Optimization of processing conditions for development of chicken meat incorporated whole wheat bread

Abstract

Present study was conducted to explore the incorporation of high level of chicken meat powder for developing protein enriched whole wheat bread. The aim was to optimise meat level and processing conditions for development of chicken meat bread. Box–Beheken design of response surface methodology was used for optimising the processing conditions of chicken meat incorporated whole wheat bread as processing conditions strongly influence the product characteristics. Meat level (30–35%), proofing time (60–120 min) and cooking time (10–12 min) were contemplated as constrains or variable factors for their effect on responses such as baking yield, moisture, protein, fat, ash, redness and yellowness value, flavour, porosity and overall acceptability which are essential for product acceptability and marketability, while the cooking temperature was kept constant at 220 °C. The responses were assessed by evaluating the physicochemical, proximate, colour units and sensory evaluation. A high coefficient of regression > 0.90 was obtained for all the responses indicating the fit of model. The desirability achieved for these responses was 0.841 for 31.497% meat level with proofing time 107.17 min and baking time of 12.74 min. The study concluded with development of chicken meat bread having high protein content with optimised processing conditions of proofing and cooking time.

Introduction

The advent of twenty first century evidenced an increasing interest in food industry and sector. With the increasing globalization and “Vasundharaya Kutumbakam” concept whole world became a home as one big family and so did their food habits and requirements. Healthy, nutrient rich, convenient foods which require minimum preparation and can fulfill daily requirement are increasing in demand (Seto and Ramankutty 2016; Zhou and Staatz 2016). The importance of animal proteins in diet can never be denied and are even difficult to replace (Leroy and Praet 2015). Regular food items such as bread are excellent source for providing recommended dietary protein intakes of geriatric patients (Beelen et al. 2017; Song et al. 2018). Similarly, the importance of dietary fibre in diets can-not be overlooked. Breads are an important source of cereal in diet and its convenience makes it more popular among fast food sector. Breads are widely used as base material or as supporter in various recipes as its nutritional value is poor. Mostly refined flour is used for bread making that lacks essential elements of diet.

Nowadays researchers are using advanced processing techniques to such as pulse electric technology (Barba et al. 2015), sonication, ultrafiltration etc. (Deng et al. 2015) to recover active ingredients and components such as proteins, dietary fibres, natural antimicrobials and antioxidants etc. from natural sources such as blackberries (Barba et al. 2015), pink guava (Nagarajan et al. 2019) for fortification of foods. Such active ingredients have also been used to enhance nutritional value of bread by incorporating proteins such as whey protein (Song et al. 2018), soy protein (Zhou et al.2018) fish protein (Cercel et al. 2016) etc. or various dietary fibres such as elderberry, orange, pomegranate and spent yeast (Martins et al. 2017).

The development of a novel product requires too many preliminary trials for optimization of formulations or processing conditions. Response surface methodology (RSM) has emerged as a boon for such situations where with the help of the methodology more precise optimizations can be done with different combinations and fewer repetitions (Delgado-Pando et al. 2019). The application of response surface methodology (RSM) in novel areas has opened up new avenues of research towards accurate prediction of responses in the experimental region. In the present scenario response surface methodology (RSM) can be envisaged to be a still better tool if integrated with an efficient simulation system for prediction and optimization of process parameters to meet the future requirement of product and process specificity.

The aim of present study was to increase the nutritional value and status of regular wheat bread by incorporation of chicken meat protein in preparation of whole wheat flour bread using response surface methodology (RSM) for optimising the processing conditions. Such as product would open new avenues for providing protein in diets of children and geriatric patients which cannot consume meat as such.

Materials and methods

Raw materials

Spent hens were procured from Central Avian Research Institute ICAR-CARI, Izatnagar, India, slaughtered and dressed in Experimental Abattoir of LPT Division, Indian Veterinary Research Institute ICAR-IVRI, Izatnagar, India. They were deboned manually and tendon, separable connective tissue and body fat were also removed. Then lean meat was packed in clean low density polyethylene (LDPE) and brought to the laboratory and frozen at − 20 °C until further use. All ingredients were procured from recognized and well established firms. Whole wheat flour, yeast and sugar were procured from local market, Bareilly, India. The dried chicken meat powder was prepared from the chicken meat in the laboratory (Umaraw and Chauhan 2018).

