# Textural Properties of Bakery Products: A Review of Instrumental and Sensory Evaluation Studies
## 4 Discussion
Under these conditions, the samples do not break, making it possible to obtain valuable information on a high number of texture properties [38; 39]. According to some authors, instrumental texture analysis have the disadvantage that the results do not always relate well to those acquired through the sensory analysis tests [38; 39]. Nevertheless, there are also many reported cases of good agreement between instrumental and sensorial texture measurement [40; 41; 42]. Regarding the instrumental tests, the most used method reported in this review was the TPA, a particular type of compression test, followed by other compression tests, puncture, and 3-point-bending tests. Compression tests used cylinder probes with variable diameters. In instrumental texture analysis, the probe moves in a controlled manner, inputting mechanical energy into and onto the test sample, and the resulting forces generated, such as puncture, compression, shear, extrusion, snapping, etc.,..., are directly influenced by the probe geometry [43]. The deformation produced by the probe under the load cell force allows us to objectively assess and measure the effect of imitative testing, producing a true indication of the product's physical characteristics. Instrumental texture analysis uses several established procedures and techniques to manipulate the forces imposed and developed within the sample being evaluated. There are several international standard methods defined by organizations such as the ASTM (American Society for Testing and Materials), AOAC (Association of Official Agricultural Chemists), BS (British Standard), ISO (International Organization for Standardization) and DIN (German Institute for Standardization) and relate to specific geometries of test probes, sample preparation and test conditions [43]. With respect to the sensorial tests, the most frequently used was the sensory profile, a descriptive type of test, but also discriminating tests, testing differences, and affective tests were used. In the latter group, it was mostly preference tests. Sensory analysis is pivotal to assessing product quality and consumer acceptability [33; 34; 35]. Color, visual appearance, flavor, taste and texture have the greatest initial impact on consumers. However, the taste and texture determine the consumer's final decision to buy the product again or not. Several aspects can be evaluated when performing sensory analysis, such as organoleptic characteristics, quality perception and consumer acceptance. These can be assessed through different types of sensory tests [33; 35; 44]. Discriminating sensory tests include two groups, difference tests, such as triangular tests or simple paired differences among others [45], and attribute differences, in which specific product features are rated as to their intensity [46]. Descriptive sensory methods allow the detection, description and quantification of sensory attributes present in a food. The food industry uses these methods in developing new products, quality control, changes in ingredients and/or formulations, and evaluating products during storage. However, most existing descriptive techniques require trained evaluators and employ unstructured scales to evaluate products [47; 48]. Quantitative descriptive analysis is the most used sensory description technique in the food area, as it allows the survey, description and quantification of detectable sensory attributes in the product, using highly trained evaluators and robust statistical analysis of the data [49]. In this test, the judge can be asked to classify the product's characteristics on structured hedonic scales typically composed of an odd number of points (five, seven or nine) [50]. The affective tests, which include acceptance or preference, are performed using scales that can be sort-preference or pairwise comparison [45]. The evaluation panels in the studies of this review were mostly untrained, 51%, with 16% of semi-trained panels and 33% of trained panels.