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The detection information of farm products increases rapidly, and plays an important role in traceability. The existed detection processing method used the mode of testing sample with detection instruments and manual recording data. This mode leads to the hazard of unassociated batches and distorted information, so the direct and credible effects of detection information in the traceability content are needed. In this paper, the reasons why detection information is not able to be integrated in the existed traceability system were analyzed. Modified system framework with detection information extracting and identification was proposed, which combined the rapid real-time detection instrument for vegetable pesticide residues mainly using enzyme inhibition method. In the detection process, information including detection ID, farm product batch and inhibition rate was recorded. In order to implement the automation reading and extracting of detection information, the access flowchart of detection data included the steps of instrument connection, vegetable detection, data extraction and data storing. The correlation between vegetable plant batch, detection identification number and product traceability code was the other important step for detection information tracing. Because one vegetable plant batch may be divided into many package units, the relationship between the batch code of vegetable plants and product traceability code was the type of 'one to many'. Because of the similar characteristics in one plant batch, the same batch of vegetable was detected once and the relationship between vegetable plant batch code and detection identification code was the type of 'one to one'. The batch correlation between product traceability code and detection identification code was established with the link of vegetable plant batch code. Based on .Net platform and the existed vegetable safety production management system, the vegetable production management and traceability system was updated for three functions. The detection instrument linked the vegetable production management and traceability system with serial port, and linking self-checking was implemented in the instrument link function. Then detection data was obtained through special data format. The detection data could be displayed but not modified in the data management function. The updated system was applied in 153 vegetable production bases in Tianjin city. During March 1st to May 30th in 2014, 28038 vegetable samples were detected and data obtain success rate with the updated system was 100%, and the data acquirement efficiency was improved. Furthermore, the detection results of 20 samples were compared between rapid real-time detection by enterprise self-check and casual inspection by supervision departments. The 20 samples were divided into two groups with No.1-8 samples using the manual recording mode of detection data without the updated the system and No.9-20 obtaining the detection data by automatic reading with the updated the system. The application results showed that the data acquisition success rate was 100% and the difference between the enterprise self-check value and the supervision casual inspection value was low with the absolute value of 4.37 after using the updated the system. The detection value with manual recording of No.3 sample was obviously lower than the one by supervision departments, which has the possible of distort data. Furthermore, the traceability results for using and not using the updated system were compared. Through using the system, the traceability results could indicate the precision detection information through the batches' correlation. The study in this paper presents an important step for satisfying the deeper requirement in traceability system. [ABSTRACT FROM AUTHOR]

With the development of research and application of traceability systems, it plays a key role that the traceability system is established with appropriate granularity for deeper application according to the supply chain actual requirement and informatization level of subjects. The existing research in traceability width, depth, and accuracy were descriptive language, or lacked a quantitative evaluation model, which lead to the difference of the traceability system not being able to be measured well. In this paper, three layers of architecture of a basic traceability system were analyzed, which included product identification, information collection, and database construction of the center. The descriptive definition of traceability granularity was established with traceable unit size and its horizontal information capacity and longitudinal tracking depth. A two-layer index system was constructed with the core of traceability precision, width, and depth. The evaluation factors included a external traceability unit, an internal turning unit, an identification unit exchange, information collection content, information update frequency, forward tracing distance, and backward tracing distance. In order to eliminate qualitative indicators difficult to measure, a five point grading assignment method was used to obtain a qualitative index value. The evaluation factors' weight was confirmed using an AHP (analytic hierarchy process) method with three steps through investigation on 20 related areas experts. First, the initial judgment matrix and comparison matrix was established through the comparison of various elements. Secondly, a single order layer was taken. Finally, the final judgment matrix was obtained through the consistency test, and then all the target weights set were obtained. In the weights set, the external traceability unit weight was the highest with 0.1985. For the cooperative supply chain, an external traceability unit with the difference between a single product, single batch and the mixed batch, affected other unit sizes and information collection, so it played an important role in the whole index system. The lowest weight was an identification unit exchange with the value of 0.0872. Because the identification unit exchange had a correlation with an external traceability unit and an internal turning unit, the identification unit exchange had relatively less effects on traceability granularity with a determined internal and external unit. This research can provide quantitative evidence for evaluating the difference of traceability systems. [ABSTRACT FROM AUTHOR]