
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12559-5
10.1016/j.heliyon.2024.e36528
e36528
Research Article
Study on quality characteristics, shelf-life prediction and frying mass transfer of breaded tilapia nuggets
Liu Shouchun liusc_zjwlab@163.com
ab
Zhang Luyao b
Guo Yongjia a
Wang Minjie a
Cai Hongying b
Hong Pengzhi ab
Zhong Saiyi zhongsy@gdou.edu.cn
a⁎
Lin Jiayong c
a College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Product Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Guangdong Provincial Engineering Technology Research Center of Seafood, Guangdong Provincial Engineering Technology Research Center of Prefabricated Seafood Processing and Quality Control, Zhanjiang, 524088, China
b Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, 524004, China
c Gaozhou Natural Aquatic Products Co., Ltd, Maoming, 525200, China
⁎ Corresponding author. College of Food Science and Technology, Guangdong Ocean University, Zhanjiang, 525200, China. zhongsy@gdou.edu.cn
19 8 2024
15 9 2024
19 8 2024
10 17 e3652830 6 2024
5 8 2024
18 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Deep-fried breaded tilapia nuggets (DFBTNs) have good market prospects as a tilapia deep-processed product. In this study, we used pre-optimized DFBTNs to simulate the mass change from storage to consumption and investigated the changes in storage shelf-life and frying mass transfer kinetics of DFBTNs. Microbial growth trend and shelf-life prediction models at different storage temperatures were developed using a modified Gompertz equation. The R2 of the fitted equations were all greater than 0.98, and the predicted shelf-life of the products was close to the actual measurement time. The ability of the electronic nose and tongue to differentiate between odor and taste can be used as a secondary indicator to determine whether a product is spoiled or not. During the reheating process of deep-frying, the batter shell moisture decreased (18.69 %→6.89 %), and the oil content increased (2.76 %→27.35 %). The mass transfer coefficient k fitted by Fick's second law for moisture evaporation was 0.0086, and the mass transfer coefficient k fitted by the first-order kinetic equation for oil absorption was 0.1137. This study is informative for storing and consuming DFBTNs, which can provide a basis for the deep processing and high-value utilization of tilapia.

Highlights

• Growth kinetics of TPC at different storage temperatures were modeled.

• Shelf life of deep-fried breaded tilapia nuggets estimated by predictive modeling.

• The mass transfer kinetics of deep-fried breaded tilapia nuggets was investigated.

• A negative correlation was found between oil absorption and water loss during frying.

Keywords

Breaded tilapia nuggets
Microbial growth
Mass transfer kinetics
Shelf-life prediction
==== Body
pmc1 Introduction

Tilapia is a popular fish rich in protein and polyunsaturated fatty acids [1]. However, fresh tilapia has high protein and water content, making it prone to spoilage during storage [2]. Therefore, it is urgent to develop processed products that are easy to store and distribute to increase the diversity of high-value utilization of tilapia. The breaded fish is one of the common deep-processing products. After frying, it has a golden color, intense aroma, and crispy shell [3], making it popular among young consumers. Tilapia is also one of the raw materials for fried battered fish because of its rich nutrition, delicate meat, and lack of bone spurs.

Deep-frying imparts unique characteristics to products. However, during long-term storage, fried food spoils and deteriorates due to reactions such as microbial growth and multiplication, protein denaturation, and fat oxidation [4]. Since microbial growth and reproduction are important factors leading to product spoilage, microbial growth kinetic models are widely used for food shelf-life prediction. Currently, mathematical models such as modified Gompertz [5] and Baranyi [6] have been used to fit the microbial growth curves at different storage temperatures to construct shelf-life prediction models for products. For example, Jiang et al. [7] used the modified Gompertz equation to establish a microbial growth trend model and a shelf-life prediction model for pork at different storage temperatures. Rodriguez-Caturla et al. [8] described the growth mathematical model for the growth of Lactobacillus and Enterobacteriaceae based on the Baranyi model. They estimated the shelf-life of vacuum-packed refrigerated beef. Therefore, modeling the microbial growth of deep-frying breaded tilapia nuggets (DFBTNs) and predicting the shelf-life are critical for their storage.

