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At present, China’s food inspection and testing mainly concentrate in food processing, packaging and sale, so that supervision over the food production process is lacking, and the regulation of the large number of production and operation entities and of the intertwining of various risks is ineffective. The cost of controlling food quality and safety by means of inspection and testing is high, and a large amount of data has not been fully utilized, causing lack of effective support for quality control in food processing. This article summarizes the current status of research on food inspection and quality control in China, analyzes the existing problems and puts forward some suggestions such as strengthening the ability to analyze and mine data, finding the law of food safety risks, targeted detection of high-risk indicators according to the law, improving the detection efficiency and the risk control ability, and guiding the monitoring and management of food safety risks. Research on quality control in food processing by comprehensive application of test data can promote the development of quality control in food processing, which can in turn improve the level of risk control and ensure food safety.

Rosa roxburghii (R. roxburghii), native to the southwest provinces of China, is a fruit crop of important economic value in Guizhou Province. However, the changes in fruit quality and flavor during R. roxburghii fruit ripening have remained unknown. Here, this study investigated the changes of seven active components and volatile organic compounds (VOCs) during the ripening of the R. roxburghii fruit at five different ripening stages including 45, 65, 75, 90, and 105 days after anthesis. The results indicated that during the ripening process, the levels of total acid, vitamin C, and soluble sugar significantly increased (p < 0.05), while the levels of total flavonoids, superoxide dismutase (SOD), and soluble tannin significantly decreased (p < 0.05). Additionally, the content of total phenol exhibited a trend of first decreasing significantly and then increasing significantly (p < 0.05). A total of 145 VOCs were detected by HS-SPME-GC-MS at five mature stages, primarily consisting of aldehydes, alcohols, esters, and alkenes. As R. roxburghii matured, both the diversity and total quantity of VOCs in the fruit increased, with a notable rise in the contents of acids, ketones, and alkenes. By calculating the ROAV values of these VOCs, 53 key substances were identified, which included aromas such as fruit, citrus, green, caramel, grass, flower, sweet, soap, wood, and fat notes. The aromas of citrus, caramel, sweet, and wood were predominantly concentrated in the later stages of R. roxburghii fruit ripening. Cluster heatmap analysis revealed distinct distribution patterns of VOCs across five different maturity stages, serving as characteristic chemical fingerprints for each stage. Notably, stages IV and V were primarily characterized by a dominance of alkenes. OPLS-DA analysis categorized the ripening process of R. roxburghii fruit into three segments: the first segment encompassed the initial three stages (I, II, and III), the second segment corresponded to the fourth stage (IV), and the third segment pertained to the fifth stage (V). Following the variable importance in projection (VIP) > 1 criterion, a total of 30 key differential VOCs were identified across the five stages, predominantly comprising ester compounds, which significantly influenced the aroma profiles of R. roxburghii fruit. By integrating the VIP > 1 and ROAV > 1 criteria, 21 differential VOCs were further identified as key contributors to the aroma changes in R. roxburghii fruit during the ripening process. This study provided data on the changes in quality and aroma of R. roxburghii fruit during ripening and laid the foundation for the investigation of the mechanism of compound accumulation during ripening. [ABSTRACT FROM AUTHOR]

Since the actual food safety monitoring data have characteristics of high-dimension, complexity, discreteness and nonlinear properties, it is difficult to accurately predict the risk of actual food inspection process. Therefore, this paper proposes a predictive modeling approach based on analytic hierarchy process (AHP) integrated extreme learning machine (ELM) (AHP-ELM). The proposed approach utilizes the AHP model to obtain the effective process characteristic information (PCIs). Compared with the analytic hierarchy process (AHP) integrated traditional artificial neural network (ANN) approach, the AHP-ELM prediction model is effectively verified by executing a linear comparison between all PCIs and the effective PCIs through daily inspection data source from the supervision and inspection department repository of China quality supervision system. Finally, the PCIs and the prediction value are obtained to provide more reliable food information and identification of potentially emerging food safety issues. The proposed method is applied to the food safety early warning and monitoring system in China. The result shows that the proposed model is effective and feasible in processing the complex food inspection data. Meanwhile, it can help to improve the quality of food products, ensure food safety and reduce the risk of food safety. [ABSTRACT FROM AUTHOR]