Ceramic Tile Surfaces Research Articles

Performance Characterization of an Illumination-Based Low-Cost Multispectral Camera.

Spectral imaging has many applications, from methane detection using satellites to disease detection on crops. However, spectral cameras remain a costly solution ranging from 10 thousand to 100 thousand euros for the hardware alone. Here, we present a low-cost multispectral camera (LC-MSC) with 64 LEDs in eight different colors and a monochrome camera with a hardware cost of 340 euros. Our prototype reproduces spectra accurately when compared to a reference spectrometer to within the spectral width of the LEDs used and the ±1σ variation over the surface of ceramic reference tiles. The mean absolute difference in reflectance is an overestimate of 0.03 for the LC-MSC as compared to a spectrometer, due to the spectral shape of the tiles. In environmental light levels of 0.5 W m-2 (bright artificial indoor lighting) our approach shows an increase in noise, but still faithfully reproduces discrete reflectance spectra over 400 nm-1000 nm. Our approach is limited in its application by LED bandwidth and availability of specific LED wavelengths. However, unlike with conventional spectral cameras, the pixel pitch of the camera itself is not limited, providing higher image resolution than typical high-end multi- and hyperspectral cameras. For sample conditions where LED illumination bands provide suitable spectral information, our LC-MSC is an interesting low-cost alternative approach to spectral imaging.

Preparation of Spinel-Type Black Pigments Using Microwave-Assisted Calcination of Stainless Steel Dust: The Effect of Manganese Molar Content

Stainless steel dust is rich in valuable metal elements including Fe, Cr, Ni and Mn, which can be utilized to prepare Fe–Cr–Ni–Mn series black pigments. Meanwhile, manganese can absorb the majority of the visible light wavelength range, which improves the color rendering performance of Fe–Cr–Ni–Mn series black pigments. However, the coloring mechanism of manganese in the above black pigments is not clear. Therefore, the effect of manganese oxide content on the preparation of spinel-type black pigments from microwave-assisted calcination of stainless steel dust was studied in this work. The results show that with the increase in MnO content in the raw mixture, the crystal plane spacing of black pigments increases from 0.2525 nm to 0.2535 nm, the grain size grows from 61.4619 nm to 79.7171 nm, and the lattice constant grows from 0.8377 to 0.8406 nm. Moreover, the band gap is decreased from 1.483 eV to 1.244 eV, the absorbance increases significantly and has a consistent absorbance in the visible range, and the L*, a* and b* values reduce from 41.8, 0.6, 1.6 to 32.0, 1.0, 0.8, respectively. MnO can react with the spinel in stainless steel dust, forming Mn3O4, MnCr2O4 and Ni (Fe,Cr)O4 in the system, with a regular polyhedral structure. The prepared pigments have excellent thermal stability at 1100 °C and good compatibility with transparent glazes, which can be adhered to the surface of ceramic tiles after calcination to demonstrate better compatibility as the content of MnO increases.

Balanced multi-scale target score network for ceramic tile surface defect detection

Ceramic tiles, as a prevalent building material, exhibit a wide variety of types and high demand. Traditional manual inspection methods relying on human visual observation suffer from low efficiency and unreliable accuracy. Current automated detection methods mostly rely on traditional image processing techniques for feature extraction, followed by machine learning-based classification. However, faced with the diversity of tile types and defect categories, fine-tuning and deployment processes require significant human and material resources, while detection efficiency remains limited. In this study, we first construct a high-resolution dataset for studying surface defects in ceramic tiles (CT surface defects dataset), encompassing multiple batches and various patterns of tiles. Subsequently, data analysis is conducted to address the scale and quantity differences in defect distribution. We propose an improved approach by introducing a content-aware feature recombination method and a dynamic attention mechanism to enhance the classical single-stage object detection algorithm YOLOv5. These enhancements aim to reduce information loss in features and enhance the expression of multi-scale features. Furthermore, we design a loss function that mitigates score differences for multi-scale defects. The proposed approach mitigates the discrepancy in contribution among different scale targets caused by imbalanced quantities. It effectively prevents the model from excessively favoring a specific scale target during the learning process. Experimental results demonstrate the superior accuracy and efficiency of our detection method.Compared to the baseline network YOLOv5, our approach achieved improvements of 4.9% in AP (Average Precision), 6% in APs (small-scale objects), and 8% in APl (large-scale objects). Furthermore, we achieved a 3.9% improvement in detecting white point defects, which are most affected by small-scale objects, and a 4.1% improvement in detecting discolored spot defect, which are most affected by class imbalance.

MCAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects

Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing this approach, we could enhance the production workflow, boost production efficiency, minimize company expenses, and lessen the workload on workers. In this paper, we propose a lightweight tile-defect detection algorithm that strikes a balance between model parameters and accuracy. Firstly, we introduced the mobile-friendly vision transformer into the backbone network to capture global and local information. This allowed the model to comprehend the image content better and enhance defect feature extraction. Secondly, we designed a lightweight feature fusion network. This design amplified the network’s detection capability for defects of different scales and mitigated the blurriness and redundancy in the feature maps while reducing the model’s parameter count. We then devised a convolution module incorporating the normalization-based attention module, to direct the model’s focus toward defect features. This reduced background noise and filtered out features irrelevant to defects. Finally, we utilized a bounding box regression loss with a dynamic focusing mechanism. This approach facilitated the prediction of more precise object bounding boxes, thereby improving the model’s convergence rate and detection precision. Experimental results demonstrated that the improved algorithm achieved a mean average precision of 71.9%, marking a 3.1% improvement over the original algorithm. Furthermore, there was a reduction of 26.2% in the model’s parameters and a 20.9% decrease in the number of calculations.

Research on tile surface defect detection by improved YOLOv5

Traditional methods for detecting surface defects on ceramic tiles result in misdetection and missed detection, which makes it difficult to guarantee product stability and consistency within the same batch. Therefore, this article proposes an improved YOLOv5 algorithm for detecting surface defects on ceramic tiles. Firstly, the Res2Net module is combined with self-attention to fully utilize local and global information and improve the feature extraction effect of defects. Secondly, the GS-BiFPN neck network is designed to enhance the fusion capability of shallow detail and deep semantic information and alleviate ambiguity and redundancy on the feature map. Then, a lightweight attention module is introduced to improve the detection capability of difficult-to-recognize defects and anti-background interference. Finally, the SIoU loss function improves the model’s convergence speed and accuracy. Experimental results demonstrate that the improved algorithm’s mean average precision (mAP) reaches 73.3%, 6.3% higher than the baseline model. Even when compared with YOLOv7-tiny, the mAP of the improved algorithm has increased by 8.7%. Additionally, the detection speed of the model can reach 92 frames per second, which can meet the requirements of ceramic tile surface defect detection in industrial scenarios.

Non-destructive detection for mosaic ceramic surface defects based on convolutional neural networks

Abstract Mosaic ceramic art pattern with noble, elegant features, it is a unique form of art creation in ancient Greece and the ancient Rome period has been loved by artists and created a lot of classic large-scale exterior mosaic ceramic art works. Small size square mosaic ceramic as the basic raw material for the creation of large exterior mosaic art, it directly affects the quality of the work created by the artist, so these ceramic mosaic ceramic materials need to undergo rigorous inspection to meet the needs of the artist’s high-quality art creation. However, small size multi-color square mosaic ceramics are different from ordinary large target ceramics, they have the characteristics of small size and easy reflection, currently mainly using manual inspection, the existing automatic inspection methods have the problem of low efficiency and accuracy, cannot meet the needs of artists for the quantity and quality of mosaic ceramics. To solve these problems, this paper proposes a new convolutional network-based fast nondestructive testing method for detecting square mosaic tiles. The detection method is based on the convolutional neural network YOLOv5s model, and by introducing the AF-FPN module and the data enhancement module, the method further improves the recognition performance of the model relative to the original YOLOv5s model and achieves the fast detection of surface defects on square mosaic ceramics. The experimental results show that the detection method for small size multicolor square mosaic ceramic tile surface minor defects detection rate of up to 94 % or more, a single square mosaic ceramic detection time of 0.41 s. The method takes into account the detection accuracy and speed, can be fast and accurate screening of high-quality, defect-free small size multicolor square mosaic ceramic, to meet the artist’s requirements for high-quality mosaic ceramic raw materials Quality and quantity requirements, to ensure the quality of the creation of mosaic art patterns, to better show the charm of the mosaic art patterns role. At the same time, the method can not only be applied to the detection of mosaic ceramics, the method can also be applied to have a similar small volume, easy to reflect the characteristics of small target object defect detection.

