Impact of the incorporation of clove and sesame flour in the formulation of dry chicken sausages on their quality during the fermentation stage

doi:10.21203/rs.3.rs-4857092/v1

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Impact of the incorporation of clove and sesame flour in the formulation of dry chicken sausages on their quality during the fermentation stage

https://doi.org/10.21203/rs.3.rs-4857092/v1

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This study aimed to investigate the effect of sesame flour and clove on the physico-chemical, colorimetric and structural quality of sausages during their fermentation stage. The results indicated that clove could be used as an antioxidant source in sausages containing sesame flour. The principal component analysis (PCA) applied to the physicochemical and colorimetric data set allowed to clearly differentiate the sausage samples according the fermentation stage and, to a lesser extent, their recipes. This trend was confirmed by factorial discriminant analysis (FDA), since 97.22% of correct overall classification was noted. The PCA applied on the MIR spectra allowed to differentiate the formulations, and 100% of correct classification were obtained following FDA application. The partial least squares regression (PLSR) allowed a very good prediction of the physicochemical parameters from the MIR spectra, since R² higher than 0.85 was found for the calibration and validation model, respectively.

Chicken sausage. Quality. Sesame flour. Clove. Fermentation

In recent years, consumers have become increasingly demanding about the consumption of healthier meat and poultry products, due to certain health problems such as cardiovascular diseases, obesity, colorectal cancer [1]. In this context, scientists are seeking to improve the quality of these products by reducing their animal fat content and enriching them with dietary fiber while maintaining their organoleptic and physicochemical qualities intact [1]. The sausage is the oldest meat product known to man [2]. In this same dynamic, numerous strategies have been tested to produce sausages with an improved nutritional profile without affecting their sensory, textural and technological properties. For example, protein-based fat substitutes, such as wheat flour, soy flour and milk-derived proteins, are widely used in the manufacture of low-fat sausage due to their properties. nutritional and functional, including solubility, viscosity and water binding. Additionally, adzuki bean flour, a legume species, has been used as a fat substitute and demonstrated a positive impact on the physicochemical and sensory properties of low-fat beef meatballs [3]. Despite the use of many products of plant origin in deli meats, only 1 study was focused on the use of sesame flour as a source of natural antioxidant and animal fat substitute [4]. However, previous studies have shown that the sesame seed has an exceptional nutritional value, since it is rich in proteins in the order of 17 to 23%, in lipids of 42 to 50%, in monounsaturated fatty acids such as oleic and polyunsaturated fatty acids such as linoleic acid, minerals such as calcium, potassium and iron and vitamins such as E, B3, B5, B6, folic acid, biotin, inositol and choline use in sausages therefore constitutes a beneficial alternative for the meat industry and for consumers. In addition, sesame seed has a preventive role against the appearance of many disorders such as atherosclerosis, hypercholesterolemia, cancer, platelet aggregation and various diseases. In addition to these components, its sesamin and sesamolin composition gives it hepatic detoxification, hypoglycemic, hepato-protective properties, etc. For these reasons in the East, sesame is considered a health food. Its use in sausages is therefore a beneficial alternative for the meat industry and for consumers). In our previous study, we succeeded in partially substituting animal fat with sesame flour for the production of dry sausages [4]. Indeed, it was shown that sesame flour can be used as a partial animal fat substitute and an antioxidant source in dry sausages during processing. Additionally, several studies have concluded that cloves (Syzigium spiceum L.) are an important source of natural antioxidants that can be used to stabilize sausages against microbial spoilage [5]. In this context, Zahid et al. [6] used clove extract, as a natural antioxidant in cooked beef patties. Furthermore, Zahid et al. [6] claimed that clove extracts used at 0.25, 0.5, 1 and 2% in Chinese-style sausages stored for 21 days at 4°C showed a remarkable antioxidant effect. Despite this valuable information on sesame and cloves, to our knowledge, these two products have never been combined as a source of natural antioxidants in sausages. Thus, this study aims to evaluate the combined effects of sesame flour and cloves on the oxidation and structural properties of chicken sausages during the fermentation phase.

