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Leafy vegetables are consumed in most daily xdiets worldwide. As living standards improve, food quality, safety requirements, and nutrition are becoming increasingly important to consumers when purchasing leafy vegetables. This study proposes an evaluation and traceability method that can be used to track the nutritional quality of leafy vegetables. Employing the principles of the Hazard Analysis and Critical Control Point (HACCP) system combined with fault tree analysis (FTA), a traceability model for the entire production and sale process of leafy vegetables is constructed. Four common leafy vegetables, spinach, rape, lettuce, and celery are examined in this research to establish a nutritional quality index system using fuzzy mathematics subordinate function method to evaluate nutritional quality. A nutritional quality and safety traceability system based on browser/server architecture and quick response (QR) code is then designed and developed for full traceability of leafy vegetable quality. This method can ensure food safety and hygiene through the control of key factors affecting food safety throughout the entire supply chain process.

China is the largest producer and consumer of vegetables, its vegetable industry is playing an important role in the domestic agricultural sector and global vegetable export market. It is important to promote the long-term sustainable development of Chinese vegetable industry for food security and quality of people’s lives. To find out the intrinsic way to promote the development of Chinese vegetable industry, this paper analyzed the influencing factors of Chinese vegetable production by utilizing the LMDI method and demonstrated the spatial-temporal characteristics of vegetable production through application of the ArcGIS spatial autocorrelation analysis method. The results showed that the influencing factors of vegetable production were the cultivated land area, multiple cropping index, vegetable planting proportion and vegetable yield per hectare in China. The major driving factor had changed from vegetable planting proportion to vegetable yield per hectare. The influencing degrees of factors on vegetable production are different in different regions, regionalization is therefore a major feature of Chinese vegetable production. The government should take production technology, regionalization-driven effect, and marketing integration into consideration to promote the development of Chinese vegetable industry. Keywords: Vegetable production, Influencing factors, Contribution ratio, Logarithmic mean Divisia index (LMDI)

Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar−acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch “Spring Belle”) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

Tricholoma matsutake (T. matsutake) growing in Tibet is very popular for its high economic and medicinal value, but fresh T. matsutake has an extremely short shelf life. The shelf life of T. matsutake is complex, influenced by product characteristics, surrounding environmental conditions, and spoilage development. The objective of this work was to study the quality characteristics of fresh T. matsutake during its shelf life period in modified atmosphere packaging (MAP) conditions and establish its remaining shelf life prediction models in a cold chain. In this study, we measured and analyzed quality indicators of fresh T. matsutake, including hardness (cap, stipe), color, odor of sensory characteristics, pH, soluble solids content (SSC), and moisture content (MC) of physical and chemical characteristics under the temperature condition of 4 °C and relative humidity (RH) of 90%. The sensory evaluation results showed that the odor indicator in sensory characteristics was more sensitive to the freshness of T. matsutake. The changes of pH, SSC, and MC were divided into three periods to analyze the physiological changes of T. matsutake. The cap spread process could affect the changes of pH, SSC, and MC in period S1, and they changed gradually in period S2. In the period S3, they changed complicatedly because of deterioration. The remaining shelf life prediction model of T. matsutake was established by the back propagation (BP) neural network method to quantify the relationship between the quality indicators and the remaining shelf life. The shelf life characteristics are complex, which were optimized by correlation analysis. Significant benefits of this work are anticipated on the transportation and preservation of fresh T. matsutake to the market and the reduction of its losses in the postharvest chain.

Low-carbon production is one of the dominating issues in the sustainable development of the food industry with high energy consumption, especially in the table grapes cold chain. The aim of this paper is to propose a profit maximization strategy of table grapes cold chain by integrating the carbon footprint to improve the low-carbon production and sustainability of the cold chain system. The carbon footprint was evaluated by life cycle assessment (LCA) in entire cold chain system of table grapes, and the economic order quantity (EOQ) model was used to develop the profit maximization model with minimal carbon footprint and to maintain the optimal balance between stock and cost. The profit optimization performance, the sensitivity performance and the influencing factors of the decay rate, the carbon emission price, and the distance and carbon emission coefficient in refrigerated transport were analyzed according to the profit maximization model and the inventory data in actual cold chain investigated. The sensitivity performance analysis illustrated that the selling price had the highest sensitivity, and the carbon emission coefficient in storage had the lowest sensitivity. The comprehensive analysis results indicated that there is an optimal combination point between the economy and environment in actual cold chain, which not only reduced the carbon emission, but also had minimal impact on the profit in cold chain. The enterprises should integrate the carbon footprint cost into the profit maximization once the carbon emission tax is levied. The proposed strategy of the profit maximization with carbon footprint constraint is also suitable for improving profit maximization of other low-carbon supply chain applications.