Experimental design

Determination of constrains

For the optimization of formulation of chicken meat bread by response surface methodology, the lower and upper limits of the variables i.e. the constraints were selected on basis of trials. The sensory evaluation and quality characteristics revealed that the lower limit of meat incorporation can be selected as 25 while the upper limit can be at 35 percent because increasing the meat level to 40% affected the quality characteristics of bread. The processing conditions such as kneading and proofing time have prominent effect on dough formation and fermentation for preparation of bread. The preliminary trials revealed that kneading for 15 min have the most desirable dough. Increasing kneading imparted no better dough and reducing kneading time presented incomplete dough formation thus, 15 min kneading was taken as constant. Further the first proofing time is the time when fermentation starts and this phase was kept constant at 30 min to limit the variations in experiment. The second proofing time was varied to find the effect of proofing on the bread preparation. The lower and upper limit was selected as 60 and 120 min. The cooking temperature was selected as 220 °C in the preliminary trials. The minimum cooking time as observed in preliminary trials was selected as 10 min while the upper limit of cooking time was selected as 14 min.

The process of preparation of chicken meat bread was optimized on the basis of three variables including meat level, proofing time and cooking time using three levels under Box–Beheken design of RSM. For the design independent variables were meat level (X₁), proofing time (X₂) and cooking time (X3) while the dependant (response) variables were product characteristics, proximate composition, colour assessment and sensory evaluation but only the following responses were used for product optimisation, baking yield (BY), moisture, protein, fat, ash, redness value (a*), yellowness value (b*), porosity (P), flavour (F) and overall acceptability (OA).

Bread preparation

The dough was prepared according to the formulation given in Table 1. The dry yeast was first dissolved in water and was given an activation time of five minutes then all the ingredients were mixed in a paddle mixer (Hobart, Model:N50) at medium speed for 10 min. Then oil was added and was again worked for further 5 min. For fermentation the prepared dough was kept in bowl covered with a moist muslin cloth and was placed in an incubator at 30 °C for 30 min. Later the dough was punched and moulded into a cylindrical shape and was placed in a baking mould. This dough was again placed in an incubator for another proofing (X₂) for time 60, 90 and 120 min according to the runs. After proofing the dough was baked in an electric oven pre-heated to a temperature of 220 °C for varying cooking time (X₃) for given time as 10, 12, 14 min as obtained for different runs. Slicing and all analysis were done only after cooling breads for at least 4 h.

Table 1.

Formulation used for preparation of bread dough

Ingredients	Treatment
	X-1	X0	X1
Whole wheat flour (g)	75.00	70.00	65.00
SHCMP (g)	25.00	30.00	35.00
Yeast (g)	3.00	3.00	3.00
Sugar (g)	5.00	5.00	5.00
Salt (g)	1.20	1.20	1.20
Fat (g)	5.00	5.00	5.00
Water (ml)	75.00	75.00	75.00

SHCMP Spent Hen Meat Powder

Response parameters

Baking yield

Cooking yield or baking yield was determined by measuring the difference in the sample weight before and after cooking (Murphy et al. 1975).

$$Cookingyield\left(\%\right)=\frac{weightofcookedbreadloaf}{weightofuncookeddough}\times 100$$

Proximate composition

The proximate composition i.e. moisture protein, fat and ash were determined by standard methods as per AOAC (1995).

Moisture content

10 g of crumbled sample was transferred in pre-weighed flat bottom aluminium moisture cup, which was transferred to hot air oven at 100 ± 1 °C and kept for 16–18 h. Dried sample was then placed in desiccator having silica gel as desiccant. After 1 h, the cup containing dried sample was weighed. Moisture content was calculated by applying the following formula:

$$\mathrm{Moisture}\left(\%\right)=\frac{W2-W3}{W2-W1}\times 100$$

where W1 = weight of empty cup, W2 = weight of cup + sample, W3 = weight of cup + dried sample.

Protein content

The protein content of product was determined by standard methods as per AOAC (1995). The nitrogen percentage was calculated using the following formula:

$$\mathrm{Nitrogen}\left(\%\right)=\frac{\left(A,-,B\right)\times 0.0014\times Totalvolumemade}{Weightofsampletaken\times Volumeofdistillate}\times 100$$

where A = Titrated value for sample, B = Titrated value for blank.

Protein percentage was determined by conversion of nitrogen percentage to protein by using conversion factor (6.25) assuming that all the nitrogen in milk was present as protein i.e. protein percentage (%) = N% × 6.25.

Fat content

Fat Content in the sample was extracted in Soxhlet extraction unit. Soxhlet extractor was set with reflux condenser and oil flask which was previously dried and weighed. Sample (5 g) was taken into fat free extraction thimble, dried in oven for 6 h at 60 °C and placed in Soxhlet extraction apparatus. 150 ml of petroleum ether (BP: 60–80 °C) was then poured into extraction flask and condenser was joined and placed on electric heater in order to boil the solvent gently. Extraction was carried out for 16 h Fat content was calculated by using the following formula:

$$\mathrm{Fat}\left(\%\right)=\frac{W2-W1}{W3}\times 100$$

where W1 = weight of empty oil flask, W2 = weight of oil flask + Fat, W3 = weight of sample taken.

