Hypothesis

hypothesis = tentative assumption to test logical or empirical consequences of applying a variable in a research project null hypothesis = statement that applying a research variable will not make a significant difference in a research project

Some examples…

Planning an experiment

• Idea

• Justification – Develop hypothesis

• Literature review

• Designing Experiment – work from hypothesis

– Must have controls

– Verified methods

– Weights and measures

Planning an experiment

• Results

– Compare treatments using objective measurements

– Physical and sensory tests

• Discussion

– Compare your results with those of others

– Did your results support your hypothesis or not?

– Rationale

• Conclusion

– Summary of results

– Impact of study

Controlling Experimental Variables

– Variable = quantity that has no fixed value

– Independent variable=defined by researcher (e.g. type of sweetner used)

– Dependent variable=will be a measured result from the experiment (e.g. affect of sweetner on color, volume,etc.)

– Extraneous variable = added variation that is not controlled that affects experimental outcome

Conducting an Experiment

• Objective and subjective observations

• Recording data – all information when observed

• Statistical analysis

– Descriptive statistics – frequency, distribution (mean, variance, standard deviation)

– Inferential statistics – probability of predicting an occurrence by use of a statistical test (t-test, ANOVA). Use significance level P<0.05

• Report

Sensory Tests

• Can be very objective when terms are clearly defined (consumer panel – 100s of people) or a panel that is highly trained (quantitative descriptive analysis)

Sensory Tests

• Involves use of senses – physiological response

– Olfactory receptors in nose

• Odor and taste receptors blend to give flavor

– Taste receptors –tongue, taste buds (gungiforms and circumvallate)

– Sweet, sour, bitter, salt

– Thresholds – concentration of taste compound at barely detectable level

– Subthreshold – concentration of taste compound at a

level that is not detectable, but is capable of influencing other taste perception (e.g. salt on sweetness)

Sensory Tests

• Visual receptors – shape, color, texture

• Appearance can affect perceived flavor or texture (example)

• Lighting is important – must not mask or accentuate irrelevant traits during sensory testing

Sensory Characteristics

• Appearance-color being most important (kids)

• Color is exterior surface

• Interior appearance –lumps, air cells, etc.

• Appearance and color features should be included on sensory testing forms

• Aroma – second most characteristic

• Aroma ‘advertises’ food

• Consider proper temperature when evaluating food aroma

• Flavor – taste and aroma mix to form flavor

• Temperature is critical to extract flavor and aroma

• Flavor potentiator – compound that enhances flavor without adding a flavor of its own (MSG)

• Flavor inhibitors – substance that blocks perception of a taste (milk protein or starch on hot pepper)

• Texture – mouthfeel – how a food feels in your mouth

– Mouthfeel –must clearly define what panelist is to evaluate (sticky, smooth, astringent)

• Tenderness – amount of chewing action to reach a certain consistency

• Appearance, Aroma, Flavor,Texture

– Train panel how attribute is defined so all are using same criteria

– Standardized and consistent experimental protocol

• examples

Selecting a Panel

• Ability to discriminate differences you are looking for

– Depending on test, may or may not want highly sensitive people

– Screen using preliminary tests

– Interest in project and serving on a panel

– Clarity of nasal passages and ability to taste and smell

– Demographic characteristics

Training a Panel

• Trained panelists- varies with complexity of test

• Review scorecards, clarify questions, assure that panelists are using same word definitions for scoring

• Untrained panelists – need larger number for tests. Consumer panels.

• Panelist has no preparation for evaluation of product (outside of own personal experience)

• Descriptive Flavor Analysis Panel and

Quantitative Descriptive Analysis

• -trained panel to analyze flavor, texture, appearance of product in great detail

• Describe product characteristics and quantify intensity of traits

• Verify flavor and determine quality

• Great amount of work (9 week or so to train panel)

• Must use same ‘calibrated’ panel over and over again. Needs long term commitment

Types of Tests

• Descriptive – provide information on selected characteristics

• Affective – subjective attitude to a product. Acceptability or preference. Follows discriminative or descriptive testing

• Difference – determine whether there are detectable differences between products

• Descriptive – provide selective information on characteristics of food

– Selective scoring of critical attributes. These are developed by researcher, through focus group or preliminary panels

– Each characteristic to be evaluated is described over entire range (min amount to excessive amount of trait x)

– Score card with rating scales (hedonic scales – e.g. extremely sweet to not sweet). These must be carefully worded

Descriptive Tests, cont.

– Score cards with comparisons -‘the more X sample is #’

– Trained or semi-trained panel

– Profile methods (flavor and texture profiling) –

Individual judgments, or ratings by a group. Develop accurate word for each characteristic to be measured

– Can be a single sample

Attribute analysis

• Not a preference test

• Problems with central tendency error

• Scales – 6-10 marks. Use objective terms as anchors (very hard) not subjective ones (much too hard).

• Anchors must be opposites

• Use anchors that are agreed upon during panel training. Each panelist can be calibrated based upon their tendency to use the whole scale. Can be repeated with a control as part of replication.