Preparation of clove extracts and sesame flour

Dried cloves and sesame flour from Mamou and Faranah (Guinea), respectively, were purchased from the Matoto market in Conakry (Guinea). The cloves were reduced to a fine powder using a blender (Moulinex Valentin mini-chopper, France). The seeds were conventionally roasted at a local factory using pan roasting at a temperature between 100 and 120°C for 10 minutes. While roasting, the seeds were stirred in the pan to brown evenly. The roasted seeds, after being crushed and sieved, were vacuum packed away from light and stored at + 4°C.

Preparation of dry fermented sausages

20 kg and 5 kg of chicken fillets and fat, respectively, were purchased from a commercial supermarket (Arras, France). These products were weighed and cut and ground. The ground materials were mixed using a mixer model PH-201 (PH 201 50HZ 400V, France) and added to the ingredients as shown in Table 1. Then, three batches of sausages were produced on a pilot scale and compounds which are: Control batch (80% chicken fillet, 18% veal fat and 2% sesame flour), batch S1 (80% chicken fillet, 17% veal fat, 2% sesame flour and 1% cloves), batch S2 (80% chicken fillet, 16% veal fat, 2% sesame flour and 2% cloves). The mixtures were introduced into 30 mm cellulose envelopes (KOKO®, Dat-Schaud, France) using a pusher (Talleres Ramon S.L., Vilassar de Dalt-Barcelona, Spain); then marinated for 12 hours at 4°C. The method described by Sangaré et al. [4] was adopted to carry out the fermentation. All measurements were determined in triplicate.

Table 1

List and levels of additives and ingredients used in the manufacture of different sausage batches.
Ingredients	Sausages with different formulations
Ingredients	Control	S1	S2
Salt (g)	121.88	121.88	121.88
Corn starch (g)	27.00	25.70	24.40
Garlic powder (g)	81.25	81.25	81.25
Black pepper (g)	24.37	24.37	24.37
Sucrose (g)	40.62	40.62	40.62
Chili (g)	24.37	24.37	24.37
Sweet paprika (g)	81.25	81.25	81.25
Traditional maturing ferment 302	4.06	4.06	4.06

Measurement of oxidation parameters

The parameters indicative of lipid and protein oxidation were assessed by determining levels of thiobarbituric acid reactive substances (TBARS) and free thiol. Thus, TBARS values were determined by adopting the method described by Wenjiao et al. [7] and the method described by Berardo et al. [8] was adopted to determine the free thiol content.

Colour measurements

The colour measurement was carried out using Minolta Chroma Meter CR-300 (Konica Minolta Sensing Europe, Roissy Charles De Gaulle, France). The colour coordinates (lightness (L*), redness (a*), and yellowness (b*) were determined in triplicate after 1, 5, 10, and 15 days of fermentation.

Mid infrared spectroscopy measurements

The spectral range of 4000 and 700 cm^− 1 at a resolution of 4 cm^− 1 was chosen for the acquisition of MIR spectra at a temperature of 20°C. The Fourier transform spectrometer IRTracer-100 (Simadzu, Duisburg, Germany) mounted with an attenuated total reflection (ATR) accessory was used. The ATR cell consisted of a horizontal ZnSe crystal which had an incident angle of 45° and a total reflection of 10. For the acquisition of the spectra, the finely ground sausage samples were placed on the surface of the ATR and good contact between the crystal and the samples was maintained by exerting light pressure on the handle. The data was performed using LabSolution software and the raw absorbance spectra were cut between 1700 and 1600 cm^− 1 to analyse the Amide I region. The second derivative (number of points 11) was calculated on baseline correction, normalization (with position 1700), smoothing (parameter number 7). For secondary structures, integrated areas of each peak were calculated for the α helix (1654–1662 cm^− 1), the β sheet (1611–1640 cm^− 1), the β turn (1660–1690 cm^− 1) and random coil (1640–1650 cm^− 1 ).

Mathematical analysis of data

The Fisher test was applied via Xlstat V 2019 on each physicochemical and colorimetric variable to compare the three recipes. MIR spectra were normalized by reducing the area under each spectrum to a value of 1. MATLAB and Unscramble X10.4 were used for data normalization and to apply principal component analysis (PCA), factor discriminant analysis (FDA) and partial least squares regression (PLSR).

