Mass-defect filtering of isotope signatures to reveal the source of chlorinated palm oil contaminants

This paper reports new insights at the molecular level into the route of a worldwide problem of the food industry: the occurrence of monochloro-propanediol (MCPD) esters. The application of mass defect-driven workflows is described to generate a hypothesis on the identity and occurrence of those thermally labile, chlorinated contaminant precursors that may act as chlorine donors during the formation of MCPD esters. For the first time, holistic mass-defect filtering of isotope signatures is used to pinpoint completely unknown and unexpected chlorine-containing substances naturally present in various extracts of palm fruit and partially and fully refined oils. Supervised multivariate analysis showed the effective classification of samples from various stages of industrial processing, suggesting that these steps strongly impact a complex and dynamic pool of chlorinated substances. In-vitro experiments confirmed that several of these naturally occurring chlorinated plant constituents decompose upon heat treatment, thus potentially being a source of chlorine for further reactions with palm oil lipids in a subsequent chlorination cascade. It is hypothesised that during oil refining the organochlorines naturally present in palm fruits act as a 'chlorine source' for the generation MCPD diesters. This discovery implies that industrial efforts targeting the mitigation of chlorinated substances must intervene at the earliest possible production stage or preferably even prior to oil processing. Current performance and limitations of mass-defect filtering are discussed and future developments are outlined.     

Using compiler optimization techniques to detect equivalent mutants

Mutation analysis is a software testing technique that requires the tester to generate test data that will find specific, well-defined errors. Mutation testing executes many slightly differing versions, called mutants, of the same program to evaluate the quality of the data used to test the program. Although these mutants are generated and executed efficiently by automated methods, many of the mutants are functionally equivalent to the original program and are not useful for testing. Recognizing and eliminating equivalent mutants is currently done by hand, a time-consuming and arduous task. This problem is currently a major obstacle to the practical application of mutation testing. This paper presents extensions to previous work in detecting equivalent mutants; specifically, algorithms for determining several classes of equivalent mutants are presented, an implementation of these algorithms is discussed, and results from using this implementation are presented. These algorithms are based on data flow analysis and six compiler optimization techniques. Each of these techniques is described together with how they are used to detect equivalent mutants. The design of the tool and some experimental results using it are also presented.