No abstract

From the first cases of bovine spongiform encephalopathy (BSE) among cattle in the United Kingdom in 1986, the route of infection of BSE is generally believed by means of feeds containing low level of processed animal proteins (PAPs). Therefore, many feed bans and alternative and complementary techniques were resulted for the BSE safeguards in the world. Now the feed bans are expected to develop into a "species to species" ban, which requires the corresponding species-specific identification methods. Currently, banned PAPs can be detected by various methods as light microscopy, polymerase chain reaction, enzyme-linked immunosorbent assay, near infrared spectroscopy, and near infrared microscopy. Light microscopy as described in the recent Commission Regulation EC/152/2009 is the only official method for the detection and characterization of PAPs in feed in the European Union. It is able to detect the presence of constituents of animal origin in feed at the level of 1 g/kg with hardly any false negative. Nevertheless, light microscopy has the limitation of lack of species specificity. This article presents a review of legislations on the use of PAPs in feedstuff, the detection details of animal proteins by light microscopy, and also presents and discusses the analysis procedure and expected development of the technique.

One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy (LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem, this paper investigated a combination of time-resolved LIBS and convolutional neural networks (CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R 2 c =0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network (ANN), showing R 2 v =0.6318 and the root mean square error of validation (RMSEV)=0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R 2 v =0.7366 and RMSEV=0.7855. These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K. However, due to limited calibration samples, the two-dimensional models presented over-fitting. The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R 2 v =0.9968 and RMSEV=0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.

In this letter, we demonstrate a facile far-field approach to quantify the near-field local density of optical states (LDOS) of a nanorod using CdTe quantum dots (QDs) emitters tethered to the surface of nanorods as beacons for optical read-outs. Radiative decay rate was extracted to quantify the LDOS; our analysis indicates that the LDOS of the nanorod enhance both the radiative and nonradiative decay of QD, particularly radiative decay of QDs at the end of nanorod is enhanced by 1.17 times greater than that at the waist, while the nonradiative decay was uniformly enhanced over the nanorod. To the best of our knowledge, our effort constitutes the first to map the LDOS of a nanostructure via far-field method, to provide clarity on the interaction mechanism between emitters and the nanostructure, and to be potentially employed in the LDOS mapping of high-throughput nanostructures.