FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
1. TITLE OF THE INVENTION
APPLICATION OF NANOMATERIAL-BASED DETECTION SYSTEMS FOR
MYCOTOXIN MONITORING IN GRAINS AND NUTS
2. APPLICANT(S)
NAME: RK UNIVERSITY
NATIONALITY - INDIA
ADDRESS: RK UNIVERSITY, BHAVNAGAR HIGHWAY, KASTURBADHAM,
RAJKOT - 360020, GUJARAT, INDIA
3. PREAMBLE TO DESCRIPTION
COMPLETE SPECIFICATION -The following specification particularly describes the invention
and the manner in which it is to be performed.
APPLICATION OF NANOMATERIAL-BASED DETECTION SYSTEMS FOR
MYCOTOXIN MONITORING IN GRAINS AND NUTS
Field of the Invention
[0001] The present disclosure relates to nanomaterial-based biosensing systems
configured for mycotoxin detection and real-time monitoring in stored or processed grains and
nuts.
Background
[0002] The background description includes information that may be useful in
understanding the present invention. It is not an admission that any of the information provided
herein is prior art or relevant to the presently claimed invention, or that any publication specifically
or implicitly referenced is prior art.
[0003] The contamination of food supplies, especially grains and nuts, by mycotoxins
such as aflatoxins, ochratoxins, fumonisins, and zearalenone has become a significant concern for
both public health and international trade. Mycotoxins are secondary metabolites produced by
fungal species such as Aspergillus, Penicillium, and Fusarium, which commonly proliferate under
warm and humid storage conditions. Traditional methods for detection and quantification of these
toxins, including high-performance liquid chromatography (HPLC), gas chromatography-mass
spectrometry (GC-MS), and enzyme-linked immunosorbent assays (ELISA), often suffer from
limitations such as high cost, the requirement for skilled personnel, prolonged sample preparation,
and inadequate portability for field-level application. Moreover, these methods are often
inadequate for rapid or on-site analysis, making them unsuitable for continuous or batch-wise
screening during grain procurement, storage, or transport.
[0004] Recent advances in nanotechnology and biosensor integration have introduced
novel approaches for achieving higher sensitivity, faster response time, and enhanced portability.
Nanomaterial-based detection platforms, particularly those utilizing carbon nanotubes, gold
nanoparticles, graphene oxide, or quantum dots, offer high surface-area-to-volume ratios and
unique electrochemical, optical, or fluorescence properties, which can be exploited for mycotoxinspecific interactions. However, most existing biosensor designs lack system-level integration for
field-readiness, modular calibration, and data interfacing, leading to constrained real-world
deployment. Furthermore, they do not typically support multiplexed detection or wireless data
transfer for decision-support analytics. Therefore, there remains a pressing need for a
nanomaterial-integrated detection system that facilitates on-site, low-latency, and repeatable
monitoring of mycotoxins in bulk commodities, with enhanced specificity, reusability, and
computational adaptability.
[0005] All references, including publications, patent applications, and patents, cited
herein are hereby incorporated by reference to the same extent as if each reference were
individually and specifically indicated to be incorporated by reference and were set forth in its
entirety herein.
[0006] It also shall be noted that as used herein and in the appended claims, the singular
forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.
This invention can be achieved by means of hardware including several different elements or by
means of a suitably programmed computer. In the unit claims that list several means, several ones
among these means can be specifically embodied in the same hardware item. The use of such
words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[0007] The following presents a simplified summary of various aspects of this disclosure
in order to provide a basic understanding of such aspects. This summary is not an extensive
overview of all contemplated aspects, and is intended to neither identify key or critical elements
nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure
in a simplified form as a prelude to the more detailed description that is presented later.
[0008] The following paragraphs provide additional support for the claims of the subject
application.
[0009] The present disclosure relates to nanomaterial-based biosensing systems
configured for mycotoxin detection and real-time monitoring in stored or processed grains and
nuts.
[00010] The present disclosure provides a nanomaterial-based detection system
specifically configured for the monitoring of mycotoxins in grains and nuts during storage,
transportation, or processing stages. The disclosed system comprises a sample handling module
for receiving a particulate or homogenized food specimen, a nanomaterial-functionalized sensing
interface disposed within a detection chamber, a signal transduction unit operatively linked to the
sensing interface, and a data acquisition circuit integrated with a wireless communication module.