Currently, there is an increasing demand for breaded foods, but most of them have health problems due to high fat content during frying. Most of the thermal processing cooking methods use oil as the heat transfer medium, and its essence is the heat and mass transfer of food ingredients under high-temperature hot oil conditions so that the quality characteristics (color, texture, flavor, etc.) are changed [9]. Extensive studies have focused on the modeling of heat and mass transfer kinetics. Many mathematical models have been proposed, which have been used to understand the deep-frying process of various food products (fishballs, fish fillets, fried chicken, etc.). For example, Ran et al. [10] analyzed the change in mass transfer characteristics of vegetable fish balls during frying using Fick's second law and first-order kinetic equations. Fang et al. [11] analyzed the variation of moisture and oil in deep-fried, electrostatic-fried, air-fried and vacuum-fried fish fillets using the first-order kinetic model. Significantly, understanding the process of moisture and oil changes in food products during deep-frying can help to improve product quality, consumer acceptance, and commercialization.

This experiment was conducted to analyze the quality changes of DFBTNs from storage to consumption; the objectives of this study were to (1) investigate tilapia's microbial growth under different storage conditions and construct a shelf-life prediction model; (2) explore the effects of different cooking methods on the sensory properties of heated DFBTNs; (3) preliminarily analyze the changes in moisture and oil of DFBTNs during deep-frying. We hope this study provides the experimental basis for large-scale fried battered fish food production.

2 Materials and methods

2.1 Materials

Fresh tilapia was purchased from Zhanjiang Aquatic Market; wheat flour, corn starch, potato starch, yeast, salt, monosodium glutamate, sugar, cinnamon powder, clove powder, cumin powder, five-spice powder, ginger powder, pepper powder, jerry powder, golden breadcrumbs, egg, peanut oil were purchased from the nearby commercial centers; fish collagen peptide was purchased from Anyang Bio-technology Co. Ltd. (Zhanjiang, China); tea polyphenols were purchased from Jiahe Xueri Trading Company Limited (Zhanjiang, China); papain and glutamine aminotransferase were purchased from Shanghai Yuanye Biotechnology Co (Shanghai, China).

2.2 Preparation process of DFBTNs

Tilapia meat was washed, seasoned, and chopped using a Jiuyang food processor (JYL-C19V, China) at 23000 rpm for 30 s, repeated 2–4 times. Then, the fish was steamed on a Midea electromagnetic oven (WK2102T, China) at 2100 W for 10 min, and finally shaped into small slices of 1.5 cm × 1 cm × 6 cm. After the slices were coated with batter (wheat flour: corn starch: jerry flour 9:5:1) and breadcrumbs, they were deep-fried at 120 °C for 20 s, and then at 140 °C for 15 s. After drying oil and cooling, the product was aseptically packaged.

Which seasoning formula for salt 2.04 %, monosodium glutamate 1.70 %, sugar 0.10 %, in addition to fishy mix (cinnamon: fennel: cloves: pepper: five spices powder: ginger powder = 1:1:1:3:3:3) 4.32 %, white wine 0.30 % and potato starch 0.06 %; additives formulated for the papain enzyme 0.14 %, the amount of tea polyphenols 0.02 %, glutamine 0.02 %, glutamine aminotransferase 0.36 %. (The amount of seasoning addition was expressed as a mass percentage of the fish.)

2.3 Measurement of product quality characteristics

2.3.1 Sensory evaluation

The sensory evaluation of the randomized products was carried out by eight experienced staff members and postgraduate students (male-to-female ratio of 1:1) in food-related disciplines. The age range of the group members is 21–30 years old, 31–40 years old, and over 40 years old. Before tasting, the team members conducted a separate training course to familiarize the evaluators with the products and attributes of the evaluation. The scores were made in six aspects (color, appearance, bone spurs, flavor, texture and impurities), of which a full score of 100 was given. The specific scoring criteria were shown in Supplementary Table S1.