Ash percentage

The fresh ground sample (5–10 g) was transferred in a pre-weighed crucible and transferred to muffle furnace at (550 °C) for 4–5 h. Ashed sample was transferred to desiccator having silica gel as desiccant. After 1 hr, the crucible was weighed. The ash content was calculated by the following formula:

$$\mathrm{Ash}\left(\%\right)=\frac{W2-W1}{W3}\times 100$$

where W2 = Weight of the crucible + sample before ashing, W1 = Weight of the crucible + sample after ashing, W3 = Weight of the sample.

Lovibond tintometer colour units

The colour of the product was measured using a Lovibond Tintometer (Model F, Greenwich, U. K) established with cool white light (D₆₅) at 2°. Samples were finely ground in the pestle and mortar, taken in the sample holder and secured against the viewing aperture. The sample colour was matched by adjusting the red (a) and yellow (b) units, while keeping the blue unit fixed at zero. The corresponding colour units were recorded.

Sensory evaluation

Sensory evaluation of chicken meat bread was conducted using an eight point descriptive scale (Keeton 1983) with slight modifications, where 8 = excellent and 1 = extremely poor. The experienced panel consisting of seven members (scientists and post graduate students) from the Division of Livestock Products Technology, IVRI, Izatnagar evaluated the samples. The panelists were briefed about the nature of the experiments without disclosing the identity of the samples and were requested to rate them on an eight point descriptive scale on the sensory evaluation pro-forma for attributes such as flavour, porosity (crumb grain uniformity i.e. pore distribution) and overall acceptability of the product.

Data analysis, modelling and optimisation

The process of preparation of chicken meat bread was optimized on the basis of three variables including meat level, proofing time and cooking time using three levels under Box-Behnken design of RSM. The 3 factor − 3 level Box-Beken experimental design of RSM with 17 experimental runs having five centre points was used for modelling of independent factors (meat level, processing time, cooking time). The coded and uncoded values of the 3 processing variables (factors) and experimental data for various selected responses have been tabulated in Table 2. The second order polynomial equation of function x_i was fitted for analyses of each response.

$$y^{\prime }={\beta }_o+\sum _{i=1}^n{\beta }_i{x}_i+\sum _{i=1}^n{\beta }_i{x}_i^2+\sum _{i=1}^{n-1}\sum _{i=1}^{n-1}{\beta }_{\mathit{ij}}{x}_i{x}_j+\epsilon$$

where Y is the estimated response, β_o, β_i, β_ij are constant coefficients usually determined by least squares method and ε is the error involved in estimating the coefficients β from the experimental data; x_i, x_j are processing variables/independent factors (meat level, processing time, cooking time), n = 3 number of factors.

Table 2.

Second order design matrix used to evaluate the effects of process variables and values of experimental responses for various responses of chicken meat bread

Runs	Meat level (g/kg−1) X1		Proofing time (min) X2		Cooking time (min) X3
	Coded	Uncoded	Coded	Uncoded	Coded	Uncoded
1	0	30	0	90	0	12
2	− 1	25	− 1	60	0	12
3	+ 1	35	0	90	− 1	10
4	0	30	0	90	0	12
5	0	30	0	90	0	12
6	0	30	+ 1	120	− 1	10
7	0	30	0	90	0	12
8	0	30	+ 1	120	+ 1	14
9	+ 1	35	+ 1	120	0	12
10	− 1	25	0	90	− 1	10
11	− 1	25	+ 1	120	0	12
12	+ 1	35	0	90	+ 1	14
13	0	30	− 1	60	+ 1	14
14	+ 1	35	− 1	60	0	12
15	0	30	0	90	0	12
16	0	30	− 1	60	− 1	10
17	− 1	25	0	90	+ 1	14