The nanomaterial-functionalized interface is designed using surface-modified nanoparticles or
carbon-based nanostructures conjugated with mycotoxin-specific ligands or aptamers. Upon
exposure to a target analyte, the system elicits a transduction event, converting biochemical
recognition into a measurable electrical, optical, or fluorescence signal.
[00011] The signal transduction unit further processes and amplifies the acquired signal,
subsequently transmitting digital representations to an analytics subsystem for quantification,
pattern recognition, or decision-triggering thresholds. In certain configurations, the system
includes a thermal preconditioning chamber for toxin release from bound matrices, and optionally
incorporates a multiplexed microchannel array to support simultaneous monitoring of multiple
toxin types. Additional embodiments provide modular calibration routines using embedded
reference standards, as well as energy-efficient operation powered by solar-integrated power
supply or battery circuitry. The entire system is configured to be housed in a portable enclosure
suitable for integration with field kits or automated sampling conveyors. This facilitates costeffective, scalable, and accurate monitoring of mycotoxins in diverse agro-industrial contexts.
Brief Description of the Drawings
[00012] The features and advantages of the present disclosure would be more clearly
understood from the following description taken in conjunction with the accompanying drawings
in which:
[00013] FIG. 1 illustrates a system architecture diagram of a nanomaterial-based detection
system comprising interconnected modules including sample intake, thermal preconditioning,
nanomaterial-functionalized sensor, signal transduction, data acquisition, and wireless
transmission.
[00014] FIG. 2 illustrates a method flow diagram outlining the procedural steps involved
in the detection of mycotoxins, from sample loading to signal processing and data transmission.
[00015] FIG. 3 illustrates a data flow diagram emphasizing how mycotoxin detection data
is digitized, processed, classified, and visualized using local computation or cloud-based analytics.
Detailed Description
[00016] In the following detailed description of the invention, reference is made to the
accompanying drawings that form a part hereof, and in which is shown, by way of illustration,
specific embodiments in which the invention may be practiced. In the drawings, like numerals
describe substantially similar components throughout the several views. These embodiments are
described in sufficient detail to claim those skilled in the art to practice the invention. Other
embodiments may be utilized and structural, logical, and electrical changes may be made without
departing from the scope of the present invention. The following detailed description is, therefore,
not to be taken in a limiting sense, and the scope of the present invention is defined only by the
appended claims and equivalents thereof.
[00017] The use of the terms “a” and “an” and “the” and “at least one” and similar referents
in the context of describing the invention (especially in the context of the following claims) are to
be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly
contradicted by context. The use of the term “at least one” followed by a list of one or more items
(for example, “at least one of A and B”) is to be construed to mean one item selected from the
listed items (A or B) or any combination of two or more of the listed items (A and B), unless
otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,”
“including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including,
but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely
intended to serve as a shorthand method of referring individually to each separate value falling
within the range, unless otherwise indicated herein, and each separate value is incorporated into
the specification as if it were individually recited herein. All methods described herein can be
performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted
by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided
herein, is intended merely to better illuminate the invention and does not pose a limitation on the
scope of the invention unless otherwise claimed. No language in the specification should be
construed as indicating any non-claimed element as essential to the practice of the invention.
[00018] Pursuant to the "Detailed Description" section herein, whenever an element is
explicitly associated with a specific numeral for the first time, such association shall be deemed
consistent and applicable throughout the entirety of the "Detailed Description" section, unless
otherwise expressly stated or contradicted by the context.
[00019] The present disclosure relates to nanomaterial-based biosensing systems
configured for mycotoxin detection and real-time monitoring in stored or processed grains and
nuts.
[00020] Pursuant to the "Detailed Description" section herein, whenever an element is
explicitly associated with a specific numeral for the first time, such association shall be deemed
consistent and applicable throughout the entirety of the "Detailed Description" section, unless
otherwise expressly stated or contradicted by the context.
