Non-destructive Image Analysis Research Articles

The Selection of Lettuce Seedlings for Transplanting in a Plant Factory by a Non-Destructive Estimation of Leaf Area and Fresh Weight

Selecting uniform and healthy seedlings is important to ensure that a certain level of production can be reliably achieved in a plant factory. The objectives of this study were to investigate the potential of non-destructive image analysis for predicting the leaf area and shoot fresh weight of lettuce and to determine the feasibility of using a simple image analysis to select robust seedlings that can produce a uniform and dependable yield of lettuce in a plant factory. To vary the range of the leaf area and shoot fresh weight of lettuce seedlings, we applied two- and three-day irrigation intervals during the period of seedling production and calculated the projected canopy size (PCS) from the top-view images of the lettuce seedlings, although there were no significant growth differences between the irrigation regimes. A high correlation was identified between the PCS and shoot fresh weight for the lettuce seedlings during the period of seedling production, with a coefficient of determination exceeding 0.8. Therefore, the lettuce seedlings were classified into four grades (A–D) based on their PCS values calculated at transplanting. In the early stages of cultivation after transplanting, there were differences in the lettuce growth among the four grades; however, at the harvest (28 days after transplanting), there was no significant difference in the lettuce yield between grades A–C, with the exception of grade D. The lettuce seedlings in grades A–C exhibited the anticipated yield (150 g/plant) at the harvest time. In the correlation between the PCS and leaf area or the shoot fresh weight of lettuce during the cultivation period after transplanting and the entire cultivation period, the R2 values were higher than 0.9, confirming that PCS can be used to predict lettuce growth with greater accuracy. In conclusion, we demonstrated that the PCS calculation from the top-view images, a straightforward image analysis technique, can be employed to non-destructively and accurately predict lettuce leaf area and shoot fresh weight, and the seedlings with the potential to yield above a certain level after transplanting can be objectively and accurately selected based on PCS.

High-resolution compound-specific mapping in works of art via data fusion of MA-XRPD with hyperspectral data (part 1: Method evaluation)

BackgroundHyperspectral imaging techniques have emerged as powerful tools for non-invasive investigation of artworks. This paper employs either reflectance imaging spectroscopy (RIS) or macroscopic X-ray fluorescence (MA-XRF) imaging in combination with macroscopic X-ray powder diffraction (MA-XRPD) for state-of-the-art chemical imaging of painted cultural heritage artefacts. While RIS can provide molecular information and MA-XRF can offer elemental distribution maps of paintings of high lateral resolution, the unique advantage of MA-XRPD lies in its ability to visualize the distributions of specific pigments and estimate in a quantitative manner the relative concentrations of the crystalline phases at the surface of artworks. However, MA-XRPD is more time-consuming and offers a lower lateral resolution than RIS and MA-XRF. ResultsThis study introduces a machine learning (ML) approach to obtain the distribution of specific compounds on the surface of artworks with a resolution that is comparable to that of RIS and MA-XRF data but with the compound specificity of MA-XRPD. The general aim is to expedite non-destructive artwork imaging analysis by fusing data from different imaging modalities via machine learning models. The effect of preprocessing techniques to enhance the predictive accuracy of the models is explored. The paper demonstrates the method's efficacy on a 16th-century illuminated manuscript, showcasing the feasibility of predicting compound-specific distribution maps. Three evaluation methods—visual examination of the predicted distribution, root mean square errors (RMSE), and feature permutation importance (FPI)—are employed to assess model performance. Fusing MA-XRF with MA-XRPD led to the best RMSE scores overall. However, fusing the RIS and MA-XRPD data blocks also yield very satisfactory and easily interpretable high-resolution compound maps. SignificanceWhile MA-XRPD allows for highly specific imaging of artworks, its time-consuming nature and limited resolution presents a bottleneck during non-invasive imaging of painted works of art. By integrating data from more time-efficient hyperspectral techniques such as MA-XRF and RIS, and employing machine learning, we expedite the process without compromising accuracy. The fusion process can also denoise the distribution maps, improving their readability for heritage professionals and art historical scholars.

Comparison of Measured and Calculated Porosity Parameters of Woven Fabrics to Results Obtained with Image Analysis.