Runs	Responses
	BY (%)	Moisture	Protein	Fat	Ash	a*	b*	P	FL	OA
1	94.57	36.06	33.05	3.79	2.26	2.51	17.05	7.75	7.5	7.8
2	92.67	34.82	26.25	3.59	2.21	2.12	16.5	7.25	6.5	6.5
3	96.05	43.14	35.01	3.88	2.39	3.08	17.5	6.5	7.25	7.25
4	94.45	37.05	32.47	3.88	2.24	2.49	17.08	7.5	7.5	7.75
5	94.74	37.34	32.08	3.81	2.31	2.53	17.02	7.5	7.75	8.25
6	95.03	39.34	29.75	3.61	2.09	2.47	17.12	7.75	7.5	7
7	93.97	38.48	31.65	3.75	2.32	2.55	17.06	7.8	7.5	8
8	93.91	36.75	32.48	4.01	2.42	2.61	16.87	8	7.6	7.5
9	95.88	44.26	36.46	4.18	2.56	3.11	17.34	6.85	7.55	7.5
10	93.01	36.27	24.41	3.15	2.05	2.09	16.68	7.65	6.5	6.5
11	92.57	35.46	26.03	3.71	2.16	2.16	16.34	8	7	6.85
12	94.21	41.63	37.07	4.28	2.71	3.22	17.28	6.5	7.5	7.65
13	92.76	36.48	31.92	3.95	2.38	2.59	16.92	7.5	7	7
14	95.03	44.51	36.44	4.21	2.51	3.15	17.45	6	7	7.5
15	94.23	38.412	31.82	3.85	2.29	2.54	17.05	7.5	7.5	8
16	95.04	37.12	30.46	3.57	2.09	2.3	17.18	7.25	7	7
17	91.94	33.46	26.81	3.68	2.29	2.2	16.28	7.75	7	7

BY baking yield (%), a* redness value, b* yellowness value, P porosity, FL flavour and OA overall acceptability

The analysis of variance was used to indicate significant terms at 5% level of significance in the model along with model adequacy terms such as model F value, Lack of Fit, coefficient of variance (CV), determination coefficient (R²) and adjusted determination of coefficient (Adjusted R²). The response surface analysis was carried out using statistical software package (Design Expert 8.0.4.1, 2010 Trial version) for regression analysis of data and for generating response surface plots.

Results and discussion

Experimental results were fitted to a full quadratic second-order polynomial equation by applying multiple regression analysis. The analyses of variance were performed to determine the lack of fit and the significance of the linear, quadratic and interaction effects of the independent variables on the dependent variables. The result of analysis of variance (ANOVA) for physico-chemical responses has been presented in Table 3 and that of colour and sensory attributes have been tabulated in Table 4. The lack of fit test is a measure of the failure of a model to represent data in the experimental domain at which points were not included in the regression (Varnalis et al. 2004). Coefficient of determination or R² is the proportion of variation in the response attributed to the model rather than to random error and was suggested that for good fit model. The responses were analyzed by using coded units to evaluate the significant effects of the three factors.

Table 3.

The reference can be made as usual

Source	BY			Moisture			Protein
	F-value	P value	R2	F-value	P value	R2	F-value	P value	R2
Model	40.23*	< 0.0001	0.9810	20.54*	0.0003	0.9635	140.48*	< 0.0001	0.9945
X1	247.80*	< 0.0001		156.73*	< 0.0001		1183.26*	< 0.0001
X2	7.34*	0.0302		1.16	0.3179		0.084	0.7800
X3	81.84*	< 0.0001		7.95*	0.0258		51.46*	0.0002
X12	9.40	0.0955		0.22	0.6527		0.079	0.7865
X13	0.014	0.1624		0.47	0.5145		0.16	0.7020
X23	3.38	0.0509		1.06	0.3374		2.22	0.1800
X11	3.71*	0.0182		14.35*	0.0068		9.00*	0.0200
X22	2.44	0.9105		1.40	0.2755		2.03	0.1976
X33	5.53	0.1085		1.64	0.2406		13.85*	0.0078
Lack of fit	0.24	0.8639		0.71	0.5932		0.02	0.9963
CV %	0.26			2.47			1.36
Advanced R2			0.9567			0.9166			0.9874
Coded equation	94.39 + 1.37X1 + 0.24X2 − 0.79X3 + 0.24X1X2 − 0.19 X1 X3 + 0.29X2 X3 − 0.37X12 + 0.014 X22 − 0.22X32			37.47 + 4.19X1 + 0.36X2 − 0.94X3 − 0.22X1X2 + 0.33X1 X3 − 0.49X2 X3 + 1.75X12 + 0.55X22 − 0.59X32			32.21 + 5.19X1 − 0.044X2 + 1.08X3 + 0.06X1X2 − 0.085X1 X3 + 0.32X2 X3 − 0.62X12 − 0.30X22 − 0.77X32

