Next Article in Journal
Isolation of Extracellular Vesicles from Agri-Food Wastes: A Novel Perspective in the Valorization of Agri-Food Wastes and By-Products
Previous Article in Journal
Volatile Constituents of Some Myrtaceous Edible and Medicinal Fruits from the Brazilian Amazon
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Foods
Volume 13
Issue 10
10.3390/foods13101491
foods-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Utilization of Sustainable Ingredients (Cañihua Flour, Whey, and Potato Starch) in Gluten-Free Cookie Development: Analysis of Technological and Sensorial Attributes

by
Olivia M. Luque-Vilca
1,
Jover Y. Paredes-Erquinigo
1,
Lenin Quille-Quille
1,
Tania J. Choque-Rivera
1,
Domingo J. Cabel-Moscoso
2,
Thalía A. Rivera-Ashqui
3 and
Reynaldo J. Silva-Paz
4,*
1
Facultad de Ingeniería de Procesos Industriales, Universidad Nacional de Juliaca, Av. Nueva Zelandia 631, Juliaca 21101, Peru
2
Ingeniería Ambiental, Universidad Nacional San Luis Gonzaga, Ica 11001, Peru
3
Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, km 19 Carretera Central, Ñaña, Lima 15457, Peru
4
Escuela de Ingeniería en Industrias Alimentarias, Departamento de Ingeniería, Universidad Nacional de Barranca, Av. Toribio de Luzuriaga N° 376 Mz J. Urb. La Florida, Barranca 15169, Peru
*
Author to whom correspondence should be addressed.
Foods 2024, 13(10), 1491; https://doi.org/10.3390/foods13101491
Submission received: 24 March 2024 / Revised: 12 April 2024 / Accepted: 17 April 2024 / Published: 11 May 2024
(This article belongs to the Section Sensory and Consumer Sciences)
Browse Figures
Versions Notes

Abstract

:
In recent years, the consumption of gluten-free products has increased due to the increasing prevalence of celiac disease and the increased preference for gluten-free diets. This study aimed to make cookies using a mixture of cañihua flour, whey, and potato starch. The use of a Box–Behnken design allowed for flexible ingredient proportions and physicochemical properties, centesimal composition, color, texture, and sensory attributes to be evaluated through consumer tests (Sorting and acceptability). The results highlighted significant variations in physicochemical data, composition, color, and texture across formulations. The blend with 38.51% cañihua flour, 10.91% sweet whey, 25.69% potato starch, 8.34% margarine, 11.10% sugar, 0.19% sodium chloride, 0.51% baking powder, 0.51% vanilla essence, and 4.24% egg exhibited superior sensory appeal. This formulation boasted excellent texture, aroma, flavor, color, and appearance, indicating high sensory and physicochemical quality. The use of cañihua flour, sweet whey, and potato starch not only provides a gluten-free option but also delivers a nutritious and sensorily pleasing choice for those with dietary restrictions. Future research could explore the commercial viability of producing these cookies on a larger scale, as well as investigating the potential health benefits of these ingredients.

1. Introduction

The rapid rise in the consumption of gluten-free products [1] can be attributed to the escalating prevalence of celiac disease globally (>1.30%) [2], a condition often undiagnosed despite evident symptoms [3]. Celiac disease, a chronic inflammatory bowel disorder affecting approximately one in every hundred individuals worldwide [4]. Peru has a prevalence of 1.20% in an age range between 18 and 29 years [5], was necessitates a strict gluten-free diet as the sole treatment option. Consequently, a heightened awareness of celiac disease [6] and non-celiac gluten sensitivity has fueled the demand for gluten-free products [7]. However, many available gluten-free products exhibit subpar nutritional profiles, often laden with excessive fats and sugars compared to their gluten-containing counterparts [8]. Thus, there is a pressing need to develop nutritionally complete, healthy, and palatable gluten-free alternatives that cater to consumer preferences [9].
The use of sustainable ingredients in food product development reflects a comprehensive commitment to long-term human, environmental, economic, and social welfare. Cañihua flour, whey, and potato starch are chosen for their natural and sustainable origins, including cañihua, a traditional Andean crop cultivated sustainably for centuries. Whey, a cheese production by-product, and potato starch, sourced from renewable outlets, emphasize resource efficiency and waste reduction. These ingredients also offer potential economic and social benefits by supporting local communities and promoting agricultural diversification, contributing to sustainable development.
In contemporary baking practices, there is a notable inclination towards utilizing high Andean crops [10], indigenous to the South American Andean regions and cultivated for centuries [11]. Among these, quinoa [12], amaranth [13], and cañihua [14], stand out as prime candidates due to their richness in essential amino acids and gluten-free nature, rendering them a wholesome and nutritious substitute [15] for conventional grains in baked goods. Cañihua, in particular, emerges as a promising contender owing to its protein content (ranging between 15 and 19%) [16] and a balanced amino acid profile, meeting the FAO/WHO/UNU recommendations [17].
Notably, cañihua boasts a plethora of bioactive compounds including phenolic acids (16.8 to 59.7 mg/100 g), soluble phenolic acids (7 to 61%), and flavonoids [18], endowing it with potential health benefits and therapeutic implications attributed to its antioxidant and anti-inflammatory properties [19]. Furthermore, cañihua is a rich source of essential minerals such as iron, calcium, magnesium, and zinc, as well as B complex vitamins including thiamine (B1), riboflavin (B2), niacin (B3), and folic acid (B9) [20].
However, the development of products like gluten-free cookies continues to pose technological challenges, particularly concerning texture and appearance [21]. Addressing these challenges necessitates the strategic incorporation of additives such as cross-linking enzymes, hydrocolloids, and amylose to enhance volume and stabilize the dough [22], thereby reducing hardness [23]. Additionally, the inclusion of native or modified starch enhances water retention, leading to increased volume and a softer texture [24], while the incorporation of whey improves the crust and provides both nutritional enrichment and sensory enhancement [25,26,27].
This emphasis on enhancing sensory attributes is pivotal, as taste and aroma significantly influence consumer acceptance [28]. Given the direct impact of inputs on the color and sensory attributes of cookies, the thorough evaluation of ingredients is imperative to optimize the overall product quality [29]. Experimental designs, such as the Box–Behnken design, offer a systematic approach to achieve this goal, facilitating the identification of optimal ingredient combinations and processes while reducing formulation time and costs [30,31].
Currently, various formulations tailored for individuals with gluten-related disorders have been developed, incorporating gluten-free inputs such as heat-treated cormorant Colocasia spp. flour, rice flour, rice proteins, peas, egg white, whey, β-conglycinin concentrate extracted from defatted soybean flour, and composite rice and chickpea flour [32,33,34,35]. These formulations underscore ongoing efforts to meet the dietary needs of individuals with gluten-related disorders, highlighting the versatility and potential of alternative ingredients in cookie production.
In this context, the present study aims to develop gluten-free cookies based on cañihua flour (Chenopodium pallidicaule Aellen), whey, and potato starch (Solanum tuberosum) while evaluating their physicochemical and sensory properties.

