The present invention generally relates to a field of food technology, and
specifically relates to an internet of things (IoT) system with smart
sensor to detect food adulteration of subjects.
BACKGROUND OF THE INVENTION
10 The Internet of Things (IoT) is emerging technology and is now
completely transforming the ways in which industries operate. The
Internet of Things is considered as a giant network of connected things,
with relationships between people a people, people and things and
things. The Internet of Things defined as “the infrastructure of the
15 information society”. The IoT allows objects to be sensed or controlled
remotely across existing network infrastructure, creating opportunities
for more direct integration of the physical world into computer-based
system. The internet of things can be employed in detection and
monitoring of ingredients and different values in food samples and can
20 be remotely accessed anywhere across the globe.
There, is an example of a food adulteration which is common a process
in which the quality of food is lowered either by the addition of inferior
quality materials or by extraction valuable ingredient. As per the data
released by the FSSAI, the percentage of food samples found
25 adulterated is on the rise. The food adulteration is very harmful for
consumers causing long term side effects on animals and especially
plants where food is taken from.
3
5 The fruits produced across Himalayan range in India have high demand
in the market and also of distance from farm to markets so cultivators,
suppliers are adulterating these fruits, to give them fresh appearance.
The apple farmers in the U.S. claim that the chemical is benign and safe
for use on their produce. European authorities are of the opposite
10 opinion; they believe diphenylamine is toxic to humans. In 2014, the
European Union imposed a restriction on apples and pears that were
treated with the chemical preservative. The ban dealt a serious
economic blow to American exporters. The European officials are
worried that diphenylamine is linked to the appearance
15 of nitrosamines on treated fruits. Nitrosamines are chemical compounds
that are formed when amines react with nitrogen-containing compounds
like diphenylamine. These compounds become more concentrated as
time passes by, such as when they are sitting on the shelves of stores.
Many nitrosamines display carcinogenic qualities. Studies show that
20 these compounds can cause cancer in lab animals and humans alike.
Eating nitrosamine-contaminated food increases the risk of stomach and
esophageal cancer. The health risks of nitrosamines have been known to
government agencies since the 1970s. There are regulations that control
the amount of chemicals that could form nitrosamines. The last time the
25 U.S. Environmental Protection Agency (EPA) reviewed diphenylamine
was in 1998. During that time, the agency considered the pesticide as
safe for human use and posing no threat to the environment. The
maximum allowed levels in each level are 10 ppm, while five ppm are
allowed in pears. The EPA is slated to review the safety of
4
5 diphenylamine within 2018. In the meantime, consumers who are
worried about the potential harm of diphenylamine can avoid it by
buying certified organically-grown fruits, vegetables, and products.
The present invention therefore presents a system which can be
employed to easily detect adulteration of foods consumed everyday
10 through some technical advancements of existing and conventional
system and by combining IoT smart interface.
SUMMARY OF THE INVENTION
The present invention generally relates to an IoT based smart food
adulteration detection system and method and includes a number of
15 sensors and interface modules in order to be remotely accessed.
In an embodiment of the present invention an IoT based smart food
adulteration detection system is disclosed. The system comprising: a
detection module configured to receive a plurality of subject samples,
wherein said detection module comprises a plurality of sensors
20 configured to detect a pH value of each of said plurality of subject
samples, wherein each of said subject samples comprises of different
levels of diphenylamine (DPA) in part-per-million (ppm) and each
subject sample is prepared by spraying at least 50% aqueous and
alcoholic solution of said DPA with at least four different concentrations,
25 wherein said subject samples are configured to be kept at ambient
temperature and pressure conditions for around five days, wherein said
plurality of sensors consists of a pH sensor and is configured to measure
said pH value of said sprayed samples after said samples are washed
5
5 with ethanol in order to remove coating of said DPA and thereby to
detect change in said pH value; a microcontroller unit operatively
coupled to said detection module and configured to receive an input
from said detection module, wherein said processing unit comprises a
processing unit and a storage unit, wherein said processing unit is
10 configured to receive said pH values of said plurality of samples
detected before and after washing said samples with said ethanol,
wherein said processing unit is configured to process said sample values
of said subject samples with each having different concentration of said
diphenylamine (DPA) are matched with stored values of pH for each
15 subject sample; and a display unit communicatively coupled to said
microcontroller unit and is configured to receive an input from said
processing unit, wherein said display unit comprises a display screen
which is configured to display said input received from said processing
unit, wherein when said detected pH value of said subject samples
20 match with said stored pH values, said subject sample is adulterated
with said diphenylamine (DPA) and is displayed over said screen.