A New Gradient Based Surface Defect Detection Method for the Ceramic Tile

Ceramic tiles are controlled to detect surface defects after production because many defects may occur on their surface during production. The detection of ceramic tile surface defects is usually performed by human observations in most factories. In this paper, an image processing method was proposed to detect the defects. In the proposed method, first, the user selects the homogenous region in the image. Then the gradient-based image processing algorithm is applied. We conducted our study using simulated and real images to which we applied the conventional image processing methods and our proposed method. Performance of the proposed method was evaluated with quality metric and subjective evaluation. The obtained results demonstrate that the proposed method has very good performance and is very promising for ceramic tile application.

Efficacy of Spinetoram for the Control of Bean Weevil, Acanthoscelides obtectus (Say.) (Coleoptera: Chrysomelidae) on Different Surfaces.

Simple SummaryContact toxicity of spinetoram on three different surfaces, concrete, ceramic floor tile and laminate flooring, against Acanthocelides obtectus (Say.) was evaluated in laboratory bioassays. Our results provide data on the insecticidal effect of spinetoram for the control of A. obtectus on various surfaces; however, its efficacy varies according to the surface type, exposure time and concentration. In conclusion, our laboratory tests indicated that spinetoram at 0.025 and 0.05 mg active ingredient (AI)/cm2 achieved satisfactory control at relatively short exposures by contact action of A. obtectus adults on three surfaces, commonly encountered in legume storage facilities and warehouses.In this study, the contact toxicity of spinetoram on three different surfaces, concrete, ceramic floor tile and laminate flooring, against Acanthocelides obtectus (Say.) (Coleoptera: Chrysomelidae) was evaluated in laboratory bioassays. Different concentrations were evaluated ranging from 0.0025 to 0.05 mg AI/cm2, against adults of A. obtectus. Adult mortality was measured after 1-, 3-, 5- and 7-day exposure. After 1-day exposure, the mortality was low on all surfaces, ranging from 0 to 27.2%. After 5- and 7-day exposure, spinetoram at concentrations of 0.01 mg/cm2 and above achieved 100% or close mortality on concrete and laminate flooring surface, whereas low concentrations (0.0025, 0.005 and 0.0075 mg AI/cm2) resulted in significantly lower mortality levels, ranging from 1.6 to 30.8%, than high concentrations. In the case of ceramic floor tile surface, spinetoram treatments at all tested concentrations did not result in 100% mortality. Significant differences were recorded among the surfaces, depending on concentrations and exposure intervals. After 3-, 5- and 7-day exposure, mortality levels on ceramic floor tile surface were generally higher at low concentrations than those on the concrete and laminate flooring surfaces, whereas those on concrete and laminate flooring surfaces were significantly higher at high concentrations than ceramic floor tile surface. These results indicate that spinetoram at 0.025 and 0.05 mg AI/cm2 achieve satisfactory control at relatively short exposures on common types of surfaces and thus can be used as an effective insecticide against A. obtectus.