The MIR spectra were normalised by reducing the area under each spectrum to a value of 1. Then, separately, principal component analysis (PCA) was applied to the normalised physico-chemical and MIR spectra. Factorial discriminant analysis (FDA) was carried out on the first 5 PCs of the PCA applied to the physico-chemical and colorimetric data and to the MIR spectra representing more than 99% of the total variance. Prior to FDA application with leave-one-out cross-validation, 12 groups were created, namely day 1 Control, day 5 Control, day 10 Control, and day 15 Control, S1 at day 1, day 5, day 10 and S1 day 15; and S2 on day 1, S2 on day 5, S2 on day 10 and S2 on day 15 taking into account the duration of fermentation and the nature of the recipes. In order to confirm this trend, a partial least squares regression (PLSR) on the normalised MIR spectra was applied in order to determine the ability of MIR to predict the amounts of TBARS and thiols. Thus, two groups were formed: the first group designated as a calibration model was composed of 24 spectra and the second containing 12 spectra and representing all classes of fermented dry sausages constituted the validation model. The robustness of the model was assessed by determining the square of the correlation ratio of the standard deviation (SD) to the square root of the squared prediction error (RMSEP), called the ratio of the prediction deviation (RPD). This ratio must be greater than 2 for a good calibration. An RPD ratio below 1.5 indicates poor predictions and the model cannot be used for new predictions [9].

Evolution of oxidation parameters

During the fermentation stage (15 days), the parameters indicating lipid oxidation (TBARS) and proteins (thiol) were determined. Regarding the TBARS values, they went from 0.03 ± 0.03 to 0.15 ± 0.33 mg MDA/kg of sausage in the control batch, from 0.03 ± 1.09 to 0, 02 ± 0.001 mg MDA/kg of sausage in batch S1, and from 0.03 ± 0.001 to 0.01 ± 0.02 mg MDA/kg of sausage in batch S2 (Table 2a). These results show that the fermentation stage and the level of clove addition significantly affected the TBARS values (p > 0.05); since the TBARS values gradually decreased throughout the fermentation stage and with the level of clove addition. This trend could be attributed to the antioxidant effects of cloves. This observation is similar to the work of Sharma et al [10], who found that clove oil significantly decreased TBARS levels in sausages. Thus, the results obtained show that clove extracts were effective in protecting sausages against lipid oxidation during the fermentation phase.

Table 2: (a) physico-chemical and colorimetric parameters, (b) Secondary structure determined from MIR spectra of different sausage batches during the fermentation stage, (c) classification table of FDA with leave-one-out cross-validation for physico-chemical and colorimetric and MIR data sets of different sausage batches during the fermentation stage taking into account the fermentation stage , and (d) classification table of FDA with leave-one-out cross-validation for physico-chemical and colorimetric and MIR data sets of different sausage batches during the fermentation stage taking into account the formulation.

Table 2a:

Fermentation (day)	Control	S1	S2
	Thiol (nmol mg/ protein)
1	2.58 ± 0.007^a	3.03 ± 0.001^b	3.04 ± 0.909^b
5	2.88 ± 0.21^a	3.06 ± 0.43^b	3.79 ± 0.21^b
10	2.78 ±0 1.09^a	3.00 ± 0.72^b	3.01 ± 0.01^b
15	2.45 ± 1.21^a	3.00 ± 1.04^b	3.00 ± 0.45^b
TBARS (mg MDA / kg of sausages)
1	0.03 ± 0.03^a	0.03 ± 1.09^a	0.03 ± 0.001^a
5	0.07 ± 0.01^a	0.04 ± 0.32^b	0.04 ± 0.01^b
10	0.07 ± 0.01^a	0.04 ± 0.03^b	0.03 ± 0.01^b
15	0.15 ± 0.33^a	0.02 ± 0.001^b	0.01 ± 0.02^b
L*
1	42.97 ± 0.45^a	40.59 ± 1.09^b	41.86 ± 0.32^b
5	38.14 ± 0.21^a	41.28 ± 0.21^b	40.87 ± 0.25^b
10	38.14 ± 0.56^a	41.28 ± 0.42^b	41.42 ± 1.90^b
15	36.83 ± 2.00^a	39.85 ± 1.07^b	40.07 ± 0.01^b
a*
1	16.00 ± 0.12^a	13.82 ± 1.04^b	13.29 ± 1.08^b
5	14.68 ± 0.25^a	13.56 ± 0.60^b	12.51 ±0.00^b
10	13.68 ± 1.23^a	12.56 ± 1.52^b	12.66 ±0.01^b
15	13.11 ± 0.43^a	12.00 ± 1.21^a	12.03 ± 0.21^a
b*
1	37.48 ± 1.98^a	33.53 ±1.104^b	33.44 ± 1.12^b
5	31.78 ± 0.09^a	30.42 ± 0.21^b	29.62 ± 1.09^c
10	30.62 ± 0.43^a	29.42 ± 1.53^b	29.28 ± 1.21^b
15	27.50 ± 0.54^a	25.70 ± 2.09^b	24.90 ± 1.07^b

