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The incidence of patulin (PAT) in fruit products is a worldwide concern due to its acute and chronic toxic effects. Therefore, accurate and reliable PAT measurements are important for preventing consumer health risks. Our previous method, which was based on a common technique that uses ethyl acetate extraction and liquid chromatography-tandem mass spectrometry with isotope dilution (ID-LC-MS/MS), has shown great performance for the determination of PAT in apple products. However, prolonged extraction times and multistep clean-up processes were required to sufficiently eliminate the matrix interferences. Herein, a feasible alternative ID-LC-MS/MS method was successfully established, employing simplified and reliable sample preparation steps. The clean-up process was performed using molecularly imprinted polymer-solid-phase extraction (MIP-SPE) cartridges, which eliminated matrix interferences and facilitated the trace quantification. While the previous method used a multimode LC column for the retention of polar patulin, the current method used a UPLC HSS T3 column, which further improved the peak sharpness and reduced the run time. The method was validated by measuring fortified samples in the concentration range of 5‒100 µg/kg. The accuracy varied between 97.8 and 102.0%, with relative standard deviation for interday and intraday precision being below 3%. The measurement uncertainty was lower than 4% (at a 95% level of confidence). Therefore, this method demonstrated adequate metrological quality with greatly enhanced performance over various reported methods. Additional key benefits of this method are easy manipulation, short preparation time, and lower consumption of hazardous solvents. Finally, the method was successfully applied to commercially available apple-based products.

A method for analyzing fluoroquinolone antibiotics, viz. enrofloxacin and ciprofloxacin, in chicken meat has been developed with the aim of reducing the total analysis time. Certified reference materials (CRMs) for the chicken meat powder, i.e. KRISS CRM 108-03-003 and KRISS CRM 108-03-004, were used for method development. Isotope dilution ultra-performance liquid chromatography–mass spectrometry (ID-UPLC-MS) was employed for sample analysis. The liquid chromatography (LC) conditions were optimized with an “in-sample addition” method using a trace amount of ethylenediaminetetraacetic acid (EDTA), in which EDTA was not added to the mobile phase but to the sample alone. The optimal EDTA amount was determined, and its influence on the separation of the target analyte was investigated. After the feasibility of the “in sample-addition” approach was confirmed, we attempted to improve the previously established sample pretreatment method by varying the conditions for liquid–liquid extraction and solid phase extraction. The developed method was validated using raw chicken meat for recovery evaluation. The “in-sample addition” method afforded highly improved peak shapes without adversely affecting the LC/MS method performance, and the quantification values of enrofloxacin and ciprofloxacin corresponded well with those obtained using the established method. Consequently, the total analysis time for the determination of enrofloxacin and ciprofloxacin in chicken meat CRM could be reduced by simplifying the sample pretreatment procedure. The developed method delivered better reliability compared to the previously established analytical method. The method will be used for stability monitoring of the chicken meat CRM and the development of other meat CRMs for fluoroquinolone analysis and improved food safety applications.

We compare image domain and projection domain denoising approaches with self-supervised learning for ultra low-dose cone-beam CT (CBCT), where number of detected x-ray photons is significantly low. For image-domain self-supervised denoising, we first reconstruct CBCT images with the standard filtered backprojection. For model training, we use blind-spot filtering to partially blind images and recover the blind spots. For projection-domain self-supervised denoising, we regard the post-log projections as training examples of convolutional neural network. From experimental results with various low-dose CBCT settings, the projection-domain denoiser outperforms the image-domain denoiser both in image quality and accuracy for ultra low-dose CBCT.

A corn flour certified reference material (KRISS CRM 108-01-011) was developed to ensure accurate and reliable measurements of type B trichothecenes (deoxynivalenol, nivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol), and deoxynivalenol-3-glucoside. The material was freeze-dried, ground, sieved, and well-mixed. The final produced CRM was packaged at 14 g per unit and stored at -70 °C. The certification was performed using isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS). For simultaneous characterization and homogeneity assessments, ten units were randomly selected and analyzed. The among-unit relative standard deviations were below 1 % for all mycotoxins, indicating excellent homogeneity of the CRM. The stability of the CRM was also assessed at various designated temperatures and test periods. The uncertainties of the certified values varied between 2.4 % and 6.2 %, thereby confirming their higher-order metrological quality to provide references for testing laboratories. In case of deoxynivalenol-3-glucoside, an information value was assigned due to the lack of its traceability to the SI units.

Deep learning has recently attracted widespread interest as a means of reducing noise in low-dose CT (LDCT) images. Deep convolutional neural networks (CNNs) are typically trained to transfer high-quality image features of normal-dose CT (NDCT) images to LDCT images. However, existing deep learning approaches for denoising LDCT images often overlook the statistical property of CT images. In this paper, we propose an approach to statistical image restoration for LDCT using deep learning. We introduce a loss function to incorporate the noise property in image domain derived from the noise statistics in sinogram domain. In order to capture the spatially-varying statistics of CT images, we increase the receptive fields of the neural network to cover full-size CT slices. In addition, the proposed network utilizes $z$ -directional correlation by taking multiple consecutive CT slices as input. For performance evaluation, the proposed networks are trained and validated with a public dataset consisting of LDCT-NDCT image pairs. We also perform a retrospective study by testing the networks with clinical LDCT images. The experimental results show that the denoising networks successfully reduce the noise level and restore the image details without adding artifacts. This study demonstrates that the statistical deep learning approach can restore the image quality of LDCT without loss of anatomical information.

A method using isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC/MS/MS) was developed for the accurate determination of zearalenone (ZEN) and its five major metabolites in corn.

