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Optimization of antioxidant activity, textural and sensory characteristics of gluten-free cookies made from whole indian quinoa flour
Accepted Manuscript Optimization of antioxidant activity, textural and sensory characteristics of gluten-free cookies made from whole indian quinoa flour Khan Nadiya Jan, P.S. Panesar, Sukhcharn Singh PII:
S0023-6438(18)30319-0
DOI:
10.1016/j.lwt.2018.04.013
Reference:
YFSTL 7024
To appear in:
LWT - Food Science and Technology
Received Date: 21 December 2017 Revised Date:
4 April 2018
Accepted Date: 6 April 2018
Please cite this article as: Jan, K.N., Panesar, P.S., Singh, S., Optimization of antioxidant activity, textural and sensory characteristics of gluten-free cookies made from whole indian quinoa flour, LWT Food Science and Technology (2018), doi: 10.1016/j.lwt.2018.04.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Optimization of antioxidant activity, textural and sensory characteristics of gluten-free
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cookies made from whole Indian quinoa flour
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Khan Nadiya Jan*, P.S.Panesar and Sukhcharn Singh
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Department of Food Engineering and Technology, Sant Longowal Institute of Engineering &
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Technology, (SLIET), Longowal, Punjab, INDIA
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Corresponding author*
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Email: [email protected] TEL: +91- 9815980334
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Abstract
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The present study deals with optimization of the process parameters for formulation of
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gluten-free cookies from quinoa flour. The levels of major ingredients and process conditions
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were varied to determine their effect on responses (color, spread factor, hardness, antioxidant
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activity and overall acceptability) defining consumer acceptance of cookies. Response
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surface methodology was used to optimize levels of ingredients and process conditions and
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the selected variables had a dominant effect on responses. Increase in fat and sugar content
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increased spread factor and decreased the hardness of cookies, while an increase in baking
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temperature and time decreased spread factor and increased hardness. The optimized values
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obtained for independent variables i.e. fat content, sugar content, baking temperature and
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baking time were 41.83 %, 33.95 %, 181 °C and 18 min, respectively. Experimentally
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determined values for responses were color 53.05 spread factor 7.16, hardness 47.05,
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antioxidant activity 20.67 (% DPPH inhibition) and overall acceptability 7.61. Results
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obtained from this study validate the production of functional and acceptable gluten-free
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cookies made from quinoa.
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Keywords: Quinoa; Cookies; optimization; antioxidant activity; overall acceptability
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1. Introduction
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ACCEPTED MANUSCRIPT Quinoa, a pseudocereal is one of the oldest crops of Andean region with almost 7000 years of
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cultivation. Recently, Europe Asia and Africa have also participated in the cultivation of
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quinoa and consider it as a ‘super food’ because of its high nutritional profile. Quinoa
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consists of higher protein content with balanced amino acid composition in comparison to
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cereals. The protein, carbohydrate, fat and fiber content of quinoa ranges from 11.7-16.4%,
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55.3-75.82%, 4.69-12.4% and 1.92-3.38%, respectively. The storage proteins of quinoa
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include albumin and globulin, with little or no prolamine which is the major storage protein
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in many cereals. It is also a rich source of minerals, vitamins and antioxidants (Arneja et al.,
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2015). Quinoa has the potential of being the bioactive and functional ingredient in food
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products due to its high natural antioxidants and dietary fiber. The seed may be conical or
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ellipsoidal with saponins concentrated in pericarp (Bhargava et al., 2006). The saponins make
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the seed unpalatable and hence need to be removed before consumption. This can be done by
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washing and/or abrasive dehulling (Risi and Galwey 1984). In addition to this, quinoa is
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naturally gluten-free (low prolamine and glutamine) and can be recommended for people
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with celiac disease. Products labelled as “gluten-free” must meet standards of less than 20 mg
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gluten /Kg (i.e. 20 ppm) of final product according to the revised standards of Codex
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Alimentarius commission (Gibert et al., 2006). There is dearth in availability of gluten-free
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products in the market. Further, already available products are of low quality and poor
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nutritional value (Alvarez-Jubete et al., 2009). Quinoa in India is at nascent stage of its
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growth and FAO data for its cultivation is also not available (Jan et al., 2017 a; Jan et al 2017
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b). This limited production of quinoa seeds in India has created a shortage, resulting in the
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increased price of these seeds.
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Cookies represent the largest category of snacks in bakery industry and can serve as
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effective vehicle of nutrient supply to consumer. They refer to the baked product containing
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three major ingredients: flour, fat and sugar. Cookies shall have low water content (1-5%)
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(Pareyt and Delcour, 2008). Development of cookies can be a better choice than any other
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product because of the relatively longer shelf life, wide consumption, ready to eat form and
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better palatability (Tsen et al., 1973). Various studies (Wang et al., 2015; Harra et al., 2011;
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Wang and Zhu 2016) have evaluated the potential of quinoa for development of cookies.
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However, in these cases quinoa was used in combination with wheat flour or some other flour
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which decreases the nutritional value of quinoa and also in some cases invalidates the concept
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of the prepared cookies being gluten-free. Available gluten-free cookies are of low quality
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with poor flavour and mouthfeel (Gallagher et al., 2004; Pestorić et al., 2017). Also
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simultaneous optimization of major ingredients and baking parameters of cookies has not
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been explored. The combination of independent variables selected for this study has not been
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studied earlier as available studies show the effect of baking temperature and baking time
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only (Farris and Piergiovanni 2008, 2009). Considering the above, the objective of present
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study was to optimize the process parameters for preparation of cookies from quinoa flour
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and to determine the effect of these parameters on color, spread factor, hardness, antioxidant
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activity and overall acceptance of cookies.
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2. Materials and methods
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2.1 Raw Materials
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Quinoa germplasm was obtained from National Bureau of Plant Genetic Resources (NBPGR)
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Shimla and was then cultivated at the experimental farm of Sant Longowal Institute of
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Engineering & Technology. Harvesting was done manually and the seeds thus obtained were
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cleaned of all foreign materials, soaked and washed by rubbing till there was no foam
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formation. The seeds were then dried and ground to flour using cyclotec mill. The flour was
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then passed through 60 BSS sieve to get uniform particle size. The flour was then packed and
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stored under refrigerated conditions till further use.
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ACCEPTED MANUSCRIPT 2.2 Cookie preparation
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The process adopted for preparation of cookies included slight modification in AACC (2000)
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standard method (10-50 D). The ingredients used for modified method included; flour 100 g,
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sodium bicarbonate 1.2 g, salt 1.0 g, skim milk powder 5 g and water 16 ml. Sugar and
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shortening were mixed initially to cream followed by addition of flour and other minor
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ingredients to form dough. The dough was then kneaded and sheeted manually on a dough
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sheeter to a uniform thickness of 0.5 cm and cut into round shapes of 5 cm in diameter.
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Baking was carried out at different time-temperature combinations designed by response
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surface methodology. The cookies were then allowed to cool at room temperature and
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subjected to further analysis.
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2.3 Color of cookies
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Hunter colorimeter (Model i5 Green Macbeth, USA) was used for determination of color
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value of cookies. The color property was recorded in terms of L-value and varies from 0 to
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100 (L= lightness to darkness).
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2.4 Spread factor of cookies
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For determination of spread factor diameter (D) and thickness of cookies were measured with
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the help of vernier calliper at two places and the average was calculated. Spread factor was
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then calculated by dividing the diameter of cookies with their height (Sharma et al., 2016).
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2.5 Hardness
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Texture analyzer (TA-XT2, Stable micro systems, Surrey, UK) attached with a 3-point
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bending rig was used for determining the hardness of cookies. The distance between lower
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beams was set at 4 mm. The beam at top was brought down at a pre-test, test and post-test
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speed of 1.5, 2 and 10 mm/s. The downward movement was continued till the breakage of
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cookie. First peak force was recorded as hardness and all measurements were repeated five
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times.
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2.6 Antioxidant activity (% DPPH inhibition)
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Antioxidant activity (AOA) was measured as percent discoloration by the method of Brand-
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Williams, Cuvelier, and Berset (1995). Control sample consisted of methanol and DPPH
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solution.
