Morphometric Characterization Workflows of Praline Chocolates using X-ray Computed Tomography

Morphometric Characterization Workflows of Praline Chocolates using X-ray Computed Tomography | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Morphometric Characterization Workflows of Praline Chocolates using X-ray Computed Tomography Bayu Nugraha, Yoga Arif Firmansyah, Joko Nugroho Wahyu Kariyadi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8547296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The structural integrity of praline chocolates is a determinant factor for consumer acceptance, yet assessing it remains challenging due to the complex internal interactions between chocolate shells and fillings. This study establishes a robust non-destructive characterization protocol using X-ray Computed Tomography (CT) to evaluate the morphometrics of dark, milk, and white chocolates filled with water-based pineapple jam and fat-based peanut butter. A critical challenge addressed was the low radiodensity contrast between the chocolate matrix and fillings. To resolve this, the research compared global threshold, Volume of Interest (VOI)-based, and a Seeded Region-Growing Algorithm (Grow from Seeds/GFS) segmentations. Results indicated a strong relationship (R²=0.9255) between CT greyscale intensity and physical densities of the praline components. The GFS method demonstrated higher accuracy on low-contrast images of the praline than Otsu and VOI-based segmentation method. This method successfully reconstructed the internal architecture and matched the actual filling mass fraction (~ 15%) with high precision. Furthermore, 3D microstructural analysis revealed that physicochemical mismatches-specifically moisture migration in pineapple jam and fat migration in peanut butter-induced critical defects, including macro-voids (> 0.3 mm³). These findings validate the developed X-ray CT workflow as a powerful tool for identifying internal multi-phase food systems, such as praline chocolate. food structure non-destructive evaluation low-contrast object 3D image processing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Chocolate is one of the most popular confectionery products in the world which has many variants of end-products. Praline chocolate is one of the most exclusive types of chocolate, known for its high economic value and strong consumer appeal. By definition, praline chocolate consists of a solid chocolate shell filled with various compositions. The compositions create a complex combination of texture and flavors [1,2]. The fillings used in pralines generally fall into two general categories: water-based fillings, such as fruit jam, caramel and cream, and fat-based fillings such as nut and ganache [2,3]. Within this context, praline chocolate filled with tropical fruit jams not only enriches the praline’s character but also offers a strategic value advantage for tropical countries such as Indonesia. Although the fillings enhance the sensory appeal of pralines, they simultaneously introduce inherent instability within the product matrix. The difference in the physicochemical properties between the chocolate shell and the filling is pronounced imbalance. This phenomenon drives mass inconsistencies, structural deformation, and bloom formation. Water-based fillings accelerate moisture diffusion, triggering sugar bloom, whereas fat-based fillings promote lipid migration that leads to fat bloom [1,4,5]. In both cases, the migrated components typically accumulate on the chocolate surface, resulting in visible whitening, textural degradation, and diminished product quality [1–3]. Bloom significantly reduces consumer acceptance, shortens shelf life, and undermines the commercial value of praline products [5,6]. This phenomenon highlights a critical challenge that must be addressed in chocolate formulation and processing. Current identifications of chocolate physical structure, such as moisture increase [3], particle size distribution [5,7], melting profile [8], hardness [9], rheology behavior [10], and structure by SEM [6,7,9] reveal internal structural properties which attributes to final stability and quality [9]. However, these cases were limited to destructive approaches that inherently remove the original internal structure and restrict the dynamic observation. Since the physical attributes fundamentally contribute to the final quality and its deformation by time, there is a critical need for non-invasive technique capable of characterizing the structure fraction spatially. This technique necessitates the application of non-destructive approaches, for instance X-ray Computed Tomography (CT). X-ray CT is an imaging instrument that enables the characterization of external and internal morphology of many materials non-destructively [11–13]. For food materials, detailed information of food physical architectures ranging from macro- to microscale, such as the whole geometry shape and volume [11], air bubble distribution [14,15], and material density [16], was precisely identified and represented using X-ray CT. In addition, according to Sin, et. al. [17], the migration of internal components within porous media may also be detectable. Previous studies have demonstrated the effectiveness of X-ray CT imaging in observing the internal structure of food products, such as bread [18–20], ice cream [15], cookies [21], and cereal food [16]. In chocolate, X-ray CT has been implemented to quantify the air bubble distribution in aerated chocolate by simply separating the chocolate matrix and created air bubbles [19,22–25]. Furthermore, advanced synchrotron X-ray CT has been employed to achieve high-resolution imaging of chocolate microstructure [26]. This technique enables precise quantification of crystalline, crack, and pore networks. In praline chocolate, the filling materials typically have identical densities to the chocolate density, potentially creating a low-contrast CT image [27,28]. This low-contrast image has minimal differences in grayscale value intensities among material phases [29], presenting the segmentation difficulty of targeted material fractions. This levels up the image processing algorithm to more advanced steps for precise identification of the praline internal features. Therefore, this tendency of applying X-ray CT to praline chocolate remains underexplored. Therefore, in this research, we focused on developing an X-ray CT-based identification method for praline chocolate to address the low-contrast phenomenon. We also aimed to produce an understanding of the impact of the microstructure on the final product quality. By determining appropriate image segmentation techniques and conducting grey value analysis, we were able to characterize the internal structure of praline chocolate and revealed internal structure phenomena. This understanding will provide several benefits, including guaranteeing the appropriate filling type, ensuring filling quality, and enabling time-dependent checking, which indirectly elevates industrial product quality. 2. Material and methods 2.1 Raw materials The compound chocolates, namely dark chocolate (DC), milk chocolate (MC), and white chocolate (WC) were purchased from Gandum Mas Kencana (Yogyakarta, Indonesia). Filling products, pineapple jam (PJ) and peanut butter (PB) were obtained from Pondasi Inti Sejahtera (Bantul, Indonesia). After purchasing materials, these ingredients were temporarily kept at a cold temperature (8°C) to maintain the quality before being processed. 2.2 Sample preparation The chocolate variants (DC, MC, and WC) were combined with fillings (PJ and PB) at two added-weight levels (0.0, and 1.5 g). Of these, the 0.0 g samples, which served as the reference, and the 1.5 g samples were continuous for non-destructive X-ray CT scanning as internal structural assessment. Chocolate compounds were prepared at a melting temperature (55°C), converting the chocolate phase to be liquid. The melted chocolate was then used to construct the praline shell by pouring it into a praline mold, followed by a vibration process to release air bubbles, and then cooled at 16°C. The chocolate fillings were then poured into the formed shell and closed by another chocolate liquid to cover the rest of the praline part. A re-cooling process was applied for tamping the final praline form. The praline chocolate quality was physically assessed using an X-ray CT. 2.3 Digital measurement 2.3.1 X-ray CT Acquisition Praline chocolates were arranged in a sample holder (10x10x15 cm) with a total of 27 samples, with 9 samples of each layer (10x10x5 cm), by considering the size of the praline chocolate and the space of sample placement, which also served as a stable platform for the sample during the image acquisition. The samples were scanned using an Industrial X-ray CT V|tome|x S by gently mounting them on the rotated sample placement. The CT imaging system was initiated to operate at a voltage and current of 140 kV and 200 µA, respectively. These settings were considered according to medical standards, 20–140 kV [30], and the previous food research, 96 µA-6 mA [19,22,24,25]. The resolution used was 73.5 µm (macroscale resolution) with the rotation angle of 0.5° in order to reduce the scanning time. Based on this rotation angle, X-ray CT captured every image for a second. Therefore, this setting produced 12 minutes of scanning time. Other geometry results, such as Focus to Detector Distance (FDD) 941.26 mm, Focus to Object Distance (FOD) 345.9 mm, Magnification 2.72x, X-Sample + 0.00, Y-Sample − 96.72 mm, and Z-Sample + 345.92 mm, were obtained, resulting in the radiography image. These settings are illustrated in Fig. 1 . 2.3.2 Image preprocessing A reconstruction process (as shown in Fig. 1 ) was conducted to convert the radiography image into slice images with 3 axes representing a three-dimensional image in DICOM files by using ImageJ (1.54g, National Institutes of Health, USA). The dynamic range adjustment was then considered from 0-65535 to 15756–24018 as a clarity of the object details. Reducing the image bits from 16-bit to 8-bit (0-255) was then applied to optimize the computational processes, and filtering was also implemented using a median filter (1.0) to reduce noise in the object. Greyscale was able to produce the value distribution of the image. 2.3.3 Grayscale analysis Greyscale was obtained from a pre-processed image that represents a color range consisting of shades of grey between black and white pixels. Greyscale determination was done by measuring the light reflected or transmitted by an object in a greyscale image with a range of 0 (black region) to 255 (white region) on an 8-bit scale. The greyscale distribution data was then collected and processed into data that can be interpreted as a histogram of a 0-255 scale over the frequency of grey value. Higher value denotes higher density, lower value denotes lower density. 