. 2022 Jan 18;11(3):255. doi: 10.3390/foods11030255

Table 1.

Classification of sensory descriptive tests.

Test	Type of Evaluation	Lexicon	Statistical Analysis	Advantages	Limitations	Variations	Ref.
QDA ¹	After the training phase, assessors develop qualitative attributes and provide quantitative data about the attribute’s intensity	Provided by a trained panel	ANOVA ²; PCA ³	Allows for the determination of product profiles	Time-consuming and requires a training phase	FCP ⁴	[28,30,75]
FCP ⁴	Assessors develop qualitative attributes and provide quantitative data about attribute’s intensity without the training phase	Elicited by assessors or a predetermined list	GPA ⁵	Rapid and less time-consuming	Lack of accuracy	FP ⁶	[34,76]
OEQ ⁷	Verbal description of samples	Elicited by the assessors	MFA ⁸; CA ⁹; Chi-square test	Complete freedom of expression	Time-consuming, Has redundancy, has ambiguity, and requires the extension of terms	Textual data treatment from open-ended questions	[77,78]
Sorting; FS ¹⁰; FMS ¹¹	Classification of samples based on their similarities and differences	Elicited by the assessors orprovided by the researcher	DISTATIS; CA ⁹; MDS ¹²	A fast and straightforward method that can be used in a single session	All samples should be presented simultaneously	SBA ¹³; Q-sort method; CS ¹⁴; FS ¹⁵; FMS ¹⁶; HS ¹⁷	[70,79,80]
PM ¹⁸; Napping	Generating samples on a two-dimensional map according to their similarities and differences	Elicited by the assessors	MFA ⁸	Description through product similarities and differences, as well as the clustering samples	All samples should be presented simultaneously; difficult to understand for naïve consumers	Affective approach; intensity approach; hedonic frame; PPM ¹⁹	[40,43,51,52]
FP ²⁰	Ranking of samples on a set of selected attributes	Elicited by the assessors	GPA ⁵; CVA ²¹; PCA ³; MFA ⁸	Rapid	Two sessions are required. All samples should be presented simultaneously	Modified FP ²⁰ with napping Pivot Profile	[81,82]
PAE ²²	Ranking of attributes according to assessors’ liking intensity of those attributes	Elicited by the assessors	GPA ⁵; HCA ²³ PANOVA ²⁴	Only one session is required	A round-table discussion is necessary; all samples should be presented simultaneously	Discrete choice experiments; best-worst scaling; CLEO ²⁵	[23,65,67]
CATA ²⁶	Pre-selected terms, where assessors choose the ones that apply to the product	Provided by the researcher	Cochran Q test; MFA ⁸; Chi-square test	A fast and straightforward method that is easy to merge with affective measurements, such as hedonic tests	The design of the term list could influence the answers; not recommended for evaluating very similar samples	Check-if-apply; RATA ²⁷; TCATA ²⁸	[26,83,84]
PSP ²⁹	Evaluation of global differences between samples and a set of fixed references	Elicited by the assessors	ANOVA ²; PCA ³; MDS ¹⁴; MFA ⁸; GPA ⁵; CA ⁹	A fast and straightforward method	Stable and readily available references are needed; selection of references couldstrongly affect the results	PSP ²⁸ based on the degree of different scales and triadic PSP ²⁹	[25,69]

Legend: ¹. Quantitative descriptive analysis; ². analysis of variance; ³. principal component analysis; ⁴. free-choice profiling; ⁵. Generalized Procrustes analysis; ⁶. flash profiling; ⁷. open-ended questions; ⁸. multiple factor analysis; ⁹. correspondence analysis; ¹⁰. free sorting; ¹¹. free multiple sorting; ¹². multidimensional scaling; ¹³. sorting backbone analysis; ¹⁴. constrained sorting; ¹⁵. fixed-sorting; ¹⁶. free multiple sorting; ¹⁷. Hierarchical sorting; ¹⁸. projective mapping; ¹⁹. polarized projective mapping; ²⁰. flash profiling; ²¹. canonical variate analysis; ²². preferred attribute elicitation; ²³. hierarchical clustering analysis; ²⁴. Procrustes analysis of variance; ²⁵. combinatorial utility function joint learning and optimization; ²⁶. check-all-that-apply; ²⁷. rate-all-that-apply; ²⁸. temporal check-all-that-apply; ²⁹. polarized sensory positioning.