| Journal of Collective Sciences and Sustainability
Received: 16 January 2026; Revised: 05 March 2026; Accepted: 23 March 2026; Published Online: 26 March 2026.
J. Collect. Sci. Sustain., 2026, 2(1), 26403 | Volume 2 Issue 1 (March 2026) | DOI: https://doi.org/10.64189/css.26403
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
LactoVerus: An Intelligent Blockchain and IoT-Based
System for Dairy Product Authenticity and
Traceability
Sneha Shegar,
*
Aarti Auti, Nawaj Pathan and Toufik Pathan
Department of Computer Engineering, Samarth College of Engineering & Management, Pune, Maharashtra, 412410, India
*Email: snehashegar1@gmail.com (Sneha Shegar)
Abstract
LactoVerus is a next-generation intelligent system designed to ensure the authenticity and traceability of dairy
products using the integration of Blockchain and Internet of Things (IoT) technologies. The system addresses
critical challenges in the dairy supply chain, including adulteration, lack of transparency, and limited consumer
trust. IoT-based sensors are utilized to monitor key quality parameters such as pH, total dissolved solids (TDS),
and turbidity in real time, enabling automated and accurate quality assessment. The collected data is securely
stored in a cloud database, while cryptographic hashes are recorded on a blockchain ledger to ensure
immutability and prevent data tampering. Each product batch is associated with a unique QR code, allowing
consumers to instantly verify product authenticity and quality through a mobile application. Experimental
evaluation demonstrates reliable sensor performance, successful data transmission, secure blockchain
integration, and accurate identification of milk quality conditions, including fresh, adulterated, and spoiled
samples. The system effectively distinguishes between normal and contaminated milk based on measured
parameters, enhancing transparency and trust across the supply chain. LactoVerus provides a scalable, cost-
effective, and user-centric solution for modern dairy quality assurance and traceability in both organized and
unorganized sectors.
Keywords: Blockchain; Internet of Things (IoT); Dairy supply chain; Food traceability, Quality verification.
Dairy products such as milk, curd, paneer, yogurt, and ghee are essential components of daily nutrition, yet
issues of adulteration, poor quality control, and lack of transparency continue to affect consumer trust.
[1,2]
The
increasing demand for natural and safe food has led to the exploration of advanced technologies to ensure
authenticity and traceability throughout the supply chain. Recent advancements in the Internet of Things (IoT)
and blockchain have shown considerable potential in addressing these challenges.
[3]
IoT enables real time
monitoring of product quality parameters such as pH, temperature, and fat content, while Blockchain ensures
secure, tamper proof recording of supply chain data.
[4,5]
When integrated, these technologies can build
transparent and trustworthy ecosystems for food safety and certification.
[6]
This study provides a
comprehensive survey of existing blockchain- and IoT-based approaches for food and dairy quality verification.
Traditional dairy quality assessment still relies heavily on laboratory-based testing for parameters such as fat
content, solids-not-fat (SNF), pH value, and microbial load. While laboratory methods provide accuracy, they
are slow, expensive and often inaccessible to small producers or local vendors.
[7]
The delay between sampling
and result generation creates opportunities for spoilage or deliberate tampering before the product reaches
consumers. Furthermore, manual data entry and centralized record keeping systems are prone to manipulation,
loss, or forgery, leading to a lack of consumer trust in product authenticity.
[8]
For local or unorganized dairy
vendors, the challenges are even greater. Many of these vendors operate without access to testing laboratories
or digital certification mechanisms. As a result, consumers purchasing loose milk or unpackaged dairy products
rel
systems underscores the urgent need for automated, affordable, and decentralized quality assessment tools that
can operate seamlessly across both organized and unorganized sectors of the dairy ecosystem.
[9]
Dairy
adulteration not only results in economic loss but also poses serious health hazards.
[10]
Common adulterants
such as detergent, urea, starch, or synthetic milk can cause gastrointestinal disorders, kidney damage and other
toxic effects.