2.3.4 Image segmentation The image segmentation stage used open-source 3D Slicer (2025, The Slicer Community, Brigham and Women’s Hospital, USA), which was implemented for characterization, visualization, and analysis of material objects based on 3D. By using the certain approach of segmentation in the structure of praline chocolate, such as chocolate structure, air bubbles, and filling, each region of praline chocolate can be identified. The TIFF files resulting from image preprocessing were then processed. The segmentation aimed to obtain a volumetric representation of each component as mm³. To measure the effectiveness of the segmentation method, a basic segmentation method was used: Otsu-based segmentation. On the other hand, due to the different component characteristics, filling segmentation was considered to characterize several approaches, such as volume of interest-based (VOI-based) greyscale and Grow from Seeds (GFS) [31,32]. 2.4 Experimental measurement 2.4.1 Density X-ray CT image of a material is represented by variations in greyscale intensity arising from differences in X-ray energy attenuation associated with local material density [12,33]; a relationship that is essential for ensuring the precision and validation of the developed method. To measure the density ρ (in g/cm3), the measurement was adapted from Lewis [34], where the density of a substance is equal to the mass of the substance divided by the volume. In this case, the mass of each chocolate sample was chosen approximately 5 g, while the test flask with a volume of 100 mL was used. 2.4.2 Moisture Content The moisture content analysis was implemented based on the international standard procedure of gravimetric method from ISO 1442:1997 [35]. The method can be appropriate for food products such as chocolate, fruit jam, and peanut butter because it is based on a universal gravimetric principle. The procedure began with the preparation of a 5 g, followed by heating at a high temperature (T: 103°C ± 2°C; t: 24 h). The dry sample was moved and placed in a desiccator for 15 minutes. Then was measured and calculated into the percentage of moisture content. 2.4.3 Fat Content Fat content measurement was performed by the Soxhlet extraction method from AOAC (920.39 Cereal fat; 960.39 Meat fat) in accordance with established standards methods [36]. The sample (5 g) was weighed and wrapped by the filter paper, then placed into the Soxhlet which was already oven-dried (T: 105°C; t: 24 h). A Soxhlet set was arranged including a fat flask (bottom) and condenser (top) and a reflux process was initiated (T: 68–69°C; t: 5 h) resulting in the mixture of fat and solvent. The mixture was then separated by evaporating (T: 105°C) and cooled in a desiccator. The difference weights between before and after extraction were then calculated as a fat content. 2.4.4 Solid Content Solid content measurement was considered according to Lakshanasomya, et. al. [37], where the oven-drying approach was applied. In this research, the chocolate sample was measured by considering the weight of the chocolate sample (5 g). Then, the sample was melted (T: 55°C; t: 15 min) and mixed with n-hexane (1:4) as a solvent. The mixture then was filtered by using filter paper and was dried with oven temperature (T: 105°C; t: 24 h). 2.5 Quantitative analysis The quantitative analysis in this study was first carried out by evaluating the physicochemical characteristics of chocolate using analysis of variance (ANOVA, p < 0.05) to ensure accuracy and reliability of the experiment data. Subsequently, digital image processing was performed to quantify image extraction data. To provide comprehensive validation, a Pearson Correlation Coefficient (PCC) analysis was conducted, particularly to verify the relationship between experimentally measured density and digitally derived parameters. 3. Result and discussion 3.1 Material characteristic of the praline chocolate According to Table 1 , DC exhibited the highest density, moisture content, solid content, and fat content among all chocolate types, primarily due to its elevated cocoa solid fraction. Higher solid content in chocolate increases particle packing, while the accompanying high fat level acts as a surface coating that reduces friction among the particles and enhances viscosity behavior [4]. MC and WC had significantly lower values of the parameters (ANOVA, p < 0.05) due to their reduced cocoa solid composition and partial substitution with milk powder, which decreases viscosity and yields a less compact microstructure [38,39]. The fillings also demonstrated markedly different physicochemical characteristics. PJ contained substantially higher moisture, while PB contained high fat and lower moisture. These differences considerably influenced greyscale attenuation in X-ray CT because of moisture content. This component had a higher density than fat [40], contributing differently to X-ray absorption. On the other hand, the instability of water-rich fillings will potentially promote interfacial disruption and entrapped air formation [4]. The CT Contrast Prediction, which was based on the statistical differences in density composition, summarized the expected contrast behavior between each chocolate type and each filling. Chocolate-filling pairs that were not significantly different in density tended to produce low-contrast, as their attenuation coefficient values will become difficult to distinguish. This tendency was observed, for example, in DC-PJ, MC-PB, and WC-PB, which had similar density interactions. Conversely, as shown in DC-PB, MC-PJ, and WC-PJ, a significant difference corresponded to high contrast. Table 1 Material characteristics of dark chocolate (DC), milk chocolate (MC), white chocolate (WC) used for the praline shell, and pineapple jam (PJ) and peanut butter (PB) used for the filling. Density (g/cm³) Moisture Content (%) Fat Content (%) Solid Content (%) Possible CT image contrast DC 1.26 ± 0.04 bc 1.43 ± 0.15 ab 38.84 ± 0.08 c 84.74 ± 1.88 c DC-PJ = Low; DC-PB = High MC 1.16 ± 0.04 ab 1.09 ± 0.12 a 36.56 ± 0.05 b 78.52 ± 4.49 ab MC-PJ = High; MC-PB = Low WC 1.08 ± 0.02 a 0.59 ± 0.02 a 32.94 ± 0.20 a 71.25 ± 4.04 a WC-PJ = High; WC-PB = Low PJ 1.41 ± 0.06 c 26.25 ± 1.67 c 0.25 ± 0.01* - PB 0.89 ± 0.03 a 3.69 ± 0.03 b 45–55** - *[41] **[42] To validate the low-contrast behavior among the chocolate-filling interactions, Fig. 2 A-F present X-ray CT cross-sections of all six combinations, enabling direct comparison between predicted and observed attenuation differences. In general, praline containing PJ can be visually distinguished from the chocolate layers, as their greyscale intensity tended to deviate consistently from that of the chocolate matrices. However, the DC-PJ sample presented an important exception. Several regions of the DC-PJ exhibited low contrast greyscale intensity that closely approached that of dark chocolate, which was shown by the yellow box in Fig. 2 A. This phenomenon resulted in localized ambiguity and partially diminished boundary clarity. According to Fig. 2 D-F, low-contrast behavior was observed overall in chocolate PB-filled. This phenomenon indicated that this filling system was particularly prone to greyscale overlap in X-ray CT imaging. This tendency confirmed that the density similarity between PB and the chocolate matrix inherently limits contrast-based phase discrimination. However, according to Fig. 2 D-F, the praline chocolate with PB-filled revealed two distinct distribution patterns of PB within the praline structure. In the lower region of the chocolate shell, the oil-rich fraction of PB tended to separate from the peanut solids during processing [4,43]. This oil-dominated zone produced pronounced low-contrast regions, as lipid phases have attenuation coefficients comparable to fat-based chocolate matrices [2]. In contrast, the upper region of the praline was dominated by peanut solid particles, which exhibit higher effective density and attenuation, leading to clearer contrast and more distinguishable boundaries from the surrounding chocolate. This phase separation within PB was consistent with its fat-rich and heterogeneous composition, where vibration and gravitational effects during molding promoted oil migration and solid settling. Therefore, it was important to consider the distribution of greyscale values to ensure clarity in each region. 3.2 Greyscale value distribution The regression of greyscale value distribution presented in Fig. 3 demonstrates a strong linear relationship (R² = 0.9255) between greyscale intensity (CT images) and measured density (observation). This correlation confirmed that the distributions represented the density of the chocolate shell, filling, and air bubbles. This alignment is essential because it verifies that density-driven contrast is a reliable indicator of material differences, even within a complex multi-phase food matrix [27,33]. Such reliability strengthens the interpretability of CT-based phase differentiation and becomes increasingly important for the segmentation steps that follow, particularly when assessing the performance of basic automatic methods. Despite this strong correlation, the contrast between several fractions remained limited because their density ranges had partially similar values (Table 1 ). This similar density compressed the greyscale distribution, creating ambiguous boundaries, especially in regions where local microstructural variations further diminished contrast. Consequently, the visual distinction between phases in the raw CT images became unreliable, even though the density–greyscale relationship itself is robust. To critically assess whether the available contrast was sufficient for practical phase separation, a basic automatic segmentation approach was applied as an initial test of component separability before moving on to more refined segmentation strategies. 