Central Food Technological Research Institute
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Development and Characterization of HEC-Stabilized Pickering Emulsion for Sustainable Food Packaging Application
No full text
Integrated quality evaluation of whole milk powder: physicochemical properties, GC-FAME lipid analysis, HPLC detection of foreign and aflatoxin M1 analysis
No full textInvestigating the effects of fumonisin B1-induced Neurotoxicity and the therapeutic potential of native and nanoformulated compounds in Zebrafish
No full textMicroplastics in our diet: A review of food source contamination.
No full textMicroplastics and Nanoplastics (MNPs) are particles arising from the intentional synthesis or unintentional
degradation of plastic. They are widespread contaminants in diverse environments - from deep oceans to the
human body. These particles could enter humans via inhalation, water, or food. Despite a decade of research on
detecting MNPs in food, there is an incomplete understanding of the source of MNPs, their quantities and
behaviour in food, and the total exposure to humans. This review examines studies on MNPs detected in foods
(sans seafood), highlighting their presence in terrestrial and processed food sources. It elaborates on the diversity
of data reported and its inconsistencies, challenges in the methodology, recent developments and progress, and
key future directions. Detection of MNPs in critical routes is the first step in understanding the extent of MNP
exposure and toxicity to humans. This is essential for developing effective interventions and food safety and
health policies
Comparative and synergetic effects of cold plasma and ozone treatments on microbial safety, color stability, and quality of chili during long-term storage
No full textThis study primarily emphasizes the comparative and synergistic effects of cold plasma (CP) and ozone (O₃)
treatments on microbial safety, surface and pigment-based color stability, and key quality parameters of Byadagi
chili during 16 weeks of ambient storage (27 ± 3 ◦C). Storage was identified as the principal factor contributing
to quality deterioration; however, pre-storage CP and O₃ interventions significantly mitigated these effects.
Samples were categorized into untreated (control), CP-treated, O₃-treated, and combined CP + O₃-treated groups.
The CP + O₃ combination achieved the most substantial microbial reductions by week 16, with decreases of
1.88 log₁₀ CFU g⁻¹ for total plate count, 1.36 log₁₀ CFU g⁻¹ for yeast and mold, and 1.05 log₁₀ CFU g⁻¹ for co-
liforms, relative to control. Although aflatoxin B₁ levels increased in all groups during storage, CP + O₃ main-
tained the lowest concentration (0.71 μg kg⁻¹) versus the control (6.32 μg kg⁻¹). Surface color was best preserved
in CP + O₃ samples (ΔE*: 3.80; a*: 19.21), with minimal change in chroma, hue, and browning index. Pigment
degradation was significantly lower in the CP + O₃ group: total carotenoids (5.8 % ↓), capsanthin (5.05 % ↓), and
American Spice Trade Association (ASTA) color value (26.2 % ↓), compared to greater losses in control samples.
While CP showed the highest antioxidant retention (phenolics: 84.7 %; DPPH: 83.4 %), CP + O₃ exhibited
balanced efficacy (phenolics: 80.5 %; DPPH: 82.1 %), with high sensory acceptability (Score: 7.45). In conclu-
sion, the synergistic CP + O₃ treatment effectively reduced storage-induced quality degradation, providing a
residue-free strategy for enhancing safety, visual quality, and nutritional attributes in high-value chili. Future
research should focus on industrial-scale applications and regulatory compliance
Improving OCTA Image Quality for Alzheimer’s Diagnosis Using Autoencoder, Self-Attention and GANs
No full textOptical Coherence Tomography Angiography (OCTA) imaging has emerged as a crucial non-invasive
technique for early detection of Alzheimer’s disease (AD) by analyzing retinal microvascular changes. However,
OCTA images often suffer from noise and low contrast, which can hinder accurate feature extraction and diagnosis.
This paper presents a novel deep learning-based denoising framework that integrates an autoencoder, self-attention
mechanism, and Generative Adversarial Network (GAN) to enhance the quality of OCTA images for Alzheimer’s
detection. The autoencoder effectively extracts noise-free latent features, significantly reducing noise while preserving
essential microvascular structures. The self-attention mechanism refines these features by capturing long-range
dependencies, ensuring the preservation of fine-grained retinal details. Finally, the GAN framework enhances image
realism by discriminating between real and reconstructed images, further improving the perceptual quality of the output.