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DPPH radical scavenging activity (%) = 1 −
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2.7 Sensory analysis
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Sensory analysis of freshly prepared cookies was carried out by twenty seven semi-trained
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panellists who were familiar with the quality aspects of baked products. The panellists were
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drawn within University community. The attributes used to evaluate cookie quality were
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colour & appearance, mouth feel, texture, taste & overall acceptability. The panelists scored
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their liking of characteristics using nine-point hedonic scale (9-like extremely to 1-dislike
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extremely).
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2.8 Experimental design
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For the optimization of quality of pseudocereal cookies, experiments were conducted
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according to central composite rotatable design containing four independent variables which
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dictated 30 experimental runs. The experiments at central point were six in order to calculate
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the repeatability of the method. Independent variables used to determine optimum baking
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conditions were fat content, sugar content, baking temperature and baking time. The low and
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high levels of parameters were 35 to 45 g for fat, 25 to 35 g for sugar, 170 to 190 °C for
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baking temperature and 15 to 20 min for baking time. The level of different variables is
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shown in Table 1. Factors such as color, spread factor, texture, antioxidant activity and
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overall acceptance were selected as quality attributes of cookies.
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2.9 Data analysis
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Statistical analysis was conducted using a Design-Expert version 11 (Stat-Ease Inc.,
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Minneapolis, USA). All the responses were analysed as a function of independent variables
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using a second order polynomial equation as follows: + ! " $
#
"
%" + ! " $
& "" %"
#)$
#
+! !
" $ ' "($
"'
%" %' (1)
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Where Yk is the response variable, Xi and Xj represent the coded independent variables. βko is
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the value of the fitted response at the centre point of the design, βki, βkii, βkj represent the
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linear, quadratic and cross-product regression coefficients, respectively of the model.
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Analysis of variance (ANOVA) was used to observe the effect of variables on responses.
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3. Results and discussion
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The experimental variables in actual form along with values of responses are given in Table
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2. ANOVA data for response variables along with correlation coefficient is shown in Table 3.
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Lack-of-fit, model analysis and R2 were used to determine the adequacy of models. The lack-
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of-fit measures the ability of a model to represent the data in experimental domain and cannot
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be accounted for random error. Model is considered as adequate in describing the responses if
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lack-of-fit is insignificant. The aptness of the model to signify real relationship among
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selected parameters is given by R2. The R2 values of models for this study were > 0.70
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representing a good fit between the model and experimental data. The difference between the
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experimental and predicted values was less indicating the suitability of the model used.
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3.1 Effect of variables on responses
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3.1.1 Color
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Surface color has been considered as an important indicator of the degree of baking of
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cookies and contributes to consumer preference hence needs to be controlled strictly. The
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lightness value of the cookies ranged from 46.08 to 56.90 (Table 2). The p value (Table 3)
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indicates that linear (except fat) and quadratic of all the variables had a significant effect. In
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contrast among interactions only “sugar and time” and “temperature and time” showed a
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significant effect on color of cookies. The magnitude of regression coefficients (Table 3) showed that the linear term of
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temperature had a maximum negative effect (B= -1.81) followed by time (B= -1.15) and
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sugar content (B= -1.04). Fig 1(a and b) shows the decrease in color value with increase in
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baking temperature, time and sugar content. It can be observed that the major effect was of
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baking temperature as with rise in temperature lightness decreases remarkably. The decrease
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in lightness value with increase in temperature, time and sugar content may be due to
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caramelization of sugar and Maillard browning reactions causing malanoidin formation
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during heating and thus, resulting in the darkening of product (Manzocco et al. 2000). The
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results are in conformity with the Farris and Piergiovanni (2008) who reported the decrease in
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color (L- value) of cookies with increase in temperature and sugar content. Gan et al (2007)
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also reported the similar decreasing trend of colour value for cake.
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of 0.963 is in reasonable agreement with the Adj R-Squared of 0.983.
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3.1.2 Spread factor
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Spread factor has been considered as an important quality parameter for cookies. Higher
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spread factor means the higher product yield. The spread factor of cookies varied from 5.89
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to 7.26. The effect of process variables on the spread factor of cookies is shown in Table 2.
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The magnitude of p value indicates that linear and quadratic effect of all variables was
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significant. However, among interactions “fat and sugar”, “fat and temperature” and “sugar
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and temperature” showed a significant effect on spread factor of cookies. The analysis of
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regression coefficients (Table 3) showed that the linear term of temperature and time had a
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negative effect with temperature showing higher effect (B= -0.14) than time (B= -0.050).
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While as fat and sugar had a positive effect on spread factor with magnitude being higher for
ACCEPTED MANUSCRIPT fat content (B= +0.15) followed by sugar content (B= +0.065). The response plots for effect
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of temperature, fat content and time on spread factor are shown in Fig. 2. Increase in sugar
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content increased the spread factor of cookies. Similar increased mobility of dough and
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higher spread of cookies by increase in sugar content was observed by Doescher et al (1987).
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Kulthe et al. (2014) also observed that the spread factor is decreased by decrease in sugar
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content. Spread factor increased gradually with increase in fat content. Singh et al. (2002)
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also observed the increase in spread factor by increase in sugar level and attributed it to the
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increase in fluidity of dough allowing two dimensional extensible film formation rather than
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three-dimensional elastic network formation. The spread factor increased from 6.66 to 7.26
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with increase in temperature from 170 to 180 °C. However, further increase in temperature
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decreased the spread factor to 5.89. Cookie spread rate is controlled by the viscosity of
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dough. Sugar contributes to dough viscosity and is related to the dough expansion during
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baking (Abboud and Hoseney 1984). During low temperature and time combinations sugar
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gets dissolved in available water content of the dough which lowers the initial viscosity of
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dough and the cookie spreads at a faster rate and vice-versa occurs during high temperature
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and time combinations due to lower availability of water content. Another reason for decrease
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in spread factor of cookies at higher temperature may be that the cookies set up before getting
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a chance to spread.
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The model F-value of 378 implies that the model is significant. The predicted R-
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Squared of 0.986 is in reasonable agreement with the Adjusted R-Squared of 0.994.
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3.1.3 Hardness
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Hardness has been considered as an important characteristic of cookie quality as it affects
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consumer acceptance and repeat sales (Gaines et al., 1992). Hardness refers to the ease with
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which the product will break. Hardness of the cookies varied from 34.05 N to 58.09 N. Table
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2 depicts the effect of process variables on the hardness of cookies.
ACCEPTED MANUSCRIPT The magnitude of p value indicates that linear as well as quadratic terms of all
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variables had a significant effect on hardness of cookies (Table 3). Among interactions “fat
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and temperature”, fat and time and “time and temperature” showed significant effect on
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hardness of cookies. The regression coefficients revealed that linear term of fat showed
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negative effect on the hardness of cookies (B= -2.96). The response plots for effect of
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temperature, fat and time on hardness of cookies are shown in Fig. 3 (a and b). The decrease
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in hardness with increase in fat content may be due to the tenderizing effect exerted by fat.
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The decreased hardness may also be due to the encapsulation of flour particles by fat, thereby
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isolating the flour particles from each other and making them more easily detachable. The
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linear terms of temperature, sugar and time showed positive effect (Table 3) on the hardness
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of cookies with the higher magnitude observed for temperature (B= +5.57) than time (B=
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+2.45). Increased hardness with increase in baking temperature and time may be due to the
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higher water loss from the dough which may lead to a more rigid fiber frame after baking.
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Further formation of fiber-protein complexes during high temperature time combinations can
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also promote the hardening of product (Farris and Piergiovanni 2009). Sugar content showed
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the non-significant positive effect of lower magnitude (B= +0.12) on hardness. Increase in
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hardness with increase in sugar content may be due to its conversion from solution to harder
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glass-like state after cooling. Singh et al. (2002) also observed the increase in hardness of
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cookies by sugar and attributed it to its conversion to hard glassy state.
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The model F-value of 202 implies that the model is significant. The pred R-Squared
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of 0.975 is in reasonable agreement with the Adj R-Squared of 0.989.