[11]
Several studies and reports across various economies and nations have highlighted that a
significant proportion of tested samples fail to meet established purity and quality standards due to
adulteration.
[12]
The lack of milk traceability mechanisms allows adulterated milk to enter the supply chain, and
tracing its origin becomes extremely difficult. Moreover, antibiotic resistance in milk has emerged as a growing
concern due to the excessive use of antibiotics in dairy cattle.
[13]
Inadequate withdrawal periods and improper
antibiotic administration can lead to the persistence of antibiotic residues in milk, which, upon prolonged
consumption, may contribute to the development of antimicrobial resistance in humans. Such contamination
issues further highlight the weaknesses of existing quality monitoring mechanisms and the widespread lack of
regulated real-time, end-to-end quality control systems supported by emerging digital technologies. The
proposed system is designed to incorporate the applicable quality and safety criteria. The primary objectives
of this study are to analyze and present existing dairy verification frameworks and real-time quality monitoring
systems, with a focus on advancements integrating Internet of Things (IoT), Blockchain, Artificial Intelligence
(AI), and traceability networks. The study aims to provide a comparative evaluation of recent digital dairy
quality frameworks to understand their effectiveness in ensuring product authenticity and safety. Furthermore,
it seeks to identify key research gaps related to scalability, security, interoperability, and data analytics within
current systems. Based on these insights, the paper proposes future directions for developing robust, secure,
scalable, and cost-effective dairy authentication solutions capable of real-time implementation across both
organized and unorganized sectors of the dairy supply chain.
Tingting Qu proposed a Non-Linear Fingerprint Mathematical Model for dairy product quality inspection using
oscillation fingerprint technology.
[14]
The study developed nonlinear fingerprint libraries and mathematical
models to distinguish between authentic, adulterated, and different branded dairy products. By analyzing
system similarity values and response surface experimental parameters, the proposed method demonstrated
improved accuracy and sensitivity in detecting dairy adulteration and quality deviations. The research
highlights the potential of nonlinear fingerprint technology as a reliable, scalable, and cost-effective approach
for dairy product safety and quality control.
2.2 Lock-in Amplifier (LIA)-based sensor for the of ciprofloxacin
Devasagayam et al. focuses on the development of a Lock-in Amplifier (LIA)-based dairy sensing system for the
detection of ciprofloxacin residues in milk, which can pose serious health risks and regulatory concerns when
present above permissible limits.
[15]
Conventional detection techniques such as chromatography and
spectroscopy are often expensive, time-consuming, and unsuitable for rapid on-site analysis. To address these
limitations, the proposed system integrates fluorescence spectroscopy with lock-in amplification to improve
detection sensitivity and noise immunity. The Lock-in Amplifier operates by extracting weak fluorescence
signals buried in background noise through phase-sensitive detection synchronized with a reference frequency.
This significantly enhances the signal-to-noise ratio (SNR), enabling the accurate detection of very low
concentrations of ciprofloxacin in milk samples. The system detects variations in the fluorescence
characteristics of milk caused by the presence of antibiotic residues and processes these weak optical signals to
obtain precise quantitative measurements.
Fig. 1: A dairy sensor for detection of ciprofloxacin in milk samples.
[15]
Experimental results demonstrate that the LIA-based sensing system provides high sensitivity, stable response,
and reliable performance under varying sample conditions. The study reports a substantial improvement in the
limit of detection compared with conventional fluorescence-based systems, achieving detection levels below
the regulatory maximum residue limit for ciprofloxacin in milk. Furthermore, the sensor offers rapid analysis
capability and potential portability, making it suitable for future on-farm and real-time dairy monitoring
applications without dependence on large laboratory infrastructure. Fig. 1 shows a dairy sensor for detection
of ciprofloxacin in milk samples. Overall, the study demonstrates that Lock-in Amplifier-based fluorescence
sensing technology provides a cost-effective, precise, and portable approach for antibiotic residue detection in
dairy products. The proposed method represents an important advancement toward automated and intelligent
dairy quality inspection systems, supporting food safety regulations and public health protection.