Porosity, the measure of the open spaces within a fabric structure, is a decisive factor in the performance of textiles. It influences breathability, permeability to liquids or gases, and suitability for various industries such as apparel, medical, and technical textiles. This study compares classical porosity calculation methods with non-destructive image analysis for 24 woven fabric samples that differ in density and weave pattern. Factors such as fabric density, weave pattern, illumination conditions, magnification, and the influence of the Otsu and Yen threshold algorithms were considered. The multifactor ANOVA statistical analysis shows that fabric density and weave pattern significantly influence porosity, with illumination playing an important role, while the threshold algorithm has a minor influence. A strong correlation is found between the actual fabric porosity and the results of the image analysis, except for double-sided illumination (reflective and transmissive), where the correlation is weakest. This comprehensive investigation provides valuable insights into the reliability of different porosity assessment approaches, which is essential for applications in various textile industries.

The Quality Assessment of Sour Cherries Dried Using an Innovative Simultaneous Osmotic–Microwave–Vacuum Approach Based on Image Textures, Color Parameters, and Sensory Attributes

Sour cherries are a perishable raw material, and their preservation is needed to extend their availability to consumers. Improving drying techniques is desirable to ensure the highest quality of products. This study aimed to determine image textures from color channels R, G, B, L, a, b, X, Y, and Z; color parameters L*, a*, and b*; the color difference (ΔE) of raw materials and dried fruit; and the sensory attributes of dried sour cherry products prepared using an innovative approach. Three sour cherry cultivars, ‘Nefris’, ‘Debreceni Botermo’, and ‘Łutówka’, were used in the experiment. Sour cherries were subjected to freezing and pit removal before drying. The simultaneous osmotic–microwave–vacuum drying was carried out in one process lasting an hour and combining osmotic dehydration using a 40 °Bx sucrose solution and microwave–vacuum drying at microwave powers of 100 W for 900 s, 300 W for 900 s, 250 W for 900 s, and 0 W for stabilization for 900 s and a pressure of 30 ± 2 hPa. After drying, the quality assessment of products was performed using non-destructive image analysis and color measurements, as well as sensory analysis, including non-destructively and destructively assessed attributes. The highest changes in textures occurred for the GHMean (histogram’s mean for color channel G) (from 30.69 to 22.64) and LHMean (histogram’s mean for color channel L) (from 66.93 to 59.07) of images of the cultivar ‘Łutówka’, and the smallest changes were found for the cultivar ‘Nefris’. Drying had a statistically significant effect on the color parameters of the ‘Debreceni Botermo’ and ‘Łutówka’ sour cherries. The value of ΔE was the highest (10.44) for ‘Debreceni Botermo’ and the smallest (1.98) for ‘Nefris’. All cultivars of dried sour cherries had very high values of overall quality, reaching 8.9 for ‘Nefris’ and ‘Debreceni Botermo’ and 8.8 for ‘Łutówka’. The ‘Nefris’ sour cherry was characterized by the highest value of flavor of 9.0. All dried samples were attractive in terms of their external appearance. The sensory parameters related to taste, texture, and crunchiness were also satisfactory. Innovative simultaneous osmotic–microwave–vacuum drying allowed for the obtainment of dried sour cherries with a high quality, including acceptable sensory attributes.

Effect of pore structure on compressive strength and permeability of planting recycled concrete

The relationship between strength, permeability, and pore structure of planting recycled concrete (PRC) was investigated using industrial computed tomography non-destructive testing technology and image analysis methods. An increase in the aggregate particle size and porosity increased the pore size and fractal dimension, and the relationship between the compressive strength and porosity conformed to the Ryshkewitch model. The fractal dimension had a weaker influence on compressive strength and permeability than did aperture size and porosity. Although vegetation growth reduced the compressive strength and permeability coefficient of PRC, the rainwater infiltration and vegetation compatibility of PRC was confirmed.

Synthesis and in-situ characterization of photochromic yttrium oxyhydride grown by reactive e−-beam evaporation

We report on controlled growth of photochromic yttrium oxyhydride thin films monitored by in-situ composition depth profiling. Films were grown by reactive e−-beam evaporation and subsequently oxidized, while simultaneously tracking the oxygen and hydrogen concentrations. Sample composition and photochromic response were characterized in-situ using non-destructive ion beam analysis and image analysis, respectively – as well as complementary ex-situ ion beam methods, X-ray diffraction and optical spectrophotometry. We show that photochromic yttrium oxyhydride can be grown as yttrium dihydride, which is then oxidized to O/H ratios triggering the photochromic response.