2. Materials and Methods

2.1. Raw Material

To make the cookies, cañihua flour (Chenopodium pallidicaule Aellen) variety INIA ILLPA was used, which was acquired from the National Institute of Agrarian Innovation (INIA)—Puno, ground in a disc mill and sifted with a mesh system N°. 40 particle size (0.18 mm). The native starch was obtained from the native potato variety Imilla negra acquired from INIA—Puno, using the wet milling technique with a slight modification [36]. The sweet whey was obtained from the pariah cheese making process. Margarine (Famossa), brown sugar (Cartavio), iodized kitchen salt (Emsal), baking powder and vanilla essence (Fleischmann), and eggs (Calera) were purchased from the supermarket in the city of Juliaca, Puno.

2.2. Methodology

2.2.1. Preparation of the Cookie

The sweet cookie formulation from the American Association of Cereal Chemists [37] was used with modifications. Table 1 shows the percentages of the different formulations to be prepared for the research. The formulation includes the variation of cañihua flour, whey, and potato starch. The rest of the ingredients were margarine, sugar, sodium chloride, baking powder, vanilla essence, and liquid egg. The preparation process consisted of weighing the ingredients and mixing them in a dough mixer (Nova brand, model K25, Lima, Peru) to cream for 10 min at a level 2 speed; then, it was kneaded for 10 min at speed level 1 to continue with the lamination (Nova Brand, model MK500, Lima, Peru) until a thickness of 0.5 cm was reached. The samples were cut manually into circular pieces of 20 mm diameter with a mold. Finally, the baking process was carried out in a rotary oven (Nova Brand, Model MAX 1000, Lima, Peru) at 150 °C for 15 min [37]. The samples were evaluated after 24 h of storage at room temperature.

2.2.2. Physicochemical Analysis and Proximal Composition

The physicochemical analysis was carried out on the cañihua flour, potato starch and whey. The humidity was quantified by the gravimetric method No. 950.46 and ashes by the method No. 935.08 [38], the pH by the NTP 206.014:1981 [39], the titratable acidity by the method described NTP. 206.013:2011 [40], and water activity (Aw) using an AquaLab vapor absorption analyzer (model VSA, Washington, DC, USA) [41]. In addition, the physicochemical properties and proximal composition of the cookies were then analyzed according to RM N°1020-2010/MINSA Sanitary Standard for the Manufacture, Preparation and Marketing of Baking, Cookies and Pastry Products [42] and the guidelines of the Association of Official Analytical Chemists—AOAC [38]: Moisture was calculated by the gravimetric method No. 950.46, proteins by the Kjeldahl method No. 984.13, fat by the Soxhlet method No. 203.05, crude fiber by method 962.09, ash by the method No. 935.08 and carbohydrates by difference. The energy value was calculated using: Energy value = 4(%carbohydrates) + 4(%Protein) + 9(%fat) expressed in Kcal/100 g [43].

2.2.3. Colorimetric Properties

The color measurements, by method (CIE L*a*b*), of the cookies were obtained with a Konica Minolta colorimeter (model 700d, Ramsey, NJ, USA), where L* is defined as the Luminosity, a* as the red/green coordinates, and b* as the yellow/blue coordinates [44]. Each test was performed in triplicate. Additionally, the color intensity (chroma) was calculated: C* = root (a*2 + b*2) and the hue angle: h* = arctan (b*/a*).

2.2.4. Instrumental Texture Analysis

Texture profile analysis was performed with the Instron universal testing equipment (model 34TM-5 Dual Column Table Mode, Instron Corp. Canton, Norwood, MA, USA). The crosshead moved at a constant speed of 0.33 mm/s. From the resulting force–time curve, the hardness (the maximum peak force during the first compression) was determined, equipped with 500 N load cell, Kramer S543A cutting cell, Drip Tray Food S5400A, and frame Food Support Frame S4427A. The test consisted of placing the cookie on the support, so that it was penetrated and compressed; in this way the cookies’ breakage or fracture resistance was determined [45].

2.2.5. Sensory Analysis

Discriminative Test—Sorting

We worked with 102 consumers, aged between 18 and 30. Each consumer was given a cookie, on disposable plates coded with three random digits, randomly for each consumer. The test was carried out in the laboratory of the National University of Juliaca. The sorting test procedure was carried out in a single session divided into two stages according to what was established [46,47]. In the first stage, consumers tried each cookie, with the aim of forming groups of cookies according to their similarities or dissimilarities based on their overall sensory perception. In the second stage, each consumer assigned words (sensory attributes) to characterize the groups of cookies previously formed according to their perception [48].

Acceptability Test

The gluten-free cookie formulations were evaluated by the degree of consumer satisfaction based on the characteristics of smell, color, flavor, and general acceptance, using a nine-point hedonic scale. The samples were presented individually to 102 consumers from the city of Juliaca who regularly consume cookies. Samples were presented on disposable plates labeled with three-digit random numbers [49,50]. In both sensory tests, consumers agreed to participate voluntarily by giving their informed consent.

2.2.6. Statistical Design

A Box–Behnken (DBB) design was applied considering cañihua flour, whey, and potato starch as variables in the formulation process, and the other inputs (Table 1) varied according to the design applied, obtaining a total of twelve treatments and three central points (Table 2). Through the experimental design, we sought to determine the influence of the factors of cañihua flour, whey, and potato starch. Furthermore, each formulation is a unique experimental condition, where each variation in cookie ingredients was carefully designed to explore its impact on different aspects of the cookies’ properties.

2.2.7. Statistical Analysis

The statistical analysis of the experimental design data was carried out with the Statistic 13.0 program (trials version) in order to identify the significance of the factors (p < 0.05), and after that, contour graphs of the significant factors were created. For the analysis of sensory data for acceptability, a completely randomized block design and a comparison of means were applied using the 95% Tukey test, and for the discriminative sorting task test, a multivariate factor analysis was used. The data obtained were statistically treated with the statistical program R version 4.1.1.0.