3.3 Basic automatic segmentation Based on the preceding contrast analysis which demonstrated that several chocolate-filling combinations exhibited low contrast separation, a basic automatic segmentation approach was first evaluated to determine whether the chocolate shell, filling, and air bubbles can be separated without additional correction. Figure 4 A-F illustrate the application of the Otsu segmentation method, which relies on a bimodal greyscale distribution to generate a global threshold [19,25,44]. Despite the fact that several fillings exhibited sufficiently higher contrast relative to the chocolate matrix, the Otsu approach was still unable to correctly identify and segment the filling areas. Consequently, although Otsu performed reasonably well in detecting air bubbles, its inability to segment the filling regions confirmed that it cannot identify specific material regions, even when their contrast against the chocolate is relatively high. 3.4 VOI-based segmentation To better address the high or low contrast of the filling region, a layer-based greyscale distribution analysis was conducted as the foundation for subsequent image correction. Figure 5 A-I illustrate the greyscale histogram based on the three depth positions during image acquisition. Across these layers, several patterns indicated notable greyscale overlapping, particularly in the top (A-C) and bottom (G-I) regions. In contrast, the middle layer (D-F) consistently exhibited separation between the chocolate and filling phases of all chocolate variants. In addition, the filling regions appeared more distinct, although slight overlap in greyscale value was still present. The overall greyscale curves in these two layers tended to shift toward lower intensity values compared to the middle layer, indicating reduced contrast and lower signal quality. As a result, applying segmentation directly yielded a lower precision for a particular region because the global histogram across the entire volume was insufficient to separate the material fractions. Therefore, a VOI-based histogram approach was introduced to obtain cleaner, localized greyscale separation. The VOI-based approach was a method that is able to isolate the regions that truly represent single materials. The key principle is reliable detection of low-contrast internal features depends on quantifying the minimum contrast level that a CT system can distinguish with statistical confidence [32]. To ensure that the analysis captured only the pure material regions, VOI sizes were adjusted according to the area of each component, using 50 pixels for chocolate regions and 20 pixels for filling regions (as shown in Fig. 6 ). This selective extraction allowed the greyscale range of each praline component to be isolated more accurately. According to Fig. 7 , which explains the VOI-based greyscale of all praline chocolate samples, it revealed a clearer threshold between the filling and chocolate fraction. The curves shown in Fig. 7 A, D, and G, indicate that DC exhibited a greyscale range of approximately 160–215, which is positioned close to PJ, with values around 185–215 (A), 210–250 (D), and 215–255 (G), while PB was significantly lower, with values around 75–140 (A), 60–150 (D), and 60–140 (G). This proximity indicated a partial similarity in density between DC and PJ, explaining why their intensity distributions appeared near each other. In Fig. 7 B, E, and H, the greyscale distribution of milk chocolate ranged from approximately 150 to 210, while PJ occupied a higher range of around 180–220 (B), 205–250 (E), and 200–240 (H). On the other hand, PB showed a similar lower greyscale pattern to PB in the DC graph with around 70–125 (B), 80–145 (E), and 55–130 (H). This pattern aligned with the expected density hierarchy, with MC having lower density than both DC and PJ [4,10]. However, despite being consistent with density data, the substantial overlap between MC and PJ indicated a pronounced low-contrast condition. This behavior contrasts with the initial expectation that MC exhibits better separation from PJ due to its intermediate formulation. The overlap suggested that the attenuation contribution of the moisture from PJ and milk components (particularly milk fat and milk solids) reduced contrast and compressed the dynamic range, making MC more difficult to distinguish from the higher-density PJ phase [45]. Furthermore, Fig. 7 C, F, and I, white chocolate, show a greyscale range of roughly 145–200, reflecting its lower density and absence of cocoa solids. However, similar to MC, the WC curve still overlapped with the left-shifted portion of the PJ distribution: 185–230 (C), 200–245 (F), and 200–250 (I). This distinction was noteworthy because WC exhibited the greatest separation from PJ due to its significantly different composition. Instead, the greyscale results revealed that WC retained a tendency to overlap with PJ rather than PB (55–145 (C), 60–150 (F), and 55–145 (I)), which, unlike PJ, does not overlap with any of the chocolate types. This persistent overlap again suggested that milk powder constituents in WC elevate its attenuation sufficiently to shift its greyscale region toward PJ, thereby reducing the expected contrast despite substantial compositional differences. Subsequently, according to Fig. 8 , the threshold for each region was implemented to separate the specific region. The results showed that the filling regions in the middle layer exhibited markedly higher accuracy when matched with the VOI-based greyscale thresholds, unlike the top and bottom layers. The reduced accuracy in these two layers remained less consistent and reflected irregular X-ray absorption across the sample depth. This phenomenon demonstrated that the interface between chocolate and filling became defined near the surface and base. This inconsistency indicated that histogram-based greyscale segmentation cannot reliably be applied, reinforcing the need to establish a different approach to segmentation that accounts for depth-dependent variability. 3.5 Grow from Seeds (GFS) Segmentation As the previous segmentation approaches were insufficient to separate the filling matrix, an alternative segmentation method was therefore adopted. The GFS algorithm implemented in 3D Slicer is a semi-automatic, region-growing segmentation method that uses user-defined seed regions to help classify voxels. For this investigation, three different seed classes-chocolate, filling, and background, were manually initialized on representative slices to capture the intrinsic greyscale characteristic of each phase (as illustrated in Fig. 9 ). From a technical perspective, GFS iteratively propagated these labels by evaluating local greyscale similarity and neighborhood connectivity. The use of multiple seed classes allowed GFS to resolve interfaces in systems with overlapping greyscale distributions, such as praline chocolate, where global thresholding failed due to limited contrast between shell and filling. The segmentation comparison presented in Fig. 10 demonstrates that the GFS method, a semi-automatic method, yielded substantially improved delineation of filling regions compared with the VOI-based approach previously described. While VOI segmentation provided an initial estimation of the filling zone, several ambiguities remained, particularly at boundaries where greyscale overlap between filling and chocolate caused local misclassification. The GFS method resolved these limitations by integrating a broader intensity context, resulting in cleaner separation, fewer boundary discontinuities, and reduced leakage into the chocolate matrix. This enhancement in visual clarity highlighted the ability of GFS to capture the true spatial distribution of the filling within praline samples. Figure 11 . Comparison of actual filling percentage (blue bar) and segmented CT image-based filling percentage using VOI (orange bar) and GFS (grey bar) approaches in praline chocolates. Figure 11 illustrates the filling percentage of praline chocolate based on three measurements: the actual filling mass (represented by blue bars), VOI-based segmentation (represented by orange bars), and GFS-based segmentation (represented by grey bars). The actual filling percentage remained constant at approximately 15% across all samples, reflecting the fixed filling mass of 1.5 g relative to the total praline weight of around 9.5 g. In contrast, the VOI segmentation produced highly inconsistent estimates, especially in PB samples, where the calculated filling percentage exceeded 25%, while PJ samples were underestimated to values around 10%. This discrepancy indicated substantial inaccuracy and poor reliability of the VOI approach, which represents the true mass fraction. The GFS method, however, showed markedly improved agreement with the actual values, producing filling percentages that consistently fall near the 15% benchmark. This alignment demonstrated that GFS provides a more robust and precise segmentation outcome, particularly in accurately delineating the filling region within a complex multi-phase chocolate structure. 3.6 Characterization of Praline physical structure Based on the demonstrated accuracy of GFS in both visual and quantitative assessments, this method was then continued to reconstruct the full 3D structure of the praline structure. After implementing the GFS method, Otsu segmentation was applied to separate the background and air bubbles from the object. To separate these two regions, a logical operator algorithm was applied to correctly subtract bubble voxels from the background and avoid misclassification. The final 3D visualization of all praline samples is presented in Fig. 12 , illustrating the spatial distribution of the two fillings (PJ (yellow) and PB (green)) along with air bubbles represented in multiple colours. The reconstructions capture the internal geometry and filling placement across the nine sample types, revealing structural differences influenced by formulation and processing conditions. PJ maintained a compact and centralized distribution, while PB displayed higher variability and broader dispersion across the praline volume. The visualization also highlighted variations in air inclusion that reflect compositional and rheological differences, particularly in PB, where the lower-density oil fraction tended to migrate upward during vibration while the heavier peanut solids settled toward the bottom [1,2]. Additionally, variations in air bubble distribution, which were affected by formulation, were also captured clearly by the GFS output, further highlighting the algorithm’s ability to resolve heterogeneity arising from differences in material composition and processing behaviour. According to Fig. 13 , the air bubble distribution across the three chocolate types was dominated by small bubbles in the range of 0.05–0.3 mm³, with milk chocolate exhibiting the lowest overall bubble volume (Fig. 13 A–C). Although dark chocolate contained more fat than milk chocolate (Table 1 ), most of this fat was structurally bound to cocoa solids [9]. The amount of cocoa solids content will increase particle interaction [46], which elevates the need for fat content. The fat content within chocolate increases the distance between the solid particles hence increasing viscosity drops [4]. Moreover, high cocoa solids also contribute to higher moisture content. Consequently, any increase in moisture becomes particularly critical, as even small amounts of water (0.3%) must be balanced by approximately 1% cocoa butter to maintain a stable suspension and ensure proper chocolate quality [4]. On the other hand, white chocolate, which is made without cocoa and instead uses milk powder, has a high intrinsic viscosity because of its low fat content [10]. This white chocolate phenomenon led to a larger and more irregular bubble distribution due to the absence of cocoa solids as a particle connection. This process weakened structural cohesion and amplified the matrix’s susceptibility to void formation [4]. These observations collectively highlighted that bubble formation was strongly influenced by the interplay between density, fat content, solid loading, and moisture. In pralines containing fillings, structural disturbances were more pronounced, reflected by the appearance of larger air cavities and cracks exceeding 1 mm³. These defects arose because the fillings introduced components with different physicochemical properties, such as water-rich PJ or fat-rich PB, which disrupted the equilibrium between fat, solids, and moisture within the chocolate matrix [5]. High-moisture fillings can induce water migration into the chocolate, increasing interfacial tension and internal pressure, thereby expanding void formation [3]. Conversely, high-fat fillings introduce an additional liquid phase that can destabilize the emulsion and promote entrapped air. Figure 13 D shows a prominent crack surrounding the filling region, illustrating how differential contraction between the chocolate shell and the filling during cooling imposes internal stress. This mismatch, particularly between fat-based chocolate and water- or fat-dominant fillings, can initiate crack propagation, compromise matrix integrity, and ultimately reduce praline quality [1,3]. These internal structural phenomena provided critical insight that enhanced the ability to predict formulation performance and shelf-life stability. 