Degradation of Aflatoxin B1 in Peanut Oil by Ultraviolet-LED Cold-Light Irradiation and Structure Elucidation of the Degradation Products.

This study aimed to determine the efficiency of ultraviolet (UV)-LED cold light treatment on the degradation of aflatoxin (AF)B1 in peanut oils. The peanut oil samples obtained from different places in China and abroad were determined for AFB1 degradation efficiency of the UV-LED cold-light irradiation method. The degradation products were analyzed by ultra-high performance liquid chromatography coupled to quadrupole orbitrap high-resolution mass spectrometry (UPLC-Q-Exactive MS). The results indicated that the AFB1 content in all peanut oil samples decreased rapidly after 5 min of irradiation. Four main photodegradation products (C18H16O7, C17H14O7, C17H14O7, and C17H14O8) were identified using the established LC-MS method. Their chemical structures were postulated based on the LC-MS data. Also, the degradation pathways were proposed based on the data obtained. Oxidation and reduction reactions were mainly responsible for AFB1-decomposition. The reactions occurred at the furan and lactone rings. These findings demonstrated that UV-LED cold-light irradiation was an effective method for treating AFB1- contaminated peanut oil.

Phylogeny inference based on spectral graph clustering.

Phylogeny inference is an importance issue in computational biology. Some early approaches based on characteristics such as the maximum parsimony algorithm and the maximum likelihood algorithm will become intractable when the number of taxonomic units is large. Recent algorithms based on distance data which adopt an agglomerative scheme are widely used for phylogeny inference. However, they have to recursively merge the nearest pair of taxa and estimate a distance matrix; this may enlarge the error gradually, and lead to an inaccurate tree topology. In this study, a splitting algorithm is proposed for phylogeny inference by using the spectral graph clustering (SGC) technique. The SGC algorithm splits graphs by using the maximum cut criterion and circumvents optimization problems through solving a generalized eigenvalue system. The promising features of the proposed algorithm are the following: (i) using a heuristic strategy for constructing phylogenies from certain distance functions, which are not even additive; (ii) distance matrices do not have to be estimated recursively; (iii) inferring a more accurate tree topology than that of the Neighbor-joining (NJ) algorithm on simulated datasets; and (iv) strongly supporting hypotheses induced by other methods for Baculovirus genomes. Our numerical experiments confirm that the SGC algorithm is efficient for phylogeny inference.