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3.1.4 Antioxidant activity ( % DPPH inhibition)
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Antioxidants have gained increased interest among consumers because the epidemiological
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studies have revealed the lower risk of cancer and cardiovascular diseases with frequent
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consumption of antioxidants (Temple 2000). Antioxidant activity of optimized cookies varied
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from 18.12 % to 20.97 % (Table 2). The p value indicates that linear and quadratic effect of
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all variables (except fat content) was significant (Table 3). However, in case of interactions
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“sugar-time”, “sugar-temperature” and “temperature-time” showed the significant effect. The analysis of regression coefficients showed that the variables sugar, temperature
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and time had a positive effect on antioxidant activity with magnitude being higher for
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temperature (B= +0.630) followed by time (B= +0.233) and sugar content (B= +0.185). The
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response plots for effect of temperature, sugar content and time on antioxidant activity are
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shown in Fig. 4. It can be observed that the major effect was that of temperature as with
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increase in temperature antioxidant activity of cookies increased remarkably. Increase in
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antioxidant activity with increase in sugar, temperature and time may be due to the formation
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of melanoidins during baking process. These compounds have been reported to show
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antioxidant activity (Manzocco et al. 2000). The antioxidant activity of cookies remained
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almost constant after 180 °C suggesting the stability of molecules bearing radical scavenging
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ability. Increase in antioxidant activity can also be attributed to the possible breakdown of
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phenolics or their degradation products which could react with the reagent (Sun et al., 2014).
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Lindenmeier and Hofmann (2004) determined the influence of baking conditions on
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antioxidant activity of bread and the antioxidant activity was found to be higher in crust in
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comparison to the crumb and untreated flour. They found a 3-5 fold increase in antioxidant
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activity with increase in baking temperature and time and attributed it to the formation of
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antioxidant compound pronyl-L-lysine. The increase in antioxidant activity due to baking was
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also observed by Sharma and Gujral (2014).
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The model F-value of 84 implies that the model is significant. The pred R-Squared of
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0.937 is in reasonable agreement with the Adj R-Squared of 0.975.
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3.1.5 Overall acceptability
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Overall acceptances of cookies as the sum of various quality characteristics (surface color,
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texture, mouth feel and taste) can be used to assess the quality of the cookies prepared. The
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overall acceptance score of cookies varied from 5.54 to 7.84 (Table 2). The magnitude of p value indicates that linear as well as quadratic terms of all
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variables had a significant effect on overall acceptability of cookies (Table 3). Among
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interactions “fat and sugar” and “time and temperature” showed a significant effect on overall
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acceptability of cookies. The regression coefficients revealed that linear terms of fat and
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sugar showed positive effect on the overall acceptability of cookies with the sugar content
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(B= +0.16) showing effect of slightly higher magnitude than fat content (B= +0.13). While as
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linear terms of temperature and time showed negative effect on the overall acceptability of
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cookies with the temperature showing effect of higher magnitude (B= -0.10) than time (B= -
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0.05).
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Fig. 5 shows the effect of temperature, fat, sugar content and time on over all
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acceptability of cookies. The cookies with higher fat and sugar content seemed to be more
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acceptable by the panellists. Positive effect of fat and sugar can be due to the reason that
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sugar acts as flavour enhancer and fat provides the better mouth feel. The negative effect of
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temperature on overall acceptability of cookies may be due to higher temperature resulting in
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decreased color (L-value) and increased hardness of cookies.
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The model F-value of 153 implies that the model is significant. The pred R-
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Squared of 0.969 is in reasonable agreement with the Adj R-Squared of 0.986.
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3.2 Optimization
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The simultaneous optimization of the responses was done numerically using statistical
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software Design-expert 11.The criterion for optimization of cookies was to obtain maximum
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values for spread factor, antioxidant activity and overall acceptability However, color and
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hardness were kept in range (Table 4). Numerical analysis report showed that fat content,
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sugar content, baking temperature and baking time of 41.83 %, 33.95 %, 181 °C and 18 min,
ACCEPTED MANUSCRIPT respectively gave an optimized product of desirability 0.93. High desirability value indicated
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the suitability of process conditions for achieving favourable results in terms of responses.
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Predicted and experimentally determined values for responses were color 52.72 and 53.05
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spread factor 7.26 and 7.16, hardness 46.40 and 47.05, antioxidant activity 20.54 and 20.67
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(% DPPH inhibition) and overall acceptability 7.76 and 7.61, respectively. Optimized
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solution provided the optimum range of variables for production of best quality cookies in
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terms of color, spread factor, hardness, antioxidant activity and overall acceptability. The
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optimum conditions obtained may be recommended for preparation of quinoa cookies.
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4. Conclusion
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Response surface methodology was effective in optimization of different formulations and
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processing parameters for development of quinoa cookies. Model analysis revealed that all
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models were adequate. Validity of the models was evaluated using the relevant statistical aids
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like coefficient of determination (R2), F-value and coefficient of variation. These statistical
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tools revealed that the models were statistically adequate. The selected independent variables,
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like contents of sugar and fat and baking temperature and time markedly affected the
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responses like color, spread factor, hardness, antioxidant activity and overall acceptability of
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quinoa cookies. Quinoa cookies can be used as substitute for cereal in Indian subcontinent
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during fasting days. The linear terms of temperature, fat content, sugar content and time was
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found to have significant effect on all responses (color, texture, spread factor, antioxidant
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activity and overall acceptability).
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Acknowledgement
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The authors would like to acknowledge UGC for providing Maulana azad national fellowship
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(MANF).
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Conflict of interest
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The authors declare that there is no conflict of interest
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Ethical Review
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This study does not involve any human or animal testing
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replacement for all purpose flour in a peanut butter cookie. Journal of the American
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Dietetic Association, 111(9), A45.
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Jan, K. N., Panesar, P. S., & Singh, S. (2017 b). Process standardization for isolation of
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quinoa starch and its characterization in comparison with other starches. Journal of
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Food Measurement and Characterization, 1-9.
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Jan, K. N., Panesar, P. S., Rana, J. C., & Singh, S. (2017 a). Structural, thermal and
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rheological properties of starches isolated from Indian quinoa varieties. International Journal of Biological Macromolecules, 102, 315-322
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supplementation on the amounts of the antioxidant pronyl-L-lysine in bakery
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non-enzymatic browning and antioxidant capacity in processed foods. Trends in food
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Pareyt, B., & Delcour, J. A. (2008). The role of wheat flour constituents, sugar, and fat in low
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moisture cereal based products: a review on sugar-snap cookies. Critical reviews in
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Pestorić, M., Sakač, M., Pezo, L., Škrobot, D., Nedeljković, N., Jovanov, P., & Mandić, A.
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(2017). Physicochemical characteristics as the markers in predicting the self-life of
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gluten-free cookies. Journal of Cereal Science, 77, 172-179.
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Risi, J.C., & Galwey, N. W. (1984). Chenopodium grains of the Andes: Inca crops for modern agriculture. Advances in applied biology, pp. 145-217
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Sharma, P., & Gujral, H. S. (2014). Cookie making behavior of wheat–barley flour blends
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and effects on antioxidant properties. LWT-Food Science and Technology, 55(1), 301-
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Sharma, S., Saxena, D. C., & Riar, C. S. (2016). Nutritional, sensory and in-vitro antioxidant
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characteristics of gluten free cookies prepared from flour blends of minor millets. Journal of Cereal Science, 72, 153-161.
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Singh, N., Gupta, S., Singh Sodhi, N., & Singh, R. P. (2002). Effect of additives on dough
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and cookie making properties of flour. International Journal of Food Properties, 5(3),
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547-562.
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Sun, H., Mu, T., Xi, L., & Song, Z. (2014). Effects of domestic cooking methods on
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polyphenols and antioxidant activity of sweet potato leaves. Journal of agricultural
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and food chemistry, 62(36), 8982-8989.
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research, 20(3), 449-459.
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Temple, N. J. (2000). Antioxidants and disease: more questions than answers. Nutrition
Tsen, C. C., Peters, E. M., Schaffer, T., & Hoover, W. J. (1973). High protein cookies. Effect of soy fortification and surfactants. Bakers Digest, 47(4), 34-39.
Wang, S., & Zhu, F. (2016). Formulation and quality attributes of quinoa food
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products. Food and bioprocess technology, 9(1), 49-68.