5. Conclusion This research successfully demonstrated the efficacy of X-ray CT as a non-destructive method for characterizing the internal microstructure of praline chocolates. This tendency was shown by the strong value of correlation (R² 0.9255) between experiment and digital measurements. Moreover, a primary contribution of this study was the validation of a segmentation workflow capable of overcoming low radiodensity contrast between chocolate shells and fillings. This research established that while global thresholding techniques (Otsu) and VOI-based were insufficient for multi-phase chocolates, the semi-automatic Grow from Seeds (GFS) algorithm yielded accurate volumetric quantification. This was evidenced by the strong correlation between the GFS-derived filling volume and the actual formulation mass (15%). From a material science perspective, the 3D reconstruction elucidated how the intrinsic properties of praline chocolate governed internal structural stability and air bubble formation. Although DC (38.84 ± 0.08%) contained a higher fat fraction than MC (36.56 ± 0.05) and WC (32.94 ± 0.20%), it exhibited a higher number and volume of air bubbles than milk chocolate due to its higher moisture content. This tendency disrupted suspension equilibrium, thereby promoting void formation. The incorporation of fillings further amplified structural heterogeneity, increasing bubble size and inducing internal cracking, particularly for PJ water-based fillings whose physicochemical properties differ markedly from the chocolate matrix. From an industrial standpoint, these findings highlighted the necessity of formulation and process-aware structural control to mitigate internal defects and ensure consistent product quality and shelf-life performance. Declarations Acknowledgment This research was supported by the Academic Excellence 2025 Grant from Universitas Gadjah Mada (UGM), Indonesia (1908/UN1/DITLIT/Dit-Lit/PT.01.00.2025). Author Contributions Bayu Nugraha : Conceptualization, Methodology, Writing – original draft preparation, Writing – review and editing, Funding Acquisition, Supervision. Yoga Arif Firmansyah : Conceptualization, Formal Analysis and Investigation, Visualization, Writing - original draft preparation, Project Administration. Joko Nugroho Wahyu Kariyadi and Fahrizal Yusuf Affandi: Writing - review and editing. Arifin Dwi Saputro : Methodology, Writing - review and editing, Resources, Supervision Conflict of Interest The authors declare no conflict of interest. References Marvig CL, Kristiansen RM, Madsen MG, et al. (2014) Identification and characterisation of organisms associated with chocolate pralines and sugar syrups used for their production. Int J Food Microbiol 185: 167–176. Tatar HD, Glicerina VT, Foligni R, et al. (2024) Microstructural and rheological influence of different strategies to mitigate oil migration in chocolate pralines during storage in limiting conditions. LWT 208: 116672. Franke K, Middendorf D, Heinz V, et al. (2022) Alcohol in praline fillings influences the water migration within the surrounding chocolate shell. 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16:13:01","extension":"xml","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83773,"visible":true,"origin":"","legend":"","description":"","filename":"78eb5a99aced42ffaa4f24fa15db158b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/adb7848f965f0363e54640ab.xml"},{"id":100069327,"identity":"916fc374-4422-4953-aa4c-d2239f49d2e9","added_by":"auto","created_at":"2026-01-12 16:12:59","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92455,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/2978216933028018d056c008.html"},{"id":100069276,"identity":"33ac3608-be56-41b3-bd28-3eba40f200da","added_by":"auto","created_at":"2026-01-12 16:12:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228518,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of X-ray CT scanning for the praline chocolates (top), and CT image reconstruction and pre-processing (bottom).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/437f9408a829750268ce7bd7.png"},{"id":100069303,"identity":"78c8d3fb-39ac-4cfe-92d8-3f2068ecec0f","added_by":"auto","created_at":"2026-01-12 16:12:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":801548,"visible":true,"origin":"","legend":"\u003cp\u003eThe X-Z planes of the six praline CT images: A) dark chocolate with pineapple jam (DC-PJ); B) milk chocolate with pineapple jam (MC-PJ); C) white chocolate with pineapple jam (WC-PJ); D) dark chocolate with peanut butter (DC-PB); (E) milk chocolate with peanut butter (MC-PB); F) white chocolate with peanut butter (WC-PB). Yellow boxes indicate the contrast interface between the two fractions (filling and chocolate).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/1986ecb3e712189a1c53958c.png"},{"id":100069292,"identity":"adcc8c3d-3690-4394-a067-ba289952a9fc","added_by":"auto","created_at":"2026-01-12 16:12:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61787,"visible":true,"origin":"","legend":"\u003cp\u003eRegression between greyscale intensity value and the densities of each praline fraction (shell, filling).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/57c911daf9e72212b357b531.png"},{"id":100069295,"identity":"31faaa1e-d568-402d-a67f-b007278fc7fc","added_by":"auto","created_at":"2026-01-12 16:12:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":869483,"visible":true,"origin":"","legend":"\u003cp\u003eThe top-row images show the unsegmented or original greyscale CT images of (A) the chocolate reference (without filling), (B) chocolate-PJ, (C) chocolate-PB. The bottom-row images present the corresponding segmented image for (D) the chocolate reference, (E) chocolate-PJ, (F) chocolate-PB based on automatic Otsu’s thresholding (blue: segmented region). The yellow boxes indicate two fractions that were not separated by the segmentation.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/c48f3a66685cd4ad949c0d7b.png"},{"id":100069326,"identity":"b26ca5c7-29d3-4912-ab8f-54f4385b4e57","added_by":"auto","created_at":"2026-01-12 16:12:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":467230,"visible":true,"origin":"","legend":"\u003cp\u003eGreyscale intensity histogram of the intact praline chocolate placed on top (A-C), middle (D-F), and bottom (G-I) layers during the X-ray CT scanning: A, D, and G are dark chocolate (DC); B, E and H are milk chocolates (MC); C, F, and I are white chocolates (WC). All are combined with pineapple jam (PJ) and peanut butter (PB). Different graph colours represent the praline combinations.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/d206f83ec71603d61322c5a0.png"},{"id":100069288,"identity":"ab8e7a7f-4b9b-402f-9188-fa6f8ee0de88","added_by":"auto","created_at":"2026-01-12 16:12:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":320456,"visible":true,"origin":"","legend":"\u003cp\u003eSelected Volumes of Interest (VOIs) for greyscale extraction. A 50-pixel VOI is used for the chocolate reference (left), while smaller 20-pixel VOIs are applied to the filling regions (middle: PJ, right: PB) to capture pure material areas.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/32b8fe0421dff2674fcf0595.png"},{"id":100069331,"identity":"2320d398-5dbe-4ab2-83db-179ae3968fa2","added_by":"auto","created_at":"2026-01-12 16:13:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":485749,"visible":true,"origin":"","legend":"\u003cp\u003eThe composite chocolate samples across top (A-C), middle (D-F), and bottom (G-I) layers. The pilots illustrate the pixel value distribution for three distinct components: Reference (black line), PJ (Orange line), and PB (green line). The panels are categorized by the type of chocolate matrix used: DC (A, D, G), MC (B, E, H), and WC (C, F, I).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/cccb8c31cd4602e6954a583b.png"},{"id":100069337,"identity":"66788bbb-8279-4918-93f3-51b579379bdc","added_by":"auto","created_at":"2026-01-12 16:13:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1139015,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented filling regions based on Volume of Interest (VOIs) from greyscale extraction (blue: segmented filling region).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/8f629d9f2b5cf3831753a157.png"},{"id":100069308,"identity":"b52e1ff8-851b-4a61-b362-065baa857e19","added_by":"auto","created_at":"2026-01-12 16:12:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":266541,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of Grow from Seeds (GFS) segmentation, where there were three selections of each region (white: background, yellow: filling, red: chocolate).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/6183b67519ac07414caa792a.png"},{"id":100069296,"identity":"c99e3784-0d10-4b20-bc84-ac4011131e70","added_by":"auto","created_at":"2026-01-12 16:12:43","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":734041,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented filling regions based on Grow from Seeds (GFS) from greyscale extraction (yellow: PJ region, dark green: PB region).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/8df57df788058cf77ebfb592.png"},{"id":100069282,"identity":"44f70905-c6a7-45c5-96a5-cdd030a62bc2","added_by":"auto","created_at":"2026-01-12 16:12:36","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":280653,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of actual filling percentage (blue bar) and segmented CT image-based filling percentage using VOI (orange bar) and GFS (grey bar) approaches in praline chocolates.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/dbdc211d0b0ffa1454974f97.png"},{"id":100365131,"identity":"4c542485-f055-4b1f-87d7-897ffea5da29","added_by":"auto","created_at":"2026-01-16 07:54:42","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":952989,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the GFS segmentation results for all praline samples: (A) DC Reference, (B) MC Reference, (C) WC Reference, (D) DC-PJ, (E) MC-PJ, (F) WC-PJ, (G) DC-PB, (H) MC-PB, and (I) WC-PB (yellow: PJ, green: PB, and the multiple colour: air bubbles).