3. Results and Discussion

3.1. Physicochemical and Proximal Analysis

To identify the nutritional contribution of the ingredients in the gluten-free cookies, the centesimal composition of the cañihua flour and of the potato starch was quantified: Moisture 9.00 ± 0.1 and 7.69 ± 0.3%, carbohydrates 69.59 ± 0.18 and 91.54 ± 0.34%, fat 3.61 ± 0.13 and 0.020 ± 0.01%, proteins 14.80 ± 0.2 and 0.570 ± 0.1%, and ashes 3.40 ± 0.25 and 0.150 ± 0.05%, respectively; the sweet whey had pH 5.80 ± 0.15, titratable acidity expressed in lactic acid was 0.110 ± 0.02%, fat was 0.30 ± 0.09%, proteins were 1.20 ± 0.03%, non-fat solids were 7.44 ± 0.04%, salts were 0.610 ± 0.01%, and lactose was 4.09 ± 0.01%.
The moisture content, ash, pH, acidity, and water activity (physicochemical properties) of the gluten-free cookies are shown in Table 3. Significant differences between the proposed formulations, where formulations 2 and 15 have a greater percentage of moisture, is probably due to the starch content, which retains water on its surface [24]. Meanwhile, the pH ranges from 5.94 to 6.27 units, and the acidity (expressed in lactic acid) from 0.150 to 0.492%, a result that can be influenced by the addition of cañihua flour and whey in the formulation of the cookies that had a pH value of 5.80 units. According to the data shown in Table 3 with respect to the DBB model, the addition of starch, cañihua, and whey does not affect the variables of moisture, ash, and water activity for optimization purposes, but it does influence acidity and pH. Figure 1 shows the contour surface of the DBB, for cañihua flour and whey on pH (a), acidity (b), a* (c), b* (d), and C* (e), observing the effects of whey and cañihua flour on the response variable, which present a minimum acidity value for each substitution value in the formulation, which implies finding appropriate parameters so that they do not exceed the maximum acidity limit according to the regulations. In addition, a linear effect was presented in the pH, demonstrating the interaction between cañihua flour and whey; however, a quadratic effect was presented with respect to acidity. Likewise, the formulation with the lowest acidity content (F12) was the one with the greatest acceptability by consumers; it ranged from 0.150 ± 0.05% acidity, expressed in lactic acid, and a pH of 6.27 ± 0.08, presenting slight acidity; value that is within that reported in research that indicates a pH 5.80 to 6.51 [51]. These results are similar to those reported in cañihua bread with the addition of whey that presented a pH of 6.10 [14].

3.2. Texture Analysis

Table 4 shows recorded values of the texture of gluten-free cookies; this parameter may represent a factor of acceptance or rejection by consumers. Of the proposed treatments, formulation F12 (32.53 ± 0.88 N) has lower hardness compared to the other formulations. These results show that hardness increases as the percentage of cañihua flour increases; in the same way, the starch content influences the texture, which can be attributed to the interaction of starch-proteins due to hydrogen bonds. Figure 2 shows the contour surface of the DBB for the cañihua flour and potato starch on the texture, indicating that the second-degree polynomial shows a better fit for the variables related to the texture when cañihua flour was increased in the formulation.
Texture is a critical aspect in gluten-free products [9] due to sensory perception, which is influenced by moisture content, with harder products requiring more extensive chewing [52]. The texture of the different formulations varied: formulation F1 (37.06 ± 0.59 N) presented a higher hardness value compared to F12 (32.53 ± 0.88 N), which was the formulation that had greater acceptability to consumers; this was due to the proportion of cañihua flour, potato starch, and whey in the formulation, which provides the variation in the moisture of the dough [52], meaning that the hardness ranged from 32.53 ± 0.88 N to 37.06 ± 0.59 N, with the values of hardness within those reported for cookies; thus, the carbohydrate content influences the variability of the texture of the product. On the other hand, the thickness of the cookies significantly influenced the variation in the texture parameters, and these variations are explained from the changes in the product after the thermal baking treatment, such as the gelatinization of starches, the denaturation of proteins, and the reduction of moisture [53].

3.3. Color Properties

Table 4 presents color values for the different cookie formulations, color being one of the most important quality properties for acceptability that influences consumer perception. The formulation with the highest acceptability, F12, presented 47.10 ± 0.65 CIELAB units of clarity (L*), which is reduced when the percentage of cañihua flour in the formulation increased. Likewise, the chromatic a* value presented a positive value of 9.78 ± 0.98 and a b* value of 15.44 ± 0.89, the chroma being the value that indicates the intensity of the color, decreasing when the formulation contained a higher content of cañihua flour and increasing when there was a higher content of potato starch in the formulation. Figure 2 shows the contour surface of the DBB for whey and potato starch on acidity, L*, a*, b*, and C*.
Therefore, the chromatic parameters shown in Table 4 show values of L*, a*, b* h*, and C*, which demonstrates that the values are high and positive due to the increase in cañihua flour in the formulation, finding the same relationship as [54], which mentions that the cookies have lower surface luminosity (L*) in the treatments that contain a lower cassava starch content and observes the tendency of cassava starch to decrease the color values a* and b*. On the other hand, the hue value (H*) increased with a higher content of whey and potato starch in the formulation, with a tendency to decrease as the cañihua flour increased, while the chroma value decreased as the flour increased. of cañihua and whey to the formulation, while its value increases with the addition of potato starch to at least 40 g, and above this there is a tendency to decrease, indicating that the chromatic parameter is lost due to the incorporation of cañihua flour into the formulation, which is why it has an impact on the color of the cookies and why the cookies differ in color [55]; however, the darker appearance of the cookies may be related to the Maillard reaction or caramelization that takes place during baking [56]. The results analyzed using the DBB show that the quadratic model fits better with respect to the chromatic parameters (a*, b*, C*, and L*). In the same way, the significance of the models was evaluated by analysis of variance, which presented a significant effect (p < 0.05). Figure 3 shows the contours generated to assess the significant influence of whey and potato starch on the acidity, L*, a*, b*, and C* parameters.

3.4. Sensory Analysis

3.4.1. Sorting Task

The sensory evaluation was carried out with 102 consumers who regularly consume cookies. Figure 4 shows the proportion of cookie samples which consumers and correspondence analysis agreed upon during the sorting task. Figure 4a shows the groupings of the samples made by consumers: the formation of nine groups is observed, the first group being F1 and F3, the second group F6 and F9, the third group F10 and F5, the fourth group F11 and F8, and the fifth F13, F14, and F15 (same formulation), indicating that participants perceived the repetitions in a similar way. The sixth, seventh, eighth and ninth groups independently comprised F2, F4, F7, and F12, respectively. The different groupings of the cookie samples present a clear separation between groups. Figure 4b shows the representation of consumers; an expected heterogeneity can be seen in the grouping of the samples, explained by the inter-individual variability between consumers. The introduction of heterogeneity in choice models, market segmentation, positioning, and micro-marketing allows us to see a group of consumers with the same tendencies or choices, because consumers are diverse and expect their individuality to be recognized [57].
Figure 4c presents the sensory map through CA, explaining 71.44% of the total variability in the first two dimensions. The location of the samples was made based on the attributes described by consumers, and six groups were formed, the first group (F1 and F3) described as crunchy, the second group (F2, F4, and F12) characterized by a sandy texture, normal texture, and pleasant odor. The third group (F7) was recognized by a soft and fragile texture, the fourth group (F11 and F8) by a light color, the fifth group (F9, F13, F14, and F15) by a semi-hard texture and sweet-sour taste, and finally the sixth group (F5, F6, and F10) by a Cañihua smell, unpleasant odor, unpleasant flavor, and unpleasant appearance. Sensorily, the samples of F9 are not expected to be described the same way as those of F13, F14, and F15 (the same product) due to the absence of potato starch; however, consumers did not perceive this absence and described it similarly to these samples. The sorting task method allows us to know the perception of consumers regarding the sensory characteristics of cookies, enabling the classification or grouping of the samples according to their similarities and/or dissimilarities.