Application of confocal laser scanning microscopy in dendrochronology

We used the Confocal Laser Scanning Microscope (CLSM) Olympus LEXT OLS5000 for non-destructive observation and image analysis of wood anatomy traits in growth layers of tree species from different climatic zones. In European beech (Fagus sylvatica), where tree rings can generally be recognised, we discuss the changes in tree-ring structure due to adverse effects (insect attacks). Growth layers in Mediterranean Aleppo pine (Pinus halepensis) from south-eastern Spain are not always annual and contain numerous intra-annual density fluctuations (IADFs). Ocote pine (Pinus oocarpa) growing at high elevation in Honduras showed growth layers with clear growth ring boundaries and IADFs. In both pines, CLSM allowed us to recognise and measure tracheid parameters to define density fluctuations. In tropical true mahogany (Swietenia macrophylla) from Venezuela and cedrela (Cedrela odorata) from Costa Rica, we studied the growth layers with variable dimensions of vessels demarcated by marginal axial parenchyma.

The effects of pozzolanic powder on foam concrete pore structure and frost resistance

Abstract Pore structure and porosity of foam concrete play decisive roles in its performance, especially in terms of frost resistance. This study aimed to explore the effect of pozzolanic powder, e.g., slag powder and silica fume on pore structure and frost resistance of foam concrete. The relationship between pore structure and frost resistance and the mechanisms of frost damage were analyzed using ultrasonic nondestructive testing, image analysis, and scanning electron microscope. The results showed that when slag powder and silica fume content were 30% and 6% respectively, frost resistance maximized, with the mass-loss of 1.76% and 1.99%, and stress loss of 13.61% and 14.49%, respectively. Slag powder and silica fume reduced porosity to 0.503 and 0.513, which were 0.102 and 0.992 less than that of the control group. The number of pores sized 100–300 μm gained an increase of 6.08% and 4.47% compared to the corresponding control group. The results also indicated that pore structure optimized when slag powder and silica fume content were 30% and 6%, respectively.

Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network

Cold damage is one of the disasters that cause significant loss and irreversible damage in crop production. To avoid yield loss, high-throughput phenotyping can be used to select the crop varieties with cold stress resistance. Nowadays, non-destructive spectral image analysis has become an effective way and is widely used in high-throughput phenotyping, which reflects the structural, physiological, biochemical characteristics and traits of plant structure and composition, plant growth and development processes and outcomes. This study used convolutional neural network (CNN) model to extract spectral features in the visible-near-infrared range to estimate cold damage of corn seedlings. The hyperspectral images of cold treated corn seedlings from five varieties were used as research objects in this study. The spectral range of the images was 450-885nm. Gaussian low-pass filter and the Savitzky-Golay smoothing method combined with the first-order derivative was used to do pre-processing for spectral data. For each corn variety, 3600 pixel samples obtained from the selected region of interests in each variety of corn seedlings were used for the CNN modeling. After the CNN modeling, 400 pixel samples extracted from the hyperspectral images were used as the testing set for each variety. Finally, a 10-layer knot CNN model was determined by analyzing the classification accuracy and computational efficiency. CNN detected the cold damage level of different types of corn seedlings as W22 (41.8 %), BxM (35%), B73 (25.6%), PH207 (20%), Mo17 (14%), which had high correlation with the ranking given by chemical method. The coefficient of correlation between cold damage detection results of CNN and results from chemical method is 0.8219. Therefore, it proves that spectral analysis based on CNN modeling can provide reference for detecting cold damage in corn seedlings.

Preliminary high-throughput phenotyping analysis in grapevines under drought

This study reports correlative information between leaf water potential (ψ), total leaf area of draughted grapevines (Vitis vinifera L.) and non-destructive image analysis techniques. Four groups of 20 potted vines each were subjected to various irrigation treatments restoring 100% (control), 75%, 50% and 25% of daily water consumption within a 22-day period of drought imposition. Leaf gas exchanges (Li-Cor 6400), ψ (Scholander chamber), fluorescence (PAM − 2500), RGB and NIR (Scanalyzer 3D system, LemnaTec GmbH phenotyping platform) data were collected before and at the end of drought imposition. Values of ψ in severely stressed vines (25%) reached −1.2 MPa pre-dawn, in turn stomatal conductance and photosynthesis reached values as low as approx. 0.02 mol H2O m−2 s−1 and 1.0 μmol CO2 m−2 s−1, respectively. The high-throughput analysis preliminarily revealed a correlation between ψ (stem) and NIR Color Class (R2=0.80), and that plant leaf area might be accurately estimated through imagine analysis (R2=0.90).