Wang, S., Opassathavorn, A., & Zhu, F. (2015). Influence of quinoa flour on quality
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characteristics of cookie, bread and Chinese steamed bread. Journal of Texture
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Studies, 46(4), 281-292.
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ACCEPTED MANUSCRIPT Table 1. Level of different variables in coded form for cookies preparation Independent variables
Units
Symbols
Levels -α
-1
0
1
α
g 100g-1 Flour
X1
30
35
40
45
50
Sugar
g 100g-1 Flour
X2
20
25
30
35
40
°C
X3
160
170
180
190
200
Min
X4
12.5
15
17.5
20
22.5
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Baking Temperature Baking Time
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Fat
ACCEPTED MANUSCRIPT Table 2.The central composite rotatable design with process variables and experimental results of responses.
170 170 170 170 190 190 190 190 170 170 170 170 190 190 190 190 180 180 180 180 160 200 180 180 180 180 180 180 180 180
15 15 15 15 15 15 15 15 20 20 20 20 20 20 20 20 17.5 17.5 17.5 17.5 17.5 17.5 12.5 22.5 17.5 17.5 17.5 17.5 17.5 17.5
Y1
Y2
56.90 56.73 54.48 54.37 52.97 52.90 49.99 49.81 53.48 53.36 51.57 51.48 50.58 50.49 48.92 48.71 53.29 52.87 53.90 50.40 53.75 46.08 55.62 51.56 54.10 54.26 54.87 54.47 54.15 54.71
6.66 6.94 6.82 7.19 6.47 6.69 6.51 6.84 6.60 6.89 6.68 7.07 6.38 6.64 6.44 6.74 6.63 7.22 6.99 7.26 6.52 5.89 6.78 6.52 7.22 7.20 7.21 7.19 7.24 7.23
Y3 39.33 35.45 39.36 35.54 48.00 43.15 48.10 43.20 41.98 36.60 42.00 36.63 56.94 49.00 56.87 49.05 47.61 34.05 44.80 46.14 34.90 58.09 36.22 47.15 46.01 46.11 46.03 47.08 46.95 47.00
Y4
Y5
18.83 18.90 19.14 19.16 20.23 20.26 20.42 20.45 19.25 19.27 19.98 20.00 20.50 20.52 20.78 20.81 19.80 19.85 19.75 20.48 18.12 20.97 19.97 20.91 20.25 20.37 20.20 20.36 20.40 20.34
6.30 6.46 6.57 6.91 6.40 6.53 6.65 6.96 6.45 6.62 6.75 7.04 6.10 6.15 6.20 6.44 6.68 7.33 6.76 7.47 5.88 5.54 6.57 6.48 7.73 7.84 7.70 7.67 7.82 7.79
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25 25 35 35 25 25 35 35 25 25 35 35 25 25 35 35 30 30 20 40 30 30 30 30 30 30 30 30 30 30
X4
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35 45 35 45 35 45 35 45 35 45 35 45 35 45 35 45 30 50 40 40 40 40 40 40 40 40 40 40 40 40
X3
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X2
Responses
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Variables
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Where Y1= color (L-value); Y2= spread factor; Y3= hardness (N); Y4= antioxidant activity (% DPPH inhibition); Y5 = overall acceptability.
ACCEPTED MANUSCRIPT Table 3. Estimated regression coefficients of the fitted second order polynomial and their significance Parameters Model X1 X2 X3
DF 14 1 1 1
X4 X12 X22 X32 X42 X1 X2
Y2
Y3
Y4
Y5
53.42 -0.078 -1.04***
7.22
46.53
20.32
7.76
0.151*** 0.065***
-2.96*** 0.124
0.014 0.185***
0.125*** 0.163***
-1.81***
-0.142***
5.57***
0.630***
-0.098***
1 1 1 1 1 1
-1.15***
-0.050***
2.45***
0.233***
0.050**
-0.318**
-0.073***
-1.34***
-0.136***
-0.198***
-0.554** -1.10*** -0.198** -0.008
-0.023** -0.253*** -0.142***
-0.223*** 1.34 -1.17***
-0.064** -0.206*** 0.018
-0.170*** -0.521*** -0.317***
0.021**
0.009
-0.003
0.042*
X1 X3 X1 X4 X2 X3 X2 X4
1 1 1 1
-0.004
-0.014*
-0.441*
-0.001
-0.014
0.001
0.003
-0.566*
-0.004
-0.012
-0.059
-0.020*
-0.003
-0.068*
-0.023
0.226*
-0.011
-0.015
-0.068*
-0.018
X3 X4 R2 Lack of fit
1
0.351**
0.004
1.37***
-0.076***
-0.142***
0.991 NS
0.997 NS
0.994 NS
0.987 NS
0.993 NS
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*Significant at p ≤0.05 **Significant at p ≤0.01 ***Significant at p ≤0.001 NS- Not-significant; DF- Degree of freedom
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Where Y1= color (L-value); Y2= spread factor; Y3= hardness (N);Y4= antioxidant activity (% DPPH inhibition); Y5 = overall acceptability.
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Table 4.Criteria and outputs of the numerical optimization of the responses for cookies
X1 X2 X3 X4
Experimental range
Goal In range In range In range In range
Min 35 25 170 15
Max 45 35 190 20
Importance 3 3 3 3
Responses 46.08 5.89 34.05 18.12 5.54
56.90 7.26 58.09 20.97 7.84
3 3 3 3 3
41.83 33.95 181 18 Predicted values 52.72 7.26 46.40 20.54 7.76
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In range Maximum In range Maximum Maximum
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Optimum values
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Variables
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Where Y1= color (L-value); Y2= spread factor; Y3=hardness (N); Y4= antioxidant activity (% DPPH inhibition); Y5 = overall acceptability.
ACCEPTED MANUSCRIPT
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Fig 1. Response plots showing the effect of a) Time, sugar and their mutual effect on color (L-value). Other variables are constant: Fat, 40 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on color (L-value). Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour.
58 56
52
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50 48 46
20 19
M AN U
Color (L-value)
54
35
33
18
31
17
Baking Time (min)
29
27
Sugar (g)
TE D
16
15
a)
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58
25
56
Color (L-value)
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50
48
46
20 190
19 185
18 180
17
Baking Time (min)
b)
175
16 15
170
Baking Temperature (°C)
ACCEPTED MANUSCRIPT
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Fig 2. Response plots showing the effect of a) Temperature, fat and their mutual effect on spread factor. Other variables are constant: sugar 30 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on spread factor. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour.
7.5
7
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Spread factor
6.5
5.5
190 185 180
Baking Temperature (°C)
M AN U
6
41
39
175
37
7.5
Fat (g)
EP
35
6.5
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Spread factor
7
TE D
170
a)
45 43
6
5.5
20
190 19
185 18
180
17
Baking Time (min)
b)
175 Baking Temperature (°C)
16 15
170
ACCEPTED MANUSCRIPT Fig. 3. Response plots showing the effect of a) Temperature, fat and their mutual effect on hardness. Other variables are constant: sugar, 30 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on hardness. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour. 60
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55
Hardness (N)
50 45 40 35
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30
M AN U
190 185 180 Baking Temperature (°C) 175 170
a)
37
35
39
43
41
45
Fat (g)
TE D
60 55
45
40
EP
Hardness (N)
50
35
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30
20 19
Baking Time (min)
18 17 16
b)
15
170
175
180
185
Baking Temperature (°C)
190
ACCEPTED MANUSCRIPT Figure 4: Response plots showing the effect of a) Temperature, sugar and their mutual effect on antioxidant activity. Other variables are constant: fat, 40 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on antioxidant activity. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour. a)
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21
Antioxidant activity (%)
20.5 20
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19.5 19
190
35
33
185
31
180
29
175
Baking Temperature (°C)
21
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20.5
19.5
25
TE D
170
20
Sugar (g)
27
a)
Antioxidant activity (%)
M AN U
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18.5
20
190
19
185 18
180
17
Baking Time (min)
b)
175
16 15
170
Baking Temperature (°C)
ACCEPTED MANUSCRIPT Figure 5: Response plots showing the effect of a) Sugar, fat and their mutual effect on overall acceptability. Other variables are constant: temp, 180 °C and time 17.50 min b) Temperature, time and their mutual effect on overall acceptability. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour.