\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/59726be30af4b342b4106784.png"},{"id":100069342,"identity":"0b427c55-fa3c-46ef-b975-cd06cf997ea3","added_by":"auto","created_at":"2026-01-12 16:13:05","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":670953,"visible":true,"origin":"","legend":"\u003cp\u003eAir bubble size distribution of dark chocolate (A), milk chocolate (B), white chocolate (C) (grey: reference; orange: PJ; brown: PB), and the visualization of crack within praline chocolate, where blue, yellow, and green show crack, PJ, and PB, respectively (D).\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/d62af9b1b8e1767fa5e09a65.png"},{"id":100382000,"identity":"28371f9f-18fc-4ac6-a38f-e85afed81f10","added_by":"auto","created_at":"2026-01-16 10:40:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7733404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8547296/v1/9142a2b6-e652-4f02-96f7-fdf59fcd3dd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Morphometric Characterization Workflows of Praline Chocolates using X-ray Computed Tomography","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChocolate is one of the most popular confectionery products in the world which has many variants of end-products. Praline chocolate is one of the most exclusive types of chocolate, known for its high economic value and strong consumer appeal. By definition, praline chocolate consists of a solid chocolate shell filled with various compositions. The compositions create a complex combination of texture and flavors [1,2]. The fillings used in pralines generally fall into two general categories: water-based fillings, such as fruit jam, caramel and cream, and fat-based fillings such as nut and ganache [2,3]. Within this context, praline chocolate filled with tropical fruit jams not only enriches the praline\u0026rsquo;s character but also offers a strategic value advantage for tropical countries such as Indonesia.\u003c/p\u003e \u003cp\u003eAlthough the fillings enhance the sensory appeal of pralines, they simultaneously introduce inherent instability within the product matrix. The difference in the physicochemical properties between the chocolate shell and the filling is pronounced imbalance. This phenomenon drives mass inconsistencies, structural deformation, and bloom formation. Water-based fillings accelerate moisture diffusion, triggering sugar bloom, whereas fat-based fillings promote lipid migration that leads to fat bloom [1,4,5]. In both cases, the migrated components typically accumulate on the chocolate surface, resulting in visible whitening, textural degradation, and diminished product quality [1\u0026ndash;3]. Bloom significantly reduces consumer acceptance, shortens shelf life, and undermines the commercial value of praline products [5,6]. This phenomenon highlights a critical challenge that must be addressed in chocolate formulation and processing. Current identifications of chocolate physical structure, such as moisture increase [3], particle size distribution [5,7], melting profile [8], hardness [9], rheology behavior [10], and structure by SEM [6,7,9] reveal internal structural properties which attributes to final stability and quality [9]. However, these cases were limited to destructive approaches that inherently remove the original internal structure and restrict the dynamic observation. Since the physical attributes fundamentally contribute to the final quality and its deformation by time, there is a critical need for non-invasive technique capable of characterizing the structure fraction spatially. This technique necessitates the application of non-destructive approaches, for instance X-ray Computed Tomography (CT).\u003c/p\u003e \u003cp\u003eX-ray CT is an imaging instrument that enables the characterization of external and internal morphology of many materials non-destructively [11\u0026ndash;13]. For food materials, detailed information of food physical architectures ranging from macro- to microscale, such as the whole geometry shape and volume [11], air bubble distribution [14,15], and material density [16], was precisely identified and represented using X-ray CT. In addition, according to Sin, et. al. [17], the migration of internal components within porous media may also be detectable. Previous studies have demonstrated the effectiveness of X-ray CT imaging in observing the internal structure of food products, such as bread [18\u0026ndash;20], ice cream [15], cookies [21], and cereal food [16]. In chocolate, X-ray CT has been implemented to quantify the air bubble distribution in aerated chocolate by simply separating the chocolate matrix and created air bubbles [19,22\u0026ndash;25]. Furthermore, advanced synchrotron X-ray CT has been employed to achieve high-resolution imaging of chocolate microstructure [26]. This technique enables precise quantification of crystalline, crack, and pore networks. In praline chocolate, the filling materials typically have identical densities to the chocolate density, potentially creating a low-contrast CT image [27,28]. This low-contrast image has minimal differences in grayscale value intensities among material phases [29], presenting the segmentation difficulty of targeted material fractions. This levels up the image processing algorithm to more advanced steps for precise identification of the praline internal features. Therefore, this tendency of applying X-ray CT to praline chocolate remains underexplored.\u003c/p\u003e \u003cp\u003eTherefore, in this research, we focused on developing an X-ray CT-based identification method for praline chocolate to address the low-contrast phenomenon. We also aimed to produce an understanding of the impact of the microstructure on the final product quality. By determining appropriate image segmentation techniques and conducting grey value analysis, we were able to characterize the internal structure of praline chocolate and revealed internal structure phenomena. This understanding will provide several benefits, including guaranteeing the appropriate filling type, ensuring filling quality, and enabling time-dependent checking, which indirectly elevates industrial product quality.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Raw materials\u003c/h2\u003e \u003cp\u003eThe compound chocolates, namely dark chocolate (DC), milk chocolate (MC), and white chocolate (WC) were purchased from Gandum Mas Kencana (Yogyakarta, Indonesia). Filling products, pineapple jam (PJ) and peanut butter (PB) were obtained from Pondasi Inti Sejahtera (Bantul, Indonesia). After purchasing materials, these ingredients were temporarily kept at a cold temperature (8\u0026deg;C) to maintain the quality before being processed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample preparation\u003c/h2\u003e \u003cp\u003eThe chocolate variants (DC, MC, and WC) were combined with fillings (PJ and PB) at two added-weight levels (0.0, and 1.5 g). Of these, the 0.0 g samples, which served as the reference, and the 1.5 g samples were continuous for non-destructive X-ray CT scanning as internal structural assessment. Chocolate compounds were prepared at a melting temperature (55\u0026deg;C), converting the chocolate phase to be liquid. The melted chocolate was then used to construct the praline shell by pouring it into a praline mold, followed by a vibration process to release air bubbles, and then cooled at 16\u0026deg;C. The chocolate fillings were then poured into the formed shell and closed by another chocolate liquid to cover the rest of the praline part. A re-cooling process was applied for tamping the final praline form. The praline chocolate quality was physically assessed using an X-ray CT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Digital measurement\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 X-ray CT Acquisition\u003c/h2\u003e \u003cp\u003ePraline chocolates were arranged in a sample holder (10x10x15 cm) with a total of 27 samples, with 9 samples of each layer (10x10x5 cm), by considering the size of the praline chocolate and the space of sample placement, which also served as a stable platform for the sample during the image acquisition. The samples were scanned using an Industrial X-ray CT V|tome|x S by gently mounting them on the rotated sample placement. The CT imaging system was initiated to operate at a voltage and current of 140 kV and 200 \u0026micro;A, respectively. These settings were considered according to medical standards, 20\u0026ndash;140 kV [30], and the previous food research, 96 \u0026micro;A-6 mA [19,22,24,25].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe resolution used was 73.5 \u0026micro;m (macroscale resolution) with the rotation angle of 0.5\u0026deg; in order to reduce the scanning time. Based on this rotation angle, X-ray CT captured every image for a second. Therefore, this setting produced 12 minutes of scanning time. Other geometry results, such as Focus to Detector Distance (FDD) 941.26 mm, Focus to Object Distance (FOD) 345.9 mm, Magnification 2.72x, X-Sample\u0026thinsp;+\u0026thinsp;0.00, Y-Sample \u0026minus;\u0026thinsp;96.72 mm, and Z-Sample\u0026thinsp;+\u0026thinsp;345.92 mm, were obtained, resulting in the radiography image. These settings are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Image preprocessing\u003c/h2\u003e \u003cp\u003eA reconstruction process (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was conducted to convert the radiography image into slice images with 3 axes representing a three-dimensional image in DICOM files by using ImageJ (1.54g, National Institutes of Health, USA). The dynamic range adjustment was then considered from 0-65535 to 15756\u0026ndash;24018 as a clarity of the object details. Reducing the image bits from 16-bit to 8-bit (0-255) was then applied to optimize the computational processes, and filtering was also implemented using a median filter (1.0) to reduce noise in the object. Greyscale was able to produce the value distribution of the image.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Grayscale analysis\u003c/h2\u003e \u003cp\u003eGreyscale was obtained from a pre-processed image that represents a color range consisting of shades of grey between black and white pixels. Greyscale determination was done by measuring the light reflected or transmitted by an object in a greyscale image with a range of 0 (black region) to 255 (white region) on an 8-bit scale. The greyscale distribution data was then collected and processed into data that can be interpreted as a histogram of a 0-255 scale over the frequency of grey value. Higher value denotes higher density, lower value denotes lower density.