Determination of bovine lactoferrin in lactoferrin-supplemented dairy products and raw milk by an automated latex assay

Latex immune agglutination method with a multipurpose auto-analyser (the automated latex assay) was validated for determination of bovine lactoferrin (BLF) in various dairy products. Reproducibility-within-laboratory (intermediate precision) due to day for infant formula, UHT milk and yogurt supplemented with BLF at 50 mg/100 g for infant formula and UHT milk, and at 100 mg/100 g for yogurt were 1.62 to 3.10 mg/100 g. Reproducibility-within-laboratory due to analysis (morning, noon, and evening) for raw milk was 1.59 mg/100 g. Trueness, accuracy in determining known amounts added for BLF-supplemented dairy products was -4.7 to -2.0 mg/100 g. BLF concentration in raw milks was 20.3 to 21.8 mg/100 g. Although interference by the matrixes took place in infant formula and raw milk, BLF assays were accurately carried out by 2000-fold dilution or standard-addition method. Automated latex assay for BLF is simple, rapid, precise and accurate enough for a routine method in various dairy products containing BLF.     

Twitter analysis algorithms for Intelligence Circulation System

The convergence of broadcasting and broadband communications network technologies has attracted increasing attention as a means to enrich the television viewing experience of viewers. Toward this end, this study proposes the ‘Intelligence Circulation System (ICS)’, which provides several services, by using newly developed algorithms for analysing Twitter messages. Twitter users often post messages about on-air TV programmes. ICS obtains viewer responses from tweets without requiring any new infrastructure or changes in users’ habits or behaviours, and it generates and provides several outputs to heterogeneous devices based on the analysis results. The algorithms—designed by considering the characteristics of Twitter messages about TV programmes—use auxiliary programme information, similarity between messages, and time series of messages. An evaluation of our algorithms using Twitter messages about all programme genres for a month showed that the accuracy of topic extraction was 85 % for an emphasis on quality (with 56 % of messages processed) and 65 % for an emphasis on quantity (with 95 % of messages processed). The accuracy of message sentimental classification was 66 %. We also describe social recommendation services using the analysis result. We have created a Social TV site for a large-scale field trial, and we have analysed users’ behaviours by comparing four types of social recommendation services on it. The experimental result shows that active and passive communication users had different needs with regard to the recommendations. ICS can generate recommendations for satisfying the needs of both user types by using the analysis result of Twitter messages.