[00021] FIG. 1 illustrates a system architecture diagram of a nanomaterial-based detection
apparatus for identifying mycotoxins in grains and nuts using a coordinated series of hardware
modules arranged in a spatial and logical sequence. A sample intake unit receives either
homogenized or particulate food matter through a mechanically actuated valve or gravity-fed
funnel, allowing seamless entry into a thermal preconditioning module. This thermal module
optionally includes a resistive heating coil and solvent reservoir configured to extract bound toxins
through mild heating and solubilization. The prepared sample then proceeds into a detection
chamber, where the sensing interface comprises a surface modified with nanostructured materials
such as gold nanoparticles, graphene oxide, or carbon nanotubes conjugated with toxin-specific
aptamers or antibodies. A signal transduction unit, which may comprise an optical detector,
electrochemical interface, or field-effect transistor, is operatively positioned adjacent to or within
the sensing region and captures perturbations arising from ligand-toxin binding events. The output
from the transduction unit is relayed to a data acquisition unit consisting of analog-to-digital
conversion circuitry, an embedded microcontroller, and memory buffers. Finally, the digitized
signal is transferred to a wireless communication module incorporating one or more transceivers
supporting BLE, Zigbee, or LoRaWAN. This wireless data is then dispatched to an external
dashboard or cloud server. The integrated modularity and spatial coupling across these units yield
rapid response times, low power usage, and reusability, optimizing deployment in agricultural
monitoring stations or field kits. The disclosed nanomaterial-based
detection system for monitoring mycotoxins in grains and nuts is operatively structured to enable
on-site quantification of hazardous fungal metabolites through a sequence of interconnected
modules. The sample introduction module receives grain or nut fragments or a homogenized
particulate suspension through a mechanical hopper or microfluidic intake valve. This intake
configuration allows gravity-assisted or pump-driven flow to a thermally stabilized preparation
zone. Within this zone, optional preconditioning steps are applied to dislodge or extract
mycotoxins bound within the sample matrix. For instance, low-boiling-point solvents such as
methanol or aqueous acetonitrile may be dispensed into the sample chamber, followed by
controlled heating to 50–60°C to enhance desorption without degrading target molecules.
Following this, the prepared sample is conveyed through a microchannel into the primary detection
chamber.
[00022] The detection chamber includes a nanomaterial-functionalized sensing interface
composed of gold nanoparticles, carbon nanotubes, or graphene oxide sheets. These nanostructures
are surface-modified using thiolated aptamers or antibodies exhibiting high affinity to specific
mycotoxins such as aflatoxin B1 or ochratoxin A. Upon molecular recognition, the analyte-ligand
binding event causes a quantifiable perturbation in the electrical or optical properties of the
nanomaterial. Depending on the selected transduction pathway, this perturbation may manifest as
a change in surface plasmon resonance, quenching or enhancement of fluorescence, or modulation
of field-effect transistor conductivity.
[00023] The signal transduction unit comprises components selected based on the nature
of the nanomaterial and detection method. In optical implementations, photodiode arrays or
spectrometric sensors are used to capture wavelength-specific intensity variations. In electrical
configurations, a low-noise amplifier circuit coupled with a field-effect transistor translates
changes in surface charge into voltage signals. These analog signals are forwarded to the data
acquisition circuit, which includes an analog-to-digital converter and microcontroller for data
framing, calibration lookup, and compensation routines.
[00024] Processed data packets are then routed to the wireless communication module,
which may include a low-energy Bluetooth transceiver or a LoRaWAN-compatible board for longrange, low-bandwidth data transmission. Data encryption protocols may be applied at this stage to
ensure secure transmission. The system is powered either through a battery regulated by a power
management integrated circuit or a solar panel array integrated on the outer enclosure for energy
autonomy in field settings.
[00025] In a first embodiment, the system may be configured for single-toxin detection,
where the nanomaterial sensing surface is tuned to a specific aptamer-antigen interaction. This is
suitable for applications focused on aflatoxin-only screening in groundnuts or maize. The technical
benefit of this embodiment lies in its simplified calibration protocol, lower cross-reactivity, and
reduced power consumption.
[00026] In a second embodiment, the system may adopt a multiplexed architecture where
a microfluidic array directs aliquots of the sample into discrete detection channels. Each channel
comprises a unique nanomaterial-ligand configuration, enabling concurrent quantification of
multiple toxins. This embodiment is especially beneficial in export-quality assessment facilities
where regulatory compliance requires multianalyte validation.
[00027] In a third embodiment, the system is integrated with a mobile sampling head
mounted on an automated conveyor or silo intake chute. The sensor intermittently diverts small
volumes of material during bulk transfer, performs on-the-fly analysis, and issues real-time
contamination alerts to the procurement database. This variant enhances operational scalability
and reduces sampling bias associated with manual collection.