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Image segmentation\u003c/h2\u003e \u003cp\u003eThe image segmentation stage used open-source 3D Slicer (2025, The Slicer Community, Brigham and Women\u0026rsquo;s Hospital, USA), which was implemented for characterization, visualization, and analysis of material objects based on 3D. By using the certain approach of segmentation in the structure of praline chocolate, such as chocolate structure, air bubbles, and filling, each region of praline chocolate can be identified. The TIFF files resulting from image preprocessing were then processed. The segmentation aimed to obtain a volumetric representation of each component as mm\u0026sup3;. To measure the effectiveness of the segmentation method, a basic segmentation method was used: Otsu-based segmentation. On the other hand, due to the different component characteristics, filling segmentation was considered to characterize several approaches, such as volume of interest-based (VOI-based) greyscale and Grow from Seeds (GFS) [31,32].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Experimental measurement\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Density\u003c/h2\u003e \u003cp\u003eX-ray CT image of a material is represented by variations in greyscale intensity arising from differences in X-ray energy attenuation associated with local material density [12,33]; a relationship that is essential for ensuring the precision and validation of the developed method. To measure the density ρ (in g/cm3), the measurement was adapted from Lewis [34], where the density of a substance is equal to the mass of the substance divided by the volume. In this case, the mass of each chocolate sample was chosen approximately 5 g, while the test flask with a volume of 100 mL was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Moisture Content\u003c/h2\u003e \u003cp\u003eThe moisture content analysis was implemented based on the international standard procedure of gravimetric method from ISO 1442:1997 [35]. The method can be appropriate for food products such as chocolate, fruit jam, and peanut butter because it is based on a universal gravimetric principle. The procedure began with the preparation of a 5 g, followed by heating at a high temperature (T: 103\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C; t: 24 h). The dry sample was moved and placed in a desiccator for 15 minutes. Then was measured and calculated into the percentage of moisture content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Fat Content\u003c/h2\u003e \u003cp\u003e Fat content measurement was performed by the Soxhlet extraction method from AOAC (920.39 Cereal fat; 960.39 Meat fat) in accordance with established standards methods [36]. The sample (5 g) was weighed and wrapped by the filter paper, then placed into the Soxhlet which was already oven-dried (T: 105\u0026deg;C; t: 24 h). A Soxhlet set was arranged including a fat flask (bottom) and condenser (top) and a reflux process was initiated (T: 68\u0026ndash;69\u0026deg;C; t: 5 h) resulting in the mixture of fat and solvent. The mixture was then separated by evaporating (T: 105\u0026deg;C) and cooled in a desiccator. The difference weights between before and after extraction were then calculated as a fat content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Solid Content\u003c/h2\u003e \u003cp\u003eSolid content measurement was considered according to Lakshanasomya, et. al. [37], where the oven-drying approach was applied. In this research, the chocolate sample was measured by considering the weight of the chocolate sample (5 g). Then, the sample was melted (T: 55\u0026deg;C; t: 15 min) and mixed with n-hexane (1:4) as a solvent. The mixture then was filtered by using filter paper and was dried with oven temperature (T: 105\u0026deg;C; t: 24 h).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Quantitative analysis\u003c/h2\u003e \u003cp\u003eThe quantitative analysis in this study was first carried out by evaluating the physicochemical characteristics of chocolate using analysis of variance (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to ensure accuracy and reliability of the experiment data. Subsequently, digital image processing was performed to quantify image extraction data. To provide comprehensive validation, a Pearson Correlation Coefficient (PCC) analysis was conducted, particularly to verify the relationship between experimentally measured density and digitally derived parameters.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Material characteristic of the praline chocolate\u003c/h2\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, DC exhibited the highest density, moisture content, solid content, and fat content among all chocolate types, primarily due to its elevated cocoa solid fraction. Higher solid content in chocolate increases particle packing, while the accompanying high fat level acts as a surface coating that reduces friction among the particles and enhances viscosity behavior [4]. MC and WC had significantly lower values of the parameters (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) due to their reduced cocoa solid composition and partial substitution with milk powder, which decreases viscosity and yields a less compact microstructure [38,39].\u003c/p\u003e \u003cp\u003eThe fillings also demonstrated markedly different physicochemical characteristics. PJ contained substantially higher moisture, while PB contained high fat and lower moisture. These differences considerably influenced greyscale attenuation in X-ray CT because of moisture content. This component had a higher density than fat [40], contributing differently to X-ray absorption. On the other hand, the instability of water-rich fillings will potentially promote interfacial disruption and entrapped air formation [4]. The CT Contrast Prediction, which was based on the statistical differences in density composition, summarized the expected contrast behavior between each chocolate type and each filling. Chocolate-filling pairs that were not significantly different in density tended to produce low-contrast, as their attenuation coefficient values will become difficult to distinguish. This tendency was observed, for example, in DC-PJ, MC-PB, and WC-PB, which had similar density interactions. Conversely, as shown in DC-PB, MC-PJ, and WC-PJ, a significant difference corresponded to high contrast.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaterial characteristics of dark chocolate (DC), milk chocolate (MC), white chocolate (WC) used for the praline shell, and pineapple jam (PJ) and peanut butter (PB) used for the filling.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003cp\u003e(g/cm\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoisture Content\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFat Content\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSolid Content\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePossible CT image contrast\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDC-PJ\u0026thinsp;=\u0026thinsp;Low;\u003c/p\u003e \u003cp\u003eDC-PB\u0026thinsp;=\u0026thinsp;High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMC-PJ\u0026thinsp;=\u0026thinsp;High;\u003c/p\u003e \u003cp\u003eMC-PB\u0026thinsp;=\u0026thinsp;Low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWC-PJ\u0026thinsp;=\u0026thinsp;High;\u003c/p\u003e \u003cp\u003eWC-PB\u0026thinsp;=\u0026thinsp;Low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u0026ndash;55**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*[41] **[42]\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo validate the low-contrast behavior among the chocolate-filling interactions, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-F present X-ray CT cross-sections of all six combinations, enabling direct comparison between predicted and observed attenuation differences. In general, praline containing PJ can be visually distinguished from the chocolate layers, as their greyscale intensity tended to deviate consistently from that of the chocolate matrices. However, the DC-PJ sample presented an important exception. Several regions of the DC-PJ exhibited low contrast greyscale intensity that closely approached that of dark chocolate, which was shown by the yellow box in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. This phenomenon resulted in localized ambiguity and partially diminished boundary clarity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F, low-contrast behavior was observed overall in chocolate PB-filled. This phenomenon indicated that this filling system was particularly prone to greyscale overlap in X-ray CT imaging. This tendency confirmed that the density similarity between PB and the chocolate matrix inherently limits contrast-based phase discrimination. However, according to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F, the praline chocolate with PB-filled revealed two distinct distribution patterns of PB within the praline structure. In the lower region of the chocolate shell, the oil-rich fraction of PB tended to separate from the peanut solids during processing [4,43]. This oil-dominated zone produced pronounced low-contrast regions, as lipid phases have attenuation coefficients comparable to fat-based chocolate matrices [2]. In contrast, the upper region of the praline was dominated by peanut solid particles, which exhibit higher effective density and attenuation, leading to clearer contrast and more distinguishable boundaries from the surrounding chocolate. This phase separation within PB was consistent with its fat-rich and heterogeneous composition, where vibration and gravitational effects during molding promoted oil migration and solid settling. Therefore, it was important to consider the distribution of greyscale values to ensure clarity in each region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Greyscale value distribution\u003c/h2\u003e \u003cp\u003eThe regression of greyscale value distribution presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates a strong linear relationship (R\u0026sup2; = 0.9255) between greyscale intensity (CT images) and measured density (observation). This correlation confirmed that the distributions represented the density of the chocolate shell, filling, and air bubbles. This alignment is essential because it verifies that density-driven contrast is a reliable indicator of material differences, even within a complex multi-phase food matrix [27,33]. Such reliability strengthens the interpretability of CT-based phase differentiation and becomes increasingly important for the segmentation steps that follow, particularly when assessing the performance of basic automatic methods.