March 2026 | Volume 2 | Issue 1 | Article No. 26403
DOI: https://doi.org/10.64189/css.26403
© The Author(s) 2026
This article is licensed under Creative Commons Attribution Non-Commercial 4.0 International (CC-BY-NC 4.0) J. Collect. Sci. Sustain., 2026, 2, 26403 | 1
\
Journal of Collective Sciences and Sustainability
Research Article | Open Access |
LactoVerus: An Intelligent Blockchain and IoT-Based System
for Dairy Product Authencity 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 (S. R. Shegar)
1. Introduction
Dairy products such as milk, curd, paneer, yogurt, and ghee
are essential components of daily nutrition,
[1]
yet issues of
adulteration, poor quality control, and lack of transparency
continue to affect consumer trust.
[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
Abstract
LactoVerus is a next-generaon intelligent system designed to ensure the authencity and traceability of dairy products
using the integraon of Blockchain and Internet of Things (IoT) technologies. The system addresses crical challenges in the
dairy supply chain, including adulteraon, lack of transparency, and limited consumer trust. IoT-based sensors are ulized
to monitor key quality parameters such as pH, total dissolved solids (TDS), and turbidity in real me, 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 authencity and quality through a mobile applicaon.
Experimental evaluaon demonstrates reliable sensor performance, successful data transmission, secure blockchain
integraon, and accurate idencaon of milk quality condions, including fresh, adulterated, and spoiled samples. The
system eecvely disnguishes between normal and contaminated milk based on measured parameters, enhancing
transparency and trust across the supply chain. LactoVerus provides a scalable, cost-eecve, and user-centric soluon 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.
Received: 16 January 2026; Revised: 05 March 2026; Accepted: 23 March 2026; Published Online: 26 March 2026.
Research article Volume 2 Issue 1 (March 2025)
2 | J. Collect. Sci. Sustain., 2026, 2, 26403 GR Scholastic
without access to testing laboratories or digital certification
mechanisms. As a result, consumers purchasing loose milk
or unpackaged dairy products rely solely on vendor
credibility, with no objective way to verify the product’s
purity. This gap in verification 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.
2. Literature review
2.1 Application of non-linear fingerprint numerical
finger printing in dairy product quality inspection
Tingting Qu
[14]
proposed a Non-Linear Fingerprint
Mathematical Model for dairy product quality inspection
using oscillation fingerprint technology. 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.
[15]
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. 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 shows a dairy sensor for detection of
ciprofloxacin in milk samples.
Fig. 1: A dairy sensor for detection of ciprofloxacin in milk
samples. Reproduced from [15].
Experimental results demonstrate that the LIA-based
sensing system provides high sensitivity, stable response, and
reliable performance under varying sample conditions. The
Volume 2 Issue 1 (March 2026) Research article
GR Scholastic J. Collect. Sci. Sustain., 2026, 2, 26403 | 3
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.
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
[16]
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. 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.
[17]
presents an IoT-enabled multispectral sensor
system for rapid milk adulteration detection. The system uses
AS7265X spectral sensors and an Arduino microcontroller to
collect milk spectral data across wavelengths from 410 nm to
940 nm. Four adulterants—sodium salicylate, dextrose,
hydrogen peroxide, and ammonium sulphate—were
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.
[18]
present a blockchain-enabled milk supply
chain platform called “Milk Chain” to address challenges
such as milk adulteration, poor traceability, and quality
degradation in global dairy supply chains. 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.
[19]
developed a non-destructive, AI-assisted
spectro-analytical system for rapid milk adulteration
detection using a portable reflectance spectrophotometer
operating in the 410–940 nm wavelength range. 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
Research article Volume 2 Issue 1 (March 2025)
4 | J. Collect. Sci. Sustain., 2026, 2, 26403 GR Scholastic
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 analyzers 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 analyzer 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.
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
Fig. 2: Architecture of proposed LactoVerus: Blockchain and IoT-enabled dairy product authenticity verification system.