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8
7
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6.5 6 5.5
35 33
41
29
Sugar (g)
39
27
35
TE D
8
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7.5
Overall acceptability
Fat (g)
37
25
a)
6.5
45
43
31
7
M AN U
Overall acceptability
7.5
6
5.5
20
190 19
185 18
180
17
Baking Time (min)
b)
175 Baking Temperature (°C)
16 15
170
ACCEPTED MANUSCRIPT Highlights The suitability of gluten-free quinoa flour for good quality cookies was studied.
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Physical, functional, textural and sensory properties of cookies are reported
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Antioxidant potential increased with increase in sugar content, temperature and time.
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Study improved the low spread factor which is a major drawback in gluten-free
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cookies
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Gluten-free cookies with high acceptability were obtained.
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S0023-6438(18)30319-0
DOI:
10.1016/j.lwt.2018.04.013
Reference:
YFSTL 7024
To appear in:
LWT - Food Science and Technology
Received Date: 21 December 2017 Revised Date:
4 April 2018
Accepted Date: 6 April 2018
Please cite this article as: Jan, K.N., Panesar, P.S., Singh, S., Optimization of antioxidant activity, textural and sensory characteristics of gluten-free cookies made from whole indian quinoa flour, LWT Food Science and Technology (2018), doi: 10.1016/j.lwt.2018.04.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Optimization of antioxidant activity, textural and sensory characteristics of gluten-free
2
cookies made from whole Indian quinoa flour
3
Khan Nadiya Jan*, P.S.Panesar and Sukhcharn Singh
4
Department of Food Engineering and Technology, Sant Longowal Institute of Engineering &
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Technology, (SLIET), Longowal, Punjab, INDIA
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Corresponding author*
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Email: [email protected] TEL: +91- 9815980334
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Abstract
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The present study deals with optimization of the process parameters for formulation of
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gluten-free cookies from quinoa flour. The levels of major ingredients and process conditions
11
were varied to determine their effect on responses (color, spread factor, hardness, antioxidant
12
activity and overall acceptability) defining consumer acceptance of cookies. Response
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surface methodology was used to optimize levels of ingredients and process conditions and
14
the selected variables had a dominant effect on responses. Increase in fat and sugar content
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increased spread factor and decreased the hardness of cookies, while an increase in baking
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temperature and time decreased spread factor and increased hardness. The optimized values
17
obtained for independent variables i.e. fat content, sugar content, baking temperature and
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baking time were 41.83 %, 33.95 %, 181 °C and 18 min, respectively. Experimentally
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determined values for responses were color 53.05 spread factor 7.16, hardness 47.05,
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antioxidant activity 20.67 (% DPPH inhibition) and overall acceptability 7.61. Results
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obtained from this study validate the production of functional and acceptable gluten-free
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cookies made from quinoa.
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Keywords: Quinoa; Cookies; optimization; antioxidant activity; overall acceptability
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1. Introduction
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ACCEPTED MANUSCRIPT Quinoa, a pseudocereal is one of the oldest crops of Andean region with almost 7000 years of
26
cultivation. Recently, Europe Asia and Africa have also participated in the cultivation of
27
quinoa and consider it as a ‘super food’ because of its high nutritional profile. Quinoa
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consists of higher protein content with balanced amino acid composition in comparison to
29
cereals. The protein, carbohydrate, fat and fiber content of quinoa ranges from 11.7-16.4%,
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55.3-75.82%, 4.69-12.4% and 1.92-3.38%, respectively. The storage proteins of quinoa
31
include albumin and globulin, with little or no prolamine which is the major storage protein
32
in many cereals. It is also a rich source of minerals, vitamins and antioxidants (Arneja et al.,
33
2015). Quinoa has the potential of being the bioactive and functional ingredient in food
34
products due to its high natural antioxidants and dietary fiber. The seed may be conical or
35
ellipsoidal with saponins concentrated in pericarp (Bhargava et al., 2006). The saponins make
36
the seed unpalatable and hence need to be removed before consumption. This can be done by
37
washing and/or abrasive dehulling (Risi and Galwey 1984). In addition to this, quinoa is
38
naturally gluten-free (low prolamine and glutamine) and can be recommended for people
39
with celiac disease. Products labelled as “gluten-free” must meet standards of less than 20 mg
40
gluten /Kg (i.e. 20 ppm) of final product according to the revised standards of Codex
41
Alimentarius commission (Gibert et al., 2006). There is dearth in availability of gluten-free
42
products in the market. Further, already available products are of low quality and poor
43
nutritional value (Alvarez-Jubete et al., 2009). Quinoa in India is at nascent stage of its
44
growth and FAO data for its cultivation is also not available (Jan et al., 2017 a; Jan et al 2017
45
b). This limited production of quinoa seeds in India has created a shortage, resulting in the
46
increased price of these seeds.
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Cookies represent the largest category of snacks in bakery industry and can serve as
48
effective vehicle of nutrient supply to consumer. They refer to the baked product containing
49
three major ingredients: flour, fat and sugar. Cookies shall have low water content (1-5%)
ACCEPTED MANUSCRIPT and can also contain minor ingredients like leavening agents, salt, emulsifiers and yeast
51
(Pareyt and Delcour, 2008). Development of cookies can be a better choice than any other
52
product because of the relatively longer shelf life, wide consumption, ready to eat form and
53
better palatability (Tsen et al., 1973). Various studies (Wang et al., 2015; Harra et al., 2011;
54
Wang and Zhu 2016) have evaluated the potential of quinoa for development of cookies.
55
However, in these cases quinoa was used in combination with wheat flour or some other flour
56
which decreases the nutritional value of quinoa and also in some cases invalidates the concept
57
of the prepared cookies being gluten-free. Available gluten-free cookies are of low quality
58
with poor flavour and mouthfeel (Gallagher et al., 2004; Pestorić et al., 2017). Also
59
simultaneous optimization of major ingredients and baking parameters of cookies has not
60
been explored. The combination of independent variables selected for this study has not been
61
studied earlier as available studies show the effect of baking temperature and baking time
62
only (Farris and Piergiovanni 2008, 2009). Considering the above, the objective of present
63
study was to optimize the process parameters for preparation of cookies from quinoa flour
64
and to determine the effect of these parameters on color, spread factor, hardness, antioxidant
65
activity and overall acceptance of cookies.
66
2. Materials and methods
67
2.1 Raw Materials
68
Quinoa germplasm was obtained from National Bureau of Plant Genetic Resources (NBPGR)
69
Shimla and was then cultivated at the experimental farm of Sant Longowal Institute of
70
Engineering & Technology. Harvesting was done manually and the seeds thus obtained were
71
cleaned of all foreign materials, soaked and washed by rubbing till there was no foam
72
formation. The seeds were then dried and ground to flour using cyclotec mill. The flour was
73
then passed through 60 BSS sieve to get uniform particle size. The flour was then packed and
74
stored under refrigerated conditions till further use.
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ACCEPTED MANUSCRIPT 2.2 Cookie preparation
76
The process adopted for preparation of cookies included slight modification in AACC (2000)
77
standard method (10-50 D). The ingredients used for modified method included; flour 100 g,
78
sodium bicarbonate 1.2 g, salt 1.0 g, skim milk powder 5 g and water 16 ml. Sugar and
79
shortening were mixed initially to cream followed by addition of flour and other minor
80
ingredients to form dough. The dough was then kneaded and sheeted manually on a dough
81
sheeter to a uniform thickness of 0.5 cm and cut into round shapes of 5 cm in diameter.
82
Baking was carried out at different time-temperature combinations designed by response
83
surface methodology. The cookies were then allowed to cool at room temperature and
84
subjected to further analysis.
85
2.3 Color of cookies
86
Hunter colorimeter (Model i5 Green Macbeth, USA) was used for determination of color
87
value of cookies. The color property was recorded in terms of L-value and varies from 0 to
88
100 (L= lightness to darkness).
89
2.4 Spread factor of cookies
90
For determination of spread factor diameter (D) and thickness of cookies were measured with
91
the help of vernier calliper at two places and the average was calculated. Spread factor was
92
then calculated by dividing the diameter of cookies with their height (Sharma et al., 2016).