2.3.2 Color

The color difference meter (3nh NS810, China Guangdong) was used to determine the surface layer color of the product. The sample shell (1 × 1 cm) was placed in the sample tank, and the brightness value L*, redness value a*, and yellowness value b* were measured [12].

2.3.3 Physicochemical properties

pH and total volatile base nitrogen (TVB-N) value: Referring to the methods of Liu et al. [13], the pH value of the sample was determined by a pH meter (Sedolis PB-10, Germany), and the TVB-N content in the sample was determined by micro-diffusion method.

2.3.4 Fat oxidation index

(1) Fat content

The fat content of the samples was determined by Soxhlet extraction regarding the method of Oladejo et al. [14]. Take the homogenized sample in the filter paper cartridge, transfer it to the extraction tube, connect it to the receiving flask with constant weight, add petroleum ether to two-thirds of the volume of the receiving flask from the extractor, and extract it with heat in a water bath for 10 h. Remove the receiving flask and dry it to constant weight. The fat content was the mass percentage of the sample before and after extraction.(2) Acid value (AV)

The determination method referred to the cold solvent indicator titration method of GB 5009.229-1816. 30.0 g sample was weighed and extracted with petroleum ether. The extract was titrated, the consumption of titrant was recorded, and the acidity was calculated.(3) Peroxide value (POV)

POV values were determined concerning the method of Erol et al. [15] with minor modifications. 2–3 g of sample was dissolved in 30 mL trichloromethane-glacial acetic acid mixture (2:3, v/v). Then, add 1 mL of saturated potassium iodide solution and let it stand for 2 min. 100 mL of distilled water was immediately added, shaken well and titrated with 0.01 mol/L sodium thiosulfate standard solution.(4) Carbonyl value (CV)

CV was measured by spectrophotometry according to GB 5009.230-2016. The extracted oil sample was dissolved in 5 mL benzene, and then 3 mL trichloroacetic acid and 5 mL 2,4-dinitrophenylhydrazine solution were added to mix well. It was heated in a 60 °C water bath for 30 min, then cooled to room temperature, and slowly added with 10 mL 4 % potassium hydroxide-ethanol solution (w/v) for standing for 10 min. Spectrophotometric analysis was performed at 440 nm wavelength.(5) Malondialdehyde (MDA) content

The MDA content was determined by visible spectrophotometry with reference to the method of Erol et al. [15]. 4 g of melted grease sample was dissolved in 50 mL of trichloroacetic acid solution, and the mixture was filtered out by double-layer filter paper to remove the grease. Then take the supernatant and add 5 mL of thiobarbituric acid reagent, mix well, and immerse in a boiling water bath for 30 min; take out and cool down at room temperature for 1h, and record the absorbance at 532 nm.

2.4 Microbiological determination and shelf-life prediction

2.4.1 Determination of total plate count (TPC)

The TPC of the samples was determined according to the method of Liu et al. [13]. The samples were weighed for 10-fold serial dilution. Three suitable dilutions were selected, the plate agar was poured and the Petri dish was rotated to mix well. After solidification of the agar, it was turned over and incubated at 30 °C for 72 h. The number of colonies on the plate was recorded to calculate the TPC.

2.4.2 Shelf-life determination

Detect the TPC of the product stored at 25 °C, 4 °C and −18 °C for different times, and establish the dynamic growth equation of the total number of colonies to predict the shelf-life.(1) First-level kinetic model of microbial growth

The samples stored at different constant temperatures were respectively counted for colony counting to obtain the experimental data of TPC, and the growth dynamics under different temperature conditions were described by the Modified Gompertz equation [16].(1) N(t)=N0+(Nmax－N0)×Exp{−Exp[(μmax×2.718/(Nmax－N0))×(λ－t)+1]}

where N(t) is the colony value (lg CFU/g) at time t; N0 corresponds to the initial colony number value (lg CFU/g); Nmax is the maximum colony value (lg CFU/g) when it increases to the stabilization period; μmax is the maximum specific growth rate of microbial growth (h−1); λ is the retardation time of microbial growth (h); and t is the storage time (h).(2) Kinetic modeling of the effect of temperature on microbial growth (Secondary model)