Source	Fat			Ash
	F-value	P value	R2	F-value	P value	R2
Model	45.11*8	< 0.0001	0.9831	67.19*	< 0.0001	0.9886
X1	251.87*	< 0.0001		331.88*	< 0.0001
X2	1.55	0.2528		0.25	0.6330
X3	125.76*	< 0.0001		216.79*	< 0.0001
X12	1.94	0.2068		3.11	0.1210
X13	1.45	0.2671		1.19	0.2009
X23	0.034	0.8581		0.50	0.5031
X11	1.72	0.2305		47.83*	0.0002
X22	7.51*	0.0289		1.99	0.2008
X33	15.37*	0.0057		1.99	0.2008
Lack of fit	1.30	0.3909		0.32	0.8089
CV %	1.41			1.23
Advanced R2			0.9613			0.9738
Coded equation			3.82 + 0.3X1 + 0.024X2 + 0.21X3 − 0.038.X1X2 − 0.032X1 X3 + 5X2 X3 + 0.034X12 +0.072X22 − 0.1X32			2.28 + 0.18X1 + 5X2 + 0.15X3 + 0.025X1X2 + 0.02X1 X3 + 0.01X2 X3 + 0.096X12 − 0.019X22 − 0.02X32

*Significance (P≤0.05) X₁ meat level (g/kg-1), X₂ Proofing Time (min), X₃ Cooking Time (min), BY baking yield, R² coefficient of determination, CV coefficient of variance

Table 4.

The reference can be made as usual and can be maintained

Source	a*			b*			Porosity
	F-value	P value	R2	F-value	P value	R2	F-value	P value	R2
Model	151.05*	< 0.0001	0.9949	182.49*	< 0.0001	0.9958	25.20*	0.0002	0.9701
X1	1266.95*	< 0.0001		1441.89*	< 0.0001		128.41*	< 0.0001
X2	2.87	0.1339		14.65*	0.0065		37.68*	0.0005
X3	36.80*	0.0005		129.54*	< 0.0001		2.01	0.1996
X12	47.42	0.3464		45.19	0.4994		0.11	0.7483
X13	1.30	0.7163		2.58*	0.0373		0.11	0.7483
X23	0.24	0.1003		0.000	0.8907		0.00	1.0000
X11	1.02*	0.0002		0.51	0.0003		57.83*	0.0001
X22	0.14	0.2922		6.57	0.1520		0.17	0.6933
X33	3.58	0.6379		0.02	1.0000		0.38	0.5570
Lack of fit	4.99	0.0774		5.06	0.0757		0.94	0.4995
CV %	1.54			0.21			2.04
Adjusted R2	0.9883			0.9903			0.9316
Coded equation	2.52 + 0.5X1 + 0.024X2 + 0.085X3 – 0.02.X1X2 + 7.5X1 X3 – 0.038X2 X3 + 0.13X12 – 0.02X22 – 9.5X32			17.05 + 0.47X1 – 0.047X2 – 0.14X3 + 0.012X1X2 + 0.045X1 X3 + 2.5X2 X3 – 0.12X12 – 0.028X22 + 0.0X32			7.61 – 0.6X1 + 0.33X2 + 0.075X3 + 0.025X1X2 – 0.025 X1 X3 + 0.00X2 X3 – 0.56X12 – 0.403 X22 + 0.045X32

Source	FL		R2	OA
	F-value	P value	0.9525	F-value	P value	R2
Model	15.58*	0.0008		17.34*	0.0005	0.9571
X1	42.81*	0.0003		43.38*	0.0003
X2	37.41*	0.0005		3.37	0.1090
X3	5.85*	0.0462		9.14*	0.0193
X12	0.04	0.8463		1.14	0.3206
X13	1.01	0.3480		0.093	0.7689
X23	0.16	0.6995		2.33	0.1706
X11	38.33*	0.0004		31.63*	0.0008
X22	7.20*	0.0314		28.21*	0.0011
X33	3.45	0.1056		26.57*	0.0013
Lack of fit	1.55	0.3325		0.26	0.8513
CV %	1.72			2.23
Adjusted R2			0.8913			0.9019
Coded equation	7.55 + 0.29X1 + 0.27X2 + 0.11X3 + 0.012X1X2 – 0.062X1 X3 – 0.025X2 X3 – 0.38X12 – 0.16X22 – 0.11X32			7.96 + 0.38X1 + 0.11X2 + 0.18X3 – 0.087X1X2 – 0.025X1 X3 + 0.12X2 X3 – 0.45X12 – 0.42X22 – 0.41X32

*Significance (P ≤ 0.05) X₁ meat level (g/kg⁻¹), X₂ Proofing Time (min), X₃ Cooking Time (min), R² coefficient of determination, CV coefficient of variance, a* redness value, b* yellowness value, FL flavour and OA overall acceptability