93
2.5 Hardness
94
Texture analyzer (TA-XT2, Stable micro systems, Surrey, UK) attached with a 3-point
95
bending rig was used for determining the hardness of cookies. The distance between lower
96
beams was set at 4 mm. The beam at top was brought down at a pre-test, test and post-test
97
speed of 1.5, 2 and 10 mm/s. The downward movement was continued till the breakage of
98
cookie. First peak force was recorded as hardness and all measurements were repeated five
99
times.
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2.6 Antioxidant activity (% DPPH inhibition)
101
Antioxidant activity (AOA) was measured as percent discoloration by the method of Brand-
102
Williams, Cuvelier, and Berset (1995). Control sample consisted of methanol and DPPH
103
solution.
104
DPPH radical scavenging activity (%) = 1 −
105
2.7 Sensory analysis
106
Sensory analysis of freshly prepared cookies was carried out by twenty seven semi-trained
107
panellists who were familiar with the quality aspects of baked products. The panellists were
108
drawn within University community. The attributes used to evaluate cookie quality were
109
colour & appearance, mouth feel, texture, taste & overall acceptability. The panelists scored
110
their liking of characteristics using nine-point hedonic scale (9-like extremely to 1-dislike
111
extremely).
112
2.8 Experimental design
113
For the optimization of quality of pseudocereal cookies, experiments were conducted
114
according to central composite rotatable design containing four independent variables which
115
dictated 30 experimental runs. The experiments at central point were six in order to calculate
116
the repeatability of the method. Independent variables used to determine optimum baking
117
conditions were fat content, sugar content, baking temperature and baking time. The low and
118
high levels of parameters were 35 to 45 g for fat, 25 to 35 g for sugar, 170 to 190 °C for
119
baking temperature and 15 to 20 min for baking time. The level of different variables is
120
shown in Table 1. Factors such as color, spread factor, texture, antioxidant activity and
121
overall acceptance were selected as quality attributes of cookies.
122
2.9 Data analysis
× 100
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Statistical analysis was conducted using a Design-Expert version 11 (Stat-Ease Inc.,
124
Minneapolis, USA). All the responses were analysed as a function of independent variables
125
using a second order polynomial equation as follows: + ! " $
#
"
%" + ! " $
& "" %"
#)$
#
+! !
" $ ' "($
"'
%" %' (1)
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=
#
Where Yk is the response variable, Xi and Xj represent the coded independent variables. βko is
127
the value of the fitted response at the centre point of the design, βki, βkii, βkj represent the
128
linear, quadratic and cross-product regression coefficients, respectively of the model.
129
Analysis of variance (ANOVA) was used to observe the effect of variables on responses.
130
3. Results and discussion
131
The experimental variables in actual form along with values of responses are given in Table
132
2. ANOVA data for response variables along with correlation coefficient is shown in Table 3.
133
Lack-of-fit, model analysis and R2 were used to determine the adequacy of models. The lack-
134
of-fit measures the ability of a model to represent the data in experimental domain and cannot
135
be accounted for random error. Model is considered as adequate in describing the responses if
136
lack-of-fit is insignificant. The aptness of the model to signify real relationship among
137
selected parameters is given by R2. The R2 values of models for this study were > 0.70
138
representing a good fit between the model and experimental data. The difference between the
139
experimental and predicted values was less indicating the suitability of the model used.
140
3.1 Effect of variables on responses
141
3.1.1 Color
142
Surface color has been considered as an important indicator of the degree of baking of
143
cookies and contributes to consumer preference hence needs to be controlled strictly. The
144
lightness value of the cookies ranged from 46.08 to 56.90 (Table 2). The p value (Table 3)
145
indicates that linear (except fat) and quadratic of all the variables had a significant effect. In
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contrast among interactions only “sugar and time” and “temperature and time” showed a
147
significant effect on color of cookies. The magnitude of regression coefficients (Table 3) showed that the linear term of
149
temperature had a maximum negative effect (B= -1.81) followed by time (B= -1.15) and
150
sugar content (B= -1.04). Fig 1(a and b) shows the decrease in color value with increase in
151
baking temperature, time and sugar content. It can be observed that the major effect was of
152
baking temperature as with rise in temperature lightness decreases remarkably. The decrease
153
in lightness value with increase in temperature, time and sugar content may be due to
154
caramelization of sugar and Maillard browning reactions causing malanoidin formation
155
during heating and thus, resulting in the darkening of product (Manzocco et al. 2000). The
156
results are in conformity with the Farris and Piergiovanni (2008) who reported the decrease in
157
color (L- value) of cookies with increase in temperature and sugar content. Gan et al (2007)
158
also reported the similar decreasing trend of colour value for cake.
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of 0.963 is in reasonable agreement with the Adj R-Squared of 0.983.
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3.1.2 Spread factor
162
Spread factor has been considered as an important quality parameter for cookies. Higher
163
spread factor means the higher product yield. The spread factor of cookies varied from 5.89
164
to 7.26. The effect of process variables on the spread factor of cookies is shown in Table 2.
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The magnitude of p value indicates that linear and quadratic effect of all variables was
166
significant. However, among interactions “fat and sugar”, “fat and temperature” and “sugar
167
and temperature” showed a significant effect on spread factor of cookies. The analysis of
168
regression coefficients (Table 3) showed that the linear term of temperature and time had a
169
negative effect with temperature showing higher effect (B= -0.14) than time (B= -0.050).
170
While as fat and sugar had a positive effect on spread factor with magnitude being higher for
ACCEPTED MANUSCRIPT fat content (B= +0.15) followed by sugar content (B= +0.065). The response plots for effect
172
of temperature, fat content and time on spread factor are shown in Fig. 2. Increase in sugar
173
content increased the spread factor of cookies. Similar increased mobility of dough and
174
higher spread of cookies by increase in sugar content was observed by Doescher et al (1987).
175
Kulthe et al. (2014) also observed that the spread factor is decreased by decrease in sugar
176
content. Spread factor increased gradually with increase in fat content. Singh et al. (2002)
177
also observed the increase in spread factor by increase in sugar level and attributed it to the
178
increase in fluidity of dough allowing two dimensional extensible film formation rather than
179
three-dimensional elastic network formation. The spread factor increased from 6.66 to 7.26
180
with increase in temperature from 170 to 180 °C. However, further increase in temperature
181
decreased the spread factor to 5.89. Cookie spread rate is controlled by the viscosity of
182
dough. Sugar contributes to dough viscosity and is related to the dough expansion during
183
baking (Abboud and Hoseney 1984). During low temperature and time combinations sugar
184
gets dissolved in available water content of the dough which lowers the initial viscosity of
185
dough and the cookie spreads at a faster rate and vice-versa occurs during high temperature
186
and time combinations due to lower availability of water content. Another reason for decrease
187
in spread factor of cookies at higher temperature may be that the cookies set up before getting
188
a chance to spread.
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The model F-value of 378 implies that the model is significant. The predicted R-
190
Squared of 0.986 is in reasonable agreement with the Adjusted R-Squared of 0.994.
191
3.1.3 Hardness
192
Hardness has been considered as an important characteristic of cookie quality as it affects
193
consumer acceptance and repeat sales (Gaines et al., 1992). Hardness refers to the ease with
194
which the product will break. Hardness of the cookies varied from 34.05 N to 58.09 N. Table
195
2 depicts the effect of process variables on the hardness of cookies.
ACCEPTED MANUSCRIPT The magnitude of p value indicates that linear as well as quadratic terms of all
197
variables had a significant effect on hardness of cookies (Table 3). Among interactions “fat
198
and temperature”, fat and time and “time and temperature” showed significant effect on
199
hardness of cookies. The regression coefficients revealed that linear term of fat showed
200
negative effect on the hardness of cookies (B= -2.96). The response plots for effect of
201
temperature, fat and time on hardness of cookies are shown in Fig. 3 (a and b). The decrease
202
in hardness with increase in fat content may be due to the tenderizing effect exerted by fat.