The difference in storage temperature on the growth of TPC in DFBTNs was described by the square root model of the Belehradek equation [17], whose model theory is based on the square root of μmax and the linear relationship between 1/λ and temperature. The relational equation is as follows:(2) μmax=bμ(T−Tminμ)

(3) 1/λ=bλ(T−Tminλ)

where T is the temperature (°C), Tmin is a hypothetical concept for the theoretical minimum growth temperature; and b is the constant.(3) Validation and reliability evaluation

Bias factor (Bf) and Accuracy factor (Af) were used to evaluate the reliability of specific microbial growth kinetic models [18]. The formulas for Bf and Af are given below:(4) Bf=10∑log(Nprojected/Npractice)n

(5) Af=10∑|log(Nprojected/Npractice)|n

where Npractice is the experimentally determined TPC, Npredicted is the predicted value of TPC obtained by growth kinetic modeling at the same time, and n is the number of experiments.(5) Shelf-life prediction and validation

The shelf period prediction equation is as follows:(6) SL=λ−Nmax−N02.718μmax×{ln[−lnNs−N0Nmax−N0]−1}

According to GB 2726-2016, the total number of colonies in a sample of cooked meat products (except fermented meat products) should not exceed 105 CFU/g in any of the five tests, so the minimum spoilage (Ns) was chosen to be 5 lg CFU/g. Shelf-life prediction can be made from the time required from N0 to Ns based on modeling growth kinetics.

2.5 Determination of flavor during storage period

2.5.1 Electronic nose

The electronic nose detection system (Airsense PEN3, Germany) was used to determine the flavor of the product at different storage temperatures and storage times. The sensor array of the electronic nose and its performance was described in Supplementary Table S2. 10 g of the sample were weighed in a small beaker, plastic wrap seal (require sealing without wrinkles), after 2 h of resting time with the electronic nose to determine the detection time of 100 s.

2.5.2 Electronic tongue

The electronic tongue detection system (INSENT SA402B, Japan) was used to determine the flavor attributes of the products under different storage temperatures and storage times. The samples were weighed 50 g and mixed with 200 mL of pure water at 40 °C in a blender. The samples were centrifuged at 5000 rpm for 15 min, and the supernatant was filtered to remove impurities and then subjected to the electronic tongue test.

2.6 Family cooking simulation

2.6.1 Reheating method

Frozen samples were taken and left for a period to allow the product to rewarm. Heat them in different ways, such as frying (130 °C, 30 s), microwaving (800 W, 3 min), baking (160 °C, 10 min), and air-frying (170 °C, 12 min), for sensory evaluation and to determine the best way of reheating at home.

2.6.2 Determination of moisture and oil content of frying

Take the frozen finished product samples and fry them at 130 °C for 10, 20, 30, 60, 90, 120, 150, and 180 s, and put the fried fish pieces into a stainless steel filter to drain off the excess oil on the surface naturally. The moisture and oil contents of the products were determined by referring to the experimental method of Ran et al. [10].

2.6.3 Frying mass transfer kinetics

Frying operation includes the process of food loss of moisture and inhalation of oil and fat, and Fick's second law [14] is used to describe the process of moisture loss during frying.(7) δMδt=Deffδ2Mδx2

where M is the instantaneous moisture content, t is the time, Deff is the effective moisture diffusion coefficient, and x is the center of the sample.

DFBTNs consist of two parts, the outer shell and the inner fish block; the initial moisture content and the temperature distribution after frying of DFBTNs are not uniform, which does not satisfy the conditions for the applicability of Fick's second law, so the outer shell is fitted with Fick's second law alone.

The shell is assumed to be an infinitesimal flat plate mass transfer process occurs on both sides of the shell [19] as in Eq. (8).(8) M_(r)=M−MeM0−Me=8π2exp(－Deffπ24L2)

where Mr is the moisture ratio, M0 is the initial moisture content, Me is the equilibrium moisture content, and L is half the thickness of the sample.

