Optimizing Acceptability of Chicken Nuggets Containing Fermented Cowpea and Peanut Flours

Prinyawiwatkul, Witoon; McWatters, Kay H.; Beuchat, Larry R.; Phillips, R. Dixon

doi:10.1111/j.1365-2621.1997.tb15480.x

Cited by 47 publications

(26 citation statements)

References 20 publications

(47 reference statements)

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“…Response Surface Methodology (RSM) is a statistical technique that can be used to determine systematically the effects of independent variables on response variables while minimizing the number of evaluations. Also, it has been applied successfully for description of sensory evaluation of rice flour yeast breads (Ylimaki et al ., 1991), tortillas (Holt et al ., 1992), liquid coffee whitener (Abdullah et al ., 1993), for physical measurements such as rice flour yeast breads (Ylimaki et al ., 1988) and for optimization of a variety of food products such as sour rye bread (Heiniö et al ., 1997), soft tofu (Shih et al ., 1997) and chicken nuggets (Prinyawiwatkul et al ., 1997).…”

Section: Introductionmentioning

confidence: 99%

Determination of optimum hazelnut roasting conditions

Saklar¹,

Katnas²,

Ungan³

2001

Int J of Food Sci Tech

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The objective of this study was to test the hypothesis that the roasting conditions used for hazelnuts, such as the air temperature, air velocity and roasting time (independent variables), could be optimized by using Response Surface Methodology. Effects of independent variables on sensory and physical characteristics were determined. A consumer test was used to determine the acceptable samples. Very dark and very light roasted samples, corresponding to 165 °C, 3 m/s, 25 min and 125 °C, 1 m/s, 15 min process conditions, respectively, were unacceptable. Superimposed contour plots were used to determine the values of independent variables and these showed the process conditions where all product characteristics were acceptable to consumers. At low velocity (0.3 m/s), acceptable products were produced at about 165–179 °C for 20–25 min. When air velocity increased, air temperature shifted to lower temperatures. Samples roasted at 145 °C, 2 m/s, 28 min, 165 °C, 1 m/s, 25 min and 145 °C, 3.7 m/s, 20 min produced the most acceptable products. The sample roasted at 165 °C, 1 m/s, 25 min required the least air velocity and was the most economical in terms of energy consumed among the samples rated most acceptable by consumers.

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Section: Introductionmentioning

confidence: 99%

Determination of optimum hazelnut roasting conditions

Saklar¹,

Katnas²,

Ungan³

2001

Int J of Food Sci Tech

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“…Based on LRA, the 3 most influential attributes for purchase intent ( P < 0.05) were taste, mouthfeel, and overall liking (Table 4). LRA has been applied, in conjunction with the mixture response surface methodology, for consumer acceptance and consumer‐oriented product optimization (Prinyawiwatkul and Chompreeda 2007). The mixture response surface contour plots generated for these 3 attributes, along with their predictive models are shown in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

Sensory Optimization of a Mayonnaise‐Type Spread Made with Rice Bran Oil and Soy Protein

Garcia¹,

Sriwattana²,

No³

et al. 2009

Journal of Food Science

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The optimum formulation for mayonnaise-type spreads containing rice bran oil (RBO) and soy protein concentrate (SPC) was determined based on sensory acceptability. RBO and SPC were used due to their health benefit claims such as lowering risk of heart disease. The effects of the proportions of high-impact ingredients (RBO, SPC, and water) on sensory acceptability of the spreads were determined. The 10 spread formulations, prepared following a 3-component constrained simplex lattice mixture design, were subjected to a consumer acceptance test to identify sensory attributes driving consumer acceptance and purchase intent. Predictive regression models were used to plot mixture response surfaces of the critical sensory attributes (taste, mouthfeel, and overall liking) that influenced purchase intent. Areas within the contour plots of these critical sensory attributes, having acceptability scores > or = 68 ("moderately like" on the 100-mm bidirectional labeled affective magnitude scale) were chosen and superimposed to obtain a predicted optimum formulation range (37% to 43% RBO, 4% to 7% SPC, and 52% to 57% water). The formulation containing 37% RBO, 6% SPC, and 57% water, which was located within the optimum region, was selected as a base for further developing flavored (sour cream & onion, cheddar & sour cream, or Monterrey Jack dried cheese) products. All flavored spreads were significantly more acceptable than the plain formulation. Purchase intent of all flavored products also significantly increased once consumers had been given the information about potential health benefits associated with RBO and SPC in the spreads.

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“…Optimization has been defined as a process of deriving the best formulations from several components (Prinyawiwatkul et al, 1997). Based upon this, numerical optimization was conducted after assigning chemical and sensory parameters either to maximum or minimum values in the parameters in the mixes.…”

Section: Optimal Mixture Compositionsmentioning

confidence: 99%