Baking yield

The significance (P < 0.05) of model was indicated by 40.23 F value and 0.24 lack of fit (Table 3). The fitness of the model was further confirmed by a satisfactory value of determination coefficient. The baking yield was significantly (P < 0.05) affected by all three constrains meat level, proofing time and cooking time. Rising level of chicken meat powder (25–35 g) improved baking yield of bread might be due to higher moisture retention activity. In a study on meat balls prepared with incorporation of wheat flour, whey proteins and soy proteins separately, Ulu (2004) reported higher moisture retention and yield in samples containing whey protein and soy protein than that containing wheat flour. Proofing time had significant (P < 0.05) positive linear effect (Fig. 1a) on baking yield which is attributed to fermentation of meat protein during incubation leads to formation of peptides which have higher water holding capacity. However the cooking time exhibited negative impact on baking yield of bread due to higher loss of moisture as the cooking time increased (Fig. 1b). Moisture content of products is responsible for yield product, thus loss of moisture consequently reduce the product yield. Similarly, Danowska-Oziewicz et al. (2007) in chicken and Konieczny et al. (2007) in beef jerky also stated that cooking time has opposite correlation with product yield. Meat level also evinced a significant negative quadratic effect on yield of the product (Fig. 1c).

Fig. 1.

3-D graphical representation of level of meat, proofing time & cooking time on baking yield

Moisture

The model fitted well as indicated by F value and lack of fit which was further confirmed by a satisfactory value of determination coefficient (Table 3). The model indicated that only 3.65% of the variability in the response could not be predicted which is desired for a model to fit. Meat level had significantly (P < 0.05) positive linear and quadratic effect which could be due to its higher protein content which in turn provides higher water binding sites in the form of charged amino groups (Fig. 2a). On the other had cooking time had significantly negative linear effect on moisture which can be related to loss of moisture in vapour form on extended cooking time (Fig. 2b, c). Umaraw et al. (2015) reported that the incorporation of raw, cooked or chicken meat powder form resulted in increased moisture content of bread. Protein fortification with chickpea flour by replacing wheat improved the water absorption capacity with rising amount of chickpea flour (Mohammed et al. 2012).

Fig. 2.

3-D graphical representation of level of meat, proofing time & cooking time on moisture

Protein

The regression model for protein evinced good and significant fit having model F value of 140.48,non-significant lack of fit F-value of 0.02 and satisfactory coefficient of determination, 0.9945 (Table 3). Similarly, Mridula et al. (2016) reported significant effect of groundnut meal on protein content of pasta with R² as 0.887 and insignificant lack of fit. The protein content in chicken meat bread was significantly (P < 0.05) affected by meat level and cooking time both in linear and quadratic terms (Fig. 3a, b). Osimani et al. (2018) also observed a linear correlation of protein with added cricket (insect) powder till 30% substitution of wheat flour in bread making. In another protein fortification study Roncolini et al. (2019) reported that incorporation of mealworm powder significantly increased the protein level of breads and fortification of bread with red meat (Farouk et al. 2018) evinced congruent results. The positive linear effect of meat and cooking time could be because of higher moisture loss on prolonged cooking as well as higher level of protein source. Cakmak et al. (2013) reported that incorporation of chicken meat and chicken meat powder enhances protein contents in treated groups than control. They also observed that addition of the level of chicken meat powder have positive correlation with increased protein content. Incorporation of Similarly, Nadeem et al. (2012) also reported that was a linear increasing trend in protein content with the incorporation of whey protein concentrate and vetch protein isolate in bread. Protein value also increased as the cooking time was prolonge this might be due to loss of moisture and thus the solid content in bread is increased (Fig. 3c).

Fig. 3.

3-D graphical representation of level of meat, proofing time & cooking time on protein

Fat

The model F value of 45.11 indicates that the model is significant while the lack of fit F-value of 1.3 indicates that it is not significant relative to pure error (Table 3). Only 1.62% variability in response was not predicted by the model as reflected from the coefficient of determination. The surface plots (Fig. 4a–c) indicate that meat level and cooking time had significant (P < 0.05) positive linear effect on fat content (Fig. 4b) of the product while quadratic effect of processing and cooking time was also significant (Fig. 4c). Roncolini et al. (2019) observed linear increase in fat content of breads prepared with incorporation of mealworm flour in wheat breads. Chicken meat powder has a greater fat content than whole wheat flour, so replacing wheat flour with it might be the possible reason for positive linear effect of meat powder. Positive linear effect of cooking time on fat might be due to the concentration effect i.e. loss of moisture. Verma et al. (2015) reported similar higher fat content in chicken noodles by replacing of flour with meat powder which was attributed to presence of higher fat content in meat than flour. Similarly, El-Beltagi et al. (2017) also documented that addition of carp fish powder considerably increased the fat content of pizza.

Fig. 4.