Means ± standard deviations; Different capital letters (A. B. C) represent statistical difference between the formulations (p < 0.05); Control: sausages with 80% chicken fillet, 18% veal fat and 2% sesame flour; S1: sausages with 80% chicken fillet, 17% veal fat, 2 % sesame flour, and 1% cloves; S2: sausages with 80% chicken fillet, 16% veal fat and 2% sesame flour, and 2% cloves

Table 2b:

Fermentation time	Control	S1	S2
	α-helix (%)
1	17.41^a	17.63^b	17.66^c
5	17.50^a	17.54^b	17.39^c
10	17.50^a	17.39^b	17.40^c
15	17.50^a	17.28^b	17.40^c
	β-sheet (%)
1	36.37^a	35.99b	36.40b
5	37.70^a	37.65^a	37.95^a
10	37.23^a	37.29^a	38.80^a
15	37.22^a	38.70^a	38.80^a
	β-turn (%)
1	37.98^a	36.76^b	37.56^b
5	34.80^a	34.80^a	34.80^a
10	34.60^a	34.63^a	34.70^a
15	34.50^a	34.70^a	34.70^a
	Random coil (%)
1	6.23^a	7.98^b	6.67^c
5	9.10^a	9.15^a	9.10^a
10	9.20^a	9.20^a	9.20^a
15	9.20^a	9.30^b	9.30^b

Control: sausages with 80% chicken fillet, 18% veal fat and 2% sesame flour; S1: sausages with 80% chicken fillet, 17% veal fat, 2 % sesame flour, and 1% cloves; S2: sausages with 80% chicken fillet, 16% veal fat and 2% sesame flour, and 2% cloves

Table 2c :

	Physico-chemical and textural measurements
Predicted \Observation	S1 J1	S1 J10	S1 J15	S1 J5	S2 J1	S2 J10	S2 J15	S2 J5	Control J1	Control J10	Control J15	Control J5	Total	% correct
S1J1	3	0	0	0	0	0	0	0	0	0	0	0	3	100.00%
S1J10	0	3	0	0	0	0	0	0	0	0	0	0	3	100.00%
S1J15	0	0	3	0	0	0	0	0	0	0	0	0	3	100.00%
S1J5	0	0	0	3	0	0	0	0	0	0	0	0	3	100.00%
S2J1	0	0	0	0	3	0	0	0	0	0	0	0	3	100.00%
S2J10	0	0	0	0	0	3	0	0	0	0	0	0	3	100.00%
S2J15	0	0	0	0	0	0	3	0	0	0	0	0	3	100.00%
S2J5	0	0	0	0	0	0	0	3	0	0	0	0	3	100.00%
Control J1	0	0	0	0	0	0	0	0	3	0	0	0	3	100.00%
Control J10	0	1	0	0	0	0	0	0	0	2	0	0	3	66.67%
Control J15	0	0	0	0	0	0	0	0	0	0	3	0	3	100.00%
Control J5	0	0	0	0	0	0	0	0	0	0	0	3	3	100.00%
Total	3	4	3	3	3	3	3	3	3	2	3	3	36	97.22%
		Mid infrared measurements (4000-900 cm^-1)
S1J1	3	0	0	0	0	0	0	0	0	0	0	0	3	100.00%
S1J10	0	3	0	0	0	0	0	0	0	0	0	0	3	100.00%
S1J15	0	0	3	0	0	0	0	0	0	0	0	0	3	100.00%
S1J5	0	0	0	3	0	0	0	0	0	0	0	0	3	100.00%
S2J1	0	0	0	0	3	0	0	0	0	0	0	0	3	100.00%
S2J10	0	0	0	0	0	3	0	0	0	0	0	0	3	100.00%
S2J15	0	0	0	0	0	0	3	0	0	0	0	0	3	100.00%
S2J5	0	0	0	0	0	0	0	3	0	0	0	0	3	100.00%
Control J1	0	0	0	0	0	0	0	0	3	0	0	0	3	100.00%
Control J10	0	0	0	0	0	0	0	0	0	3	0	0	3	100.00%
Control J15	0	0	0	0	0	0	0	0	0	0	3	0	3	100.00%
Control J5	0	0	0	0	0	0	0	0	0	0	0	3	3	100.00%
Total	3	3	3	3	3	3	3	3	3	3	3	3	36	100.00%