Another embodiment of the present invention states a method of an IoT
based smart food adulteration detection. The method comprising steps:
detecting a pH value of each of a plurality of subject samples by a
25 plurality of sensors, wherein each of said subject samples comprises of
different levels of diphenylamine (DPA) in part-per-million (ppm);
spraying each subject sample with at least 50% aqueous and alcoholic
solution of said DPA with at least four different concentrations, wherein
said subject samples are configured to be kept at ambient temperature
6
5 and pressure conditions for around five days; removing a coating of
diphenylamine (DPA) by dipping each of said plurality of subjects in a
specific quantity of ethanol; measuring pH values of each of said
plurality of sample subjects by said plurality of sensors which consists of
a pH sensor after said samples are washed with ethanol in order to
10 remove coating of said DPA and thereby to detect change in said pH
value; transmitting said detected values of pH to a microcontroller unit
operatively coupled to said detection module and configured to receive
an input from said detection module, wherein said processing unit
comprises a processing unit and a storage unit, wherein said processing
15 unit is configured to receive said pH values of said plurality of samples
detected before and after washing said samples with said ethanol;
comparing said detected pH values of said plurality of subject samples
with each having different concentration of said diphenylamine (DPA)
with stored values of pH for each subject sample; and displaying over a
20 display screen of a display unit which is communicatively coupled to said
microcontroller unit when said detected pH value of said subject
samples match with said stored pH values, said subject sample is
adulterated with said diphenylamine (DPA) and is displayed over said
screen.
25 To further clarify advantages and features of the present invention, a
more particular description of the invention will be rendered by
reference to specific embodiments thereof, which is illustrated in the
appended drawings. It is appreciated that these drawings depict only
typical embodiments of the invention and are therefore not to be
7
5 considered limiting of its scope. The invention will be described and
explained with additional specificity and detail with the accompanying
drawings
BRIEF DESCRIPTION OF FIGURES
These and other features, aspects, and advantages of the present
10 invention will become better understood when the following detailed
description is read with reference to the accompanying drawings in
which like characters represent like parts throughout the drawings,
wherein:
Figure 1 illustrates a block diagram of components installed in an IoT
15 based smart food adulteration detection system.
Figure 2 illustrates a flowchart of steps involved in a method of
operating said IoT based smart food adulteration detection.
Figure 3 illustrates a model for said IoT based smart food adulteration
detection system.
20 Figure 4 illustrates a flow diagram of components of said IoT based
smart food adulteration detection system.
Figure 5 illustrates an architecture of said IoT based smart food
adulteration detection system.
Figure 6 illustrates an internet of things (IoT) model employed in said
25 IoT based smart food adulteration detection system.
8
5 Figure 7 illustrates a plot of pH values of subject samples versus ppm
of diphenylamine (DPA).
Figure 8- Table 1 shows pH Value of aqueous solution of 50% ethanol
before spraying over said subject samples.
Figure 9- Table 2 shows pH value of ethanol in which said subject
10 samples are dipped to remove the coating of Diphenylamine.
Further, skilled artisans will appreciate that elements in the drawings
are illustrated for simplicity and may not have been necessarily been
drawn to scale. For example, the flow charts illustrate the method in
terms of the most prominent steps involved to help to improve
15 understanding of aspects of the present invention. Furthermore, in
terms of the construction of the device, one or more components of the
device may have been represented in the drawings by conventional
symbols, and the drawings may show only those specific details that are
pertinent to understanding the embodiments of the present invention so
20 as not to obscure the drawings with details that will be readily apparent
to those of ordinary skill in the art having benefit of the description
herein.
DETAILED DESCRIPTION
For the purpose of promoting an understanding of the principles of the
25 invention, reference will now be made to the embodiment illustrated in
the drawings and specific language will be used to describe the same. It
will nevertheless be understood that no limitation of the scope of the
invention is thereby intended, such alterations and further modifications
9
5 in the illustrated system, and such further applications of the principles
of the invention as illustrated therein being contemplated as would
normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing
general description and the following detailed description are exemplary
10 and explanatory of the invention and are not intended to be restrictive
thereof.
