IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring DOI Creative Commons

Siva Peddareddigari,

S. Vijayan,

Annamalai Manickavasagan

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 105 - 105

Published: Dec. 26, 2024

Technological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, risk of contamination has increased severalfold, often resulting significant damage terms wastage, economic loss to producers, danger public health, or all these. In general, governments across world recognized importance having safety processes place impose recalls as required. However, primary challenges existing practices are delays identifying unsafe food, siloed data handling, delayed decision making, and tracing source contamination. Leveraging Internet Things (IoT), 5G, blockchains, cloud computing, big data, a novel framework been proposed address current challenges. The enables real-time gathering situ application machine learning-powered algorithms predict facilitate instant making. Since processed real approach be identified early informed decisions made confidently, thereby helping reduce significantly. also throws up new implementation changes collection phases production, onboarding various stockholders, adaptation process.

Language: Английский

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades DOI Creative Commons
Marietta Fodor,

Anna Matkovits,

Eszter Benes

et al.

Foods, Journal Year: 2024, Volume and Issue: 13(21), P. 3501 - 3501

Published: Oct. 31, 2024

During food quality control, NIR technology enables the rapid and non-destructive determination of typical characteristics categories, their origin, detection potential counterfeits. Over past 20 years, results for a variety groups—including meat products, milk baked goods, pasta, honey, vegetables, fruits, luxury items like coffee, tea, chocolate—have been compiled. This review aims to give broad overview NIRS processes that have used thus far assist researchers employing techniques in comparing findings with earlier data determining new research directions.

Language: Английский

Citations

8
Detecting Starch-Adulterated Turmeric Using Vis-NIR Spectroscopy and Multispectral Imaging with Machine Learning DOI
Madhusudan G. Lanjewar,

Satyam S. Asolkar,

Jivan S. Parab

et al.

Journal of Food Composition and Analysis, Journal Year: 2024, Volume and Issue: 136, P. 106700 - 106700

Published: Aug. 30, 2024

Language: Английский

Citations

5
Detection of amylose content in rice samples with spectral augmentation and advanced machine learning DOI
Kamini G. Panchbhai, Madhusudan G. Lanjewar

Journal of Food Composition and Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 107455 - 107455

Published: March 1, 2025

Language: Английский

Citations

0
A Systematic Review of Spectroscopic Techniques for Detecting Milk Adulteration DOI

Parsa Joolaei Ahranjani,

Kamine Dehghan,

Zahra Esfandiari

et al.

Critical Reviews in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 32

Published: April 14, 2025

Milk adulteration is a crucial worldwide concern that endangers food safety and public health, as it involves the deliberate tampering with milk by adding foreign substances or removing essential nutrients, often to boost profits hinder microbial growth. Traditional detection methods frequently lack sensitivity speed required identify adulterants within milk's complex matrix. This systematic review critically examines application of spectroscopic techniques for detecting adulteration, focusing on Nuclear Magnetic Resonance (NMR), Infrared (IR) Spectroscopy, Raman Ultraviolet-Visible (UV-Vis) Mass Spectrometry, Laser-Based Techniques, Dielectric X-Ray Spectroscopy. Each technique's principles, advantages, limitations, specific applications in identifying adulterants, such water, urea, melamine, added sugars, fats, preservatives, heavy metals are discussed. The highlights how these offer rapid, non-destructive, sensitive analysis, enhancing ability detect at molecular levels. Despite advancements, challenges persist, including complexity natural variability composition, high costs advanced equipment, need specialized expertise, standardized protocols. Future directions emphasize developing portable cost-effective devices, integrating artificial intelligence machine learning data fostering international collaboration establish methodologies comprehensive spectral databases. By addressing challenges, can be more widely implemented, ultimately safeguarding ensuring integrity dairy products, maintaining consumer trust global supply chain.

Language: Английский

Citations

0
Determination of malathion content in sorghum grains using hyperspectral imaging technology combined with stacked machine learning models DOI
Jianheng Peng,

Jiahong Zhang,

Lipeng Han

et al.

Journal of Food Composition and Analysis, Journal Year: 2024, Volume and Issue: 135, P. 106635 - 106635

Published: Aug. 8, 2024

Language: Английский

Citations

3
Detection Technologies, and Machine Learning in Food: Recent Advances and Future Trends DOI
Qiong He, Heng-Yu Huang,

Yuanzhong Wang

et al.

Food Bioscience, Journal Year: 2024, Volume and Issue: unknown, P. 105558 - 105558

Published: Nov. 1, 2024

Language: Английский

Citations

1
IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring DOI Creative Commons

Siva Peddareddigari,

S. Vijayan,

Annamalai Manickavasagan

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 105 - 105

Published: Dec. 26, 2024

Technological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, risk of contamination has increased severalfold, often resulting significant damage terms wastage, economic loss to producers, danger public health, or all these. In general, governments across world recognized importance having safety processes place impose recalls as required. However, primary challenges existing practices are delays identifying unsafe food, siloed data handling, delayed decision making, and tracing source contamination. Leveraging Internet Things (IoT), 5G, blockchains, cloud computing, big data, a novel framework been proposed address current challenges. The enables real-time gathering situ application machine learning-powered algorithms predict facilitate instant making. Since processed real approach be identified early informed decisions made confidently, thereby helping reduce significantly. also throws up new implementation changes collection phases production, onboarding various stockholders, adaptation process.

Language: Английский

Citations

0