Table 2d:

Physico-chemical and textural measurements
Predicted \Observation	S1	S2	Control	Total	% correct
S1	5	1	6	12	41.67%
S2	3	9	0	12	75.00%
Control	6	0	6	12	50.00%
Total	14	10	12	36	55.56%
Mid infrared measurements (4000-900 cm^-1)
S1	6	6	0	12	50.00%
S2	3	9	0	12	75.00%
Control	0	1	11	12	91.67%
Total	9	16	11	36	72.22%

Control: sausages with 80% chicken fillet, 18% veal fat and 2% sesame flour; S1: sausages with 80% chicken fillet, 18% veal fat, 2 % sesame flour, and 1% cloves; S2: sausages with 80% chicken fillet, 16% veal fat and 4% sesame flour, and 2% cloves

The levels of thiol content found in the Control, S1 and S2 batches for 1, 5, 10 and 15 days decreased significantly during the fermentation stage, since these values were 2.58 ± 0.007, 2.88 ± 0.21, 2.78 ± 0 1.09, and 2.45 ± 1.21 nmol mg/ protein in the Control batch, 3.03 ± 0.001, 3.06 ± 0.43, 3.00 ± 0.72, and 3.00 ± 1.04 nmol mg/ protein in S1 batch and 0.04 ± 0.909, 3.79 ± 0.21, 3.01 ± 0.01, and 3.00 ± 0.45 nmol mg/ protein in S1 batch (Table 2a). No significant difference was observed between S1 and S2 batches (p > 0.05) compared to Control batch; in agreement with the findings of Zhang et al. [11] who depicted that the addition of Salvia officinalis decreased the loss of thiol groups. These results indicate that the addition of clove had a positive effect on retarding protein oxidation in sausages.

Regarding the color measurements (Table 2a), it appeared that the incorporation of clove induced a decrease of L* values. Indeed, L* values at 1 day for Control, S1 and S2 batches were 42.97 ± 0.45, 40.59 ± 1.09 and 41.86 ± 0.32, respectively. While at 15 days, these values were 36.83 ± 2.00, 39.85 ± 1.07, and 40.07 ± 0.01, respectively, and no significant difference (p > 0.05) was observed between the S1 and S2 batches compared to the Control batch. Another explanation for this decrease would be due to the polyphenolic compounds present in the clove being able to oxidize into the relevant quinines and then into darker compounds in the sausages, in agreement with the observations of Zhang et al. [12] who observed a decrease in the value of L* with the addition of clove extract. Considering a given formulation in relation to fermentation time, a gradual decrease was observed. This trend shows both the clove effect and the fermentation time.

The Control batch samples showed higher a* values (p > 0.05) than S1 and S2 batches (Table 2a). Throughout the fermentation stage, the a* value for Control batch decreased significantly compared to the S1 and S2 batches. From these results, it appeared that cloves could have a preventive effect on the discoloration of sausage samples during the fermentation stage. These results are similar to the work of Kong et al. [13] who noted that pork patties with clove extract significantly decreased the a* value compared to the Control batch. Our results are also in agreement with studies carried out by Zhang et al. [12] who also found that clove extract considerably affected the value of a* in sausages.

