1. Introduction
Nowadays, there is a high demand for functional foods and the supply of healthier dietary options on the market is increasing. The reformulation of existing products is a viable alternative to develop new products. In fact, many healthier foods have been developed by the addition of functional ingredients, improving original recipes. However, the modifications induced by the reformulation do necessitate the evaluation of different aspects to assert the new product’s success. Firstly, the cost of production should be reasonable and have low repercussion on the final price of the new product. Furthermore, safety should not be altered, shelf life should not be reduced and sensory properties should be satisfactory to consumers. In summary, the new product has to meet consumers´ needs and expectations. For that reason, its acceptability has to be evaluated [
1]. In addition, the knowledge about the consumers’ perception of the different sensory attributes and of the contribution of each attribute to the acceptability is a very useful information to identify potential improvements of the recipe to be moved closer to the “ideal” product [
2].
In classic sensorial science, sensory characterization has been traditionally carried out by trained judges. However, the training and maintaining a sensory panel can be quite expensive and time consuming. For that reason, sensory sciences have developed more flexible and rapid sensory tools that give extra agility to sensory characterization, both in terms of timing and training requirements [
3]. Some of these new techniques are based on the evaluation of individual attributes such as Check-All-That Apply (CATA) and flash profiling. Other methods are based on the evaluation of global differences, as with sorting and mapping; others still, such as just-about-right scales (JAR), not only describe the product but also allow to determine the ideal level of the product attributes [
4]. When the JAR scales are used, consumers are asked to rate the intensity of an attribute, indicating whether the intensity of it is just about right, too strong or too weak compared to their internal ideal [
5].
Within the most-used techniques to get information about consumers’ perception of the sensory attributes of a product are the JAR scales and attribute liking questions [
2]. The JAR scales usually have five points to assess whether there is much less, much more or a “just-about-right” level of an attribute [
6] and are a reliable tool to study the adequacy of sensory attributes [
7]. Furthermore, it should be noted that a penalty analysis is used in order to gain an understanding of the attributes that most affected liking ratings [
8]. This method provides guidance for product reformulation or a better understanding of attribute adequacy in relation to liking in terms of direction, with the assumption that the maximum hedonic score will occur at the “just-about-right” point [
9,
10].
Muffins are one of the most frequently consumed bakery products. Moreover, fibre is, among others, a healthy functional ingredient that has had a high impact over recent decades [
11]; therefore, adding fibre to muffins without affecting the sensory characteristics is a significant challenge. Different studies have determined that grape pomace, a winemaking by-product, is an excellent source of dietary fibre to use in nutraceutical, medical and alimentary applications [
12,
13]. There are numerous food matrices that incorporate different additives obtained from white and red grape pomace (bread, biscuits, muffins, yoghurt, cheese, sausages, seafood, purée, etc.) at levels that range between 0.1% in cheese to 100% in infusions [
14]. However, one of the problems associated with the incorporation of these products in food matrices is that they can modify sensorial properties [
14]. In this sense, Acun and Gül [
15] demonstrated that the use of flours prepared with grape-skin extract at concentrations of 5% in biscuits improved their acceptability, but high levels of this extract led to consumer rejection, due to a bitter taste and a darkening of the product. Therefore, when products obtained from winemaking by-products are used in the reformulation of new products, it is necessary to determine the maximum acceptable dose and to evaluate all those organoleptic properties that can affect the global acceptability of the product by the consumer.
It should be highlighted that, although recent consumer trends have embraced the concepts of sustainability and health, the sensory properties of foods are the most important reason people eat the foods they eat [
16]. Therefore, the main aim of this work was to evaluate the sensory properties and adequacy of sensory attributes, by the use of JAR scales and penalty analysis, of new healthy whole-wheat muffins. Furthermore, their nutritional, colour and texture properties were evaluated.
2. Materials and Methods
2.1. Ingredients
The ingredients used in the preparation were whole-wheat flour, sunflower oil, brown sugar, semi-skimmed milk, baking powder and salt, which were obtained from a local supermarket.
The products used in this study were obtained from white and red grape pomace and were named WP (white product) and RP (red product), respectively. They were obtained according to the patented method CCP:ES2524870 [
17]. Briefly, dried grape pomace free of seed was milled, sieved (particle size <250 µm mesh) and microbiologically stabilized, obtaining powder products useful to be used as food ingredients. Obtained products were kept under vacuum and darkness until their use. The composition of the RP was: total dietary fibre (48.6%), total protein (14.4%), total lipid (3.7%) and ash (14.4% of dry matter; potassium: 43.3 mg/g of dry matter; total phenolic content: 25.9 mg gallic acid/g) [
9]. Moreover, the composition of the WP was: total dietary fibre (53.3%), total protein (14.4%), total lipid (3.7%) and ash (9.8% of dry matter; potassium: 36.5 mg/g of dry matter; total phenolic content: 13.8.9 mg gallic acid/g) [
18].
2.2. Elaboration of the Muffins
The following muffins were prepared: a control muffin with 100% of whole-wheat flour (C) and muffins with 10 and 20% of each grape pomace product, white (WP-10 and WP-20) and red (RP-10 and RP-20), replacing the part corresponding to whole-wheat flour with the product.
The formulation employed in each muffin is listed in
Table 1.
