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NF2016N Introduction 2011

Introduces the topics in this module

Richard Marshall

on 10 February 2012

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Transcript of NF2016N Introduction 2011

NF2016N Food Processing & Preservation
Introduction 2012 What's the programme?
Introduction - today!
5½ weeks of labs
4½ weeks of lectures/tutorials Canning practical
3 groups of about 20 students
4 students per team - 5 teams/group
Canning chick peas Group A 3.10 pm
Group B 3.50 pm
Group C 4.30 pm Week 2
2 - 3.30pm - Principles of food analysis
4 pm All groups open cans, prepare contents for drying Today
After Intro - Canning SCG-15 Food Tech Weeks 3, 4, 5 - Analysis of food, 2 - 6pm
Week 6 - Finish analysis, 2 - 3.30pm. Tutorial European Food Industry 4pm Week 7 - Tutorial: Food spoilage, 2 - 3.30pm;
Data analysis, 4pm
Week 8, - Tutorials: Food preservation 1 & 2
Week 9, - Data analysis - all afternoon
Weeks 10, 11, Tutorials: Food preservation 3 & 4,
Nutrition & processing Analysis of chick peas
Sample preparation - homogenisation
Moisture - drying
Protein - Kjeldahl
Fat - Soxtec
Fibre - Acid detergent fibre
Ash - total minerals - by combustion
Minerals - Na, K, Ca - flame photometry, AAS
Energy - bomb calorimetry DIfferent foods - different methods of homogenisation
Soft foods - blend in food blender
Brittle, crumbly foods - use food blender
Brittle, hard foods - pestle & mortar
Very tough foods - powerful homogeniser Why homogenise? Food samples usually too large to analyse directly
Need to have a smaller, sub-sample
Must still be representative of the whole
Mix all the ingredients together and take sample Weighing of samples
VERY IMPORTANT!!!! For ALL analytical work
MUST use 3 or 4 place balance Methods of analysis
Most are 'proximates'
ie groups of similar substances, not individual proteins, fats, sugars etc 1. Moisture
Sample homogenised
Small sample weighed into drying tins Dried under vacuum, 70 C to constant weight
Cooled, reweighed 2. Protein
By measuring nitrogen (N) content
'Kjeldahl' method
Dried food, wet oxidised in sulphuric acid
N in proteins (etc) converted to NH (SO )
Distilled off, measure NH by titration
N value converted to protein using standard factor 2 4 2 3 3. Fat
Different foods require different methods
Can use solvent extraction for many
We'll be using Soxtec method
Dried sample washed with petroleum spirit
Weighed before and after 4. Fibre
Should use Englyst, Southgate or AOAC method
Simulate digestion, take over 24 h
We'll be using Acid Detergent Fibre (ADF)
Dried sample boiled with acid/detergent reagent
Weighed before and after filtering off the solubilised parts 5. Ash
Not a true analyte
Represents total minerals
Used to calculate carbohydrate by difference NOT this time BUT
CHO by difference = 100 - (P+F+Ash+M)
Sugars = 100 - (P+F+Fibre+Ash+M) Ash.....
Dried sample burned at 550 deg C
Weighed before and after 6. Minerals (specifics) - Na, K, Ca
Dried samples completely oxidised in very strong acid - totally solubilised
Solutions analysed in Atomic Adsorbtion Spectrophotometer (AAS) 7. Energy
Dried samples burned in oxygen in sealed container (bomb)
Heat produced calculated as kJ
Can also calculate energy from the protein, fat and sugar values Getting the results
As results come in, will upload onto a Google Spreadsheet
Need your email addresses to authorise your use
Everyone can enter data and share it
Export it later to Excel https://spreadsheets.google.com/ccc?key=0AqcqjzIhzFcicDNfLWZCSHE3M0o4U0NhYkN0T2dwYUE&hl=en_GB#gid=0 Statistical analysis of results
What affects their reliability?
Compare results across class teams
Calculate 'z scores'
Plot out the data z scores should be within +/- 2 of zero (= mean)
95 % of reliable results fall within this range
ie only 5% probability that they fall outside by chance
Less an 1% chance they fall more than +/- 3 z scores What are possible causes of errors?
Method errors - methods have built in un-reliability
Technical errors - methods may require training, skill, experience
Calculation errors - some are easy, others more difficult
Good analysis tries to minimise these Accuracy and Precision
You should know the definitions..... Accuracy
How close a mean value is to the true value

How close a single value is to the mean http://learning.londonmet.ac.uk/HHS/NF2016N/accuracy&precision.gif BUT........
We don't know what the true values are
Can only use our analytical methods and they only estimate a value
The precision is often very high
Long experience gives confidence
Don't need to know values of proximates to very high accuracy Must have reliable methods
Need to know how well a method performs
What are the 'issues' - technical, methodological etc

How consistent results are when repeated in one lab

How consistent results are when repeated in different labs

You're going to be working as if your teams are different labs
Comparing results will help you see where problems are Resources
See Web Learn for this module
Module booklet
Analytical methods
Power Points
Other presentations
Podcasts (go with the PPs)
Info on various areas of the module End of first tutorial
Start of next week's! Practical plan
Full transcript