3.4.2. Acceptability

Figure 5 shows the results of consumer acceptability using a nine-point structured hedonic scale. Significant differences were found between the samples studied (p < 0.05). The samples of F7, F2, and F12 had greater acceptability (I like it a lot); these cookies did not present significant differences (p > 0.05), that is, they were statistically similar. The least acceptability was presented for F5, F6, and F10 (neither like nor dislike). The rest of the samples were rated from like it slightly to like it moderately. These results are similar to those reported by Krajewska et al. [58], in cookies enriched with fruits and by-products, and Silva et al. [59], in gluten-free cookies. In general, cookies can be enriched with Cañihua flour, whey, and potato starch without a significant impact on the consumer’s perception of the products. However, if a higher level of supplementation is desired (<80%), further experiments need to be performed to improve the sensory characteristics of the final cookies.

3.5. Evaluation of the Formulation with the Best Physicochemical and Sensory Characteristics

The product with the best physicochemical and sensory characteristics was found to be the F12 formulation, which presented values of moisture 4.83 ± 0.05%, protein 7.15 ± 0.87%, fat 15.09 ± 0.15%, crude fiber 1.05 ± 0.05%, ash 2.70 ± 0.08%, carbohydrates 73.51%, and 458.45 kcal/100 g. Likewise, the high protein content of the cookie with the best acceptability could be attributed to the proteins from cañihua flour and whey. Whey protein is effective for protein synthesis in muscles, demonstrating functionality due to its content in bioactive components derived from whey, such as essential amino acids, micronutrients, β-Lactoglobulin, α-Lactalbumin, Immunoglobulins, Bovine serum albumin, Guanosine monophosphate, Lactoferrin, and Lipopolysaccharide, which have many biomedical, pharmaceutical, and therapeutic applications, such as the prevention of type 2 diabetes, obesity, cardiovascular diseases, phenylketonuria, the elimination of excess free radicals produced by oxidative stress, the suppression of tumor development, anti-proliferative effects, and the treatment of metastatic carcinoma [60]. Thus, its use in the formulation allows for improvements in the technological properties of the dough to produce gluten-free functional foods [61].
In general, cañihua flour is characterized by its excellent nutritional profile, being a rich source of protein compared to similar raw materials such as quinoa, which contains 14.1% [62]; this is why there is growing interest in its use for the formulation of healthy food products with better nutritional value [63], allowing for an improvement in the nutritional quality of cookies [64,65] and being an alternative to meet the demand for gluten-free bakery products by groups with celiac disease, gluten sensitivity, and/or health awareness [66].

4. Conclusions

Gluten-free cookies were developed with Cañihua flour, whey, and potato starch. The incorporation of these ingredients produces significant differences in the physicochemical, nutritional, colorimetric, and texture parameters. The F12 formulation presented better physicochemical characteristics and sensory attributes. This formulation contains 38.51% Cañihua flour, 10.91% whey, 25.69% potato starch, 8.34% margarine, 11.10% sugar, 0.19% sodium chloride, 0.51% baking powder, 0.51% vanilla essence, and 4.24% egg, as well as containing 0% gluten and showing a good protein profile of 7.75%; it also met all the physicochemical and nutritional quality parameters, so the results of this research demonstrate that it is possible to formulate cookies using flour from high Andean crops such as cañihua flour, which is an adequate and acceptable resource for celiac consumers and can be used for the production of cookies. In addition, the production of such cookies would take advantage of the whey produced and discarded by dairy plants.

Author Contributions

Conceptualization, O.M.L.-V., L.Q.-Q. and T.J.C.-R.; methodology, O.M.L.-V., J.Y.P.-E., T.A.R.-A. and L.Q.-Q.; software, D.J.C.-M. and O.M.L.-V.; validation, L.Q.-Q., T.A.R.-A. and R.J.S.-P.; investigation, T.J.C.-R.; resources, D.J.C.-M.; data curation, O.M.L.-V., J.Y.P.-E. and T.A.R.-A.; writing—original draft preparation, O.M.L.-V., J.Y.P.-E. and T.A.R.-A.; writing—review and editing, R.J.S.-P.; visualization, J.Y.P.-E. and T.A.R.-A.; supervision, R.J.S.-P.; project administration, R.J.S.-P.; funding acquisition, O.M.L.-V. All authors have read and agreed to the published version of the manuscript.

Funding

This research and payment by APC was funded by the National University of Juliaca (number 406-2019).

Institutional Review Board Statement

Approval was obtained from the Human Ethics Committee at the Peruvian Union University—Juliaca campus (N° 019-2021).