2.3 Development of spectroscopic sensor system for an IOT application of adulteration identification on
milk using machine learning
Sowmya and Ponnusamy focuses on the design and development of an IoT-enabled multispectral sensor system
integrated with machine learning algorithms for rapid and real-time detection of milk adulteration.
[16]
The
research addresses the limitations of traditional chemical testing methods, which are often time-consuming,
expensive, laboratory-dependent, and unsuitable for field-level applications. The developed system integrates
affordable multispectral sensors covering wavelengths from 410 nm to 940 nm with an Arduino-based
microcontroller to capture optical spectral responses from milk samples. Various adulterants such as sodium
salicylate, dextrose, hydrogen peroxide, and ammonium sulphate were tested to generate a spectral fingerprint
dataset. The collected spectral data were analyzed using machine learning algorithms including Naive Bayes,
Support Vector Machine (SVM), Decision Tree, Linear Discriminant Analysis (LDA), and Neural Network models
for adulteration classification. The system provides a non-destructive and cost-effective solution for milk quality
monitoring through an IoT-enabled cloud platform, allowing remote monitoring and real-time visualization of
results through a web interface. This integration improves transparency, portability, and digital traceability for
dairy producers and regulatory authorities without requiring advanced laboratory infrastructure. The
developed prototype is portable and economically feasible, with an approximate system cost of INR 8,624,
making it suitable for rural and small-scale dairy environments. Overall, the study demonstrates that IoT-
integrated spectroscopic sensing combined with artificial intelligence can provide an intelligent, real-time, and
reliable solution for milk adulteration detection and food safety management.
2.4 IoT integrated fuzzy classification analysis for detecting adulterants in cow milk
Lal et al. presents an IoT-enabled multispectral sensor system for rapid milk adulteration detection.
[17]
The
system uses AS7265X spectral sensors and an Arduino microcontroller to collect milk spectral data across
wavelengths from 410 nm to 940 nm. Four adulterantssodium salicylate, dextrose, hydrogen peroxide, and
ammonium sulphatewere analyzed using machine learning techniques. Algorithms such as Naive Bayes, SVM,
Decision Tree, LDA, and Neural Networks were applied for classification. The neural network achieved up to
100% accuracy after hyperparameter tuning using a genetic algorithm. The proposed system is non-destructive,
portable, and cost-effective, with a total cost of about INR 8,624. IoT integration enables real-time monitoring
and remote visualization of adulteration results through a web interface. The study demonstrates that
multispectral sensing and artificial intelligence can provide an efficient solution for dairy quality monitoring
and food safety management.
2.5 Milk Chain: A blockchain-enabled dairy traceability platform
Nukapeyi et al. present a blockchain-
challenges such as milk adulteration, poor traceability, and quality degradation in global dairy supply chains.
[18]
The study highlights the limitations of traditional manual monitoring methods, which can lead to health risks
and economic losses, especially in densely populated regions such as India. The proposed system integrates IoT
sensors, blockchain technology, and machine learning to ensure transparency, tamper-proof data management,
and reliable quality monitoring. The platform focuses on preserving nutritional quality, detecting adulteration,
supporting dairy farmers economically, and ensuring product authenticity. Various milk quality parameters,
including pH, temperature, taste, odor, fat content, turbidity, color, and grade, were analyzed using the CatBoost
machine learning algorithm. The findings identify critical factors affecting milk quality and demonstrate the
potential of intelligent digital technologies in building a secure and sustainable dairy supply chain system.
2.6 Portable multispectral milk quality monitoring using machine learning
Sharma et al. developed a non-destructive, AI-assisted spectro-analytical system for rapid milk adulteration
detection using a portable reflectance spectrophotometer operating in the 410940 nm wavelength range.