When the frying process reaches equilibrium, the equilibrium moisture content (Me) is very small and is assumed to be negligible. In order to calculate the moisture diffusion coefficient k, the equation can be expressed as Eq. (9).(9) Mr=M−MeM0−Me=8π2exp(－Deffπ24L2)=8π2exp(－kt)

For the oil absorption process in the frying process, Krokida et al. [20] proposed to use the first order kinetic equation (First order kinetic model) to describe, such as Eq. (10).(10) FC=C0[1−exp(－kt)]

where FC is instantaneous oil content, C0 is balanced oil content, k is the mass transfer coefficient for oil absorption and t is the time.

2.7 Statistical analysis

The experiment was repeated three times and the average value was calculated. The Duncan comparison range of SPSS 17.0 software was used to test and analyze the significant difference of the data (P < 0.05), and Origin 2020 was used for plotting.

3 Results and discussion

3.1 Product quality characteristics

3.1.1 Sensory evaluation

Fig. 1(A) showed that the product was golden in color and intact in shape. The product's sensory evaluation was 93.5 points (Fig. 1(B)); the product had no bone spurs, compact structure, uniform lumps with the characteristic aroma of fried food. After deep-frying, the product was salty and light, crispy outside and tender inside, crunchy and delicious. The results showed that the experimentally produced DFBTNs had high sensory quality and were readily accepted by consumers.Fig. 1 (A) Appearances, (B) sensory scores and (C) color of samples.

Fig. 1

3.1.2 Color

During deep-frying, the carbohydrates and proteins in the outer batter undergo a Meladic reaction, affecting the product's color. In determining the surface color difference, the L* and b* values were good, and the color difference between the products was slight, which could present an excellent golden color (Fig. 1 (C)).

3.1.3 Physicochemical properties

Food pH is an essential characteristic of food products and can be used to evaluate product attributes. Product pH 6.64 (<7) was considered acidic. TVB-N refers to the production of alkaline nitrogen-containing substances such as ammonia and amines in animal foods due to the action of enzymes and bacteria, which break down the proteins during the spoilage process [21]. Therefore, the TVB-N value is widely used as an essential indicator to determine the degree of spoilage of aquatic products. The final production TVB-N was 9.1 mg/100 g < 30 mg/100 g [13], much lower than the volatile saline nitrogen limit value.

3.1.4 Fat oxidation

The fat content of fish is low and primarily unsaturated fatty acids. Due to the high temperature and intense conditions during processing, the fish lipids oxidized and deteriorated, and the quality and flavor changed. The AV, POV, CV, and MDA content can reflect the degree of hydrolysis and rancidity of fats and oils, and they are the main indexes of fat oxidation. The fat oxidation indexes of the product were determined. Table 1 showed that the AV, POV, CV, and MDA content were all lower than the national limited standard. The product complied with the food safety standards and was suitable for consumption.Table 1 Physicochemical and fat oxidation indexes of finished products.

Table 1Measurement indicators	Concentration	Standardized limit	Standard sources	
pH	6.64 ± 0.03	–	–	
TVB-N (mg/100g)	0.091 ± 0.007	30	GB 10136-1815	
Fat content (%)	8.67 ± 0.04	–	–	
Acid value (mg/g)	1.40 ± 0.12	2.5	GB 10146-1815	
Peroxide value (g/100g)	0.030 ± 0.001	0.2	GB 10146-1815	
Carbonyl value (meq/kg)	5.84 ± 0.04	50	GB 7102.1–1803	
Malondialdehyde (mg/100g)	0.024 ± 0.004	0.25	GB 10146-1815	