HFENet: A lightweight hand‐crafted feature enhanced CNN for ceramic tile surface defect detection

Inkjet printing technology can make tiles with very rich and realistic patterns, so it is widely adopted in the ceramic industry. However, the frequent nozzle blockage and inconsistent inkjet volume by inkjet printing devices, usually leads to defects such as stayguy and color blocks in the tile surface. Especially, the stayguy in complex pattern is difficult to identify by naked eyes due to it is easily covered by complex patterns and becomes invisible, this brings great challenge to tile quality inspection. Nowadays, the machine learning is employed to address the issues. The existing machine learning methods based on hand-crafted features are capable of stayguy detection of the tiles with a simple pattern, but not applicable for complex patterns due to the interference of pattern in feature extraction. The emerging deep-learning-based methods have the potential to be applied for stayguy detection with complex patterns, but cannot achieve real-time detection due to high complexity. In this paper, a lightweight hand-crafted feature enhanced convolutional neural network (named HFENet) is proposed for rapid defect detection of tile surface. First, we perform data enhancement on the original image by global histogram equalization and image addition. Second, for the special shape of stayguy which is usually vertical, we embed the extended vertical edge detection operator (Prewitt) as convolution kernel into HFENet to extract the hand-crafted vertical edge features of the test image and eliminate the interference of complex pattern in the feature extraction. Third, the 5 × 1 asymmetric convolution kernel with a dilation rate of 2 is used to improve the utilization of convolution kernel and reduce the complexity of the model. Fourth, to reach the real-time requirements, a memory access cost-aware design is proposed, which can orchestrate the number of shallow convolution layers and deep convolution layers in feature extraction. The experiments were performed on the ceramic tile image data set captured by high-resolution industrial cameras in ceramic tile production line. Experimental results show that the HFENet outperforms the state-of-the-art semantic segmentation networks (i.e., UNet, FCN-8s, SegNet, DeepLabV3+, etc.) and lightweight networks (i.e., ShuffleNet, MobileNet, and SqueezeNet). All the code and data are available at a GitHub repository (https://github.com/RobotvisionLab/HFENet).

Research on defect detection algorithm of complex texture ceramic tiles based on visual attention mechanism

Aiming at the difficulty of detecting the surface defects of complex texture tiles, a salient target detection method based on the human visual attention mechanism is proposed and used for the detection of tile surface defects. Firstly, the image of ceramic tile surface is pretreated using the single-scale SSR light correction method and bilateral filtering method; Secondly, according to the principle of contrast and high-frequency suppression in the visual attention mechanism, aiming at the "imaging" and "aggregation" characteristics of complex background textures, a detection model based on the visual attention mechanism is established to determine and mark defects.According to the contrast principle and high-frequency suppression principle in visual attention mechanism, feature extraction of ceramic tile surface is carried out. Then, the image color patch weight salient map and image feature fused salient map are obtained, and the two maps are fused according to the image saliency criteria.Finally, the marked ceramic tile defects are determined and marked.Finally the marked ceramic tile defects are obtained. This defect detection algorithm and the other two algorithms are applied to three kinds of randomly selected complex texture ceramic tiles. The experimental results show that compared with other algorithms, our algorithm can achieve a comprehensive detection rate of more than 96% for complex texture ceramic tiles, and can obtain a good effect of ceramic tile defect detection as well.

A rapid detection method for the surface defects of mosaic ceramic tiles

Due to its unique artistic value, mosaic ceramics are widely used in construction-related fields. To meet the artist's demand for high-quality mosaic ceramic to create artistic works, it is necessary to meet the needs for efficient screening of mosaic ceramic tiles. Different from the ordinary large-target ceramics, mosaic ceramics exhibit characteristics of small tile sizes, a variety of colors, large demand for quantities, and easy reflection on the surface. Common manual detection methods show problems of low efficiency or accuracy, easy to fatigue, and many others. To solve these problems, this paper proposes a new detection method to identify surface defects of mosaic ceramic tiles and designs a detection system platform to achieve rapid detection. The experiment proves that the detection system has a detection rate of 93.99% for small defects on the surface of mosaic ceramic tiles, and the detection time of a single mosaic ceramic tile is less than 0.06 s. The detection method can quickly and accurately screen out high-quality, defect-free mosaic ceramic tiles, which can effectively improve the quality and artistic value of mosaic ceramic art creation.