[19]
The study evaluated six adulterants-urea, formalin, hydrogen peroxide, baking soda, sucrose, and cornstarch
to establish compositional and spectral relationships. Physicochemical analysis showed significant variations in
SNF, fat, and protein content due to adulteration. Spectral analysis identified important wavelength regions
associated with chromophore absorption and near-infrared (NIR) responses. Principal Component Analysis
(PCA) confirmed clear adulterant-specific clustering, while ANOVA-based feature selection identified key
discriminatory spectral zones. Machine learning models including Decision Tree, Logistic Regression, Support
Vector Machine (SVM), and ensemble methods achieved classification accuracies above 99% with very low
inference time. The study demonstrates the effectiveness of compact, low-cost, AI-enabled multispectral sensing
systems for real-time milk quality monitoring and adulteration detection.
3. System design and architecture
Architecture of the proposed LactoVerus: Blockchain and IoT-enabled dairy product authenticity verification
system is shown in Fig. 2. It is designed to ensure end-to-end transparency and reliable consumer verification of
dairy products across both organized manufacturers and automated local vendors. The system integrates IoT,
blockchain, cloud, web, and unorganized vendor technologies into a unified framework that securely connects
manufacturers, distributors, vendors, and automated testing units through trusted data exchange and quality
assurance mechanisms. At the manufacturer side, each dairy product batch undergoes quality testing using IoT-
based analysers or standard laboratory equipment to measure critical parameters such as fat percentage, SNF
(solid-not-fat), protein content, pH value, and the presence of adulterants like urea, starch, or detergent. After
testing, the quality results are transmitted to a cloud server (such as Firebase or SQL) for secure storage, while a
cryptographic hash of the test report is generated using SHA-256 and recorded on a blockchain ledger (e.g.,
Hyperledger or Ethereum), ensuring immutability and preventing any future tampering of quality data.
For every verified product batch, a unique and non-replicable QR code is generated containing essential details
such as batch ID, production and expiry dates, blockchain transaction ID, and a verification link to the test report.
This QR code is printed on the product packaging to allow consumers to authenticate the product through a
simple scan. Once packaged, the verified dairy products are dispatched to distributors, whose details and transfer
information are also recorded on the blockchain to maintain complete supply chain traceability. Distributors can
additionally update transportation and storage conditions, such as temperature logs, ensuring quality
preservation during transit. On the local vendor side, sellers of loose dairy products such as milk, curd, paneer,
yogurt, and ghee are equipped with a portable IoT-based testing kit comprising sensors such as a pH sensor, fat
analyser or lactometer, turbidity sensor, temperature sensor (DS18B20), conductivity sensor, and adulteration
test strips for starch, urea, and detergent. This kit, powered by an ESP32 or Arduino microcontroller, collects and
processes sensor readings in real time, converts raw data into meaningful quality parameters using calibration
equations, and instantly displays the results on a built-in digital screen, enabling consumers to view real-time
quality information directly at the point of sale without requiring a mobile application or QR code.
Fig. 2: Architecture of proposed LactoVerus: Blockchain and IoT-enabled dairy product authenticity verification
system.
Supporting the entire ecosystem is a unified web and mobile application layer that enables seamless interaction
among all stakeholders. The web interface allows manufacturers and distributors to upload and manage product
batch details, monitor blockchain transaction logs, view quality verification records, and generate QR codes,
while the Android application empowers consumers to scan QR codes on packaged dairy products, retrieve
verified batch information from the cloud, confirm authenticity through blockchain-backed data, view origin and
expiry details, and provide feedback or report issues. Through this integrated architecture, LactoVerus delivers
transparent, tamper-proof, and consumer-centric dairy product authentication, fostering trust, accountability,
and quality assurance across the complete dairy supply chain.
3.1 Implementation
purity, and organic quality of dairy products across both organized (manufacturer) and unorganized (local
vendor) sectors of the dairy supply chain. This study addresses major challenges in the food industry, including
milk adulteration and the lack of consumer trust, by combining the capabilities of the Internet of Things (IoT)
for real-time quality analysis and blockchain technology for transparent and tamper-proof record keeping. The
system also includes an Android mobile application that enables end users to instantly verify the authenticity
and quality of the dairy products they consume.