3.2 Microbial growth modeling and shelf-life prediction

3.2.1 TPC

DFBTNs were contaminated by various microorganisms during processing, and the initial colony number of products was 3.45 lg CFU/g. With the extension of storage time, TPC showed a growing trend, while the higher the storage temperature, the faster the microbial growth (Fig. 2(A)). The psychrophilic bacteria contaminated during processing, such as Psychrobacter and Pseudomonas, due to the cold-resistant characteristics of these psychrophilic bacteria, will still grow and reproduce at low temperatures, eventually leading to spoilage of cooling products [22]. However, their metabolic activities are extremely slow, and their growth and reproduction are also prolonged at low temperatures, thus leading to slower deterioration of chilled and frozen foods. The recommended standard for TPC in the hygienic indicators of cooked meat products: the total number of colonies in a sample of cooked meat products (except fermented meat products) should not exceed 105 CFU/g for 5 consecutive times. Therefore, the product was corrupted on the 4th day of normal temperature and the 6th day of refrigeration, and the frozen product did not reach the corruption limit during storage. The results of the TPC showed that frozen storage is more favorable for slowing down spoilage to prolong the products' shelf-life.Fig. 2 (A) Changes in total plate count (TPC) of samples at different storage temperatures, (B) growth curves of colony counts fitted using the Modified Gompertz equation. (C) Curves of the square root of the growth rate (μmax) and temperature, (D) square root of the inverse of the delay period (1/λ) and temperature.

Fig. 2

3.2.2 First-level kinetic model of microbial growth

Based on the counting results of TPC at constant temperature, the first-level model of TPC was fitted, and the growth curves at different temperatures were plotted (Fig. 2(B)). The R2 values of the fitted equations at 25 °C, 4 °C, and −18 °C were 0.997, 0.981, and 0.972, respectively. The R2 values were all greater than 95 %, which indicated that the fit of the equations at each temperature is better and can better reflect the growth of TPC at the corresponding temperature. In addition, Table 2 showed some differences in the growth of microorganisms at different temperatures. With temperature increase, the μmax increased significantly, mainly due to the accelerated enzyme reaction and metabolic rate in the cells, resulting in the accelerated growth rate. However, with temperature decrease, the λ was prolonged, and the Nmax showed a decreasing trend. This might be due to the delayed metabolism of microorganisms at low temperatures, where the growth was inhibited, thus prolonging the time required to reach the Nmax [23]. Therefore, temperature has an important effect on colony counts, and suitable storage temperature should be selected to ensure product quality.Table 2 The initial colony value (N0), the maximum colony value (Nmax), the maximum specific growth rate of microbial growth (μmax), the retardation time of microbial growth (λ) and R2 of samples stored at 25 °C, 4 °C and −18 °C.

Table 2Storage temperature (°C)	N0 (log CFU/g)	Nmax (log CFU/g)	μmax (h－1)	λ (h)	R2	Equation	
25	3.45	6.206	0.02386	12.95207	0.997	Nt = 3.45+(6.20595–3.45)*exp(-exp(2.718*0.023866.20595−3.45*(12.95207-t)+1))	
4	3.45	6.116	0.01756	23.98889	0.981	Nt = 3.45+(6.11632–3.45)*exp(-exp(2.718*0.017566.11632−3.45*(23.98889-x)+1))	
−18	3.45	5.129	0.01032	59.26622	0.997	Nt = 3.45+(5.1294–3.45)*exp(-exp(2.718*0.010325.1294−3.45*(59.26622-x)+1))	

3.2.3 Secondary model fitting

In order to explore the effect of temperature on microbial growth, the square root model of Belehradek's equation was used to describe the linear relationship between the square root of μmax and 1/λ and temperature, and the second-level model of microbial growth was obtained (Fig. 2(C and D)). The temperature showed an excellent linear relationship with the square root of μmax and the square root of 1/λ of microbial growth. The R2 was 0.992 and 0.989, respectively, which indicated that the secondary model could accurately describe the development of TPC at different temperatures. Therefore, the effect of temperature on the kinetics of colony growth can be evaluated using the Belehradek equation.

3.2.4 Model reliability evaluation

The quantitative reliability of the developed microbial growth model was evaluated according to the evaluation criteria proposed by Ross [24]. The closer Af is to 1, the higher the model prediction accuracy. While Bf was between 0.90 and 1.05, the model was the best. As shown in Table S3, the mathematical model developed in this experiment could predict the growth dynamics of DFBTNs well during storage at −18, 4 and 25 °C. The deviations of the predicted colony count ranged from 0.994 to 1.002, and the accuracies ranged from 1.013 to 1.031. It can also be seen from Fig. 2(B) that the growth curves of the predicted colony count under storage at different temperatures almost overlapped with the experimental growth curves. In other words, the prediction model established in this study could accurately predict the TPC growth of DFBTNs at these three different storage temperatures.