The synthesis, characterization and decontamination of surface radioactive contamination of ethyl cellulose/polyacrylate strippable detergent at low temperature

It is an important issue to handle the surface contamination of nuclear facilities in the development of nuclear industry and nuclear emergency, especially in low temperature environment. In order to solve the problem of frozen, poor film-forming and strippability of the detergent at low temperature (below 0ºC), a novel ethyl cellulose/polyacrylate detergent was synthesized by free radical polymerization from ethyl cellulose, methyl methacrylate (MMA), butyl acrylate (BA), ethyl methacrylate (EMA), and isooctyl acrylate (IOA). The wettability of the detergent on different surfaces was determined by contact angle testing method. The film-forming and peeling property, rheological property of ethyl cellulose/polyacrylate detergent at low temperature were studied and the mechanical properties of the peeling film of ethyl cellulose/polyacrylate were tested. The decontamination of simulated radionuclide on the different plates by the ethyl cellulose/polyacrylate detergent at the temperature range of − 15ºC ~ 0ºC was also explored. The mechanism of simulated radionuclide transferring and decontamination was analyzed by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM) and energy spectrum (EDS). The results show that the detergent can not be frozen even at low temperature (−25ºC), and is easy to be sprayed, forming film and peeled off on the plates of stainless steel, ceramic tiles, glass, marble and concrete within 18 h at the low temperature range of − 15ºC ~ 0ºC. The decontamination efficiency of the ethyl cellulose/polyacrylate detergent is above 90% for radionuclide contamination on the surface of stainless steel, ceramic tiles, glass and marble plates, the decontamination efficiency on concrete is above 80%, at the temperature range of − 15ºC ~ 0ºC. The mechanism of simulated radionuclide transferring from the surface of contaminated plates into the detergent and being removed were diffusion, adhesion and embedding. Therefore, the ethyl cellulose/polyacrylate detergent has a good application prospect in surface decontamination of nuclear facility decommisioning due to its excellent fluidity, spraying, rheological property, film forming, strippable property, high decontamination efficiency at low temperature.

Ricochet of AK bullets (7.62 MM X 39 MM) on glazed ceramic tiles: An empirical study in support of shooting incident reconstructions

Indoor environments provide numerous hard surfaces and nearby objects which facilitate the shots fired to ricochet off and hit victims. Out of many surface types, ceramic tile surfaces are considered to be one of the most commonly available and encountered surface type in indoor shootings. However, no studies had attempted to understand the ricochet behaviour and surface evidence of ceramic tiles with any bullet type. This study explores the ricochet behaviour of one of the most commonly reported bullet type in recent shooting incidents; AK bullets (7.62 mm × 39 mm/ M43) on two glazed ceramic tile samples used for indoor walls and flooring. The study’s results present the critical angles of glazed ceramic floor and wall tile samples along with a few significant and currently not reported ricochet-surface mark characteristics with greater forensic significance for use in AK gun-related ricochet investigations. This study further emphasises the need for case-by-case empirical approaches to understanding the ricochet behaviour of different bullet-target combinations during ricochet investigations. The study also opens up a new research area to explore whether the observed results are common to steel core AK bullets or common to other ammunition types and tile surfaces with different compositions.

Ceramic tile surface defect detection based on deep learning

Ceramic tiles are widely used in the construction field. In actual production, the difficulty in extracting texture ceramic tile features and the small size of defects lead to low detection accuracy and inefficiency. To solve these problems, we propose a detection method based on improved YOLOv5s. Firstly, the network layer is deepened in the backbone network and the attention mechanism CBAM module is added. Then, a small-scale detection layer is added, and the model is increased from a three-output prediction layer to a four-output prediction layer. Thirdly, the network feature fusion is enhanced in the neck network. Finally, the original convolution is replaced with depthwise separable convolution, and a lightweight ceramic tile detection system is constructed. Experimental results show that our model can solve the problems caused by small defects and insufficient feature information effectively.

