New Sweet Potato Line having low Gelatinization Temperature and altered Starch Structure

A new sweet potato breeding line, Kanto 116, was developed, featuring low gelatinization temperature and an altered starch fine structure. Starch granules from Kanto 116 showed an abnormal morphology characterized by cracking into granules. Starch content, amylose content and tuberous root appearance of Kanto 116 were similar to those of the control and the parents. Pasting temperatures of Kanto 116 starch determined by the Rapid Visco Analyser were 51.4 — 52.6 °C, approximately 20 °C lower than those of the control and parents starches. Onset, peak, and conclusion temperature of gelatinization, and gelatinization enthalpy of Kanto 116 starch determined by differential scanning calorimetry were 39.0 °C, 46.9 °C, 64.8 °C, and 8.8 J/g, respectively, and much lower than those of the control and parents starches. The chain‐length distribution of the amylopectin molecules, determined by high‐performance anion‐exchange chromatography, showed that Kanto 116 starch had a higher proportion of short chains (DP 6 — 11) and a lower proportion of chains between DP 12 — 28 than control and parent starches. The debranched β‐limit dextrin of Kanto 116 starch also showed that the proportion of both short and long B1 chains was different from those of the control and parents starches.     

Evolutionary selection of interesting class association rules using genetic relation algorithm

During the last years, several association rule‐based classification methods have been proposed, these algorithms may quickly generate accurate rules. However, the generated rules are often very large in terms of the number of rules and usually complex and hardly understandable for users. Among all the rules generated by the algorithms, only some of them are likely to be of any interest to the domain expert analyzing the data. Most of the rules are either redundant, irrelevant or obvious. In this paper, a new method for selecting the interesting class association rules is proposed by an evolutionary method named genetic relation algorithm. The algorithm evaluates the relevance and interestingness of the discovered association rules by the relationships between the rules in each generation using a specific measure of distance among them giving a reduced set of rules as the result in the final generation. This small rule set has the following properties: (i) accurate as it has at least the same classification accuracy as the complete association rule set, (ii) interesting because of the diversity of rules and (iii) comprehensible because it is more understandable for the users as the number of attributes involved in the rules is also small. The efficiency of the proposed method is compared with other conventional methods including genetic network programming‐based mining using ten databases and the experimental results show that it outperforms others keeping a good balance between the classification accuracy and the comprehensibility of the rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.