3.2 Technologies used
The study was designed to develop a unified digital ecosystem that connects manufacturers, distributors,
vendors, and consumers through a transparent and verifiable quality tracking mechanism. The study integrates
advanced technologies within a multi-layered architecture to ensure efficiency, scalability, and reliability across
all operational levels. Table 1 summarizes the Implementation stages and technologies used the proposed
LactoVerus system.
Testing is a crucial phase to ensure that the proposed dairy product quality monitoring system functions
accurately, reliably, and securely. The system is evaluated using unit testing and integration testing to validate
both individual components and their interaction.
3.3.1 Unit testing: Unit testing is performed to verify the functionality of individual modules independently.
1) Sensor Module Testing: All sensors provided stable and accurate readings within acceptable tolerance limits.
2) Data Acquisition Unit Testing: Analog sensor signals were converted into digital values using the
microcontroller ADC. Sampling rate, accuracy, and calibration logic were verified to ensure correct data
acquisition.
3) Cloud Database Testing: The cloud database successfully stored and retrieved real-time sensor data.
4) Blockchain Module Testing: The system-generated data hashes were validated and securely stored on the
blockchain ledger. Immutability and transaction verification were success fully tested.
5) QR Code Generation Testing: QR codes were generated for each dairy product batch containing the blockchain
reference. Readability and decoding accuracy were tested using multiple mobile devices.
3.3.2 Integration testing: Integration testing ensures smooth interaction between all system modules.
1) Sensor to Microcontroller Integration: Sensors were interfaced with the IoT controller, and real-time data
trans mission was verified under normal and fault conditions.
2) Microcontroller to Cloud Integration: Sensor data was transmitted to the cloud platform using Wi-Fi
connectivity. Network failure and reconnection scenarios were also tested.
3) Cloud to Blockchain Integration: Cloud-stored data was hashed and recorded on the blockchain network.
Transaction confirmations were verified successfully.
4) QR Code to Consumer Verification Integration: QR codes were scanned using a mobile application to retrieve
blockchain data and display product quality information to consumers.
4. Results and Discussion
4.1 Results
Table 2 represents the quality analysis of different milk samples based on parameters like pH, TDS, and turbidity.
It helps identify whether the milk is good, adulterated, or spoiled (chemically or naturally) using measured
values.
Table 2: Results after testing.
Sr. No.
Product name
PH value
TDS
Turbidity
Result
1
Fresh Milk
6.7
150
4.5
Good Milk
2
Boiled Milk
6.6
155
4.8
Good Milk
3
Packaged Milk
6.5
160
4.6
Good Milk
4
Diluted Milk
7.2
95
1.8
Adulterated
5
Urea Mixed Milk
8.1
380
1.5
Chemically Spoiled
6
Stored Milk(2 days)
5.6
210
8.8
Naturally Spoiled
4.2 Discussion
The review of various dairy quality assurance systems reveals that the integration of Blockchain and IoT
introduces a significant transformation in transparency, traceability, and data reliability within the dairy
industry. Traditional dairy monitoring systems primarily relied on manual record keeping, laboratory-based
analysis, and centralized supervision, which often resulted in delays, limited accessibility, and vulnerability to
data manipulation. In contrast, IoT-enabled systems facilitate real-time and automated data acquisition,
allowing critical quality parameters such as fat content, pH value, SNF, and temperature to be monitored directly
through sensors with minimal human intervention. Blockchain technology further strengthens this framework
by storing quality records in a decentralized and tamper-proof ledger, thereby ensuring data integrity and
authenticity throughout the dairy supply chain. The combined Blockchain-IoT architecture significantly
improves traceability by enabling producers, distributors, retailers, and consumers to access verified records
regarding product origin, quality status, and handling conditions. The integration of Blockchain with an
Android-based consumer application enhances user participation by allowing consumers to scan QR codes
linked to immutable Blockchain records for instant verification of product purity and organic certification. This
transparency not only minimizes the risks of adulteration and fraudulent labelling but also improves consumer
confidence and trust in dairy products. Additionally, the implementation of Blockchain smart contracts can
automate verification procedures, reduce administrative complexity, and improve the efficiency of certification
and compliance workflows. Despite these advantages, several practical challenges remain in implementing
Blockchain-IoT-based dairy verification systems on a large scale. High initial deployment costs, sensor
calibration requirements, maintenance complexity, and dependence on continuous internet connectivity may
limit adoption, especially in rural and small-scale dairy environments. Furthermore, interoperability issues
among different Blockchain platforms and IoT devices highlight the need for technical standardization and
regulatory support. Addressing these limitations through government initiatives, open-source development
frameworks, affordable IoT hardware, and scalable cloud infrastructure can accelerate the adoption of
intelligent dairy verification systems and promote a more transparent, secure, and sustainable dairy supply
chain ecosystem.