Informed Consent Statement

All participants received written information about the study. All participants gave voluntary consent to participate. Specifically, participants were assured that their responses would be kept confidential and that they could withdraw from the study at any time.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Capriles, V.D.; Santos, F.G.; Aguiar, E.V. Innovative gluten-free breadmaking. In Trends in Wheat and Bread Making; Elsevier: Amsterdam, The Netherlands, 2021; pp. 371–404. [Google Scholar]
  2. Lebwohl, B.; Rubio-Tapia, A. Epidemiology, Presentation, and Diagnosis of Celiac Disease. Gastroenterology 2021, 160, 63–75. [Google Scholar] [PubMed]
  3. Sana, M.K.; Hussain, Z.M.; Shah, P.A.; Maqsood, M.H. Artificial intelligence in celiac disease. Comput. Biol. Med. 2020, 125, 103996. [Google Scholar] [CrossRef] [PubMed]
  4. Koh, J.E.W.; De Michele, S.; Sudarshan, V.K.; Jahmunah, V.; Ciaccio, E.J.; Ooi, C.P.; Gururajan, R.; Gururajan, R.; Oh, S.L.; Lewis, S.K.; et al. Automated Interpretation of Biopsy Images for the Detection of Celiac Disease Using a Machine Learning Approach. Comput. Methods Programs Biomed. 2021, 203, 106010. [Google Scholar] [CrossRef] [PubMed]
  5. Baldera, K.; Chaupis-Meza, D.; Cárcamo, C.; Holmes, K.; García, P. Population seroprevalence of celiac disease in urban areas of Peru. Rev. Peru Med. Exp. Salud. Publica 2020, 37, 63–66. [Google Scholar] [CrossRef] [PubMed]
  6. Schnedl, W.J.; Mangge, H.; Schenk, M.; Enko, D. Non-responsive celiac disease may coincide with additional food intolerance/malabsorption, including histamine intolerance. Med. Hypotheses 2020, 146, 110404. [Google Scholar] [CrossRef] [PubMed]
  7. Henggeler, J.C.; Veríssimo, M.; Ramos, F. Non-coeliac gluten sensitivity: A review of the literature. Trends Food Sci. Technol. 2017, 66, 84–92. [Google Scholar] [CrossRef]
  8. Jnawali, P.; Kumar, V.; Tanwar, B. Celiac disease: Overview and considerations for development of gluten-free foods. Food Sci. Hum. Wellness 2016, 5, 169–176. [Google Scholar] [CrossRef]
  9. Puerta, P.; Laguna, L.; Villegas, B.; Rizo, A.; Fiszman, S.; Tarrega, A. Oral processing and dynamics of texture perception in commercial gluten-free breads. Food Res. Int. 2020, 134, 109233. [Google Scholar] [CrossRef] [PubMed]
  10. Coronel, E.B.; Guiotto, E.N.; Aspiroz, M.C.; Tomás, M.C.; Nolasco, S.M.; Capitani, M.I. Development of gluten-free premixes with buckwheat and chia flours: Application in a bread product. LWT 2021, 141, 110916. [Google Scholar] [CrossRef]
  11. Zhu, F. Development of quinoa grain as a sustainable crop. In Quinoa; Academic Press: Cambridge, MA, USA, 2023; pp. 1–15. [Google Scholar] [CrossRef]
  12. Wang, X.; Lao, X.; Bao, Y.; Guan, X.; Li, C. Effect of whole quinoa flour substitution on the texture and in vitro starch digestibility of wheat bread. Food Hydrocoll. 2021, 119, 106840. [Google Scholar] [CrossRef]
  13. Miranda-Ramos, K.C.; Sanz-Ponce, N.; Haros, C.M. Evaluation of technological and nutritional quality of bread enriched with amaranth flour. LWT 2019, 114, 108418. [Google Scholar] [CrossRef]
  14. Zegarra, S.; Muñoz, A.M.; Ramos-Escudero, F. Elaboration of a gluten-free bread based on cañihua (Chenopodium pallidicaule Aellen) flour and sensory acceptability evaluation. Rev. Chil. Nutr. 2019, 46, 561–570. [Google Scholar] [CrossRef]
  15. Chaudhary, N.; Walia, S.; Kumar, R. Functional composition, physiological effect and agronomy of future food quinoa (Chenopodium quinoa Willd.): A review. J. Food Compos. Anal. 2023, 118, 105192. [Google Scholar] [CrossRef]
  16. Moscoso-Mujica, G.; Zavaleta, A.I.; Mujica, Á.; Arnao, I.; Moscoso-Neira, C.; Santos, M.; Sánchez, J. Antimicrobial peptides purified from hydrolysates of kanihua (Chenopodium pallidicaule Aellen) seed protein fractions. Food Chem. 2021, 360, 129951. [Google Scholar] [CrossRef]
  17. FAO; WHO; UNU. Protein and Amino Acid Requirements in Human Nutrition: Report of a Joint WHO/FAO/UNU Expert Consultation; WHO: Geneva, Switzerland, 2007. [Google Scholar]
  18. Repo-Carrasco-Valencia, R.; Hellström, J.K.; Pihlava, J.-M.; Mattila, P.H. Flavonoids and other phenolic compounds in Andean indigenous grains: Quinoa (Chenopodium quinoa), kañiwa (Chenopodium pallidicaule) and kiwicha (Amaranthus caudatus). Food Chem. 2010, 120, 128–133. [Google Scholar] [CrossRef]
  19. Magni, G.; Riboldi, B.; Petroni, K.; Ceruti, S. Flavonoids bridging the gut and the brain: Intestinal metabolic fate, and direct or indirect effects of natural supporters against neuroinflammation and neurodegeneration. Biochem. Pharmacol. 2022, 205, 115257. [Google Scholar] [CrossRef]
  20. Rosell, C.M.; Cortez, G.; Repo-Carrasco, R. Breadmaking Use of Andean Crops Quinoa, Kañiwa, Kiwicha, and Tarwi. Cereal Chem. 2009, 86, 386–392. [Google Scholar] [CrossRef]
  21. Bender, D.; Schönlechner, R. Innovative approaches towards improved gluten-free bread properties. J. Cereal Sci. 2020, 91, 102904. [Google Scholar] [CrossRef]
  22. Aoki, N.; Kataoka, T.; Nishiba, Y. Crucial role of amylose in the rising of gluten- and additive-free rice bread. J. Cereal Sci. 2020, 92, 102905. [Google Scholar] [CrossRef]
  23. Martínez, M.M.; Gómez, M. Rheological and microstructural evolution of the most common gluten-free flours and starches during bread fermentation and baking. J. Food Eng. 2017, 197, 78–86. [Google Scholar] [CrossRef]
  24. Agama-Acevedo, E.; Flores-Silva, P.C.; Bello-Perez, L.A. Cereal Starch Production for Food Applications; Elsevier Inc.: Amsterdam, The Netherlands, 2018. [Google Scholar] [CrossRef]
  25. Pico, J.; Reguilón, M.P.; Bernal, J.; Gómez, M. Effect of rice, pea, egg white and whey proteins on crust quality of rice flour-corn starch-based gluten-free breads. J. Cereal Sci. 2019, 86, 92–101. [Google Scholar] [CrossRef]
