Currently, food quality has become a major concern for the food industry. To efficiently detect food quality problems during the production process, food enterprises must build quality monitoring systems. However, in a traditional quality monitoring system, data tampering and centralized storage have become barriers to reliability. In addition, due to lack of sufficient automation, traditional quality monitoring approaches are usually inefficient. Fortunately, blockchain is a promising technology that is tamper-proof and decentralized. Moreover, smart contracts, which are executable codes on the blockchain platform, are able to conduct transactions between mutually untrusted parties and are self-executing and self-verifying. By combining smart contracts and quality evaluation models, this paper presents an intelligent quality monitoring system for fruit juice production. This system has the characteristics of high automation and high reliability. In this system, response surface models are established based on preproduction data, and the optimal production condition for each stage is identified. During the actual production process, smart contracts are executed to record production data on a blockchain. These data serve as the inputs for evaluation models. Based on the evaluation outcome, smart contracts will decide whether the production process can be resumed or not. To evaluate the feasibility of the presented system, a prototype version of the quality monitoring system for flat peach juice production is implemented based on the Ethereum platform and executed in the Remix IDE.

Peppertree prickly ash, Amomum tsao-ko, cumin, and ginger have long been used in Asian countries to modify the flavor and to partially neutralize any unpleasant odors present in roast lamb. The purpose of this study was to evaluate the change in the amount of volatile components present in roast lamb compared to meat added with peppertree prickly ash, Amomum tsao-ko, cumin, and ginger. Principal component analysis was carried out on the 27 initially selected from 88 volatile substances, and 15 substances with a projection of more than 0.25 in the load matrix were used as indicators to study the different contents in roasted mutton and lamb prepared by adding peppertree prickly ash, Amomum tsao-ko, cumin, and ginger. The types of VOCs (volatile organic compounds) detected in roast meat without adding spices were the least. Roast meat with the addition of cumin leads to the strongest content of aldehydes, followed by the addition of Amomum tsao-ko. Additionally, roast meat with the addition of Chinese prickly ash leads to the strongest content of terpenes, followed by the addition of ginger. Moreover, with the addition of spices, the content of volatiles responsible for the presence of a mutton odor (such as hexanal, heptanal, pentanal, (z)-4-decenal, benzaldehyde, p-propyl-anisole, and dimethyl ether) was not significantly decreased, and in fact some volatiles increased in amount such as pentanal, hexanal, octanal, and (z)-4-decenal. In conclusion, the effect of addition of spices on the volatile profile of roasted mutton and lamb can be attributed to the generation of flavor volatiles mainly derived from raw spices’ hot action, with few additional volatiles formed during boiling.