\u003c/p\u003e \u003cp\u003eDespite this strong correlation, the contrast between several fractions remained limited because their density ranges had partially similar values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This similar density compressed the greyscale distribution, creating ambiguous boundaries, especially in regions where local microstructural variations further diminished contrast. Consequently, the visual distinction between phases in the raw CT images became unreliable, even though the density\u0026ndash;greyscale relationship itself is robust. To critically assess whether the available contrast was sufficient for practical phase separation, a basic automatic segmentation approach was applied as an initial test of component separability before moving on to more refined segmentation strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Basic automatic segmentation\u003c/h2\u003e \u003cp\u003eBased on the preceding contrast analysis which demonstrated that several chocolate-filling combinations exhibited low contrast separation, a basic automatic segmentation approach was first evaluated to determine whether the chocolate shell, filling, and air bubbles can be separated without additional correction. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-F illustrate the application of the Otsu segmentation method, which relies on a bimodal greyscale distribution to generate a global threshold [19,25,44]. Despite the fact that several fillings exhibited sufficiently higher contrast relative to the chocolate matrix, the Otsu approach was still unable to correctly identify and segment the filling areas. Consequently, although Otsu performed reasonably well in detecting air bubbles, its inability to segment the filling regions confirmed that it cannot identify specific material regions, even when their contrast against the chocolate is relatively high.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 VOI-based segmentation\u003c/h2\u003e \u003cp\u003eTo better address the high or low contrast of the filling region, a layer-based greyscale distribution analysis was conducted as the foundation for subsequent image correction. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-I illustrate the greyscale histogram based on the three depth positions during image acquisition. Across these layers, several patterns indicated notable greyscale overlapping, particularly in the top (A-C) and bottom (G-I) regions. In contrast, the middle layer (D-F) consistently exhibited separation between the chocolate and filling phases of all chocolate variants. In addition, the filling regions appeared more distinct, although slight overlap in greyscale value was still present. The overall greyscale curves in these two layers tended to shift toward lower intensity values compared to the middle layer, indicating reduced contrast and lower signal quality. As a result, applying segmentation directly yielded a lower precision for a particular region because the global histogram across the entire volume was insufficient to separate the material fractions. Therefore, a VOI-based histogram approach was introduced to obtain cleaner, localized greyscale separation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe VOI-based approach was a method that is able to isolate the regions that truly represent single materials. The key principle is reliable detection of low-contrast internal features depends on quantifying the minimum contrast level that a CT system can distinguish with statistical confidence [32]. To ensure that the analysis captured only the pure material regions, VOI sizes were adjusted according to the area of each component, using 50 pixels for chocolate regions and 20 pixels for filling regions (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This selective extraction allowed the greyscale range of each praline component to be isolated more accurately.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which explains the VOI-based greyscale of all praline chocolate samples, it revealed a clearer threshold between the filling and chocolate fraction. The curves shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, D, and G, indicate that DC exhibited a greyscale range of approximately 160\u0026ndash;215, which is positioned close to PJ, with values around 185\u0026ndash;215 (A), 210\u0026ndash;250 (D), and 215\u0026ndash;255 (G), while PB was significantly lower, with values around 75\u0026ndash;140 (A), 60\u0026ndash;150 (D), and 60\u0026ndash;140 (G). This proximity indicated a partial similarity in density between DC and PJ, explaining why their intensity distributions appeared near each other.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, E, and H, the greyscale distribution of milk chocolate ranged from approximately 150 to 210, while PJ occupied a higher range of around 180\u0026ndash;220 (B), 205\u0026ndash;250 (E), and 200\u0026ndash;240 (H). On the other hand, PB showed a similar lower greyscale pattern to PB in the DC graph with around 70\u0026ndash;125 (B), 80\u0026ndash;145 (E), and 55\u0026ndash;130 (H). This pattern aligned with the expected density hierarchy, with MC having lower density than both DC and PJ [4,10]. However, despite being consistent with density data, the substantial overlap between MC and PJ indicated a pronounced low-contrast condition. This behavior contrasts with the initial expectation that MC exhibits better separation from PJ due to its intermediate formulation. The overlap suggested that the attenuation contribution of the moisture from PJ and milk components (particularly milk fat and milk solids) reduced contrast and compressed the dynamic range, making MC more difficult to distinguish from the higher-density PJ phase [45].\u003c/p\u003e \u003cp\u003eFurthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, F, and I, white chocolate, show a greyscale range of roughly 145\u0026ndash;200, reflecting its lower density and absence of cocoa solids. However, similar to MC, the WC curve still overlapped with the left-shifted portion of the PJ distribution: 185\u0026ndash;230 (C), 200\u0026ndash;245 (F), and 200\u0026ndash;250 (I). This distinction was noteworthy because WC exhibited the greatest separation from PJ due to its significantly different composition. Instead, the greyscale results revealed that WC retained a tendency to overlap with PJ rather than PB (55\u0026ndash;145 (C), 60\u0026ndash;150 (F), and 55\u0026ndash;145 (I)), which, unlike PJ, does not overlap with any of the chocolate types. This persistent overlap again suggested that milk powder constituents in WC elevate its attenuation sufficiently to shift its greyscale region toward PJ, thereby reducing the expected contrast despite substantial compositional differences.\u003c/p\u003e \u003cp\u003eSubsequently, according to Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the threshold for each region was implemented to separate the specific region. The results showed that the filling regions in the middle layer exhibited markedly higher accuracy when matched with the VOI-based greyscale thresholds, unlike the top and bottom layers. The reduced accuracy in these two layers remained less consistent and reflected irregular X-ray absorption across the sample depth. This phenomenon demonstrated that the interface between chocolate and filling became defined near the surface and base. This inconsistency indicated that histogram-based greyscale segmentation cannot reliably be applied, reinforcing the need to establish a different approach to segmentation that accounts for depth-dependent variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Grow from Seeds (GFS) Segmentation\u003c/h2\u003e \u003cp\u003eAs the previous segmentation approaches were insufficient to separate the filling matrix, an alternative segmentation method was therefore adopted. The GFS algorithm implemented in 3D Slicer is a semi-automatic, region-growing segmentation method that uses user-defined seed regions to help classify voxels. For this investigation, three different seed classes-chocolate, filling, and background, were manually initialized on representative slices to capture the intrinsic greyscale characteristic of each phase (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). From a technical perspective, GFS iteratively propagated these labels by evaluating local greyscale similarity and neighborhood connectivity. The use of multiple seed classes allowed GFS to resolve interfaces in systems with overlapping greyscale distributions, such as praline chocolate, where global thresholding failed due to limited contrast between shell and filling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe segmentation comparison presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e demonstrates that the GFS method, a semi-automatic method, yielded substantially improved delineation of filling regions compared with the VOI-based approach previously described. While VOI segmentation provided an initial estimation of the filling zone, several ambiguities remained, particularly at boundaries where greyscale overlap between filling and chocolate caused local misclassification. The GFS method resolved these limitations by integrating a broader intensity context, resulting in cleaner separation, fewer boundary discontinuities, and reduced leakage into the chocolate matrix. This enhancement in visual clarity highlighted the ability of GFS to capture the true spatial distribution of the filling within praline samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;11\u003c/b\u003e. Comparison of actual filling percentage (blue bar) and segmented CT image-based filling percentage using VOI (orange bar) and GFS (grey bar) approaches in praline chocolates.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;11 illustrates the filling percentage of praline chocolate based on three measurements: the actual filling mass (represented by blue bars), VOI-based segmentation (represented by orange bars), and GFS-based segmentation (represented by grey bars). The actual filling percentage remained constant at approximately 15% across all samples, reflecting the fixed filling mass of 1.5 g relative to the total praline weight of around 9.5 g. In contrast, the VOI segmentation produced highly inconsistent estimates, especially in PB samples, where the calculated filling percentage exceeded 25%, while PJ samples were underestimated to values around 10%. This discrepancy indicated substantial inaccuracy and poor reliability of the VOI approach, which represents the true mass fraction. The GFS method, however, showed markedly improved agreement with the actual values, producing filling percentages that consistently fall near the 15% benchmark. This alignment demonstrated that GFS provides a more robust and precise segmentation outcome, particularly in accurately delineating the filling region within a complex multi-phase chocolate structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Characterization of Praline physical structure\u003c/h2\u003e \u003cp\u003eBased on the demonstrated accuracy of GFS in both visual and quantitative assessments, this method was then continued to reconstruct the full 3D structure of the praline structure. After implementing the GFS method, Otsu segmentation was applied to separate the background and air bubbles from the object. To separate these two regions, a logical operator algorithm was applied to correctly subtract bubble voxels from the background and avoid misclassification. The final 3D visualization of all praline samples is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e, illustrating the spatial distribution of the two fillings (PJ (yellow) and PB (green)) along with air bubbles represented in multiple colours. The reconstructions capture the internal geometry and filling placement across the nine sample types, revealing structural differences influenced by formulation and processing conditions. PJ maintained a compact and centralized distribution, while PB displayed higher variability and broader dispersion across the praline volume. The visualization also highlighted variations in air inclusion that reflect compositional and rheological differences, particularly in PB, where the lower-density oil fraction tended to migrate upward during vibration while the heavier peanut solids settled toward the bottom [1,2]. Additionally, variations in air bubble distribution, which were affected by formulation, were also captured clearly by the GFS output, further highlighting the algorithm\u0026rsquo;s ability to resolve heterogeneity arising from differences in material composition and processing behaviour.