Reference throughout this specification to “an aspect”, “another aspect”
or similar language means that a particular feature, structure, or
characteristic described in connection with the embodiment is included
15 in at least one embodiment of the present invention. Thus, appearances
of the phrase “in an embodiment”, “in another embodiment” and similar
language throughout this specification may, but do not necessarily, all
refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof,
20 are intended to cover a non-exclusive inclusion, such that a process or
method that comprises a list of steps does not include only those steps
but may include other steps not expressly listed or inherent to such
process or method. Similarly, one or more devices or sub-systems or
elements or structures or components proceeded by "comprises...a"
25 does not, without more constraints, preclude the existence of other
devices or other sub-systems or other elements or other structures or
other components or additional devices or additional sub-systems or
additional elements or additional structures or additional components.
10
5 Unless otherwise defined, all technical and scientific terms used herein
have the same meaning as commonly understood by one of ordinary
skill in the art to which this invention belongs. The system, methods,
and examples provided herein are illustrative only and not intended to
be limiting.
10 Embodiments of the present invention will be described below in detail
with reference to the accompanying drawings.
Figure 1 illustrates a block diagram of components installed in an IoT
based smart food adulteration detection system. Tn IoT based smart
food adulteration detection system mainly includes a detection module
15 (104) configured to receive a plurality of subject samples (102), wherein
said detection module (104) comprises a plurality of sensors (106)
configured to detect a pH value of each of said plurality of subject
samples (102), wherein each of said subject samples (102) comprises of
different levels of diphenylamine (DPA) in part-per-million (ppm) and
20 each subject sample is prepared by spraying at least 50% aqueous and
alcoholic solution of said DPA with at least four different concentrations,
wherein said subject samples (102) are configured to be kept at
ambient temperature and pressure conditions for around five days,
wherein said plurality of sensors (106) consists of a pH sensor and is
25 configured to measure said pH value of said sprayed samples after said
samples (102) are washed with ethanol in order to remove coating of
said DPA and thereby to detect change in said pH value.
A microcontroller unit (108) is operatively coupled to said detection
module (104) and configured to receive an input from said detection
11
5 module (102), wherein said microcontroller unit (108) comprises a
processing unit (110) and a storage unit (112), wherein said processing
unit (110) is configured to receive said pH values of said plurality of
samples (102) detected before and after washing said samples with said
ethanol, wherein said processing unit (110) is configured to process said
10 sample values of said subject samples with each having different
concentration of said diphenylamine (DPA) are matched with stored
values of pH for each subject sample.
A display unit (114) is provided and is communicatively coupled to said
microcontroller unit (108) and is configured to receive an input from
15 said processing unit (110), wherein said display unit (114) comprises a
display screen (116) which is configured to display said input received
from said processing unit (110), wherein when said detected pH value of
said subject samples (102) match with said stored pH values, said
subject sample is adulterated with said diphenylamine (DPA) and is
20 displayed over said screen (116).
In an embodiment, the microcontroller unit (108) is configured to
receive said pH values of each of said subject samples (102).
In an embodiment, the pH values of each of said subject samples (102)
are detected before spraying said aqueous solution and after washing
25 and/or removing said DPA coating from said samples, wherein said
microcontroller unit (108) is configured to automatically match said
values of pH detected by said pH sensors (106).
12
5 Figure 2 illustrates a flowchart of steps involved in a method of
operating said IoT based smart food adulteration detection. The method
of an IoT based smart food adulteration detection is explained stepwise
as follows.
The step (202) involves detecting a pH value of each of a plurality of
10 subject samples by a plurality of sensors, wherein each of said subject
samples comprises of different levels of diphenylamine (DPA) in partper-million (ppm). The step (204) involves spraying each subject sample
with at least 50% aqueous and alcoholic solution of said DPA with at
least four different concentrations, wherein said subject samples are
15 configured to be kept at ambient temperature and pressure conditions
for around five days.
The step (206) involves removing a coating of diphenylamine (DPA) by
dipping each of said plurality of subjects in a specific quantity of ethanol.
Step (208) involves measuring pH values of each of said plurality of
20 sample subjects by said plurality of sensors which consists of a pH
sensor after said samples are washed with ethanol in order to remove
coating of said DPA and thereby to detect change in said pH value.
The step (210) involves transmitting said detected values of pH to a
microcontroller unit operatively coupled to said detection module and
25 configured to receive an input from said detection module, wherein said
processing unit comprises a processing unit and a storage unit, wherein
said processing unit is configured to receive said pH values of said
plurality of samples detected before and after washing said samples with
said ethanol.