203
The decreased hardness may also be due to the encapsulation of flour particles by fat, thereby
204
isolating the flour particles from each other and making them more easily detachable. The
205
linear terms of temperature, sugar and time showed positive effect (Table 3) on the hardness
206
of cookies with the higher magnitude observed for temperature (B= +5.57) than time (B=
207
+2.45). Increased hardness with increase in baking temperature and time may be due to the
208
higher water loss from the dough which may lead to a more rigid fiber frame after baking.
209
Further formation of fiber-protein complexes during high temperature time combinations can
210
also promote the hardening of product (Farris and Piergiovanni 2009). Sugar content showed
211
the non-significant positive effect of lower magnitude (B= +0.12) on hardness. Increase in
212
hardness with increase in sugar content may be due to its conversion from solution to harder
213
glass-like state after cooling. Singh et al. (2002) also observed the increase in hardness of
214
cookies by sugar and attributed it to its conversion to hard glassy state.
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The model F-value of 202 implies that the model is significant. The pred R-Squared
216
of 0.975 is in reasonable agreement with the Adj R-Squared of 0.989.
217
3.1.4 Antioxidant activity ( % DPPH inhibition)
218
Antioxidants have gained increased interest among consumers because the epidemiological
219
studies have revealed the lower risk of cancer and cardiovascular diseases with frequent
220
consumption of antioxidants (Temple 2000). Antioxidant activity of optimized cookies varied
ACCEPTED MANUSCRIPT 221
from 18.12 % to 20.97 % (Table 2). The p value indicates that linear and quadratic effect of
222
all variables (except fat content) was significant (Table 3). However, in case of interactions
223
“sugar-time”, “sugar-temperature” and “temperature-time” showed the significant effect. The analysis of regression coefficients showed that the variables sugar, temperature
225
and time had a positive effect on antioxidant activity with magnitude being higher for
226
temperature (B= +0.630) followed by time (B= +0.233) and sugar content (B= +0.185). The
227
response plots for effect of temperature, sugar content and time on antioxidant activity are
228
shown in Fig. 4. It can be observed that the major effect was that of temperature as with
229
increase in temperature antioxidant activity of cookies increased remarkably. Increase in
230
antioxidant activity with increase in sugar, temperature and time may be due to the formation
231
of melanoidins during baking process. These compounds have been reported to show
232
antioxidant activity (Manzocco et al. 2000). The antioxidant activity of cookies remained
233
almost constant after 180 °C suggesting the stability of molecules bearing radical scavenging
234
ability. Increase in antioxidant activity can also be attributed to the possible breakdown of
235
phenolics or their degradation products which could react with the reagent (Sun et al., 2014).
236
Lindenmeier and Hofmann (2004) determined the influence of baking conditions on
237
antioxidant activity of bread and the antioxidant activity was found to be higher in crust in
238
comparison to the crumb and untreated flour. They found a 3-5 fold increase in antioxidant
239
activity with increase in baking temperature and time and attributed it to the formation of
240
antioxidant compound pronyl-L-lysine. The increase in antioxidant activity due to baking was
241
also observed by Sharma and Gujral (2014).
242
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The model F-value of 84 implies that the model is significant. The pred R-Squared of
243
0.937 is in reasonable agreement with the Adj R-Squared of 0.975.
244
3.1.5 Overall acceptability
245
Overall acceptances of cookies as the sum of various quality characteristics (surface color,
ACCEPTED MANUSCRIPT 246
texture, mouth feel and taste) can be used to assess the quality of the cookies prepared. The
247
overall acceptance score of cookies varied from 5.54 to 7.84 (Table 2). The magnitude of p value indicates that linear as well as quadratic terms of all
249
variables had a significant effect on overall acceptability of cookies (Table 3). Among
250
interactions “fat and sugar” and “time and temperature” showed a significant effect on overall
251
acceptability of cookies. The regression coefficients revealed that linear terms of fat and
252
sugar showed positive effect on the overall acceptability of cookies with the sugar content
253
(B= +0.16) showing effect of slightly higher magnitude than fat content (B= +0.13). While as
254
linear terms of temperature and time showed negative effect on the overall acceptability of
255
cookies with the temperature showing effect of higher magnitude (B= -0.10) than time (B= -
256
0.05).
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Fig. 5 shows the effect of temperature, fat, sugar content and time on over all
258
acceptability of cookies. The cookies with higher fat and sugar content seemed to be more
259
acceptable by the panellists. Positive effect of fat and sugar can be due to the reason that
260
sugar acts as flavour enhancer and fat provides the better mouth feel. The negative effect of
261
temperature on overall acceptability of cookies may be due to higher temperature resulting in
262
decreased color (L-value) and increased hardness of cookies.
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The model F-value of 153 implies that the model is significant. The pred R-
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264
Squared of 0.969 is in reasonable agreement with the Adj R-Squared of 0.986.
265
3.2 Optimization
266
The simultaneous optimization of the responses was done numerically using statistical
267
software Design-expert 11.The criterion for optimization of cookies was to obtain maximum
268
values for spread factor, antioxidant activity and overall acceptability However, color and
269
hardness were kept in range (Table 4). Numerical analysis report showed that fat content,
270
sugar content, baking temperature and baking time of 41.83 %, 33.95 %, 181 °C and 18 min,
ACCEPTED MANUSCRIPT respectively gave an optimized product of desirability 0.93. High desirability value indicated
272
the suitability of process conditions for achieving favourable results in terms of responses.
273
Predicted and experimentally determined values for responses were color 52.72 and 53.05
274
spread factor 7.26 and 7.16, hardness 46.40 and 47.05, antioxidant activity 20.54 and 20.67
275
(% DPPH inhibition) and overall acceptability 7.76 and 7.61, respectively. Optimized
276
solution provided the optimum range of variables for production of best quality cookies in
277
terms of color, spread factor, hardness, antioxidant activity and overall acceptability. The
278
optimum conditions obtained may be recommended for preparation of quinoa cookies.
279
4. Conclusion
280
Response surface methodology was effective in optimization of different formulations and
281
processing parameters for development of quinoa cookies. Model analysis revealed that all
282
models were adequate. Validity of the models was evaluated using the relevant statistical aids
283
like coefficient of determination (R2), F-value and coefficient of variation. These statistical
284
tools revealed that the models were statistically adequate. The selected independent variables,
285
like contents of sugar and fat and baking temperature and time markedly affected the
286
responses like color, spread factor, hardness, antioxidant activity and overall acceptability of
287
quinoa cookies. Quinoa cookies can be used as substitute for cereal in Indian subcontinent
288
during fasting days. The linear terms of temperature, fat content, sugar content and time was
289
found to have significant effect on all responses (color, texture, spread factor, antioxidant
290
activity and overall acceptability).
291
Acknowledgement
292
The authors would like to acknowledge UGC for providing Maulana azad national fellowship
293
(MANF).
294
Conflict of interest
295
The authors declare that there is no conflict of interest
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Ethical Review
297
This study does not involve any human or animal testing
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312 313 314 315 316 317 318 319 320
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References
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Jan, K. N., Panesar, P. S., & Singh, S. (2017 b). Process standardization for isolation of
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Studies, 46(4), 281-292.
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ACCEPTED MANUSCRIPT Table 1. Level of different variables in coded form for cookies preparation Independent variables
Units
Symbols
Levels -α
-1
0
1
α
g 100g-1 Flour
X1
30
35
40
45
50
Sugar
g 100g-1 Flour
X2
20
25
30
35
40
°C
X3
160
170
180
190
200
Min
X4
12.5
15
17.5
20
22.5
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Baking Temperature Baking Time
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Fat
ACCEPTED MANUSCRIPT Table 2.The central composite rotatable design with process variables and experimental results of responses.
