This study aimed to investigate the potential application of image texture processing method on visible crumb structure of salty cake pogácsa, which was prepared with different baking times (5 and 7 min) and temperatures (200, 215, and 230°C). For this purpose, changes in gray level co-occurrence matrix (GLCM) features including energy, contrast, correlation, homogeneity, and entropy were monitored and their relationship with the instrumental texture parameters (hardness, adhesiveness, cohesiveness, springiness, gumminess, and chewiness) were assessed. The pore ratios were also extracted and visualized using image processing technique. Texture profile parameters indicated strong correlation (p < .01) with the image pattern parameters in different pogácsa groups. Gumminess showed strong correlation with contrast (0.503), correlation (-0.498), and homogeneity (0.401). Hardness also exhibited correlation with contrast (0.517), entropy (0.341), and correlation (-0.476). The pore ratio showed marked variation in crumb structure when different times and temperatures were used. Baking at 230°C for 7 min maximized the pore ratio (0.56). Penalty analysis revealed that oiliness, pore structure, and color of products were linked with baking time and temperature. Overall, the results suggested that the GLCM-based technique had the potential to be used as a nondestructive method for rapid quality assessment of pogácsa.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/jtxs.12619 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
Department of Electrical Engineering and Information Systems, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Organic photodetectors (OPDs) are key devices for monitoring vital signs, such as heart rate and blood oxygen level. For realizing the long-term measurement of biosignals, stable operation is essential. To improve the stability of OPDs, it is important to analyze each layer to understand the degradation mechanism.
View Article and Find Full Text PDFAnal Sci
January 2025
Chitose Institute of Science and Technology, Chitose, Hokkaido, 066-8655, Japan.
Cartilage is a connective tissue composed of mainly water, collagen (COL) and proteoglycans (PGs) including chondroitin sulfate (CS). Near-infrared (NIR) spectroscopy is adequate for examination of soft and hard tissues with large amount of water non-destructively and non-invasively. We measured tablets containing CS and COL using NIR spectroscopy to develop an evaluation method for PGs in cartilage non-destructively and non-invasively.
View Article and Find Full Text PDFCells
January 2025
Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603005 Nizhny Novgorod, Russia.
Background: The wide variability in clinical responses to anti-tumor immunotherapy drives the search for personalized strategies. One of the promising approaches is drug screening using patient-derived models composed of tumor and immune cells. In this regard, the selection of an appropriate in vitro model and the choice of cellular response assay are critical for reliable predictions.
View Article and Find Full Text PDFIn recent years, DNA metabarcoding has been used for a more efficient assessment of bulk samples. However, there remains a paucity of studies examining potential disparities in species identification methodologies. Here, we explore the outcomes of diverse clustering and filtering techniques on data from a non-destructive metabarcoding approach, compared to species-level morphological identification of Brachycera (Diptera) and Hymenoptera of two bulk samples collected with Malaise traps.
View Article and Find Full Text PDFFood Res Int
February 2025
Izmir Institute of Technology, Department of Food Engineering, Urla-Izmir, Turkiye. Electronic address:
The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spectroscopic data to detect adulteration in apple juice concentrate. Four techniques-UV-visible, fluorescence, near-infrared (NIR) spectroscopy, and time domain H nuclear magnetic resonance relaxometry (H NMR)-were used to generate data from both authentic and adulterated apple juice samples.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!