Copper oxide‐ and copper‐modified antibacterial ceramic surfaces

AbstractIn the present study, copper oxide (CuO) and copper (Cu) particles were characterized and investigated to observe the effects of antimicrobial activity on ceramic tile surfaces. Scanning electron microscopy (SEM) and X‐ray diffraction (XRD) techniques were used for particle characterization. A new formulation of antimicrobial glaze was achieved by the combination of two glaze compositions with different Cu and CuO content to obtain micro–nano hybrid surface morphology to provide the antibacterial property. Surface morphology of coated ceramic surfaces was examined by SEM in detail and water contact angles were measured with goniometer. The relationships between antibacterial effect, phase evolution, surface topography, and wettability of surfaces were determined. The tenorite phase of CuO was observed on both Cu‐ and CuO‐modified surfaces. Both Cu and CuO particles in glazed ceramic surfaces showed the similar antibacterial activity against Staphylococcus aureus and Escherichia coli. The antibacterial activity was dependent on mainly tenorite phase crystallization. The amount of the tenorite phase that was slightly higher in the case of Cu‐incorporated surfaces instead of CuO and hence the bactericidal effect was slightly more pronounced. Although the developed surfaces are not hydrophobic, they show antibacterial properties.

Optimizing Operating Parameters of Electric Ultra-Low Volume Sprayer with Slightly Acidic Electrolyzed Solution for Efficient Virucidal Activity on Environmental Surfaces.

Recently, and considering the COVID-19 pandemic, there has been a growing consensus that the disinfection of surfaces contaminated with pathogenic viral particles is essential. Chemical disinfectant sprays are effective at preventing the spread of infectious human noroviruses (Hu-NoVs) in healthcare and public areas. We assessed the virucidal activity of slightly acidic electrolyzed water (SAEW) spray on fomite surfaces. A multivariate statistical assessment that combined a response surface methodology (RSM) and a Box–Behnken design (BBD) was performed to define the optimal parameters of, and correlations among, experimental conditions. Spraying SAEW disinfectant (oxidation-reduction potential: 1123 mV, pH range: 5.12, available chlorine concentration: 33.22 ppm) resulted in the successful decontamination of Hu-NoV, with a 4-log reduction in viral particles on polyvinyl chloride, stainless steel, ceramic tile, and glass surfaces. Our experimental data revealed optimized treatment conditions for decontaminating Hu-NoV GI.6 and GII.4, using the numerical multiple optimized method (spraying rate: 218 mL/min, spraying time: 4.9 s, spraying distance: 0.9 m). These findings offer significant insights for designing optimal strategic control practices to prevent infectious disease, particularly Hu-NoV, transmission.

Detection of Surface Defects in Ceramic Tiles With Complex Texture

To solve the problem of false defect detection owing to the interference of the texture attribute of ceramic tiles, a method for detecting surface defects in complex-textured ceramic tiles is proposed. Based on the visual detection principle of ceramic tile surfaces, an image acquisition system is established to obtain the ceramic tile image. After image segmentation and correction, the surface defects are preliminarily detected using a saliency detection method. Then, the image sub-block containing the defect area is cut out for secondary detection. The false defects are eliminated, and the final detection of the ceramic tile surface defects is completed using the defect determination method. The feasibility and effectiveness of the defect detection method are studied via comparative experiments. Experimental results show that the maximum accuracy rate of the proposed defect detection method is 98.75%, which satisfies the actual detection requirements and confirms the practical significance of the proposed method.