PlantSize Offers an Affordable, Non-destructive Method to Measure Plant Size and Color in Vitro.

Plant size, shape and color are important parameters of plants, which have traditionally been measured by destructive and time-consuming methods. Non-destructive image analysis is an increasingly popular technology to characterize plant development in time. High throughput automatic phenotyping platforms can simultaneously analyze multiple morphological and physiological parameters of hundreds or thousands of plants. Such platforms are, however, expensive and are not affordable for many laboratories. Moreover, determination of basic parameters is sufficient for most studies. Here we describe a non-invasive method, which simultaneously measures basic morphological and physiological parameters of in vitro cultured plants. Changes of plant size, shape and color is monitored by repeated photography with a commercial digital camera using neutral white background. Images are analyzed with the MatLab-based computer application PlantSize, which simultaneously calculates several parameters including rosette size, convex area, convex ratio, chlorophyll and anthocyanin contents of all plants identified on the image. Numerical data are exported in MS Excel-compatible format. Subsequent data processing provides information on growth rates, chlorophyll and anthocyanin contents. Proof-of-concept validation of the imaging technology was demonstrated by revealing small but significant differences between wild type and transgenic Arabidopsis plants overexpressing the HSFA4A transcription factor or the hsfa4a knockout mutant, subjected to different stress conditions. While HSFA4A overexpression was associated with better growth, higher chlorophyll and lower anthocyanin content in saline conditions, the knockout hsfa4a mutant showed hypersensitivity to various stresses. Morphological differences were revealed by comparing rosette size, shape and color of wild type plants with phytochrome B (phyB-9) mutant. While the technology was developed with Arabidopsis plants, it is suitable to characterize plants of other species including crops, in a simple, affordable and fast way. PlantSize is publicly available (http://www.brc.hu/pub/psize/index.html).

Confocal Raman Microscopy: new perspective on the weathering of anhydrous cement

Raman spectroscopy when is combined with Confocal microscopy is a non-destructive technique that allow us to obtain information in cementitious materials. In this study, we present non-destructive image and structural analysis of anhydrous cement with carbonation evidences by Confocal Raman Microscopy (CRM). The results obtained by CRM show a direct relationship between the presence of the weathering processes of an anhydrous cement with the presence of sulphates and surprisingly, with the existence of amorphous carbon in the medium.

A new holistic 3D non-invasive analysis of cellular distribution and motility on fibroin-alginate microcarriers using light sheet fluorescent microscopy.

Cell interaction with biomaterials is one of the keystones to developing medical devices for tissue engineering applications. Biomaterials are the scaffolds that give three-dimensional support to the cells, and are vectors that deliver the cells to the injured tissue requiring repair. Features of biomaterials can influence the behaviour of the cells and consequently the efficacy of the tissue-engineered product. The adhesion, distribution and motility of the seeded cells onto the scaffold represent key aspects, and must be evaluated in vitro during the product development, especially when the efficacy of a specific tissue-engineered product depends on viable and functional cell loading. In this work, we propose a non-invasive and non-destructive imaging analysis for investigating motility, viability and distribution of Mesenchymal Stem Cells (MSCs) on silk fibroin-based alginate microcarriers, to test the adhesion capacity of the fibroin coating onto alginate which is known to be unsuitable for cell adhesion. However, in depth characterization of the biomaterial is beyond the scope of this paper. Scaffold-loaded MSCs were stained with Calcein-AM and Ethidium homodimer-1 to detect live and dead cells, respectively, and counterstained with Hoechst to label cell nuclei. Time-lapse Light Sheet Fluorescent Microscopy (LSFM) was then used to produce three-dimensional images of the entire cells-loaded fibroin/alginate microcarriers. In order to quantitatively track the cell motility over time, we also developed an open source user friendly software tool called Fluorescent Cell Tracker in Three-Dimensions (F-Tracker3D). Combining LSFM with F-Tracker3D we were able for the first time to assess the distribution and motility of stem cells in a non-invasive, non-destructive, quantitative, and three-dimensional analysis of the entire surface of the cell-loaded scaffold. We therefore propose this imaging technique as an innovative holistic tool for monitoring cell-biomaterial interactions, and as a tool for the design, fabrication and functionalization of a scaffold as a medical device.

