This study systematically quantified and analyzed the correlation between different regions of interest (ROI) characteristics (part, shape, and size) and the prediction accuracy of soluble solids content (SSC) in kiwifruits to build a kiwifruit SSC prediction model by integrating hyperspectral imaging with ROI selection. The ROI spectral data of whole fruits were preprocessed using multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, standard normal variate (SNV) transformation, or SNV-SG smoothing. A partial least squares regression model was established to predict the SSC of kiwifruits, and performance analysis was conducted to determine the optimal preprocessing strategy. Furthermore, we extracted the ROI spectral information of different shape and size combinations at the equator, calyx, and peduncle of kiwifruits to compare the accuracy of the prediction model. The results revealed that SNV preprocessing yielded the best performance, with a coefficient of determination (R_P²) of 0.8327 and a root mean square error of prediction (RMSEP) of 0.3871 for whole-fruit ROI prediction set. The ROI characteristics significantly impacted the accuracy of SSC prediction, and the effects of fruit part, shape, and size followed the decreasing order: equator > calyx > pedicel; circular > square; and small > large. Notably, the small circular ROI at the equator yielded the optimal prediction, with R_P² = 0.9173 and RMSEP = 0.2217. This study demonstrates the crucial role of ROI optimization in hyperspectral image modeling, clarifies the advantages of the “equator-circular-small” combination, and provides an effective approach for improving the prediction accuracy of SSC in kiwifruits using hyperspectral technology.

In order to solve the problems of strong subjectivity and low targeting in sampling decision-making that exist in food safety surveillance sampling, this study proposed a correlation analysis method based on an improved Frequent Pattern-growth (FP-growth) algorithm for food risk factors. First, the entropy weight method was used to assign weights to the risk indicators of food categories so as to calculate the risk indices of different food categories. Second, the risk index was used as a feature for risk clustering based on MiniBatchKmeans to obtain the risk level of food products. Finally, an improved FP-growth algorithm with constraints was used for association rule mining of food risk factors to excavate the association relationship between the risk level of food products and the information of food types, time, and geographic attributes, and the mined results were analyzed by correlation analysis so as to provide guidance for precise targeting to guide the decision making of sampling inspection. This study was based on food sampling data from certain regions of China in 2019, which were assigned with indicators to calculate the risk index. Afterwards, the risk was clustered into low (L), medium (M), and high risk (H). Finally, the data was imported into the improved FP-growth algorithm to obtain the association rules of food risk factors. For 17214 pieces of sampling data, the improved FP-growth algorithm had a shorter running time when compared with the Apriori algorithm. Compared with the traditional one, the improved FP-growth algorithm removed invalid rules and improved the analysis efficiency of the association rules of food risk factors. Thus, it provides an accurate and efficient decision-making basis for the sampling work of food regulatory authorities.