The model is trained using a composite loss function comprising mean squared error, adversarial loss, and perceptual
loss to optimize both accuracy and visual fidelity. This study specifically addresses multiple noise types including
horizontal strip noise, vertical stripe noise, Gaussian noise, speckle noise, and random stripe noise. Experimental
results, as depicted in Figure 5, illustrate both original and corresponding noisy OCTA images. A comparative
evaluation with state-of-the-art denoising methods (Table 1) demonstrates that the proposed approach achieves
superior performance, with a Peak Signal-to-Noise Ratio (PSNR) of 75.10 and a Structural Similarity Index (SSIM)
of 0.9890. These results significantly outperform existing methods such as SGIDN, StruNet, and Baseline
+Res+SWT+PL, confirming the effectiveness of the proposed framework in enhancing OCTA image clarity and
preserving diagnostic features. Consequently, this leads to more accurate and reliable Alzheimer’s detection from
OCTA scans
Multivariate Analysis of the Efficiency of Bio-Actives Extraction from Laboratory-Generated By-Products of Citrus reticulata Blanco
No full textA growing demand for sustainable methods to valorize agricultural by-products underscores the need for efficient,
green extraction techniques. This study evaluated ultrasound-assisted extraction (UAE) of bioactive compounds from Citrus
reticulata Blanco peel and pomace using multivariate analysis across six key metrics: total phenolic content (TPC), flavonoid
content, and four antioxidant assays. UAE significantly outperformed conventional extraction (CE), achieving a 207% higher
average composite performance score (2.94 vs. 0.95), on the basis of a heatmap-derived SUM(P*) index. The best UAE sample
(S9: peel–aqueous–UAE) achieved a score of 5.33 out of 6, compared to 2.01 for the best CE sample (S3), highlighting the superior
efficiency of UAE. Principal component analysis (PCA) revealed distinct clustering of UAE samples, confirming their enhanced
biochemical profiles. UAE also reduced energy consumption to 0.065 kWh (77% less than CE) while maintaining high antioxidant
performance. Fourier transform infrared spectroscopy (FTIR) confirmed functional groups characteristic of phenolics, and ultra-
high performance liquid chromatography (UHPLC)–HRMS identified catechin, neohesperidin, and nomilinic acid derivatives as
major bioactives. These findings establish a robust, data-driven framework for optimizing sustainable extraction of food-grade
antioxidants from citrus residues. This validated UAE approach enables efficient, low-energy recovery of phenolics from citrus
waste, offering immediate potential for integration into food, nutraceutical, and cosmetic industry supply chains under circular
economy models
Physical, functional, and cooking properties along with storage study of bioprocessed pigmented red and black rice
No full textThis study investigates the quality characteristics of pigmented brown rice from red and black varieties obtained through
bioprocessing via germination and subsequent hydrothermal treatment (steaming). The objective was to evaluate how these
processes affect the milling, physical, functional, cooking, sensory, and storage properties of the rice. Bioprocessing resulted
in a 30–33% reduction in milling yield in both and a 45% decrease in the hardness of red rice grains, with no significant
changes in other physical properties. The insoluble amylose content increased by 21% in bioprocessed red rice and by
25–27% in bioprocessed and steam-treated in both. Functional properties related to alkali spreading and sedimentation values
remained unchanged in both, but water absorption capacity increased by 13.7% in bioprocessed red rice and by 25% and 18%
in bioprocessed and steam-treated red and black rice, respectively. Pasting properties, such as gelatinization temperature
(GT), increased, while peak viscosity was reduced by 80% in red and ~ 15 times in black and cold paste viscosity by 54% in
red and ~ 14 times in black variety. On the other hand, subsequent hydrothermal treatment showed increase in PV and other
properties but remained lower than the control in red, whereas in black, it increased and reached control values. Solid loss
increased by 26% in bioprocessed red rice, while cooking time was reduced by 11% in both bioprocessed rice and by 22%
in both steam-treated rice. Sensory evaluation showed that bioprocessed rice had a softer texture, lower firmness, higher
stickiness, and lower overall acceptability. However, subsequent hydrothermal treatment resulted in improvements in grain
integrity, firmness, and overall acceptability. Texture analysis revealed an 82% reduction in hardness and a 20% increase in
elasticity for red rice, while the black variety showed a 305% increase in gumminess and a 240% increase in adhesiveness.
During storage, the bioprocessed and treated rice samples maintained good quality characteristics, with moisture levels
within safe limits, free fatty acids below 0.04%, and peroxide values within acceptable limits. In conclusion, bioprocessed
and hydrothermally treated red and black rice exhibited quality characteristics suitable for use as whole grain rice, with
notable improvements in various quality attributes