Volume 2 Issue 1 (March 2026) Research article
GR Scholastic J. Collect. Sci. Sustain., 2026, 2, 26403 | 5
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
The proposed “LactoVerus” aims to provide a
technologically integrated solution for ensuring the
authenticity, 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.
3.3 Testing and evaluation
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 1: Implementation stages and technologies used in the proposed LactoVerus system.
Sr. No.
Stage
Process description
Technology Used
1
Quality Testing
Sensors analyze product parameters (Fat %,
SNF, pH, Adulterants)
IoT Sensors,
Microcontroller
2
Data Collection &
Processing
Analog sensor readings are digitized and
processed for accuracy
ESP32, Arduino IDE
3
Data Storage
Test results stored in real time on a cloud
platform
Firebase / SQL Database
4
Blockchain
Recording
Cloud data hashed and recorded on blockchain
ledger
Ethereum / Hyperledger
5
QR Code
Generation
Encodes batch ID and blockchain reference for
packaged products
ZXing Library
6
Consumer
Verification
Users scan QR code or view IoT kit display to
verify quality
Android App / Display
Screen
Research article Volume 2 Issue 1 (March 2025)
6 | J. Collect. Sci. Sustain., 2026, 2, 26403 GR Scholastic
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 labeling
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
The integration of Blockchain and IoT technologies has
transformed dairy product purity and authenticity
verification by enabling real-time monitoring, secure data
management, and traceability across the supply chain.
Traditional methods such as manual testing, RFID tracking,
and centralized databases often suffer from limitations
including delayed results, data manipulation, and restricted
accessibility for consumers and vendors. In contrast, IoT
sensors continuously capture important quality parameters
such as fat content, pH, temperature, and adulteration
indicators, while Blockchain technology ensures that the
collected information remains tamper-proof, transparent, and
verifiable. This integration enhances consumer trust and
strengthens accountability throughout the dairy supply chain.
Overall, Blockchain-IoT based systems provide a scalable,
secure, and efficient solution for ensuring dairy product
purity and authenticity. By digitally connecting all
stakeholders, from farmers to consumers, these systems
minimize manual errors and improve transparency, quality
assurance, and public health protection. Furthermore, their
Volume 2 Issue 1 (March 2026) Research article
GR Scholastic J. Collect. Sci. Sustain., 2026, 2, 26403 | 7
adaptability for both organized and unorganized dairy
markets demonstrate significant potential for building a
sustainable, transparent, and consumer-centric dairy
ecosystem, setting a new benchmark for food safety and
traceability in the modern agricultural industry.
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.
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
No datasets were generated, analyzed, or used 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, 23–
42, 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,
1–10, 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, 1–12, 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, 5108–5135, 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, 4427–4438, 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, 499–514,
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, 1301–1314, 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, 184350–184365, 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, 41697–41707,
doi: 10.1109/ACCESS.2023.3270136.
[16] N. Sowmya, V. Ponnusamy, Development of
Research article Volume 2 Issue 1 (March 2025)
8 | J. Collect. Sci. Sustain., 2026, 2, 26403 GR Scholastic
spectroscopic sensor system for an IoT application of
adulteration Identification on milk using machine
learning, IEEE Access, 2021, 9, 53979–53995, 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, 1–7, 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.
Publisher Note: The views, statements, and data in all
publications solely belong to the authors and contributors.
GR Scholastic is not responsible for any injury resulting from
the ideas, methods, or products mentioned. GR Scholastic
remains neutral regarding jurisdictional claims in published
maps and institutional affiliations.
Open Access
This article is licensed under a Creative Commons
Attribution-NonCommercial 4.0 International License,
which permits the non-commercial use, sharing, adaptation,
distribution and reproduction in any medium or format, as
long as appropriate credit to the original author(s) and the
source is given by providing a link to the Creative Commons
License and changes need to be indicated if there are any.
The images or other third-party material in this article are
included in the article's Creative Commons License, unless
indicated otherwise in a credit line to the material. If material
is not included in the article's Creative Commons License
and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a
copy of this License, visit:
https://creativecommons.org/licenses/by-nc/4.0/
© The Author(s) 2026