Whole blood clot optical clearing for nondestructive 3D imaging and quantitative analysis.

A technological revolution in both light and electron microscopy imaging now allows unprecedented views of clotting, especially in animal models of hemostasis and thrombosis. However, our understanding of three-dimensional high-resolution clot structure remains incomplete since most of our recent knowledge has come from studies of relatively small clots or thrombi, due to the optical impenetrability of clots beyond a few cell layers in depth. Here, we developed an optimized optical clearing method termed cCLOT that renders large whole blood clots transparent and allows confocal imaging as deep as one millimeter inside the clot. We have tested this method by investigating the 3D structure of clots made from reconstituted pre-labeled blood components yielding new information about the effects of clot contraction on erythrocytes. Although it has been shown recently that erythrocytes are compressed to form polyhedrocytes during clot contraction, observations of this phenomenon have been impeded by the inability to easily image inside clots. As an efficient and non-destructive method, cCLOT represents a powerful research tool in studying blood clot structure and mechanisms controlling clot morphology. Additionally, cCLOT optical clearing has the potential to facilitate imaging of ex vivo clots and thrombi derived from healthy or pathological conditions.

Abstract 4835: A robust, non-destructive image analysis method for the quantitation and characterization of patient derived organoids

Abstract Patient-derived organoids are an emerging 3D model system that more closely recapitulate the organ functionality of the tissue of origin compared to traditional 2D cell lines. Further advantages to the organoid system include tunability i.e. adding additional cell types, adjusting matrix stiffness, and establishing nutrient gradients, in a physiologically relevant setting, as well as the ability to quickly scale up from a small initial sample. We have established an actively expanding biobank of primary and metastatic colorectal cancer (CRC) tumor organoids under normoxic (21% O2) and physioxic (5% O2) conditions, from a diverse patient population. Concurrently, we isolated and cultured matched cancer-associated fibroblasts (CAFs). With these samples we are able to generate data from a cohort that mimics the CRC population at large, including different ethnic groups, and mutational status. Here we detail a quantitative imaging platform that captures 3D morphometric information in addition to traditional live/dead readouts. Organoids from each patient are heterogeneous in size, shape, symmetry, and various other phenotypic features. Furthermore, the perturbation of microenviromental factors i.e. drugs, CAFs, etc. cause phenotypic changes over time. Common population-based viability assays used in drug screens, such as ATP or MTT, may be inappropriate to capture the complexities of drug response since they are often single end point measurements of an entire population of cells, and do not account for phenotypic variations. Short term quantitative live cell imaging incorporates phenotypic information, and can extend the lifetime of samples, yet still require manipulating samples by using dyes. However, these dyes are phototoxic, making them less than ideal for long term live cell imaging. By restricting image acquisition to brightfield we are able to minimize manipulation of patient samples, leaving them intact and viable. The additional benefit of imaging organoids in 3D enables us to get spatial information not available from assays with a single readout. Using our non-destructive imaging technique we can track 3D morphometric changes in the same organoid population over time. By using a flexible analysis method, and unbiased machine learning algorithms to determine relevant features we can account for these differences yet still get comparable data. Our organoid repository combined with long term image analysis and machine learning techniques provide a robust platform that is flexible enough to handle the heterogeneity seen across the patient population. This platform could be used to advance personalized medicine allowing clinicians to quickly and more accurately determine the appropriate treatment for a patient by screening their tumor prior to determining a course of treatment. Citation Format: Erin Spiller, Roy Lau, Colin Flinders, Shannon Mumenthaler. A robust, non-destructive image analysis method for the quantitation and characterization of patient derived organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4835. doi:10.1158/1538-7445.AM2017-4835

Confocal Raman microscopy as a non-destructive tool to study microstructure of hydrating cementitious materials