  26. Komeroski, M.R.; Homem, R.V.; Schmidt, H.d.O.; Rockett, F.C.; de Lira, L.; da Farias, D.V.; Kist, T.L.; Doneda, D.; Rios, A.d.O.; de Oliveira, V.R. Effect of whey protein and mixed flours on the quality parameters of gluten-free breads. Int. J. Gastron. Food Sci. 2021, 24, 100361. [Google Scholar] [CrossRef]
  27. Scalone, G.L.L.; Ioannidis, A.G.; Lamichhane, P.; Devlieghere, F.; De Kimpe, N.; Cadwallader, K.; De Meulenaer, B. Impact of whey protein hydrolysates on the formation of 2,5-dimethylpyrazine in baked food products. Food Res. Int. 2020, 132, 109089. [Google Scholar] [CrossRef] [PubMed]
  28. de Kock, H.; Magano, N. Sensory tools for the development of gluten-free bakery foods. J. Cereal Sci. 2020, 94, 102990. [Google Scholar] [CrossRef]
  29. Falsafi, S.R.; Maghsoudlou, Y.; Aalami, M.; Jafari, S.M.; Raeisi, M.; Nishinari, K.; Rostamabadi, H. Application of multi-criteria decision-making for optimizing the formulation of functional cookies containing different types of resistant starches: A physicochemical, organoleptic, in-vitro and in-vivo study. Food Chem. 2022, 393, 133376. [Google Scholar] [CrossRef] [PubMed]
  30. Polat, H.; Capar, T.D.; Inanir, C.; Ekici, L.; Yalcin, H. Formulation of functional crackers enriched with germinated lentil extract: A Response Surface Methodology Box-Behnken Design. LWT 2020, 123, 109065. [Google Scholar] [CrossRef]
  31. Ferreira, S.L.C.; Bruns, R.E.; Ferreira, H.S.; Matos, G.D.; David, J.M.; Brandão, G.C.; da Silva, E.G.P.; Portugal, L.A.; dos Reis, P.S.; Souza, A.S.; et al. Box-Behnken design: An alternative for the optimization of analytical methods. Anal. Chim. Acta 2007, 597, 179–186. [Google Scholar] [CrossRef] [PubMed]
  32. Calle, J.; Benavent-Gil, Y.; Rosell, C.M. Development of gluten free breads from Colocasia esculenta flour blended with hydrocolloids and enzymes. Food Hydrocoll. 2020, 98, 105243. [Google Scholar] [CrossRef]
  33. Wu, T.; Wang, L.; Li, Y.; Qian, H.; Liu, L.; Tong, L.; Zhou, X.; Wang, L.; Zhou, S. Effect of milling methods on the properties of rice flour and gluten-free rice bread. LWT 2019, 108, 137–144. [Google Scholar] [CrossRef]
  34. Espinosa-Ramírez, J.; Garzon, R.; Serna-Saldivar, S.O.; Rosell, C.M. Functional and nutritional replacement of gluten in gluten-free yeast-leavened breads by using β-conglycinin concentrate extracted from soybean flour. Food Hydrocoll. 2018, 84, 353–360. [Google Scholar] [CrossRef]
  35. Hamdani, A.M.; Wani, I.A.; Bhat, N.A. Gluten free cookies from rice-chickpea composite flour using exudate gums from acacia, apricot and karaya. Food Biosci. 2020, 35, 100541. [Google Scholar] [CrossRef]
  36. Singh, J.; McCarthy, O.J.; Singh, H.; Moughan, P.J. Low temperature post-harvest storage of New Zealand Taewa (Maori potato): Effects on starch physico-chemical and functional characteristics. Food Chem. 2008, 106, 583–596. [Google Scholar] [CrossRef]
  37. American Association of Cereal Chemists. Approved Methods of the American Association of Cereal Chemists; Edic; American Association of Cereal Chemists (AACC): St. Paul, MN, USA, 1997. [Google Scholar]
  38. AOAC. Official Methods of Analysis of the Association of Analytical Chemists, 18th ed.; AOAC: Gaithersburg, MD, USA, 2005; Volume 1. [Google Scholar]
  39. Norma Técnica Peruana 206.014:1981; Galletas. Determinación del pH; 1ª Ed. Norma Técnica Peruana: Lima, Perú, 2016.
  40. Norma Técnica Peruana 206.013:1981; Bizcochos, Galletas, Pastas y Fideos. Determinación de la Acidez; 1ª Ed. Norma Técnica Peruana: Lima, Perú, 2011.
  41. Borges-Machado, A.L.; Ribeiro-Sanches, M.A.; Neves-Martins, M.J.; dos Santos, P.A.; Luna-Solano, G.; Telis-Romero, J. Water desorption isotherms and thermodynamic properties of Ora-pro-nóbis (Pereskia aculeata Miller). LWT 2024, 193, 115749. [Google Scholar] [CrossRef]
  42. RM N 1020-2010/MINSA; Resolucion Ministerial. Norma Sanitaria para La Fabricación, Elaboración y Expendio de Productos de Panificación, Galletería y Pastelería. Ministerio de Salud: Lima, Perú, 2011.
  43. Maclean, W.; Harnly, J.; Chen, J.; Chevassus-Agnes, S.; Gilani, G.; Livesey, G.; Warwick, P. Food energy–Methods of analysis and conversion factors. In Food and Agriculture Organization of the United Nations Technical Workshop Report; FAO: Rome, Italy, 2003; Volume 77, pp. 8–9. [Google Scholar]
  44. Khan, M.A.I.; Ueno, K.; Horimoto, S.; Komai, F.; Someya, T.; Inoue, K.; Tanaka, K.; Ono, Y. CIELAB color variables as indicators of compost stability. Waste Manag. 2009, 29, 2969–2975. [Google Scholar] [CrossRef] [PubMed]
  45. Jan, K.N.; Panesar, P.; Singh, S. Optimization of antioxidant activity, textural and sensory characteristics of gluten-free cookies made from whole indian quinoa flour. LWT 2018, 93, 573–582. [Google Scholar] [CrossRef]
  46. Chollet, S.; Valentin, D. Impact of training on beer flavor perception and description: Are trained and untrained subjects really different? J. Sens. Stud. 2001, 16, 601–618. [Google Scholar] [CrossRef]
  47. Chollet, S.; Lelièvre, M.; Abdi, H.; Valentin, D. Sort and beer: Everything you wanted to know about the sorting task but did not dare to ask. Food Qual. Prefer. 2011, 22, 507–520. [Google Scholar] [CrossRef]
  48. Rodríguez-Miranda, J.; Juárez Barrientos, J.; Hernández-Santos, B.; Herman Lara, E.; Martínez Sánchez, C.; Torruco-Uco, J.; Gamboa, E.P.; de Jesús Ramírez-Rivera, E. Análisis sensorial descriptivo de algunas leches comerciales consumidas en Tuxtepec, Oaxaca, México. Ecosistemas Recur. Agropecu. 2014, 1, 269–279. [Google Scholar]
  49. García-Mogollon, C.; Alvis-Bermudez, A.; Romero, P. Aplicación del mapa de preferencia externo en la formulación de una bebida saborizada de lactosuero y pulpa de maracuy. Inf. Tecnol. 2015, 26, 17–24. [Google Scholar] [CrossRef]
  50. Macfie, H. Preference mapping and food product development. In Consumer-Led Food Product Development; Woodhead Publishing: Sawston, UK, 2007; pp. 551–592. [Google Scholar] [CrossRef]