The treatment plan of colorectal neoplasm differs based on histology. Although new endoscopic imaging systems have been developed, there are clear diagnostic thresholds and requirements in using them. To overcome these limitations, we trained convolutional neural networks (CNNs) with endoscopic images and developed a computer-aided diagnostic (CAD) system which predicts the pathologic histology of colorectal adenoma. We retrospectively collected colonoscopic images from two tertiary hospitals and labeled 3400 images into one of 4 classes according to the final histology: normal, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. We implemented a CAD system based on ensemble learning with three CNN models which transfer the knowledge learned from common digital photography images to the colonoscopic image domain. The deep learning models were trained to classify the colorectal adenoma into these 4 classes. We compared the outcomes of the CNN models to those of two endoscopist groups having different years of experience, and visualized the model predictions using Class Activation Mapping. In our multi-center study, our CNN-CAD system identified the histology of colorectal adenoma with as sensitivity 77.25%, specificity of 92.42%, positive predictive value of 77.16%, negative predictive value of 92.58% averaged over the 4 classes, and mean diagnostic time of 0.12 s per image. Our experiments demonstrate that the CNN-CAD showed a similar performance to that of endoscopic experts and outperformed that of trainees. The model visualization results also showed reasonable regions of interest to explain the classification decisions of CAD systems. We suggest that CNN-CAD system can predict the histology of colorectal adenoma.

Deep learning has recently attracted widespread interest as a means of reducing noise in low-dose CT (LDCT) images. Deep convolutional neural networks (CNNs) are typically trained to transfer high-quality image features of normal-dose CT (NDCT) images to LDCT images. However, existing deep learning approaches for denoising LDCT images often overlook the statistical property of CT images. In this paper, we propose an approach to statistical image restoration for LDCT using deep learning (StatCNN). We introduce a loss function to incorporate the noise property in the image domain derived from the noise statistics in the sinogram domain. In order to capture the spatially-varying statistics of axial CT images, we increase the receptive fields of the proposed network to cover full-size CT slices. In addition, the proposed network utilizes z-directional correlation by taking multiple consecutive CT slices as input. For performance evaluation, the proposed network was thoroughly trained and tested by leave-one-out cross-validation with a dataset consisting of LDCT-NDCT image pairs. The experimental results showed that the denoising networks successfully reduced the noise level and restored the image details without adding artifacts. This study demonstrates that the statistical deep learning approach can transfer the image style from NDCT images to LDCT images without loss of anatomical information.

Detection, diagnosis, and removal of colorectal neoplasms are well-accepted colorectal cancer prevention methods. Although promising endoscopic imaging techniques including narrow-band imaging have been developed, these techniques are operator-dependent and interpretations of the results may vary. To overcome these limitations, we applied deep learning to develop a computer-aided diagnostic (CAD) system of colorectal adenoma. We collected and divided 3000 colonoscopic images into 4 categories according to the final pathology, normal, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. We implemented three convolutional neural networks (CNNs) using Inception-v3, ResNet-50, and DenseNet-161 as baseline models. We further altered the models using several strategies: replacement of the top layer, transfer learning from pre-trained models, fine-tuning of the model weights, rebalancing and augmentation of the training data, and 10-fold cross-validation. We compared the outcomes of the three CNN models to those of two endoscopist groups having different years of experience, and visualized the model predictions using Class Activation Mapping (CAM). The CNN-CAD achieved the best performance in our experiments with a 92.48% classification accuracy rate. The CNN-CAD results showed a better performance in all criteria than those of endoscopic experts. The model visualization results showed reasonable regions of interest to explain pathology classification decisions. We demonstrated that CNN-CAD can distinguish the pathology of colorectal adenoma, yielding better outcomes than the endoscopic experts group.

We report the development of a highly accurate method based on isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS) for the simultaneous determination of four major type-A trichothecenes in cereal grains. Uniformly labeled [13C] analogues of the target analytes were used as internal standards. An expedient sample preparation procedure was established. After extraction with acetonitrile/water (84:16; v/v), further clean-up was performed using MycoSep 227 solid-phase extraction cartridges. Unlike the commonly used immunoaffinity columns having strong selectivity for only certain target analytes, the cartridges allowed the simultaneous recovery of all four mycotoxins and efficient elimination of co-extracted matrix interferences. The ID-LC-MS/MS method exhibited very good analytical performance in the concentration range of 10–200 µg/kg; accuracy ranged from 97 to 103% with intra-day and inter-day relative standard deviations of less than 5% and 4%, respectively. Measurement uncertainties were generally below 5%. The applicability of the method was assessed by measuring the target mycotoxins in several samples at sub‐µg/kg levels.

In the absence of duplicate high-dose CT data, it is challenging to restore high-quality images based on deep learning with only low-dose CT (LDCT) data. When different reconstruction algorithms and settings are adopted to prepare high-quality images, LDCT datasets for deep learning can be unpaired. To address this problem, we propose hierarchical deep generative adversarial networks (HD-GANs) for semi-supervised learning with the unpaired datasets. We first cluster each patient’s CT images into multiple categories, and then collect the images in the same categories across different patients to build an imageset for denoising. Each imageset is fed into a generative adversarial network that consists of a denoising network and a following classification network. The denoising network efficiently reuses feature maps from the lower layers for end-to-end learning with full-size images. The classifier is trained to distinguish between the denoised images and the high-quality images. Evaluated with a clinical LDCT dataset, the proposed semi-supervised learning approach efficiently reduces the noise level of LDCT images without loss of information, thereby addressing the major shortcomings of IR such as computation time and anatomical inaccuracy.