3.2.5 Shelf-life modeling

The shelf-life of the DFBTNs was obtained by substituting the Ns from the microbial growth model into Eq. (6). The predicted and measured shelf-life values were shown in Table 3. The predicted shelf-life of products was 78.928 h at 25 °C, 114.025 h at 4 °C and 270.223 h at −18 °C, respectively. The shelf-life predictions showed that the products deteriorated quickly at room temperature while frozen storage maintained the quality of the products better, which was consistent with the TPC results. Frozen storage slows down microbial colonization and reduces the physical deterioration of the products [2,25]. The experimental design had a long time interval between TPC measurements and failed to measure near the spoilage threshold value. However, the shelf-life prediction time was still within the actual time range, indicating that the predictive model results were reliable. Product shelf-life prediction can help minimize losses and waste due to quality degradation and the resulting adverse effects on customers.Table 3 Predicted and measured shelf-life of deep-fried breaded tilapia nuggets (DFBTNs) in storage.

Table 3Storage temperature (°C)	Predicted time (h)	Measured time (h)	
25	78.928	48–96	
4	114.025	96–144	
−18	270.223	240–360	

Compared with the prepared fried food sold in the actual market, the shelf-life of this product is shorter, which is presumed to be mainly due to two reasons: (1) microorganisms contaminated the product during processing and production, which resulted in higher initial colony counts; (2) the oil content of the product was relatively high, which accelerated the oxidation of oil and fat to promote the deterioration of the product during the period of storage; (3) preservatives were not added in the product preparation.

3.3 Flavor substance changes in DFBTNs

3.3.1 Electronic nose

The decomposition of proteins by microorganisms to produce unpleasant odors such as sulfides and nitrogen oxides can reflect the spoilage of the product [26]. From the electronic nose radar chart (Fig. 3(A)), the primary response values of the product during storage were W5S and W1W, indicating that the main odor components of the product were nitrogen oxides and inorganic sulfides, and their response values increased during storage. The high W5S and W1W response values for samples stored at room temperature for 10 days indicated that the samples had unacceptable odors and were spoiled. The high values of the main odor characteristics of the samples at room temperature for 5 days presumed that they were close to spoilage. The relatively small change in the odor of the samples in the refrigerated and frozen groups was attributed to the fact that refrigeration reduced the metabolic rate and activity of microorganisms and enzymes and slowed down the spoilage process of tilapia patties [8]. Freezing inhibited the chemical reactions and microbial growth that lead to spoilage by lowering the temperature of the food to −18 to −30 °C [27].Fig. 3 (A) Electronic nose radargram and (B) electronic tongue radargram of samples.

Fig. 3

3.3.2 Electronic tongue

Fig. 3(B) showed the electronic tongue flavor radargram of the samples. With the prolongation of storage time, the flavor of tilapia gradually deteriorated, and sourness, bitterness, bitter aftertaste, astringency, and astringent aftertaste increased to varying degrees. In general, the dominant flavor of fish muscle changed from freshness to bitterness and astringency during storage [26]. The changes in flavor values were mainly due to muscle autolysis and microorganisms during storage [28]. Among them, the most significant change in flavor value was observed in ambient storage samples, indicating that they were spoiled and inedible. The frozen flavor values showed less change in quality during storage. Thus, frozen storage can be chosen to prolong the products' shelf-life.

The results of the electronic sensor showed that the storage method and time affected the flavor response value of the product, and the increase in storage time and temperature increased the spoilage of the product. The electronic nose and tongue could distinguish the odor and taste of the product at different storage time points under the same storage temperature, which can be used as an auxiliary indicator to determine whether the product is spoiled.