  51. Aldalur, A.; Bustamante, M.; Barron, L.J.R. Effects of technological settings on yield, curd, whey, and cheese composition during the cheese-making process from raw sheep milk in small rural dairies: Emphasis on cutting and cooking conditions. J. Dairy Sci. 2019, 102, 7813–7825. [Google Scholar] [CrossRef]
  52. Aleixandre, A.; Benavent-Gil, Y.; Velickova, E.; Rosell, C.M. Mastication of crisp bread: Role of bread texture and structure on texture perception. Food Res. Int. 2021, 147, 110477. [Google Scholar] [CrossRef] [PubMed]
  53. Torres, J.; Torres-Gallo, R.; Acevedo, D.; Gallo, L. Evaluación instrumental de los parámetros de textura de galletas de limón. Rev. Vector 2015, 10, 12–25. [Google Scholar]
  54. Aguirre Torres, L.E. Diseño y Desarrollo de Galletas Dulces Destinada a Personas Celíacas; Repositorio Digital de la UTMACH; UTMACH, Unidad Académica de Ciencias Químicas Y De La Salud: Machala, Ecuador, 2019. [Google Scholar]
  55. Schmelter, L.; Rohm, H.; Struck, S. Gluten-free bakery products: Cookies made from different Vicia faba bean varieties. Futur. Foods 2021, 4, 100038. [Google Scholar] [CrossRef]
  56. Cahyana, Y.; Rangkuti, A.; Halimah, T.S.; Marta, H.; Yuliana, T. Application of heat-moisture-treated banana flour as composite material in hard biscuit. CyTA J. Food 2020, 18, 599–605. [Google Scholar] [CrossRef]
  57. Gázquez, J.C.; Sánchez Pérez, C. Consideración de la heterogeneidad en el comportamiento de elección del consumidor a través de modelos logit: Enfoque paramétrico vs. semiparamétrico. Cuad. Econ. Dir. Empresa 2007, 10, 233–263. [Google Scholar] [CrossRef]
  58. Krajewska, A.; Dziki, D. Enrichment of Cookies with Fruits and Their By-Products: Chemical Composition, Antioxidant Properties, and Sensory Changes. Molecules 2023, 28, 4005. [Google Scholar] [CrossRef] [PubMed]
  59. Silva-Paz, R.J.; Silva-Lizárraga, R.R.; Jamanca-Gonzales, N.C.; Eccoña-Sota, A. Evaluation of the physicochemical and sensory characteristics of gluten-free cookies. Front. Nutr. 2024, 10, 1304117. [Google Scholar] [CrossRef]
  60. Mehra, R.; Kumar, H.; Kumar, N.; Ranvir, S.; Jana, A.; Buttar, H.S.; Telessy, I.G.; Awuchi, C.G.; Okpala, C.O.R.; Korzeniowska, M.; et al. Whey proteins processing and emergent derivatives: An insight perspective from constituents, bioactivities, functionalities to therapeutic applications. J. Funct. Foods 2021, 87, 104760. [Google Scholar] [CrossRef]
  61. Ungureanu-Iuga, M.; Dimian, M.; Mironeasa, S. Development and quality evaluation of gluten-free pasta with grape peels and whey powders. LWT 2020, 130, 109714. [Google Scholar] [CrossRef]
  62. Ghumman, A.; Mudgal, S.; Singh, N.; Ranjan, B.; Kaur, A.; Rana, J.C. Physicochemical, functional and structural characteristics of grains, flour and protein isolates of Indian quinoa lines. Food Res. Int. 2021, 140, 109982. [Google Scholar] [CrossRef]
  63. Perez-Rea, D.; Antezana-Gomez, R. The Functionality of Pseudocereal Starches. In Starch in Food; Elsevier: Amsterdam, The Netherlands, 2018; pp. 509–542. ISBN 9780081008683. [Google Scholar]
  64. Diaz, J.M.R.; Kirjoranta, S.; Tenitz, S.; Penttilä, P.A.; Serimaa, R.; Lampi, A.-M.; Jouppila, K. Use of amaranth, quinoa and kañiwa in extruded corn-based snacks. J. Cereal Sci. 2013, 58, 59–67. [Google Scholar] [CrossRef]
  65. Shotts, M.-L.; Pujolras, M.P.; Rossell, C.; Rodriguez-Saona, L. Authentication of indigenous flours (Quinoa, Amaranth and kañiwa) from the Andean region using a portable ATR-Infrared device in combination with pattern recognition analysis. J. Cereal Sci. 2018, 82, 65–72. [Google Scholar] [CrossRef]
  66. Xu, J.; Zhang, Y.; Wang, W.; Li, Y. Advanced properties of gluten-free cookies, cakes, and crackers: A review. Trends Food Sci. Technol. 2020, 103, 200–213. [Google Scholar] [CrossRef]
Foods 13 01491 g001
Figure 1. Influence of cañihua flour and whey on pH (a), acidity (b), a* (c), b* (d) and C* (e).
Figure 1. Influence of cañihua flour and whey on pH (a), acidity (b), a* (c), b* (d) and C* (e).
Foods 13 01491 g001
Foods 13 01491 g002
Figure 2. Influence of cañihua flour and potato starch on acidity (a), texture (b), L* (c), and C* (d).
Figure 2. Influence of cañihua flour and potato starch on acidity (a), texture (b), L* (c), and C* (d).
Foods 13 01491 g002
Foods 13 01491 g003
Figure 3. Influence of whey and potato starch on acidity (a), L* (b), a* (c), b* (d), and C* (e).
Figure 3. Influence of whey and potato starch on acidity (a), L* (b), a* (c), b* (d), and C* (e).
Foods 13 01491 g003
Foods 13 01491 g004
Figure 4. Sensory map of cookies (a); consumer (b) and correspondence analysis (c) using the sorting task test.
Figure 4. Sensory map of cookies (a); consumer (b) and correspondence analysis (c) using the sorting task test.
Foods 13 01491 g004
Foods 13 01491 g005
Figure 5. Results of the acceptability of the different samples evaluated by consumers (n = 102). a,b,c,d,e,f,g,h Different letters indicate significant differences (p < 0.05).
Figure 5. Results of the acceptability of the different samples evaluated by consumers (n = 102). a,b,c,d,e,f,g,h Different letters indicate significant differences (p < 0.05).
Foods 13 01491 g005
Table 1. Formulation of the different cookies prepared (%).
Table 1. Formulation of the different cookies prepared (%).
IngredientsF1F2F3F4F5F6F7F8F9F10F11F12F13-F14-F15 *
Cañihua flour35.1552.0234.5451.3542.1959.3529.6745.7752.7251.8139.0138.5144.51
Whey13.189.7514.6810.9116.8811.8711.879.1513.1814.689.7510.9111.87
Potato starch17.5913.0017.2712.850.000.0029.6922.870.000.0026.0125.6914.85
Margarine11.428.4511.238.3413.729.659.647.4411.4411.238.458.349.64
Sugar15.2011.2514.9411.1018.2512.8312.839.9015.2014.9411.2511.1012.83
Sodium chloride0.260.200.260.190.320.220.220.170.260.260.200.190.22