13
5 The step (212) involves comparing said detected pH values of said
plurality of subject samples with each having different concentration of
said diphenylamine (DPA) with stored values of pH for each subject
sample.
The further step (214) involves displaying over a display screen of a
10 display unit which is communicatively coupled to said microcontroller
unit when said detected pH value of said subject samples match with
said stored pH values, said subject sample is adulterated with said
diphenylamine (DPA) and is displayed over said screen.
The method further comprises spraying at least four subject samples
15 having around 2000 to 5000 ppm of diphenylamine (DPA), by a 50%
aqueous solution of ethanol in order to remove coating of DPA from said
subject samples.
The method further comprises monitoring said sample subjects sprayed
with said 50% aqueous solution of ethanol for at least said five days,
20 wherein a pH value of said ethanol mixed with said 2000-5000 ppm DPA
is detected before applying over said sample subjects.
The method further comprises measuring said pH values of said
monitored samples in order to detect change in pH values before
spraying of said ethanol and after removing of said DPA coating.
25 Figure 3 illustrates a model for said IoT based smart food adulteration
detection system. The system includes taking a sample subject such as
a fruit and the sensors are employed to determine the chemical
assessment inside said sample.
14
5 The details of the chemical assessment are forwarded to a processing
unit or cloud server. The cloud server compares the chemical
assessment values such as pH scale, acidity, and the like with the
already stored or ideal assessment of an ideal sample.
The results can be displayed over a screen in a monitoring room or can
10 be transmitted to a remote device, such as a mobile phone application
where a user can retrieve all the information.
Figure 4 illustrates a flow diagram of components of said IoT based
smart food adulteration detection system. The fruit sample as
mentioned in figure 1-3 is used and different sensors are employed to
15 detect chemical assessment from said sample fruits, such as pH value
and these details are transmitted to a microcontroller which has a
processing unit and a storage unit. The processing unit starts comparing
the values with the pre-recorded one and once the match is confirmed it
is transmitted to a display screen that said sample is adulterated.
20 Figure 5 illustrates an architecture of said IoT based smart food
adulteration detection system. The sample subject herein used is an
apple as this fruit contains more amount of diphenylamine (DPA).
Figure 6 illustrates an internet of things (IoT) model employed in said
IoT based smart food adulteration detection system. A prototype of an
25 IoT model is displayed which includes different sensors and a
microcontroller and a display screen. The system is portable and
compact and can be employed for different samples to detect different
chemical details.
15
5 Figure 7 illustrates a plot of pH values of subject samples versus ppm
of diphenylamine (DPA). The graph displays two plots, with first one
when a sample subject is detected for pH value before and after
spraying ethanol and diphenylamine (DPA). The different pH values of
different samples are explained in following table 1 and 2.
10 Table 1 and Table 2 (in Figures 8 and 9 respectively) shows pH
Value of aqueous solution of 50% ethanol before spraying over said
subject samples and pH value of ethanol in which said subject samples
are dipped to remove the coating of diphenylamine. For example, the
case of apples where diphenylamine shows up more than any other
15 pesticide, possibly because it is used post-harvest rather than in the
orchard to prevent scald, which is a browning of the skin.
As test samples (apple) contain different ppm (2000, 3000, 4000 and
5000) of DPA (Diphenylamine). The apple sample for testing were
prepared by spray 50% aqueous and alcoholic solution of DPA having
20 four different concentration and were observed over the period of five
days.
As we know, pH of ethanol is almost neutral like water. The pH of 100%
ethanol is 7.33, compared to 7.00 for pure water but our laboratory
rated ethanol has pH 7. When this ethanol added with 2000, 3000, 4000
25 and 5000 ppm of DPA it gives 6.0, 6.1, 6.3 and 6.65 pH, respectively.
DPA containing 50% aqueous solution of ethanol sprayed over the
apple, and was monitored over the course of five days.
16
5 After five days the four different sample of apple, which contains
different concentration of DPA were washed with ethanol for further
monitoring the change in pH value. As a matter of fact, DPA is soluble in
ethanol. Furthermore, pH value of four different solutions was checked
with the help of device, which detects the trace of Diphenylamine. Based
10 on literature background Diphenylamine is widely used for adulteration
in apple. Next, the pH value obtained from the solution of four different
samples of 2000, 3000, 4000 and 5000 ppm of DPA were 6.0, 6.1, 6.2
and 6.45 pH, respectively. It has been observed from the experiment
that the pH value of all samples under investigation decrease as
15 compare with pure ethanol. Hence, the proposed device can easily
detect pure ethanol or ethanol contains a trace of DPA. Based on above
experimental result one can easily detect DPA adulterated apple.