3-D graphical representation of level of meat, proofing time & cooking time on fat

Ash

The model F value of 67.19 indicates that the model is significant while the lack of fit F-value of 0.32 indicates that it is not significant relative to pure error (Table 3). The fitness of the model was further confirmed by a satisfactory value of determination coefficient, indicating that 98.86% variability in response were predictable by the model selected. Meat level showed positive linear and quadratic effect at 95 percent confidence level while cooking time evinced a positive linear effect on ash content of the product (Fig. 5a–c). Meat is a rich source of minerals which could explain to some extend its positive linear effect on ash content. Külcü et al. (2019) similarly observed increase in ash content of bread with increasing level of strip loin beef powder. Similarly incorporation of insect flour in wheat bread evinced significant increase in the ash content of breads (González et al. 2019). Alu’datt et al. (2012) and El-Adawy (1997) have also reported an increase in protein and ash contents on supplementation of wheat flour with barley protein isolate and sesame protein concentrate/isolate, respectively. Verma et al. (2014) reported that, substitution of wheat flour with chicken meat increased the trend of ash content in products by increasing level of meat. The higher protein and ash contents of prepared meat bread could suggest higher nutritional value compared with control bread.

Fig. 5.

3-D graphical representation of level of meat, proofing time & cooking time on ash

Redness (a*) value

The results of analysis of variance assured the goodness of fit for the model with significant model F value, non-significant lack of fit and satisfactory coefficient of determination (Table 4). The redness value of the product was significantly affected by positive linear and quadratic effect of meat level along with positive linear effect of cooking time (Fig. 6a–c). Breads incorporated with mealworm flour reported higher L, a, b values in a study by Roncolini et al. (2019) which increased significantly with the level of meal worm flour incorporation. Similarly, bread incorporated with whey protein concentrate and buttermilk powder also evinced higher redness value with increased level of incorporation (Madenci and Bilgiçli 2014). It might be attributed to the increased protein and amino acid level which could have led to higher amino-sugar interaction leading to browning known as Maillard reaction (Roncolini et al. 2019).The redness score of developed bread was increased with substitution of wheat flour with chicken meat this increase in redness value of bread might be attributed to myoglobin and haemoglobin content of meat. Similarly the cooking time also evinced positive correlation with the redness value bread might be due to formation of brown pigment during baking (Fig. 6b). Lazo-Vélez et al. (2015) and Umaraw and Chauhan (2018) have reported that during heating Maillard reaction takes place i.e. reducing sugar react with amino acid present in food, and lead to formation of brown pigments which is also responsible for red colour.

Fig. 6.

3-D graphical representation of level of meat, proofing time & cooking time on redness

Yellowness (b*) value

The regression model for yellowness was significant (P < 0.05) with model F value of 182.49 and lack of fit F-value, 5.06 indicating insignificant relation to pure error (Table 4). The coefficient of determination proved the goodness of fit with having just 0.42% of the variability in the response unpredicted by the model. Meat level evinced significantly positive linear effect on b* value while processing and cooking time showed negative linear effect (Fig. 7a–c). Interaction of meat level and cooking time also had significant positive effect while meat level’s quadratic effect was significantly negative. Similarly, in preparation of chicken meat noodles Khare et al. (2015) observed positive quadratic effect of meat and cooking time on colour of the product. Chinma et al. (2015) observed that incorporation of naturally fermented rice bran protein concentrate and yeast-fermented rice bran protein concentrate in wheat bread increased it’s a* and b* values and the incorporation level of meat in bread had positive correlation with yellowness (b*) which was attributed to the change in the oxidation-réduction potential and non-enzymatic browning reactions during baking. O’Sullivan et al. (2003) also reported that yellowness of meat was also interrelated to brown as described by sensory panelists than to sensory evaluation of blue and yellow descriptors. Smith et al. (2012) have documented that yellowness values of uncooked meat usually increases when cooked. Farouk et al. (2018) also reported significantly higher a* and b* values in red meat incorporated breads developed for geriatric patients.

Fig. 7.

3-D graphical representation of level of meat, proofing time & cooking time on yellowness

Porosity

Correlation coefficient of R² = 0.9701 and Model F-value of 25.20 (Table 4) exhibited that response surface model for porosity was significant (P < 0.05), which was further exemplified by the lack of fit. Meat level had negative linear and quadratic effect at 95 percent confidence level on porosity while processing time had significant linear effect (Fig. 8a–c). This might be due to decrease in gluten level with increased replacement of whole wheat flour with chicken meat. The decreased porosity might also be attributed to the absence of fibre in meat which was used to replace whole wheat flour. Previously, Lazaridou et al. (2007) have reported higher porosity in dietary fiber enriched bread, which was attributed to the higher trapping and stabilization of air in fiber rich bread which prevented the coalescence of pores. Cercel et al. (2016) also reported decrease in porosity attribute of fish protein concentrate and lyophilised fish protein concentrate incorporated wheat bread.