Non-destructive investigation of hydrated cement microstructure is still a big challenge. In this study, we present non-destructive image and structural analysis of hydrated monoclinic C3S clinker by means of confocal Raman microscopy (CRM), which to the best of our knowledge is used for studying hydrating cementitious systems for the first time. In detail, CRM enabled structural and spatial characterization of calcium-silicate-hydrate phases (C-S-H), portlandite (CH) and reactants on the micrometer scale without the need for extensive sample preparation. Two complementary data analysis tools (cluster analysis and graph basis analysis) revealed that m-C3S can be fully or partly hydrated to C-S-H as the ubiquitous phase. Furthermore, CH could be identified as macroscopic flakes of different size as well as intimately mixed with the C-S-H phase on a sub-micrometer scale. In addition, good quality Raman spectra of non-synthetic C-S-H, containing rarely reported lattice vibrations at ~130cm−1 and other characteristic Raman bands, are reported over a broad spectral range from around 100 up to 3700cm−1. Hence, this study introduces CRM as a promising novel technique for microstructural characterization of hydrating cementitious pastes.Cement chemistry notation: C: CaO, S: SiO2, H: H2O

Assessment of distribution of pellets in tablets by non-destructive microfocus X-ray imaging and image analysis technique

In this study, potassium chloride (KCl) containing matrix pellets were compressed into tablets using powder or pellet form of partially spray dried lactose as filler-binder excipient. The spatial distribution of potassium chloride pellets within the tablets was examined by non-destructive microfocus X-ray imaging (MFX) and image analysis technique. The KCl content of the tablets was determined by conductivity measurement and these values were compared to the average gray values of the MFX images calculated by image analysis software. A linear relationship has been shown between the KCl content predicted from average gray values of the MFX images and the actual KCl content measured by conductometry. The non-destructive MFX method was capable to characterize the pellet distribution as well as the pellet content of the multiparticulate dosage form. Based on the observed spatial distribution of KCl pellets within the tablets made using lactose pellets or powder for direct compression as filler excipients the difference in homogeneity was not remarkable besides the applied blending method.

Analysis and interpretation of a unique Arabic finger ring from the Viking Age town of Birka, Sweden.

In this work we used non-destructive SEM imaging and EDS analysis to characterize the material composition of an Arabic finger ring, which was found in a 9(th) c. woman's grave at the Viking Age (A.D. 793-1066) trading center of Birka, Sweden. The ring is set with a violet stone inscribed with Arabic Kufic writing, here interpreted as reading "il-la-lah", i.e. "For/to Allah". The stone was previously thought to be an amethyst, but the current results show it to be coloured glass. The ring has been cast in a high-grade silver alloy (94.5/5.5 Ag/Cu) and retains the post-casting marks from the filing done to remove flash and mold lines. Thus, the ring has rarely been worn, and likely passed from the silversmith to the woman buried at Birka with few owners in between. The ring may therefore constitute material evidence for direct interactions between Viking Age Scandinavia and the Islamic world. Being the only ring with an Arabic inscription found at a Scandinavian archaeological site, it is a unique object among Swedish Viking Age material. The technical analysis presented here provides a better understanding of the properties and background of this intriguing piece of jewelry.

Mechanistic time scales in adhesive mixing investigated by dry particle sizing

This study exploits the mechanisms governing blending of adhesive mixtures, i.e. random mixing, de-agglomeration and adhesion, and their relative importance to achieve mixing homogeneity. To this end, blending of micronized particles (fines) with carrier particles was carried out using a high shear mixer. Dry particle sizing using laser diffraction coupled with a strong powder dispersion unit was employed to measure the fines content in samples collected during mixing, and hence to assess blend homogeneity. The method was also employed to evaluate the relative strength of the agglomerates present in the fines. Particle sizing using a non-destructive imaging technique was used to monitor changes in particle size during blending. It could be shown that the de-agglomeration of the fine-particle agglomerates is the slowest mechanism and hence the rate-limiting step as regards achieving a homogeneous adhesive mixture. Consequently, a longer mixing time is needed for blending of larger agglomerates. Being fast, simple and reproducible, the laser diffraction technique was shown to be an efficient method for measurement of fine particle content and homogeneity of a mixture, while the non-destructive image analysis was able to give relevant information on the rate of de-agglomeration of the fine-particle agglomerates as well as on the size of the resulting carrier-fine particle assemblies.

We become more aware of the void as we explore it

We become more aware of the void as we explore it