170 170 170 170 190 190 190 190 170 170 170 170 190 190 190 190 180 180 180 180 160 200 180 180 180 180 180 180 180 180
15 15 15 15 15 15 15 15 20 20 20 20 20 20 20 20 17.5 17.5 17.5 17.5 17.5 17.5 12.5 22.5 17.5 17.5 17.5 17.5 17.5 17.5
Y1
Y2
56.90 56.73 54.48 54.37 52.97 52.90 49.99 49.81 53.48 53.36 51.57 51.48 50.58 50.49 48.92 48.71 53.29 52.87 53.90 50.40 53.75 46.08 55.62 51.56 54.10 54.26 54.87 54.47 54.15 54.71
6.66 6.94 6.82 7.19 6.47 6.69 6.51 6.84 6.60 6.89 6.68 7.07 6.38 6.64 6.44 6.74 6.63 7.22 6.99 7.26 6.52 5.89 6.78 6.52 7.22 7.20 7.21 7.19 7.24 7.23
Y3 39.33 35.45 39.36 35.54 48.00 43.15 48.10 43.20 41.98 36.60 42.00 36.63 56.94 49.00 56.87 49.05 47.61 34.05 44.80 46.14 34.90 58.09 36.22 47.15 46.01 46.11 46.03 47.08 46.95 47.00
Y4
Y5
18.83 18.90 19.14 19.16 20.23 20.26 20.42 20.45 19.25 19.27 19.98 20.00 20.50 20.52 20.78 20.81 19.80 19.85 19.75 20.48 18.12 20.97 19.97 20.91 20.25 20.37 20.20 20.36 20.40 20.34
6.30 6.46 6.57 6.91 6.40 6.53 6.65 6.96 6.45 6.62 6.75 7.04 6.10 6.15 6.20 6.44 6.68 7.33 6.76 7.47 5.88 5.54 6.57 6.48 7.73 7.84 7.70 7.67 7.82 7.79
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25 25 35 35 25 25 35 35 25 25 35 35 25 25 35 35 30 30 20 40 30 30 30 30 30 30 30 30 30 30
X4
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35 45 35 45 35 45 35 45 35 45 35 45 35 45 35 45 30 50 40 40 40 40 40 40 40 40 40 40 40 40
X3
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X2
Responses
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X1
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Variables
EP
Runs
Where Y1= color (L-value); Y2= spread factor; Y3= hardness (N); Y4= antioxidant activity (% DPPH inhibition); Y5 = overall acceptability.
ACCEPTED MANUSCRIPT Table 3. Estimated regression coefficients of the fitted second order polynomial and their significance Parameters Model X1 X2 X3
DF 14 1 1 1
X4 X12 X22 X32 X42 X1 X2
Y2
Y3
Y4
Y5
53.42 -0.078 -1.04***
7.22
46.53
20.32
7.76
0.151*** 0.065***
-2.96*** 0.124
0.014 0.185***
0.125*** 0.163***
-1.81***
-0.142***
5.57***
0.630***
-0.098***
1 1 1 1 1 1
-1.15***
-0.050***
2.45***
0.233***
0.050**
-0.318**
-0.073***
-1.34***
-0.136***
-0.198***
-0.554** -1.10*** -0.198** -0.008
-0.023** -0.253*** -0.142***
-0.223*** 1.34 -1.17***
-0.064** -0.206*** 0.018
-0.170*** -0.521*** -0.317***
0.021**
0.009
-0.003
0.042*
X1 X3 X1 X4 X2 X3 X2 X4
1 1 1 1
-0.004
-0.014*
-0.441*
-0.001
-0.014
0.001
0.003
-0.566*
-0.004
-0.012
-0.059
-0.020*
-0.003
-0.068*
-0.023
0.226*
-0.011
-0.015
-0.068*
-0.018
X3 X4 R2 Lack of fit
1
0.351**
0.004
1.37***
-0.076***
-0.142***
0.991 NS
0.997 NS
0.994 NS
0.987 NS
0.993 NS
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10
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*Significant at p ≤0.05 **Significant at p ≤0.01 ***Significant at p ≤0.001 NS- Not-significant; DF- Degree of freedom
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Where Y1= color (L-value); Y2= spread factor; Y3= hardness (N);Y4= antioxidant activity (% DPPH inhibition); Y5 = overall acceptability.
ACCEPTED MANUSCRIPT
Table 4.Criteria and outputs of the numerical optimization of the responses for cookies
X1 X2 X3 X4
Experimental range
Goal In range In range In range In range
Min 35 25 170 15
Max 45 35 190 20
Importance 3 3 3 3
Responses 46.08 5.89 34.05 18.12 5.54
56.90 7.26 58.09 20.97 7.84
3 3 3 3 3
41.83 33.95 181 18 Predicted values 52.72 7.26 46.40 20.54 7.76
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In range Maximum In range Maximum Maximum
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Y1 Y2 Y3 Y4 Y5
Optimum values
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Variables
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Where Y1= color (L-value); Y2= spread factor; Y3=hardness (N); Y4= antioxidant activity (% DPPH inhibition); Y5 = overall acceptability.
ACCEPTED MANUSCRIPT
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Fig 1. Response plots showing the effect of a) Time, sugar and their mutual effect on color (L-value). Other variables are constant: Fat, 40 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on color (L-value). Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour.
58 56
52
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50 48 46
20 19
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Color (L-value)
54
35
33
18
31
17
Baking Time (min)
29
27
Sugar (g)
TE D
16
15
a)
EP
58
25
56
Color (L-value)
AC C
54 52
50
48
46
20 190
19 185
18 180
17
Baking Time (min)
b)
175
16 15
170
Baking Temperature (°C)
ACCEPTED MANUSCRIPT
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Fig 2. Response plots showing the effect of a) Temperature, fat and their mutual effect on spread factor. Other variables are constant: sugar 30 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on spread factor. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour.
7.5
7
SC
Spread factor
6.5
5.5
190 185 180
Baking Temperature (°C)
M AN U
6
41
39
175
37
7.5
Fat (g)
EP
35
6.5
AC C
Spread factor
7
TE D
170
a)
45 43
6
5.5
20
190 19
185 18
180
17
Baking Time (min)
b)
175 Baking Temperature (°C)
16 15
170
ACCEPTED MANUSCRIPT Fig. 3. Response plots showing the effect of a) Temperature, fat and their mutual effect on hardness. Other variables are constant: sugar, 30 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on hardness. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour. 60
RI PT
55
Hardness (N)
50 45 40 35
SC
30
M AN U
190 185 180 Baking Temperature (°C) 175 170
a)
37
35
39
43
41
45
Fat (g)
TE D
60 55
45
40
EP
Hardness (N)
50
35
AC C
30
20 19
Baking Time (min)
18 17 16
b)
15
170
175
180
185
Baking Temperature (°C)
190
ACCEPTED MANUSCRIPT Figure 4: Response plots showing the effect of a) Temperature, sugar and their mutual effect on antioxidant activity. Other variables are constant: fat, 40 g/100 g Flour and time 17.50 min b) Temperature, time and their mutual effect on antioxidant activity. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour. a)
RI PT
21
Antioxidant activity (%)
20.5 20
SC
19.5 19
190
35
33
185
31
180
29
175
Baking Temperature (°C)
21
AC C
19
EP
20.5
19.5
25
TE D
170
20
Sugar (g)
27
a)
Antioxidant activity (%)
M AN U
18.5
18.5
20
190
19
185 18
180
17
Baking Time (min)
b)
175
16 15
170
Baking Temperature (°C)
ACCEPTED MANUSCRIPT Figure 5: Response plots showing the effect of a) Sugar, fat and their mutual effect on overall acceptability. Other variables are constant: temp, 180 °C and time 17.50 min b) Temperature, time and their mutual effect on overall acceptability. Other variables are constant: Fat, 40 g/100 g Flour and sugar 30 g/100 g Flour.
RI PT
8
7
SC
6.5 6 5.5
35 33
41
29
Sugar (g)
39
27
35
TE D
8
AC C
EP
7.5
Overall acceptability
Fat (g)
37
25
a)
6.5
45
43
31
7
M AN U
Overall acceptability
7.5
6
5.5
20
190 19
185 18
180
17
Baking Time (min)
b)
175 Baking Temperature (°C)
16 15
170
ACCEPTED MANUSCRIPT Highlights The suitability of gluten-free quinoa flour for good quality cookies was studied.
•
Physical, functional, textural and sensory properties of cookies are reported
•
Antioxidant potential increased with increase in sugar content, temperature and time.
•
Study improved the low spread factor which is a major drawback in gluten-free
RI PT
•
cookies
EP
TE D
M AN U
SC
Gluten-free cookies with high acceptability were obtained.
AC C
•