4.3 Future scope
Future research and development in dairy traceability systems should focus on improving scalability,
intelligence, and sustainability through advanced technological integration. This includes the adoption of hybrid
Blockchain models that combine public and private chains to balance transparency with data privacy. The
implementation of AI-based self-calibrating IoT modules can further enhance detection accuracy and system
reliability. Additionally, the development of digital twins for dairy farm environments can enable real-time
simulation, monitoring, and optimization of production processes. Privacy-preserving techniques such as
federated learning may also be explored to support collaborative adulteration detection without sharing raw
data. Sustainable innovations, including solar-powered IoT modules, can facilitate deployment in rural and
resource-constrained areas. Furthermore, achieving cross-chain interoperability among multiple Blockchain
platforms will be essential for seamless data exchange and unified traceability across diverse dairy supply chain
networks.
5. Conclusion
CRediT Author Contribution Statement
Sneha Shegar: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing Review
& editing. Aarti Auti: Formal analysis, Methodology, Validation. Nawaj Pathan: Methodology, Writing Original
draft. Toufik Pathan: Resources, Visualization, Writing Review & editing. All authors have read and approved
the final version of the manuscript for publication and agree to be accountable for all aspects of the work,
ensuring that questions related to the accuracy or integrity of any part of the work are appropriately
investigated and resolved.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors confirm that no artificial intelligence (AI)-assisted technologies were used in the writing of the
manuscript, and no images were generated or manipulated using AI. AI-based tools were used solely for
language editing to improve grammar, clarity, and readability, in accordance with journal policy. The authors
take full responsibility for the accuracy, originality, and integrity of the work.
Supporting Information
Not applicable.
References
[1]
A. Muehlhoff, A. Bennett, D. McMahon, Milk and Dairy Products in Human Nutrition, Food and
Agriculture Organization (FAO), Rome, Italy, 2013.
[2]
A. Poonia, A. Jha, R. Sharma, H. B. Singh, A. K. Rai, N. Sharma, Detection of adulteration in milk: A review,
International Journal of Dairy Technology, 2017, 70, 2342, doi: 10.1111/1471-0307.12274.
[3]
A. David, C. Ganeshkumar, J. G. Sankar, Blockchain and artificial intelligence in food industry: Case
analysis of Ripe.Io firm, International Conference on Information Management & Machine Intelligence
(ICIMMI), 2023, 110, doi: 10.1145/3647444.3652477.
[4]
L. Sabila, L. Dwiyono, A. Hakim, A. Karuana, D. Hakika, IoT-Based Monitoring System for Temperature
and pH Control in Cocoa Fermentation, MOTIVECTION: Journal of Mechanical, Electrical and Industrial
Engineering, 2025, 7, 112, doi: 10.46574/motivection.v7i1.381.
[5]
N. Kumar, K. Kumar, A. Aeron, F. Verre, Blockchain technology in supply chain management:
Innovations, applications, and challenges, Telematics and Informatics Reports, 2025, 18, 100204, doi:
10.1016/j.teler.2025.100204.
[6]
W. Jiang, C. Liu, W. Liu, L. Zheng, Advancements in Intelligent Sensing Technologies for Food Safety
Detection, Research, 2025, 8, 0713, doi: 10.34133/research.0713.