Regarding the b* value, the control batch showed values ranging from 37.48 ± 1.98 to 27.50 ± 0.54 during fermentation, while batches S1 and S2 showed values ranging from 33.53 ± 1.104 to 25.70 ± 2.09 and from 33.44 ± 1.12 to 24.90 ± 1.07, respectively (Table 2a). In batches S1 and S2, significant differences (p > 0.05) compared to the control batch at days 1, 10 and 15 were observed. The results found show that the addition of cloves significantly impacted the b* value, due to the addition of cloves. Indeed, Zhang et al. [12] noted that the discoloration of sausages could be attributed to the antioxidant properties of phenolic compounds present in cloves which exhibit an antioxidant effect in sausages ([15].

In order to extract the information contained in the physico-chemical and colorimetric data, principal component analysis (PCA) was applied to the normalised data (Fig. 1). The map defined by PC1 and PC2, respectively 62 and 15.8% of the total variance, allowed to clearly differentiate sausage samples according to their stage of fermentation and, to a lesser extent, their recipes. This trend was confirmed by a discriminant factor analysis (FDA) with leave-one-out cross-validation carried out on the 5 PCs of the PCA. Thus, an overall correct classification of 97.22% was obtained (Table 2c). Moreover, by applying the FDA according to the level of addition of the clove, overall rates of 55.56% were obtained (Table 2d).

Evolution of MIR spectra of sausages during fermentation

The MIR spectra recorded on samples from Control and S1 and S2 batches are mentioned in Fig. 2. The spectra were characterized by a strong absorption band around 3330 cm^− 1 due to the presence of O–H stretching and N–H stretching vibrations as described by Pavli et al. [16]. A second small peak was observed at 2920 cm^− 1 which was attributed to the saturated aliphatic C–H stretching [17]. This observation is similar to that of Pebriana et al. [18] who reported that the –CH₂ functional group presented peaks at 2920, and 2922 and 2853 cm^− 1 consecutively as the results of asymmetrical and symmetrical vibration. The peak observed at: i) 2852 cm^− 1 could be due to the asymmetric and symmetrical stretching methylene (−CH₂−) group [18; 19]); ii) 1743 cm^− 1 could be attributed to the carbonyl groups (C=O); and iii) 1640 cm^− 1 could be related to the cis stretch C = C and H–O–H stretch of the water and/or Amide I group [16]. Our results are in agreement with those of Pedersen et al. [17] who stated that the carbonyl (C = O) group of the triglyceride ester presented a peak at 1744/1745 cm^− 1. Pedersen et al. [17] also reported that the peak at 1711 cm^− 1 was assigned to C = O group of free fatty acid substituted trans and cis olefin absorbed near 1674 cm^− 1 and 1659 cm^− 1 as the result of C = C stretching vibration, respectively [17]. Another peak was observed at 1551 and 1403 cm^− 1 which can be due to C–H deformation of CH₂, CH₃ asymmetric deformation or bending, CH₂ scissoring or bending [18]. Those observed at 1246 cm^− 1 and 1162 cm^− 1 could be attributed to the Amide III band and amines, free amino acids and C–N stretch, respectively [18].

The secondary structure of proteins determined on sausage samples during the fermentation stage is presented in Table 2b. The range of 1600–1700 cm^− 1 was used to determine the Amide I band. The different peaks could be attributed to different secondary structures, including α-helix (1654–1662 cm^− 1), β-sheet (1611–1640 cm^− 1), β-turn (1665–1693 cm^− 1) and random coil (1642–1650 cm^− 1). The results obtained show that the protein structure of sausages changed significantly with the fermentation stage. The levels of α-helix decreased significantly in all formulations with the increase in the fermentation stage. The total ratio of α-helix in Control, S1 and S2 batches did not change significantly (p < 0.05); since it varied from 17.41 to 17.50%, 17.63 to 17.28% and 17.66 to 17.40% respectively. On the other hand, the α-helix levels in all 3 formulations increased with the level of clove addition, except for days 5, 10 and 15, where it remained relatively stable. These results indicate that the α-helix levels were affected by both clove and fermentation stage. Interestingly, for a formulation considered in relation to the fermentation stage, the α-helix levels remained relatively stable. This trend indicates that the level of clove addition showed the greatest effect

In S1, S2 and Control batches, the β-sheet levels varied from 36.37 to 37.22%, 35.99 to 38.70% and 36.40 to 38.80%, respectively. Moreover, on day 1, the β-sheet levels were significantly higher (p > 0.05) in batches containing clove, on the other hand, they remained stable on days 5, 10 and 15, since no significant difference was observed between batches. Regarding the β-sheet and random sheet, the same trend was observed throughout the fermentation stages since except on day 1, the β-sheet and random sheet levels remained statistically unchanged (p < 0.05).