The different formulations of muffins were prepared with a mixer (KitchenAid KSM90, Benton Harbor, MI, United States). Firstly, the eggs were beaten until they peaked. Then, the milk and the sunflower oil were quickly added, ensuring that all the ingredients were well mixed. Subsequently, the mixture of whole-wheat flour, yeast, and the seasoning, or the salt in the case of the control muffins, were added and beaten at high speed for 5 min. Finally, the mixture was beaten at high speed for 3 min and spooned into paper baking cups. The selected baking temperature was 180 °C for 17 min. After baking, the samples underwent packaging with a partial vacuum of 30% in polypropylene bags and they were stored at room temperature for 24 h until the corresponding analysis.
Three batches, each one with 40 muffins, of each of the different formulations of the study were prepared on three different days.
2.3. Nutritional Composition
Moisture content was determined by drying at 130 °C for 90 min. The fat content was evaluated by Soxhlet extraction using petroleum ether in a Buchi B-811 extraction system (Büchi, Flawil, Switzerland). The dry and defatted residues were used for the analysis of protein and total dietary fibre contents. The protein content was determined using the Kjeldahl method with a conversion factor of 5.70 to convert nitrogen into protein values. The total dietary fibre was determined by the AOAC enzymatic-gravimetric method [
19,
20]. The enzymatic kit was supplied by Sigma (St Louis, MO, USA). In each batch, all these analyses were carried out by duplicate.
2.4. Height Increase and Weight Loss
Muffins’ height and weight are some of the most common parameters used to control the baking process and the homogeneity among lots. Furthermore, they are indirect indicators of some muffins’ quality parameters as sponginess and firmness.
Muffins were weighed and their height was measured before going in the oven and after baking and cooling for 1 h according to the method proposed by Martínez-Cervera et al. [
21]. Weight loss and height increase were calculated by difference between both weights and heights. Three different samples from each batch and formulation were measured.
2.5. Colour
Instrumental measurement of the colour of the muffins was performed at 24 h after baking, employing a Konica Minolta CM-2600d spectrophotometer (Konica Minolta Business Technologies Inc., Tokio, Japan). The D65 illuminance and 10º standard observe were selected. The colour coordinates were determined in the CIELAB colour space, expressing the results in terms of L* (lightness), a* (redness) and b* (yellowness).
Crust and crumb colour were measured by duplicate on five different muffins by batch and formulation. For the measure of the crumb colour, each muffin was cut in half parallel to the base.
2.6. Texture Profile Analysis (TPA)
The TPA of samples (2 × 2 × 2 cm) obtained from the mid-section of the muffins was measured using a TA.XT.plus Texture Analyzer (Stable Microsystem Ltd., Surrey, UK). A double compression cycle test was carried out with an aluminium 50 mm diameter cylinder probe and to 50% compression, at 1 mm/s speed test with 5 s of delay between the two cycles. The texture parameters (hardness, springiness, cohesiveness, chewiness and resilience) were calculated from the texture profile graphics. The texture parameters of each muffin formulation were averaged from nine measurements (three muffins for each batch).
2.7. Sensory Analysis
The standardized tasting room equipped with individual booths [
22] of the Faculty of Science (Burgos University, Burgos, Spain) was the place where consumers tasted the muffins. A total number of 104 untrained judges, habitual consumers (who consumes muffins at least once every week) participated in the assays. They were students, professors and administrative employers of the University of Burgos, with ages ranged between 18 and 55 years (69% female, 31% male).
Sensory evaluations were carried out on three different days, which means that three different batches of each formulation were evaluated. Muffins were always evaluated 24 h after their elaboration. After consumers had read the informed consent form for sensory evaluation, they evaluated five muffins in a single session (one of each formulation). Samples were presented one-by-one, following a balanced complete block experimental design. The samples were coded with random three-digit numbers. Consumers could rinse their mouth with water between samples.
Consumers indicated the degree of liking of each muffin using a nine-point hedonic scale, ranged from 1 = “dislike extremely” to 9 = “like extremely” with middle value 5 = “neither like nor dislike”, whereas the adequacy of the six attributes (“surface colour”, “crumb colour”, “sweetness”, “hardness”, “sponginess” and “flavour”) was measured using “just-about-right” (JAR) bipolar scales of five points ranged (from 1 = “much less” to 5 = “much more” with middle value 3 = “just about right”). These scales are used in studies with consumers to establish whether a particular attribute is perceived in a product at an excessive, scarce or acceptable level. The extreme ends of the scale represent the level of an attribute that moves away from the ideal theoretical point in opposite directions, while the central point is the ideal or acceptable one [
9]. Penalty analysis (PA) was carried out transforming the initial scale of five points into one of three points. For that, the responses “much less” and “less” were grouped into an unique group named “much less” and the responses “more” and “much more” were grouped in an unique group named “much more” [
1,
9,
23,
24].
2.8. Statistical Analysis
The results of physico-chemical and nutritional properties and liking scores were presented as the average ± standard deviation of the different replicates. An ANOVA test was performed with the purpose of determining whether statistically significant differences existed among the samples at a significance level of p = 0.05. The LSD (Least Significant Difference) test was performed with the objective of establishing between which samples there were statistically significant differences. The Statgraphics Centurion XVII.I software (IBM Corp., Armonk, NY, USA) program was used.
The JAR results were analysed by PA to identify potential directions for product improvement on the basis of consumer acceptability by highlighting the most penalizing attributes in liking terms [
10].