Synergetic application of thermal imaging and CCD imaging techniques to detect mutton adulteration based on data-level fusion and deep residual network.

To improve the performance of single thermal imaging and single CCD imaging in detecting unknown adulterated meat samples, these two imaging techniques combined with a deep residual network were synergistically applied to detect mutton adulteration. Considering the importance of spatial and detailed information in improving stability and accuracy, three data-level fusion methods, namely, colour image stitching, grey image stitching and grey channel stacking, were proposed for the fusion of thermal images and CCD images. Classification and prediction models were further developed based on fusion images. The results showed that the models with colour image stitching achieved the best performance. For the external validation set, the accuracy of the best classification model in discriminating five categories was 99.30%. In predicting pork proportions, the R2, RMSE, RPD and RER of the best prediction model were 0.9717, 0.0238, 7.8696 and 21.28, respectively. The best prediction model for duck proportions had a R2 of 0.9616, RMSE of 0.0277, RPD of 5.1015, and RER of 14.44. Therefore, the synergetic application of thermal imaging and CCD imaging can provide a novel and promising tool to detect mutton adulteration and the quality of other food items.

Quantifying local density of optical states of nanorods by fluorescence lifetime imaging.

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.

A Markov random field based approach to the identification of meat and bone meal in feed by near-infrared spectroscopic imaging.

Contaminated meat and bone meal (MBM) in animal feedstuff has been the source of bovine spongiform encephalopathy (BSE) disease in cattle, leading to a ban in its use, so methods for its detection are essential. In this study, five pure feed and five pure MBM samples were used to prepare two sets of sample arrangements: set A for investigating the discrimination of individual feed/MBM particles and set B for larger numbers of overlapping particles. The two sets were used to test a Markov random field (MRF)-based approach. A Fourier transform infrared (FT-IR) imaging system was used for data acquisition. The spatial resolution of the near-infrared (NIR) spectroscopic image was 25 µm × 25 µm. Each spectrum was the average of 16 scans across the wavenumber range 7,000-4,000 cm(-1), at intervals of 8 cm(-1). This study introduces an innovative approach to analyzing NIR spectroscopic images: an MRF-based approach has been developed using the iterated conditional mode (ICM) algorithm, integrating initial labeling-derived results from support vector machine discriminant analysis (SVMDA) and observation data derived from the results of principal component analysis (PCA). The results showed that MBM covered by feed could be successfully recognized with an overall accuracy of 86.59% and a Kappa coefficient of 0.68. Compared with conventional methods, the MRF-based approach is capable of extracting spectral information combined with spatial information from NIR spectroscopic images. This new approach enhances the identification of MBM using NIR spectroscopic imaging.

An overview of the legislation and light microscopy for detection of processed animal proteins in feeds.

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.