The PCA applied in order to extract information from the MIR spectra clearly allowed to differentiate Control, S1 and S2 batches (Fig. 2b). This trend was confirmed by the FDA since 100% correct classification was obtained for all formulations (Table 2c). Moreover, when FDA was applied only considering the level of clove addition, an overall rate of 72.22% was obtained (Table 2d). From the obtained results, it appeared that MIR allowed better discrimination of sausages according to their formulation and fermentation stage than the physico-chemical and textural measurements.

Prediction of some chemical parameters from mid infrared spectra

Partial least squares regression (PLSR) was applied to predict TBARS and thiol levels using the range of 4000 − 900 cm^− 1 (Table 3). Considering the calibration set as shown in Table 3, the prediction levels of TBARS (R² = 0.94, RPD = 1.00; RMSEC = 0.02) and thiols (R² = 0.97, RPD = 2.74; RMSEC = 0.43) can be considered as good. The level of thiols (R² = 0.88, RPD = 2.50; RMSEP = 0.35 (Table 3a) and TBARS (R² = 0.87, RPD = 0.87; RMSEP = 0.03 (Table 3a; Fig. 3c)) was obtained. The developed model demonstrated the potential use of MIR as a tool to predict the physico-chemical composition of sausages enriched with sesame flour and cloves extracts throughout the fermentation stage.

Table 3

Cross-validation results of TBARS and thiol contents using PLSR of the calibration and validation models
Data	Calibration			Validation
Data	R²	RPD	RMSEC	R²	RPD	RMSEP
Thiols ( nmol mg/ protein)	0.97	2.74	0.43	0.88	2.50	0.35
TBARS ( mg MDA / kg of sausages)	0.94	1.00	0.02	0.87	0.87	0.03
RMSEC: Root mean square error of calibration;
RMSEP: Root mean square error of prediction;
RPD: Ratio of prediction deviation;
TBARS: Thiobarbituric acid reactive substances

In this study, we coupled physicochemical and mid-infrared methods with chemometric tools to assess the combined effects of sesame flour and cloves on the quality of sausages during the fermentation stage. The oxidation and colorimetric parameters indicated that sesame flour (2%) associated with cloves (1 and 2%) can be used as an alternative to the partial substitution of veal fat for the production of dried sausages of chicken, since the values obtained for batches S1 and S2 were comparable to the Control sausage. L*, a* and b* values were also affected by the addition of clove; for example, the value of L* decreased with the level of clove addition throughout the fermentation stage. The principal component analysis and the discriminant factor analysis applied to the MIR spectra allowed to clearly separate Control, S1 and S2 batches according to the fermentation stage. In addition, partial least squares regression allowed to excellently predict the physicochemical parameters. Based on the results obtained, we can conclude that sesame flour and cloves could be used as a source of natural antioxidant in dry chicken sausages during the fermentation stage.

Author Contribution

MS obtained funding for the work, performed the laboratory analysis, and wrote the main manuscriptJB optimized the sausage formulation methodsCC validated the laboratory analysis methodsRK processed the data.All authors revised the manuscript..

Acknowledgments

This work has been carried out in the framework of the BIHAUTSECO de France project, which is financed by the French State, and the French Region of Hauts-de-France. We acknowledge the financial support from: i) the Major Domain of Interest (DIM) "Eco-Energy Efficiency" of Artois University; and ii) Embassy of France in Guinea.

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Impact of the incorporation of clove and sesame flour in the formulation of dry chicken sausages on their quality during the fermentation stage

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Abstract

Figures

Introduction

Materials and methods

Preparation of clove extracts and sesame flour

Preparation of dry fermented sausages

Measurement of oxidation parameters

Colour measurements

Mid infrared spectroscopy measurements

Mathematical analysis of data

Results and discussion

Evolution of oxidation parameters

Evolution of MIR spectra of sausages during fermentation

Prediction of some chemical parameters from mid infrared spectra

Conclusion

Declarations

Author Contribution

Acknowledgments

References

Additional Declarations

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