[00028] The system may also feature software-defined thresholds for contamination levels
based on regional safety norms. The onboard analytics engine classifies detected toxin levels and
issues actionable alerts. In cloud-integrated versions, time-series data across storage facilities are
aggregated and modeled using AI algorithms to predict future contamination risk, thereby
supporting just-in-time intervention or redirection of contaminated lots. All measured values are
tagged with GPS metadata and timestamped for traceability. The signal processing flow remains
consistent across embodiments: analyte binding initiates a material-specific perturbation, the
transduction unit captures and amplifies this perturbation, the data acquisition module digitizes the
signal, and the wireless module transmits the data for visualization or archival.
[00029] Overall, the system integrates chemical specificity of nanostructured biosensing
surfaces with the robustness of digital signal processing and modular data communication,
delivering a comprehensive apparatus suitable for high-throughput, real-time mycotoxin
monitoring in grains and nuts throughout the supply chain.
[00030] FIG. 2 illustrates a method flow diagram detailing the procedural logic embedded
within the detection system. The operation begins with sample collection and introduction,
followed by optional thermal preconditioning that releases toxin molecules from the matrix. Upon
completion of sample conditioning, the fluid passes into a sensing chamber containing a
nanomaterial interface. The interaction of the analyte with immobilized ligand structures elicits a
signal, whose nature depends on the specific nanostructure employed and detection method—
either photonic, electrochemical, or fluorescence-based. The transduced signal is read and filtered
by the analog frontend before undergoing digitization. Post-digitization, the signal undergoes
calibration against internal reference standards using embedded firmware routines. If toxin
concentrations exceed thresholds, the control logic initiates the wireless data transfer protocol. The
data packet, complete with timestamps and metadata, is dispatched to a central cloud analytics
dashboard where users can visualize trends or initiate batch-wise safety protocols. This sequential
method design allows flexible adaptation to different commodity types, supports on-demand or
continuous operation, and facilitates autonomous operation without requiring expert intervention
at the point of sampling.
[00031] FIG. 3 illustrates a data flow diagram focused on the informational pipeline from
sensor interface to cloud-based decision analytics. The initial data originates as a physical signal
captured by the nanostructure-on-ligand detection surface. This raw signal is processed through
amplification and conversion stages and enters the data acquisition layer where packetization,
time-stamping, and threshold-tagging are implemented. Once formatted, the structured data
proceeds through a communication stack wherein protocol encoding is applied to ensure reliable
wireless transmission. The encoded signal reaches a cloud-based platform or local edge gateway
depending on connectivity configuration. In cloud-based setups, the data enters a backend
analytics engine that utilizes statistical filters and AI-assisted classifiers to map contamination
trends across geography and time. Visual dashboards allow stakeholders to assess contamination
risk zones, initiate procurement blocks, or trigger recall mechanisms. In edge-based
implementations, a minimal AI inference engine embedded within the device itself assesses the
signal against trained contamination models. In either configuration, closed-loop feedback is
optionally provided to the sampling head to adjust future sampling rates or prioritize certain lots.
This data flow sequence not only encapsulates the transformation of physical sensing into
actionable intelligence but also enhances regulatory compliance, batch-level traceability, and
quality assurance across agro-industrial supply chains.
[00032]
[00033] Example embodiments herein have been described above with reference to block
diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each
block of the block diagrams and flowchart illustrations, and combinations of blocks in the block
diagrams and flowchart illustrations, respectively, can be implemented by various means including
hardware, software, firmware, and a combination thereof. For example, in one embodiment, each
block of the block diagrams and flowchart illustrations, and combinations of blocks in the block
diagrams and flowchart illustrations can be implemented by computer program instructions. These
computer program instructions may be loaded onto a general purpose computer, special purpose
computer, or other programmable data processing apparatus to produce a machine, such that the
instructions which execute on the computer or other programmable data processing apparatus
create means for implementing the functions specified in the flowchart block or blocks.
[00034] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used
herein relates to any mechanism or computationally intelligent system that combines knowledge,
techniques, and methodologies for controlling a bot or other element within a computing
environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and
that can adapt it-self and learn to do better in changing environments. Additionally, employing
any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to
unknown or changing environment for better performance. The artificial intelligence (AI) includes
fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or
programmatically intelligent software.
