Modelling with Response Surface Methodology of the Effects of Egg Yolk, Egg White, and Sodium Carbonate on Some of the Physical-Chemical and Sensory Properties of Beef Patties

Heterocyclic amines (HCAs) are compounds with carcinogenic potential formed during high-temperature processing of meat and meat products. Vegetables or their extracts with high antioxidant capacity can be incorporated into the meat matrix to reduce their formation, but it is necessary to find the optimal levels to achieve maximum inhibition without affecting the sensory properties. The objective of this study was to evaluate the effects of roselle extract (RE, 0–1%), potato peel flour (PP, 0–2%), and beef fat (BF, 0–15%) on the sensory properties and formation of HCAs in beef patties using response surface methodology. IQx, IQ, MeIQx, MeIQ, 4,8-DiMeIQx, and PhIP were identified and quantified by HPLC. Regression models were developed to predict sensory properties and HCAs’ formation. All models were significant (p < 0.05) and showed a R2 > 0.70. Roselle extract and beef fat had a negative linear effect on the formation of the total HCAs, while PP had a positive linear effect. The optimal formula that minimizes the formation of HCAs included 0.63% RE, 0.99% PP, and 11.96% BF. RE and PP are foods that can be used as ingredients in low-fat beef patties to minimize the formation of HCAs without affecting their sensory properties.

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Application of response surface methodology on sensory properties in the development of mushroom-based patties from grey oyster mushroom (Pleurotus pulmonarius)

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/765/1/012042 ◽

2021 ◽

Vol 765 (1) ◽

pp. 012042

Author(s):

N N Mohd Yusof ◽

Y K Yee ◽

Z Zakaria

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Sensory Properties ◽

Oyster Mushroom ◽

Pleurotus Pulmonarius

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Optimizing Physiochemical and Sensory Properties of Infrared-Hot Air Roasted Sunflower Kernels Using Response Surface Methodology

Journal of Food Quality ◽

10.1155/2018/4186050 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 4

Author(s):

Mahdis Mosayebi ◽

Mahdi Kashaninejad ◽

Leila Najafian

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Color Change ◽

Specific Energy Consumption ◽

Chemical Properties ◽

Sensory Properties ◽

Quadratic Model ◽

Total Phenolic ◽

Phenolic Contents ◽

Hot Air

Roasting sunflower kernels is a key process in production of nuts. In this study, the effect of roasting conditions, including hot air temperature (120–160°C), infrared (IR) power (400–600 W) and roasting time (3–10 min) on energy and specific energy consumption, color parameters (L∗, a∗, b∗, ΔE, BI, SI, WI, and h°), texture, moisture content, chemical properties (pH and total phenolic contents, peroxide value (PV), and sensory properties of sunflower kernel were investigated. In addition, the best models for the responses were obtained, and the proper roasting conditions were determined using response surface methodology (RSM). A quadratic model was proposed for color change (L∗, ΔE, SI, and WI), moisture and total phenol contents, linear relation for a∗, b∗, h°, and 2FI for BI, texture, PV, and pH. Roasting at 425.7 W IR power and 124.3°C for 3.7 min was found to be convenient or proper roasting conditions.

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