[7]
P. Balakrishnan, A. Anny Leema, N. Jothiaruna, P. J. Assudani, K. Sankar, M. B. Kulkarni, M. Bhaiyya,
Artificial intelligence for food safety: From predictive models to real-world safeguards, Trends in Food
Science & Technology, 2025, 163, 105153, doi: 10.1016/j.tifs.2025.105153.
[8]
I. Fernando, J. Fei, S. Cahoon, D. C. Close, A review of the emerging technologies and systems to mitigate
food fraud in supply chains, Critical Reviews in Food Science and Nutrition, 2025, 65, 51085135, doi:
10.1080/10408398.2024.2405840.
[9]
P. Devi, K. Subburamu, V. A. Giridhari, B. Dananjeyan, T. Maruthamuthu, Integration of AI based tools in
dairy quality control: Enhancing pathogen detection efficiency, Food Measure, 2025, 19, 44274438,
doi: 10.1007/s11694-025-03269-8.
[10]
A. K. Yadav, M. Gattupalli, K. Dashora, V. Kumar, Key milk adulterants in India and their Detection
techniques: A Review, Food Analytical Methods, 2023, 16, 499514, doi: 10.1007/s12161-022-02427-
8.
[11]
B. S. Acharya, S. Nair, A. A. Abdul Salam, Quality analysis and detection of adulterants and
contaminations in milk/milk powder by raman spectroscopy, Comprehensive Reviews in Food Science
and Food Safety, 2026, 25, e70403, doi: 10.1111/1541-4337.70403.
[12]
M. Aqeel, A. Sohaib, M. Iqbal, S. S. Ullah, Milk adulteration identification using hyperspectral imaging
and machine learning, Journal of Dairy Science, 2025, 108, 13011314, doi: 10.3168/jds.2024-25635.
[13]
B. Iraguha, J. P. M. Mpatswenumugabo, M. N. Gasana, E. Åsbjer, Mitigating antibiotic misuse in dairy
farming systems and milk value chain market: Insights into practices, factors, and farmers education
in Nyabihu district, Rwanda, One Health, 2024, 9, 100843, doi: 10.1016/j.onehlt.2024.100843.
[14]
T. Qu, Application of non-linear fingerprint mathematical model in dairy product quality inspection,
IEEE Access, 2024, 12, 184350184365, doi: 10.1109/ACCESS.2024.3510715.
[15]
J. Devasagayam, C. A. Leclerc, R. Bosma, L. Wood, C. M. Collier, Lock-in amplifier dairy sensor for
detection of ciprofloxacin, IEEE Access, 2023, 11, 4169741707, doi: 10.1109/ACCESS.2023.3270136.
[16]
N. Sowmya, V. Ponnusamy, Development of spectroscopic sensor system for an IoT application of
adulteration Identification on milk using machine learning, IEEE Access, 2021, 9, 5397953995, doi:
10.1109/ACCESS.2021.3070558.
[17]
P. P. Lal, A. A. Prakash, A. A. Chand, K. A. Prasad, U. Mehta, M. H. Assaf, F. S. Mani, K. A. Mamun, IoT
integrated fuzzy classification analysis for detecting adulterants in cow milk, Sensing and Bio-Sensing
Research, 2022, 36, 100486, doi: 10.1016/j.sbsr.2022.100486.
[18]
S. Nukapeyi, R. Bommala, D. S. Kondreddy, C. Amalakanti, C. Ravi, T. A. Konuri, Milk Chain with ML and
blockchain for ensuring milk purity in dairy industry, 2024 2nd World Conference on Communication
& Computing (WCONF), 2024, 17, doi: 10.1109/WCONF61366.2024.10692146.
[19]
A. Sharma, M. Yadav, N. Kumar, G. Chandu, R. Jana, P. Pandey, R. Kumar, Rapid detection of milk
adulteration using AI-driven portable colorimetric spectroscopy, Discover Food, 2026, 6, 118, doi:
10.1007/s44187-026-00855-7.
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