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003e, the air bubble distribution across the three chocolate types was dominated by small bubbles in the range of 0.05\u0026ndash;0.3 mm\u0026sup3;, with milk chocolate exhibiting the lowest overall bubble volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eA\u0026ndash;C). Although dark chocolate contained more fat than milk chocolate (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), most of this fat was structurally bound to cocoa solids [9]. The amount of cocoa solids content will increase particle interaction [46], which elevates the need for fat content. The fat content within chocolate increases the distance between the solid particles hence increasing viscosity drops [4]. Moreover, high cocoa solids also contribute to higher moisture content. Consequently, any increase in moisture becomes particularly critical, as even small amounts of water (0.3%) must be balanced by approximately 1% cocoa butter to maintain a stable suspension and ensure proper chocolate quality [4]. On the other hand, white chocolate, which is made without cocoa and instead uses milk powder, has a high intrinsic viscosity because of its low fat content [10]. This white chocolate phenomenon led to a larger and more irregular bubble distribution due to the absence of cocoa solids as a particle connection. This process weakened structural cohesion and amplified the matrix\u0026rsquo;s susceptibility to void formation [4]. These observations collectively highlighted that bubble formation was strongly influenced by the interplay between density, fat content, solid loading, and moisture.\u003c/p\u003e \u003cp\u003eIn pralines containing fillings, structural disturbances were more pronounced, reflected by the appearance of larger air cavities and cracks exceeding 1 mm\u0026sup3;. These defects arose because the fillings introduced components with different physicochemical properties, such as water-rich PJ or fat-rich PB, which disrupted the equilibrium between fat, solids, and moisture within the chocolate matrix [5]. High-moisture fillings can induce water migration into the chocolate, increasing interfacial tension and internal pressure, thereby expanding void formation [3]. Conversely, high-fat fillings introduce an additional liquid phase that can destabilize the emulsion and promote entrapped air. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eD shows a prominent crack surrounding the filling region, illustrating how differential contraction between the chocolate shell and the filling during cooling imposes internal stress. This mismatch, particularly between fat-based chocolate and water- or fat-dominant fillings, can initiate crack propagation, compromise matrix integrity, and ultimately reduce praline quality [1,3]. These internal structural phenomena provided critical insight that enhanced the ability to predict formulation performance and shelf-life stability.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research successfully demonstrated the efficacy of X-ray CT as a non-destructive method for characterizing the internal microstructure of praline chocolates. This tendency was shown by the strong value of correlation (R\u0026sup2; 0.9255) between experiment and digital measurements. Moreover, a primary contribution of this study was the validation of a segmentation workflow capable of overcoming low radiodensity contrast between chocolate shells and fillings. This research established that while global thresholding techniques (Otsu) and VOI-based were insufficient for multi-phase chocolates, the semi-automatic Grow from Seeds (GFS) algorithm yielded accurate volumetric quantification. This was evidenced by the strong correlation between the GFS-derived filling volume and the actual formulation mass (15%).\u003c/p\u003e \u003cp\u003eFrom a material science perspective, the 3D reconstruction elucidated how the intrinsic properties of praline chocolate governed internal structural stability and air bubble formation. Although DC (38.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08%) contained a higher fat fraction than MC (36.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05) and WC (32.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20%), it exhibited a higher number and volume of air bubbles than milk chocolate due to its higher moisture content. This tendency disrupted suspension equilibrium, thereby promoting void formation. The incorporation of fillings further amplified structural heterogeneity, increasing bubble size and inducing internal cracking, particularly for PJ water-based fillings whose physicochemical properties differ markedly from the chocolate matrix. From an industrial standpoint, these findings highlighted the necessity of formulation and process-aware structural control to mitigate internal defects and ensure consistent product quality and shelf-life performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Academic Excellence 2025 Grant from Universitas Gadjah Mada (UGM), Indonesia (1908/UN1/DITLIT/Dit-Lit/PT.01.00.2025).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBayu Nugraha : Conceptualization, Methodology, Writing \u0026ndash; original draft preparation, Writing \u0026ndash; review and editing, Funding Acquisition, Supervision. Yoga Arif Firmansyah : Conceptualization, Formal Analysis and Investigation, Visualization, Writing - original draft preparation, Project Administration. Joko Nugroho Wahyu Kariyadi and Fahrizal Yusuf Affandi: Writing - review and editing. Arifin Dwi Saputro : Methodology, Writing - review and editing, Resources, Supervision\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarvig CL, Kristiansen RM, Madsen MG, et al. (2014) Identification and characterisation of organisms associated with chocolate pralines and sugar syrups used for their production. \u003cem\u003eInt J Food Microbiol\u003c/em\u003e 185: 167\u0026ndash;176.\u003c/li\u003e\n\u003cli\u003eTatar HD, Glicerina VT, Foligni R, et al. 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(2017) Investigating the rheological, microstructural and textural properties of chocolates sweetened with palm sap-based sugar by partial replacement. \u003cem\u003eEur Food Res Technol\u003c/em\u003e 243: 1729\u0026ndash;1738.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-biophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Biophysics](https://www.springer.com/journal/11483)","snPcode":"11483","submissionUrl":"https://submission.nature.com/new-submission/11483/3","title":"Food Biophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"food structure, non-destructive evaluation, low-contrast object, 3D image processing","lastPublishedDoi":"10.21203/rs.3.rs-8547296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8547296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe structural integrity of praline chocolates is a determinant factor for consumer acceptance, yet assessing it remains challenging due to the complex internal interactions between chocolate shells and fillings. This study establishes a robust non-destructive characterization protocol using X-ray Computed Tomography (CT) to evaluate the morphometrics of dark, milk, and white chocolates filled with water-based pineapple jam and fat-based peanut butter. A critical challenge addressed was the low radiodensity contrast between the chocolate matrix and fillings. To resolve this, the research compared global threshold, Volume of Interest (VOI)-based, and a Seeded Region-Growing Algorithm (Grow from Seeds/GFS) segmentations. Results indicated a strong relationship (R\u0026sup2;=0.9255) between CT greyscale intensity and physical densities of the praline components. The GFS method demonstrated higher accuracy on low-contrast images of the praline than Otsu and VOI-based segmentation method. This method successfully reconstructed the internal architecture and matched the actual filling mass fraction (~\u0026thinsp;15%) with high precision. Furthermore, 3D microstructural analysis revealed that physicochemical mismatches-specifically moisture migration in pineapple jam and fat migration in peanut butter-induced critical defects, including macro-voids (\u0026gt;\u0026thinsp;0.3 mm\u0026sup3;). These findings validate the developed X-ray CT workflow as a powerful tool for identifying internal multi-phase food systems, such as praline chocolate.\u003c/p\u003e","manuscriptTitle":"Morphometric Characterization Workflows of Praline Chocolates using X-ray Computed Tomography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 15:59:54","doi":"10.21203/rs.3.rs-8547296/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-06T16:45:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T13:31:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T22:15:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57155037705422427886473146262186146103","date":"2026-01-09T06:15:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177169432678607039992756144212063879036","date":"2026-01-08T14:20:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T14:15:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T08:35:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-08T08:31:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Biophysics","date":"2026-01-08T05:36:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-biophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Biophysics](https://www.springer.com/journal/11483)","snPcode":"11483","submissionUrl":"https://submission.nature.com/new-submission/11483/3","title":"Food Biophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ae73cb26-143a-4467-b6ea-8700a17323cc","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T13:40:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 15:59:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8547296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8547296","identity":"rs-8547296","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}