• Unstructured scales are best. Eliminates problems with unequal psychological intervals between traits.

• Psychological difference between terms are important. E.g ‘extremely sweet’ and ‘very sweet’ do not represent the same difference as ‘trace sweet’ and ‘not sweet’

• Hard to apply to complex traits like texture which must be characterized as individual components

• Train panel on what property IS so all will be looking for the same thing

• Include standards as scale tends to drift with time and panel’s familiarity with the product.

Umami

Earthy

Musky

Sour Overall Aroma

Bread/ Yeasty

Metallic

Intensity Af tertaste

Bitter

Duration Af tertaste

Algae Flavor Strength

Salted Squid

Salted

Cooked Salmon

Fruity

OceanSeaweed

Floral

Fresh Fish

Fresh Salmon

Buttery

Sweet

Type of Tests

• Affective – subjective attitude to a product. Acceptability or preference. Follows discriminative or descriptive testing

• Ranking – rate by intensity of trait. Can be used to screen one or two samples from a larger group.

Must couple with another test to sort out degree of different if this is important.

– hedonic scales (like extremely/dislike extremely)

– consumer panels

Difference – detect differences between products

– also called discrimination tests

– Test sensitivity of judges to a certain trait

– Try to match experimental product with control

– New product formulations

Difference Tests

• Paired comparison

• Specific characteristic tested: ‘which sample is more sweet”

Other discrimination tests

• Triangle

• 2 out of 5

• Ranking- works well when several samples need to be evaluated for a single characteristic. Rank sample in order of intensity of characteristic being measured.

Factors affecting sensory measurements

• Choosing a panel – Best scenario –

– Panel is an analytical instrument

– Health, interest, availability, punctual, good verbal and communication skills.

• Threshold tests for primary tastes not useful to screen individuals for sensitivity to different foods

• Generally screen 2-3x as many people as you will use

• Prepare test samples as you would for ‘real experiment’

• Make sure panel understands forms used and the terms used on the forms

• Expectation error – any information a panelist receives influences the outcome

• Panels finds what they are expected to find

• Trick – provide only enough information for panelist to be able to do the test

• Try not to include people already involved in the experiment (single blind)

• Avoid codes that create inherent bias (1,A etc)

• Motivated panelists

• Leniency error – rate products based upon feelings about researcher

• Suggestion effect – response of other panelists to product (need to isolate panelists and keep them quiet)

Testing times

• Must not be too tired or hungry

• Late morning or mid afternoon are good

• Early AM bad for testing spicy foods

• Late day – lack of panelist motivation

Stimulus Error

• Influence of irrelevant questions (e.g piece size, color, uniformity)

• Try to mask unwanted difference (e.g. colored

lights)

• Logical error – associated with stimulus error – tendency to rate characteristics that appear to be logically associated (yellow and rancidity).

Control by masking differences

Halo and Proximity Effect

• Halo effect – caused by evaluating too many factors at one time. Panelists already have an impression about the product when asked about second trait – will form a logical association (e.g. dry-> tough)

• Best to structure testing so that only one factor is tested at a time (difficult to do)

• Proximity error – rate more characteristics similar when they follow in close proximity.

Convergence Effect

• Convergence effect – large difference between two samples will mask small

differences between others.

• This causes results to converge. So use random order to reduce this.

• Next slide shows how flavor interactions impact this.

Positional Effect and Contrast Effect

• Positional effect – tendency to rate second product higher or lower

• 2 products very different – panelists will exaggerate differences and rate ‘weaker’ sample lower than would otherwise

• Use random order. Use all possible presentation orders

Central Tendency Error

• Panelists done want to use whole scale.

• Mix up scale (don’t load one end with all the ‘good traits.

• Can also normalize form for each panelist

Physical Location

• Testing in special rooms. 22C, positive pressure, 45% RH,

• Special lighting

• No fumes

• No smoking

Sample preparation

• Preliminary preparation – grind, puree to reduce color differences (unless testing for color differences)

• Masking color – lights, glasses, blindfolds, black lined cups, added dye

Dilutions and carriers

• Spices or hot sauce – dilute in white sauce or syrup

• Hydrocolloids mask flavor

• Test actual food – icing ON cake

• 20-40C easiest range

Utensils and containers

• Glass is best (inert)

• Container should not have flavor or aroma

Quantity of sample

• Size limited by amount of product available

• Representative of what is needed to test variation in product as manufactured

• Test dependent (consumer sample or portion would require more sample)

• Discriminative – 16 ml liquid, 28 g solid. Double for preference test

• Market testing – use consumer size serving – what tastes ‘good’ at 20 ml may not at 200!

Controls

• Include reference sample in test as part of mix

• Use random numbers

• Balanced order of presentation to reduce physiological and psychological effects

• Use same ‘process’ between samples to reduce carry over.

• Neutral tasting room temperature water.

• Matzo crackers between samples

• High fat samples – warm tea, lemon water, apple slices

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