The present invention further states that current sensor systems used
biosensor is universally defined as “a self-contained analytical device
20 that combines a biological component with a physicochemical device for
the detection of an analytic of biological importance”. It consists of a
biological recognition element which is able to specifically interact with a
target molecule and a transducer able to convert this interaction into a
measurable signal. Chemical biosensors are based on the presence of a
25 biological element, which is specific for the analytic, and stable under
normal conditions of use and storage. Numerous recognition elements
have been used in biosensors, such as receptors, nucleic acids, whole
cells, antibodies and different class of enzymes.
17
5 Biosensors are normally classified according to the transduction method
they use. In biosensors, the transducer converts a wide array of
chemical, physical or biological reactions into an electrical signal. On this
basis, optical, calorimetric or acoustic biosensors have been built and
characterized, but the most widely used biosensors rely on the
10 electrochemical proprieties of transducers and analysts. Electrochemical
biosensors have been studied since the early 1960s when the first
glucose oxidase biosensor was developed. Electrochemical biosensors
can be impedimetric, potentiometric or amperometric biosensors, where
the biochemical signal is transduced into a quantifiable amperometric
15 signal. Enzyme based amperometric biosensors, in which the production
of a current is monitored when a fixed potential is applied between two
electrodes, have been widely studied over the last few decades as they
are easy to miniaturize, robust and can operate with small sample
volumes of rather complex matrices.
20 Designing biosensors requires consideration of both the target analytic
and the complexity of the matrix in which the analytic will be measured.
Electrochemical measurements depend strongly on the working
electrode material. Since the end of 1980s, research has focused on the
development of amperometric biosensors based on carbon paste
25 electrodes. Carbon still represents one of the most widely-used
materials for biosensing in electrocatalysis and electroanalysis,
exploiting the favourable chemical-physical proprieties of carbon
nanotubes or graphene, as well as desirable catalytic proprieties (high
18
5 surface area, good biocompatibility, chemical stability and signal
reproducibility).
In addition, metals, such as gold, platinum or palladium, have been
used as transducers in electrochemical biosensors as electron transfer is
easy and hydrogen peroxide generated by first generation oxidase10 based biosensors is efficiently electro-oxidized to generate a signal.
The detection module further comprises a plurality of biological sensors
coupled with an internet of things (IoT) module, wherein said biological
sensors consists of a biological recognition element configured to
specifically interact with a target molecule present in said subject
15 samples and a transduction element which is configured to convert said
interaction by said recognition element into a measurable signal,
wherein said recognition element comprises receptors, nucleic acids,
whole cells, antibodies and a class of enzymes.
The drawings and the forgoing description give examples of
20 embodiments. Those skilled in the art will appreciate that one or more of
the described elements may well be combined into a single functional
element. Alternatively, certain elements may be split into multiple
functional elements. Elements from one embodiment may be added to
another embodiment. For example, orders of processes described herein
25 may be changed and are not limited to the manner described herein.
Moreover, the actions of any flow diagram need not be implemented in
the order shown; nor do all of the acts necessarily need to be
performed. Also, those acts that are not dependent on other acts may
be performed in parallel with the other acts. The scope of embodiments
19
5 is by no means limited by these specific examples. Numerous variations,
whether explicitly given in the specification or not, such as differences in
structure, dimension, and use of material, are possible. The scope of
embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been
10 described above with regard to specific embodiments. However, the
benefits, advantages, solutions to problems, and any component(s) that
may cause any benefit, advantage, or solution to occur or become more
pronounced are not to be construed as a critical, required, or essential
feature or component of any or all the claims.

We Claim:
1. An IoT based smart food adulteration detection system, the
system comprising:
a detection module (104) configured to receive a plurality of subject
samples (102), wherein said detection module (104) comprises a
10 plurality of sensors (106) configured to detect a pH value of each of said
plurality of subject samples (102), wherein each of said subject samples
(102) comprises of different levels of diphenylamine (DPA) in part-permillion (ppm) and each subject sample is prepared by spraying at least
50% aqueous and alcoholic solution of said DPA with at least four
15 different concentrations, wherein said subject samples (102) are
configured to be kept at ambient temperature and pressure conditions
for around five days, wherein said plurality of sensors (106) consists of
a pH sensor and is configured to measure said pH value of said sprayed
samples after said samples (102) are washed with ethanol in order to
20 remove coating of said DPA and thereby to detect change in said pH
value;
a microcontroller unit (108) operatively coupled to said detection
module (104) and configured to receive an input from said detection
module (102), wherein said microcontroller unit (108) comprises a
25 processing unit (110) and a storage unit (112), wherein said processing
unit (110) is configured to receive said pH values of said plurality of
samples (102) detected before and after washing said samples with said
ethanol, and
21
5 a display unit (114) communicatively coupled to said microcontroller
unit (108) and is configured to receive an input from said processing
unit (110), wherein said display unit (114) comprises a display screen
(116) which is configured to display said input received from said
processing unit (110), wherein when said detected pH value of said
10 subject samples (102) match with said stored pH values, said subject
sample is adulterated with said diphenylamine (DPA) and is displayed
over said screen (116).
2. The system as claimed in claim 1, wherein said processing unit
(110) is configured to process said sample values of said subject
15 samples with each having different concentration of said
diphenylamine (DPA) are matched with stored values of pH for
each subject sample.
3. The system as claimed in claim 1, wherein said detection module
20 further comprises:
a plurality of biological sensors coupled with an internet of things (IoT)
module, wherein said biological sensors consists of a biological
recognition element configured to specifically interact with a target
molecule present in said subject samples and a transduction element
25 which is configured to convert said interaction by said recognition
element into a measurable signal, wherein said recognition element
comprises receptors, nucleic acids, whole cells, antibodies and a class of
enzymes.
22
5 4. The system as claimed in claim 1, wherein said microcontroller
unit (108) is configured to receive said pH values of each of said
subject samples (102).
5. The system as claimed in claim 4, wherein said pH values of each
10 of said subject samples (102) are detected before spraying said
aqueous solution and after washing and/or removing said DPA
coating from said samples, wherein said microcontroller unit (108)
is configured to automatically match said values of pH detected by
said pH sensors (106).
15 6. A method of an IoT based smart food adulteration detection, the
method comprising steps:
detecting a pH value of each of a plurality of subject samples by a
plurality of sensors, wherein each of said subject samples comprises of
different levels of diphenylamine (DPA) in part-per-million (ppm);
20 spraying each subject sample with at least 50% aqueous and alcoholic
solution of said DPA with at least four different concentrations, wherein
said subject samples are configured to be kept at ambient temperature
and pressure conditions for around five days,
removing a coating of diphenylamine (DPA) by dipping each of said
25 plurality of subjects in a specific quantity of ethanol;
measuring pH values of each of said plurality of sample subjects by said
plurality of sensors which consists of a pH sensor after said samples are
23
5 washed with ethanol in order to remove coating of said DPA and thereby
to detect change in said pH value;
transmitting said detected values of pH to a microcontroller unit
operatively coupled to said detection module and configured to receive
an input from said detection module, wherein said processing unit
10 comprises a processing unit and a storage unit, wherein said processing
unit is configured to receive said pH values of said plurality of samples
detected before and after washing said samples with said ethanol;
comparing said detected pH values of said plurality of subject samples
with each having different concentration of said diphenylamine (DPA)
15 with stored values of pH for each subject sample;
displaying over a display screen of a display unit which is
communicatively coupled to said microcontroller unit when said detected
pH value of said subject samples match with said stored pH values, said
subject sample is adulterated with said diphenylamine (DPA) and is
20 displayed over said screen.
7. The method as claimed in claim 6, wherein said method further
comprises:
spraying at least four subject samples having around 2000 to 5000 ppm
of diphenylamine (DPA), by a 50% aqueous solution of ethanol in order
25 to remove coating of DPA from said subject samples.
24
5 8. The method as claimed in claim 6, wherein said method further
comprises:
monitoring said sample subjects sprayed with said 50% aqueous
solution of ethanol for at least said five days, wherein a pH value of said
ethanol mixed with said 2000-5000 ppm DPA is detected before
10 applying over said sample subjects.
9. The method as claimed in claim 6, wherein said method further
comprises:
measuring said pH values of said monitored samples in order to detect
change in pH values before spraying of said ethanol and after removing
15 of said DPA coating.