[00035] Throughout the present disclosure, the term ‘processing means’ or
‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose
processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a
reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW)
microprocessor, a microprocessor implementing other types of instruction sets, or a
microprocessor implementing a combination of types of instruction sets) or a specialized processor
(such as, for example, an application specific integrated circuit (ASIC), a field programmable gate
array (FPGA), a digital signal processor (DSP), or a network processor).
[00036] The term “non-transitory storage device” or “storage” or “memory,” as used
herein relates to a random access memory, read only memory and variants thereof, in which a
computer can store data or software for any duration.
[00037] Operations in accordance with a variety of aspects of the disclosure is described
above would not have to be performed in the precise order described. Rather, various steps can be
handled in reverse order or simultaneously or not at all.
[00038] While several implementations have been described and illustrated herein, a
variety of other means and/or structures for performing the function and/or obtaining the results
and/or one or more of the advantages described herein may be utilized, and each of such variations
and/or modifications is deemed to be within the scope of the implementations described herein.
More generally, all parameters, dimensions, materials, and configurations described herein are
meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations
will depend upon the specific application or applications for which the teachings is/are used. Those
skilled in the art will recognize, or be able to ascertain using no more than routine experimentation,
many equivalents to the specific implementations described herein. It is, therefore, to be
understood that the foregoing implementations are presented by way of example only and that,
within the scope of the appended claims and equivalents thereto, implementations may be
practiced otherwise than as specifically described and claimed. Implementations of the present
disclosure are directed to each individual feature, system, article, material, kit, and/or method
described herein. In addition, any combination of two or more such features, systems, articles,
materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods
are not mutually inconsistent, is included within the scope of the present disclosure.

I/We Claim:

CLAIM 1
A detection system for monitoring mycotoxins in grains and nuts, comprising:
a sample introduction module configured to receive a particulate or homogenized food sample;
a detection chamber including a nanomaterial-functionalized sensing interface comprising
nanoparticles or carbon nanostructures conjugated with mycotoxin-specific ligands;
a signal transduction unit operatively coupled to the sensing interface, configured to generate an
electrical, optical, or fluorescence signal in response to interaction with a mycotoxin analyte;
a data acquisition circuit operatively coupled to the signal transduction unit and configured to
digitize and process signal output;
and a wireless communication module configured to transmit the processed data to an external
computing device or cloud server.
CLAIM 2
The detection system of claim 1, wherein the nanomaterial-functionalized sensing interface
comprises gold nanoparticles or graphene oxide surfaces functionalized with single-stranded DNA
aptamers specific to aflatoxins, ochratoxins, or zearalenone.
CLAIM 3
The detection system of claim 1, wherein the signal transduction unit comprises an integrated
photodiode array, field-effect transistor, or electrochemical impedance circuit configured to
convert ligand-binding events into corresponding signal patterns.
CLAIM 4
The detection system of claim 1, further comprising a thermal preconditioning module disposed
upstream of the detection chamber, configured to release bound mycotoxins from food matrices
using controlled heating and solvent exposure.
CLAIM 5
The detection system of claim 1, further comprising a multiplexed microfluidic array disposed in
fluid communication with the detection chamber, wherein each microchannel is functionalized
with a distinct nanomaterial-ligand pair for simultaneous detection of multiple mycotoxin types.
CLAIM 6
The detection system of claim 1, wherein the wireless communication module is configured for
Bluetooth, Zigbee, or LoRaWAN protocol compatibility and comprises encryption circuitry for
secure data transfer.
CLAIM 7
The detection system of claim 1, further comprising a calibration module configured with
embedded reference standards and an onboard processor executing baseline correction and drift
compensation routines based on periodically acquired control signals.
CLAIM 8
The detection system of claim 1, wherein the sample introduction module is integrated with an
automated grain-conveyor sampling head configured to continuously divert small amounts of
material into the detection chamber at user-defined intervals.
CLAIM 9
The detection system of claim 1, wherein the system is housed in a portable, impact-resistant
enclosure further comprising a solar-panel-assisted power supply and rechargeable battery backup
configured for field deployment.
CLAIM 10
The detection system of claim 1, wherein the processed data transmitted by the wireless
communication module is stored and visualized via a cloud-based dashboard configured with AIassisted analytics for predictive contamination mapping and procurement-level alert generation.