Baking powder0.700.520.690.510.840.590.590.460.700.690.520.510.59
Vanilla essence0.700.520.690.510.840.590.590.460.700.690.520.510.59
Egg5.804.295.704.246.964.904.903.785.805.704.294.244.90
* F13, F14 and F15 have the same formulation.
Table 2. Formulation of gluten-free cookies for celiac using a Box–Behnken design.
Table 2. Formulation of gluten-free cookies for celiac using a Box–Behnken design.
FormulationsCoded VariablesDecoded Variables (g)
X1X2X3HC *L *AP *
F1−1−10401520
F21−10801520
F3−110401720
F4110801720
F5−10−140160
F610−180160
F7−101401640
F8101801640
F90−1−160150
F1001−160170
F110−11601540
F12011601740
F13000601620
F14000601620
F15000601620
* HC = Cañihua flour; L = Whey and AP = Potato starch.
Table 3. Analysis of moisture, ash, pH, acidity, Aw of the cañihua cookie treatments.
Table 3. Analysis of moisture, ash, pH, acidity, Aw of the cañihua cookie treatments.
FormulationMoisture (%)Ash (%) pHAcidity (%)Aw
F14.52 ± 0.023.61 ± 0.035.94 ± 0.030.492 ± 0.080.410 ± 0.04
F25.28 ± 0.012.60 ± 0.046.20 ± 0.040.486 ± 0.090.410 ± 0.03
F33.12 ± 0.022.89 ± 0.096.18 ± 0.050.468 ± 0.080.400 ± 0.04
F44.54 ± 0.012.63 ± 0.086.08 ± 0.060.468 ± 0.050.400 ± 0.05
F54.82 ± 0.032.66 ± 0.076.14 ± 0.040.378 ± 0.060.390 ± 0.04
F64.20 ± 0.022.83 ± 0.036.01 ± 0.120.234 ± 0.040.400 ± 0.03
F75.13 ± 0.043.02 ± 0.046.19 ± 0.100.246 ± 0.080.410 ± 0.04
F84.32 ± 0.082.76 ± 0.046.27 ± 0.090.186 ± 0.090.420 ± 0.04
F94.64 ± 0.023.19 ± 0.036.11 ± 0.080.192 ± 0.080.405 ± 0.05
F104.39 ± 0.033.08 ± 0.076.12 ± 0.090.252 ± 0.070.410 ± 0.03
F113.75 ± 0.042.26 ± 0.066.19 ± 0.090.180 ± 0.040.400 ± 0.02
F124.83 ± 0.052.70 ± 0.086.27 ± 0.080.150 ± 0.050.400 ± 0.05
F133.83 ± 0.062.76 ± 0.096.14 ± 0.100.162 ± 0.060.410 ± 0.03
F144.59 ± 0.053.05 ± 0.026.19 ± 0.090.162 ± 0.040.400 ± 0.02
F155.42 ± 0.073.08 ± 0.086.26 ± 0.080.162 ± 0.030.410 ± 0.03
Regression coefficients
HC−0.118−0.1500.090−0.0600.010
HC2−0.001−0.010−0.0100.010−0.010
L6.551−0.0901.510−3.970−0.020
L2−0.236−0.020−0.0400.1300.010
AP−0.265−0.090−0.0200.020−0.020
AP20.010−0.0200.010−0.0100.010
HC*L0.0800.090−0.0500.001−0.000
HC*AP−0.010−0.0100.0100.001−0.000
L*AP0.0200.0700.010−0.0010.010
p-value
HC0.7820.1900.042 *0.031 *0.587
HC20.9690.5430.0850.001 *0.756
L0.6220.1530.4910.047 *0.424
L20.6380.3070.2180.001 *0.950
AP0.9990.9510.2150.0700.783
AP20.9740.7590.6250.004 *0.950
HC*L0.7320.3170.018 *0.9351.000
HC*AP0.9220.9850.1010.2830.009
L*AP0.4890.3360.5330.2540.698
* The variable significantly influences (p < 0.05).
Table 4. Texture analysis and chromatic parameters of the cañihua cookie treatments.
Table 4. Texture analysis and chromatic parameters of the cañihua cookie treatments.
FormulationTexture (N)L*a*b*h*C*
F137.06 ± 0.5940.52 ± 0.8914.33 ± 0.5620.63 ± 0.670.960 ± 0.1725.12 ± 0.97
F235.77 ± 0.6936.72 ± 0.9111.51 ± 0.5816.04 ± 0.780.950 ± 0.2619.75 ± 0.92
F335.97 ± 0.8838.12 ± 0.8911.25 ± 0.8615.83 ± 0.650.950 ± 0.1019.42 ± 0.34
F435.97 ± 1.0833.34 ± 0.7211.29 ± 0.5716.84 ± 0.430.980 ± 0.2120.27 ± 0.45
F536.26 ± 1.1833.54 ± 0.9610.82 ± 0.7814.21 ± 0.560.920 ± 0.1317.86 ± 0.53
F636.75 ± 0.8833.93 ± 0.7610.72 ± 0.8915.82 ± 0.450.970 ± 0.1619.11 ± 0.43
F735.97 ± 0.6943.26 ± 0.6413.59 ± 0.8320.16 ± 0.670.980 ± 0.1824.31 ± 0.65
F835.47 ± 0.5936.76 ± 0.8911.79 ± 0.5415.21 ± 0.970.910 ± 0.1219.24 ± 0.64
F935.18 ± 0.3939.36 ± 0.8711.34 ± 0.7315.04 ± 0.870.920 ± 0.0818.84 ± 0.98
F1035.28 ± 0.6934.40 ± 0.6710.44 ± 0.8914.36 ± 0.560.940 ± 0.0717.75 ± 0.78
F1134.88 ± 0.5935.23 ± 0.8613.52 ± 0.8719.52 ± 0.650.970 ± 0.0223.74 ± 0.89
F1232.53 ± 0.8847.10 ± 0.659.78 ± 0.9815.44 ± 0.891.000 ± 0.2118.28 ± 0.67
F1335.08 ± 0.7836.06 ± 0.8512.50 ± 0.7616.93 ± 0.760.930 ± 0.1221.05 ± 0.89
F1435.77 ± 0.6835.84 ± 0.7612.96 ± 0.8918.21 ± 0.870.950 ± 0.0922.36 ± 0.76
F1533.01 ± 0.8833.01 ± 0.9311.97 ± 0.6718.3 ± 0.890.990 ± 0.0721.87 ± 0.95
Regression coefficients
HC−0.3690.182−0.590−1.047−0.005−1.197
HC20.0030.0010.001−0.001−0.001−0.000
L18.415−73.07011.0897.405−0.27112.527
L2−0.5722.179−0.422−0.3700.007−0.548
AP0.538−3.1620.7421.1290.0011.343
AP2−0.0010.004−0.002−0.003−0.001−0.003
HC*L0.004−0.0120.0350.0690.0010.078
HC*AP−0.001−0.004−0.001−0.001−0.000−0.003
L*AP−0.0310.210−0.035−0.0400.000−0.054
p-value
HC0.8410.0820.1800.002 *0.3370.002 *
HC20.009 *0.9840.9000.6880.7860.804
L0.1820.8740.2900.0540.2500.797
L20.0760.1410.1980.2150.6020.097
AP0.021 *0.026 *0.004 *0.001 *0.8360.010 *
AP20.1790.1930.040 *0.003 *0.6030.001 *
HC*L0.7770.8460.048 *0.003 *0.4560.002 *
HC*AP0.3650.2100.1810.001 *0.0690.002 *
L*AP0.0550.017 *0.049 *0.019 *0.6760.008 *
* The variable significantly influences (p < 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Luque-Vilca, O.M.; Paredes-Erquinigo, J.Y.; Quille-Quille, L.; Choque-Rivera, T.J.; Cabel-Moscoso, D.J.; Rivera-Ashqui, T.A.; Silva-Paz, R.J. Utilization of Sustainable Ingredients (Cañihua Flour, Whey, and Potato Starch) in Gluten-Free Cookie Development: Analysis of Technological and Sensorial Attributes. Foods 2024, 13, 1491. https://doi.org/10.3390/foods13101491

AMA Style

Luque-Vilca OM, Paredes-Erquinigo JY, Quille-Quille L, Choque-Rivera TJ, Cabel-Moscoso DJ, Rivera-Ashqui TA, Silva-Paz RJ. Utilization of Sustainable Ingredients (Cañihua Flour, Whey, and Potato Starch) in Gluten-Free Cookie Development: Analysis of Technological and Sensorial Attributes. Foods. 2024; 13(10):1491. https://doi.org/10.3390/foods13101491

Chicago/Turabian Style

Luque-Vilca, Olivia M., Jover Y. Paredes-Erquinigo, Lenin Quille-Quille, Tania J. Choque-Rivera, Domingo J. Cabel-Moscoso, Thalía A. Rivera-Ashqui, and Reynaldo J. Silva-Paz. 2024. "Utilization of Sustainable Ingredients (Cañihua Flour, Whey, and Potato Starch) in Gluten-Free Cookie Development: Analysis of Technological and Sensorial Attributes" Foods 13, no. 10: 1491. https://doi.org/10.3390/foods13101491

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop