1. Pg diploma in food safety and qualitymanagement
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I. Contents of pg diploma in food safety and quality management
==================
About the Programme
Food safety is a fundamental public health concern. The Food Safety and Quality has become an
area of priority and necessity for consumers, retailers, manufacturers and regulators. Changing
global patterns of food production, international trade, technology, public expectations for health
protection and many other factors have created a huge demand for food safety and quality
auditing professionals.
IGNOU and APEDA have come together to launch this programme, the first such initiative in
India, with an emphasis on practical proficiency development exercises. This programme is
expected to meet the increasing human resource requirements for food safety and quality
management professionals in the agriculture and food sectors.
Objectives
The core objective of the programme is to develop professionals for development,
implementation and auditing of Food Safety and Quality Management in the country. It seeks to
develop India’s capability to meet the global food safety and quality requirements and enhance
competitiveness of food products. In long term perspective, it would contribute to ensure
consumer safety within and outside the country.
This PG programme shall enable the students to:
Comprehend the issues of safety and quality in food production, handling, processing and
trade.
Build technical proficiency in undertaking food safety and quality assurance in food
processing chain i.e., from farm to fork.
2. Ensure the safety and quality of food products as per mandatory legal requirements and
voluntary standards including export regulations, if required.
Design and implement
o Good Hygienic Practices (GHP)
o Good Manufacturing Practices (GMP)
o Hazard Analysis and Critical Control Point (HACCP)
o Quality Management Systems (QMS): ISO9001
o Food Safety Management Systems (FSMS) : ISO 22000
o Environmental Management Systems: ISO 14001
o Laboratory Management System: ISO 17025
o Retail Standards
Be able to effectively plan, conduct, report and audit as per the guidelines of the ISO
19011-2002.
Undertake Standard Microbiological and Chemical analysis of Food Products.
Apply Good Hygienic, Manufacturing, Laboratory, Transportation and Retail Practices in
Food Processing/ Hospitality industry and Retail outlets.
Programme Structure
In order to be eligible for award of the PG Diploma, a student has to complete the following 8
courses equivalent to 32 credits (1 credit is equal to 30 study hours)
Course Code Title of the Course Credits
MVP-001 Food Fundamentals & Chemistry 4
MVPI-001 Food Microbiology 4
MVP-002 Food Laws and Standards 4
MVP-003 Principles of Food Safety and Quality Management 4
MVP-004 Food Safety and Quality Management Systems 4
MVPL-001 Food Safety and Quality Auditing 4
MVPL-002 Chemical Analysis and Quality Assurance§ 4
MVPP-001 Project Work § 4
§The candidates shall be associated with a Food Laboratory, Industry or Consultancy
organization.
Video Programmes: The self learning instructional material shall be supported with the
video programmes.
3. Eligibility Requirements
Science Graduates with Chemistry/ Bio-chemistry or Microbiology as one of the subjects.
Degree in allied sciences like Agriculture/ Food Science and Technology/ Post Harvest
Technology/ Home Science/ Life Science/ Microbiology/Biochemistry/ Biotechnology/
Horticulture/ Dairy Technology/ Veterinary/ Fisheries/ Hotel Management and Catering/
Hospitality Management etc. or equivalent.
Science graduates in disciplines like Geography, Statistics with Physics & Math, Art
Subjects and Medical Lab technology and with minimum three years experience in food
processing and /or quality control. (minimum 1 year experience).
Art Graduates with diploma in Food Science disciplines viz. Fruits and vegetables, dairy
technology, meat technology, cereal, pulses and oilseeds etc. with minimum 5 years
experience in Food Processing/Food Quality Control (2 years experience in quality
control).
B.A./B.Com Graduates with minimum 7 years experience or holding senior position in
Govt./Semi Govt. Units involved in Food Quality Control.
Programme Fee
Rs.12,100/- (Rs. 12,000/- Programme Fee + Rs.100/- Registration Fee)
Duration
Minimum - 1 year
Maximum - 4 years.
Medium of Instruction
English
Target Group
The PG Diploma is intended for Graduates in Science/ Agriculture/ Food Science or allied
disciplines contemplating a career in Food Safety and Quality Management. It is also intended
for professionals in food processing and quality control for strengthening their proficiency in
design and implement quality management systems, undertake food auditing and implement new
food act- Food Safety and Standard Act 2006. The programme shall also open new vista for
entrepreneurs who intend to diversify in food safety and quality aspects.
Job Opportunities
Quality Control Officer or Quality Assurance/ Management professionals in food
/hospitality/ retail industry and laboratories,
Food Safety Officer in the regulatory bodies,
Food Auditor in Certification and Inspection bodies,
Trainer/ Counsellor in Food Safety & Quality Management Systems, and
Self-Employment as food certifying/auditing professionals for the Food Safety and
Quality Management Systems
==================
4. III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
Who filled out the check sheet
What was collected (what each check represents,
an identifying batch or lot number)
Where the collection took place (facility, room,
apparatus)
When the collection took place (hour, shift, day
of the week)
Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
5. to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
6. The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
7. being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
People: Anyone involved with the process
Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
Machines: Any equipment, computers, tools, etc.
required to accomplish the job
Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
Measurements: Data generated from the process
that are used to evaluate its quality
8. Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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