Fig. 8.

3-D graphical representation of level of meat, proofing time & cooking time on porosity

Flavour

The regression model showed goodness of fit (R² = 0.9525) and highly significant (P < 0.05) level of fitted model with non-significant lack of fit relative to pure error (Table 4). The fitness of the model was further exemplified by determination coefficient, indicating that 95.25% of the variability in the response could be predicted by the model. The flavour was significantly affected by meat level, proofing time and cooking time. All three variables had significant positive linear effect on flavour while meat level and processing time showed significant negative quadratic effect (Fig. 9a–c). Singh et al. (2015) observed significant linear effect of all variables meat level, oil level and cooking time on flavour of developed chicken caruncles using response surface methodology (RSM). The effect might be due to the increased protein level which produced more flavour compounds on fermentation and cooking. During lesser processing time, reaction of amino acid and sugar on cooking along with meat fat produces unique flavour thus incorporation of the meat improved the flavour score. However, higher meat level and increased processing time (Fig. 9a) decreased the flavour scores which might be due to production of some compounds by the microbial fermentation during incubation. Meinert et al. (2016) reported production of some “off-odor”, “chemical flavor”, and “bitter taste compound during hydrolysis of bovine heart.

Fig. 9.

3-D graphical representation of level of meat, proofing time & cooking time on flavour

Overall acceptability

Consumers are attracted towards much more towards the nutritionly and organoleptically superior food items (Zinoviadou et al. 2015; Galanakis 2018) addition of protein rich component in cereal based products also enhance its nutritional importance and health claim (Galanakis 2015).The overall acceptability of chicken bread was significantly affected by the factors studied. The regression model was well fitted with significant model F value, non-significant lack of fit and satisfactory determination coefficient (Table 4). Meat level and cooking time showed significant positive effect on overall acceptability while all the variants exhibited significant quadratic effect at 95 percent confidence level on overall acceptability (Fig. 10a–c).Similarly Khare et al. (2015) also observed that all independent variables and their interactions significantly affected the overall acceptability of chicken noodles using response surface methodology (RSM). Similarly, Mridula et al. (2016) reported increase in overall acceptability of pasta with increased level of groundnut meal. Islam (2002) also reported that addition of the milk protein in to bread increased linearly upto 5% substitution followed by reduction of the overall acceptability at higher level i.e. at the 7% level of substitution.

Fig. 10.

3-D graphical representation of level of meat, proofing time & cooking time on overall acceptability

Optimisation

Responses were optimized individually in combination using Box-Behnken design (Design Expert 8.0.4.1, 2010 trial version). In response surface analysis, the selected model was used to calculate the stationary point. A stationary point is a point at which the slope of the response surface is zeroed in all the directions. The factors (meat level, proofing time and cooking time) significantly affected the chicken bread quality. All the parameters studied, yield, moisture, protein, fat, ash, yellowness, redness, porosity, flavour and overall acceptability were used for optimisation of the processing conditions as these parameters have significant role in determining the product commercialization and consumer acceptability. These parameters were numerically optimized keeping yield, moisture, protein, fat, ash, yellowness, redness, porosity in range and flavour and overall acceptability at maximum. The desirability achieved for these responses was 0.841 (Fig. 11) at 31.497% meat levels, 107.17 min processing time and 12.743 min cooking time. Thus constraints, meat level 31.5% with a processing time of 107.2 min and cooking time of 12.7 min were optimum for development of chicken meat bread.

Fig. 11.

Effect of level of meat, proofing time & cooking time on desirability

Conclusion

Development of a novel product requires innumerable attempts for successful product design and processing conditions. The use of Response surface methodology has opened new avenues of process standardisation and optimisation (Fig. 12). The use of Box-Beken design for optimisation of three important factors, meat level, proofing time and cooking time for developing novel chicken meat bread was very effective. The regression models and surface plots evinced the effect of all the factors on various responses such as yield, moisture, protein, fat, ash, yellowness, redness, porosity, flavour and overall acceptability. These responses achieved a desirability of 0.841. Thus, it was concluded that chicken meat level of 31.5%, processing time of 107.2 min and cooking time of 12.7 min were optimum for preparation of chicken meat bread, a novel and healthy snack.

Fig. 12.

Figure of cooked chicken meat bread and bread slices

Compliance with ethical standards

Conflict of interest

None.