Food Sensory Exam 2

Affective testing

measures consumer liking, preference, and acceptance of products (ex: rating how much people like different ice cream flavors)

Purpose of affective testing

determine consumer satisfaction and product success (ex: deciding if a new soda will sell well)

Analytic vs affective tests

analytic tests detect differences, affective tests measure liking (ex: triangle test vs hedonic test)

Uses of affective testing

product development, improvement, and market research (ex: choosing best cookie formula before launch)

Preference testing

determines which product is preferred over another (ex: choosing between Coke vs Pepsi)

Acceptance testing

measures degree of liking for a product (ex: rating a yogurt on a 9

Preference methods

paired comparison and ranking tests (ex: ranking 3 chip brands from best to worst)

Acceptance methods

hedonic rating scales (ex: rating pizza from dislike to like extremely)

Preference design

direct comparison between samples (ex: tasting 2 juices side

Acceptance design

individual product evaluation (ex: tasting one product at a time and rating it)

Preference limitation

does not measure intensity of liking (ex: you know which cookie is preferred but not how much)

Acceptance limitation

subjective responses vary by consumer (ex: one person rates 9, another rates 5)

Preference statistics

often non

Acceptance statistics

t

Scale choice

depends on product type and research objective (ex: simple scale for kids, detailed for trained adults)

9

point hedonic scale

Magnitude scales

measure perceived intensity of attributes (ex: rating sweetness strength)

Unstructured line scales

continuous scale without fixed categories (ex: marking sweetness on a line)

Measurement types

ordinal (rank), interval (equal spacing), ratio (true zero) (ex: ranking chips vs rating sweetness vs measuring sugar grams)

t

test independent groups

t

test paired observations

ANOVA

compares means of three or more groups (ex: comparing 4 flavors of yogurt)

ANOVA null hypothesis

all group means are equal (ex: all flavors liked the same)

ANOVA alternative hypothesis

at least one group mean is different (ex: one flavor liked more)

ANOVA applications

test product or treatment differences (ex: effect of sugar levels on liking)

ANOVA interpretation

significant result indicates at least one difference exists (ex: p < 0.05 means not all samples equal)

Post

hoc tests

Friedman test

non

Friedman interpretation

identifies differences in rankings between samples (ex: sauce A ranked highest overall)

Attribute testing

evaluates specific sensory characteristics of products (ex: sweetness, texture, color of juice)

JAR testing

determines if an attribute is too weak, just right, or too strong (ex: sweetness too low, just right, too high)

Penalty analysis

measures how deviations from JAR affect liking (ex: too salty soup lowers liking score)

Relating attributes to liking

identifies drivers of liking or disliking (ex: creaminess increases ice cream liking)

Attribute interpretation

determines which attributes impact acceptance (ex: bitterness reduces coffee liking)

Descriptive analysis

detailed, quantitative description of sensory attributes (ex: rating sweetness, sourness, texture of juice)

Purpose of descriptive analysis

objectively measure product characteristics (ex: comparing texture of chips)

Specific types of DA

Flavor Profile, QDA, Texture Profile, Sensory Spectrum (ex: QDA panel rating sweetness intensity)

Generic DA steps

define attributes, train panel, evaluate samples, analyze data (ex: train panel to rate crispiness)

Research question (DA)

defines objective of the study (ex: does fat level affect creaminess?)

Recruiting panels

select qualified or trained panelists (ex: experienced tasters for wine study)

Training panels

improve accuracy and consistency of evaluations (ex: teaching panel what “crunchy” means)

Panelist reproducibility

ability to produce consistent results over time (ex: same rating across repeated tests)

References and standards

provide baseline for attribute comparison (ex: sugar solutions for sweetness levels)

Collecting DA data

structured sensory evaluations by panelists (ex: rating multiple attributes per sample)

Analyzing DA data

statistical analysis of sensory measurements (ex: ANOVA on attribute scores)

Interpreting DA data

identify meaningful differences and patterns (ex: one product is consistently sweeter)

Flavor Profile

qualitative method describing flavor characteristics (ex: describing soup as savory and herbal)

QDA

quantitative descriptive analysis using trained panelists (ex: scoring sweetness from 1–10)

Texture Profile

measures mechanical and geometric texture attributes (ex: hardness of candy)

Sensory Spectrum

standardized descriptive method with universal scales (ex: comparing products across studies)

Multivariate ANOVA

analyzes multiple dependent variables simultaneously (ex: analyzing sweetness, texture, and color together)

Advanced post

hoc tests

Maps

visual representations of sensory or preference data (ex: PCA map showing product similarity)

Qualitative research

explores consumer attitudes, opinions, and perceptions (ex: asking why people like a product)

Types of qualitative research

interviews, focus groups, observations (ex: one

Focus groups

guided discussions to gather consumer insights (ex: group discussing snack preferences)

Focus group interpretation

identify trends, themes, and consumer perceptions (ex: many say product is too sweet)

CATA

consumers select all attributes that apply to a product (ex: checking “sweet,” “creamy,” “fruity”)

Sorting task

consumers group products based on perceived similarity (ex: grouping similar tasting juices)