Reuse of laminated glass waste in the manufacture of ceramic frits and glazes

Frits are special glasses used in glaze compositions to cover the surface of ceramic tiles, such as floors, walls, and porcelain tiles. Therefore, glass waste can be used as raw material in the preparation of the ceramic frits for glazes. In this work, frits were formulated by mixture design using 8–15 mass % of laminated glass waste. The waste was added in frit compositions and 40 frits were melted. Test glazes were prepared with the frit compositions and were used to cover ceramic tiles. Using the glass waste, the whiteness, brightness, and Mohs hardness of the test glazes were 93.0, 2.7–91.9 and 4–7, respectively, in comparison to 92.6, 70.1 and 5 for the standard glazes. The Knoop microhardness of the test glazes was 54.5–189.3 (0.53–1.85 GPa) in comparison to 119.8 (1.18 GPa) of the standard. In addition, 92.5% of the test samples were resistant to the chemical attack of NaClO. 84.2% were resistant to HCl, but with differences in appearance of the surface. The coefficient of thermal expansion of the test glazes was 55.6–82.7 ∙ 10−7 °C−1, in comparison to 59.6–75.3 ∙ 10−7 °C−1 the standards. Finally, the results pointed to the feasibility of using ~8 mass % of laminated glass waste in the manufacture of frits used to produce ceramic glazes.

The Effect of Temperature and Milling Process on Steel Scale Utilized as a Pigment for Ceramic Glaze.

This study is focused on the evaluation of the re-utilizability of scale originated during the steel casting and steel rolling processes as a pigment for glazes. Non-oiled scale with Fe3O4 as the major phase were used as a coloring component of transparent glaze matrix in: (i) as received state, (ii) thermally pre-treated at 700 and 900 °C, (iii) mechanically treated in planetary ball mill (60, 120 and 240 min) and (iv) mechanically treated in vibratory disc mill (60 and 120 min). Prepared glazes were applied on the surface of ceramic tiles prepared from a commercially available white ceramic slurry. The resulting tiles with given glaze were thermally treated at 800, 900 and 1060 °C. The pigments were characterized by X-ray powder diffraction method (XRD), X-ray fluorescence spectroscopy (XRF), granulometry (PSD), thermogravimetric analysis (TG) and differential thermal analysis (DTA), scanning electron microscopy (SEM/EDAX). The color of the samples was described by the coordinates L*a*b* from CIELAB color space. The results showed that the non-oiled scale is suitable as the pigment for ceramic glazes. Careful control of the scale treatment process (mechanical as well as thermal) together with the temperature of final glaze firing is necessary to obtain the glaze of desired color and quality.

Evaluation of the CERTUS Environmental Listeria Species Detection Kit for the Detection of Listeria Species on Environmental Surfaces: AOAC Performance Tested MethodSM 101802.

CERTUS Environmental Listeria species Detection Kit (CERTUS EL Detection Kit) is a real-time, bio-contained assay designed to accurately detect Listeria species (L. grayi, L. innocua, L. ivanovii, L. marthii, L. monocytogenes, L. seeligeri, and L. welshimeri) from environmental surface matrixes using an antibody-coupled magnetic microparticle with a Surface Enhanced Raman Spectroscopy (SERS) nanoparticle technology test system paired with proprietary CERTUS EL Selective Growth Media and CERTUS Detection Unit. The method was evaluated for AOAC®Performance Tested MethodSM certification. Inclusivity and exclusivity, matrix studies, product consistency and stability were conducted to evaluate the CERTUS EL Detection Kit. In the matrix studies, stainless steel, ceramic tile, plastic (polystyrene) and sealed concrete environmental surfaces (4 × 4" test areas) were tested. No statistically significant differences were found by Probability of Detection analysis (POD) in any of the matrixes when results were compared to the U.S. Food and Drug Administration cultural microbiology reference method for Listeria. The CERTUS EL Detection Kit correctly identified all 50 target Listeria isolates and correctly excluded all 30 non-target strains that were analyzed. Probability of Detection analysis of CERTUS EL Detection Kit robustness, product consistency (lot-to-lot) and stability studies demonstrated no statistically significant differences, and no variation was observed between instruments. The data collected in these studies demonstrate that the CERTUS EL Detection Kit is a reliable method for the rapid and specific detection of Listeria from stainless steel, ceramic tile, plastic (polystyrene) and sealed concrete environmental surfaces.