newton's law of gravitation, and a subcluster merging strategy is proposed. To prove the effectiveness of our algorithm, we compare the LKSM_DPC with K-means, DBSCAN, DPC, and DPC derivatives for 24 datasets. A large number of experiments demonstrate that our algorithm can often outperform other algorithms.
Impact damage to apples is one of the most crucial quality factors and needs to be detected in postharvest quality sorting processes. In this study, the impact damage of the 'Red Fuji' apple fruit was investigated quantitatively by hyperspectral imaging technology. A total of 240 samples were prepared with six groups for different damage degrees. The hyperspectral imaging technique based on near-infrared (NIR) spectrometry in the range of 900-1700 nm was used to measure mechanical parameters, such as the average pressure, contact load, damaged area, absorbed energy, and damaged firmness. Four types of spectral pre-treatment, including the standard normal variate, multiplicative scatter correction, first-order derivative, and second-order derivative, were adopted to improve the model's predictive performance. The quantitative relationships between spectra and mechanical parameters were successfully modeled based on partial least squares (PLS) regression. For 'Red Fuji' apples, raw spectral data without pretreatment performed better than those after spectral pre-treatments. In this model, the characteristic wavelengths were selected by the Savitzky-Golay second-order derivative (SG 2 nd Der) and competitive adaptive reweighted sampling (CARS) method. The results indicate that the CARS-PLS regression model produced better results than the SG 2 nd Der-PLS regression model. The good prediction performances were presented by the coefficient of determination (R P 2 ) and root mean square errors of prediction (RMSEP) values. The R P 2 and RMSEP results of the average pressure, contact load, damaged area, absorbed energy, and damaged firmness are 0.66 and 0.02 MPa, 0.86 and 53.80 N, 0.83 and 116.37 mm 2 , 0.81 and 0.24 J, and 0.64 and 0.19 N, respectively. This study demonstrates the potential of the NIR hyperspectral imaging technique as a highly accurate way to quantitatively predict the mechanical parameters of apples.
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