3.4 Family cooking

3.4.1 Choice of reheating method

Different cooking methods can significantly impact the sensory and physicochemical properties of foods. Fig. 4(A) showed the sensory evaluation of DFBTNs after different cooking methods. After microwaving, DFBTNs were unsuitable for consumption because they were crunchy, rough and lacked crispiness. The quality of the breaded tilapia deteriorated because, during microwave heating, the moisture and oil in the inner core of the food moved outward [29], which made the outer skin soft and impacted the overall quality. There was no significant difference between the products of the other three cooking methods (P > 0.05). Among them, the products heated in the air fryer had a distinctive and rich flavor, but the water evaporation increased, and the texture was slightly dry. After baking heat, the batter layer gave the product a unique cake flavor and a high comprehensive score. Deep-frying did not change the flavor of the product, which was the original flavor of the processed product and received the second-highest overall score. For actual home consumption, deep-frying was convenient and did not require additional baking equipment; therefore, deep-frying was chosen for this experiment to reheat and investigate the mass transfer kinetics.Fig. 4 (A) Comparison of reheating methods; (B) changes in moisture and fat content during frying; (C) fat transfer kinetics and (D) moisture transfer kinetic.

Fig. 4

3.4.2 Water evaporation and oil penetration during frying

The samples' moisture and oil content changes during frying were shown in Fig. 4(B). The moisture content of the samples decreased rapidly during the initial stage of frying due to the evaporation of moisture from the surface of the samples [10]. During frying, most of the water was in the myofibrils, and the change in moisture content was related to the protein structure. With the prolongation of frying time, the degree of thermal denaturation of proteins increased, the proteins aggregated and contracted with each other, making the myofibril space smaller, and the water between the myofibrils was extruded, so that its moisture content decreased [28]. As for the oil, during the frying process, part of it enters the pores left by the evaporation of water from the shell [30], and part of it adheres to the surface of the shell, so the oil is absorbed more quickly and the oil mass fraction rises rapidly. During subsequent frying, water continued to evaporate from the sample's surface, which might have led to a hardening of the crust on the outer shell, thereby slowing down the evaporation of internal moisture [31]. At the same time, the shell impeded the oil flow into the sample [14], and thus the oil content stabilized.

3.4.3 Mass transfer kinetics

Substituting Mr into Eq. (9), the results of the deep-frying moisture diffusion model parameters were shown in Fig. 4(C). Mr = 8π2 × exp(-0.00859t), the moisture diffusion coefficient k of DFBTNs fitted by Fick's second law was 0.0086, and R2 was 0.96, showing that the moisture evaporation kinetic model had a high goodness-of-fit. Fick's second law model could describe the moisture evaporation kinetics in shells of the deep-frying process relatively well. The larger the water diffusion coefficient k, the faster the rate of water evaporation from the shell.

The FC of the shell obtained by the test was substituted into Eq. (10), and the results were shown in Fig. 4(D). FC = 26.01 × [1-exp(-0.1137t)], the mass transfer coefficient k of fat absorption fitted by the first-degree kinetic equation was 0.1137, and R2 was 0.72, indicating that the fat absorption kinetic model fitted well. The first-degree kinetic equation could describe the kinetics of fat absorption in the shell of the deep-frying process well. The larger the mass transfer coefficient of fat absorption, the faster the rate of fat absorption in the shell.

Therefore, to guarantee taste and health, the frying time should be controlled to minimize water loss and excessive fat intake.

4 Conclusion

This study investigated the storage flavor, shelf-life prediction and frying process of DFBTNs. According to the food regulations, the laboratory homemade tilapia products had good acceptability of sensory scores, TVB-N values, fat oxidation indexes, and TPC. During the storage period, the growth kinetic model of TPC at different storage temperatures was established using the modified Gompertz equation combined with the square root equation, and a prediction model for the shelf-life of tilapia was developed. The results showed that the increase in storage time and temperature exacerbates the products' deterioration, and the experimentally constructed TPC growth model could predict the shelf-life of DFBTNs. The electronic nose and tongue distinguished the odor and taste of the product at different storage time points under the same storage temperature, which could be used as an auxiliary indicator to determine whether the product is spoiled. In addition, in the reheating process, the frying reheating method was selected through sensory evaluation and the actual situation of the family cooking, and the changes in moisture and fat content during the frying process were investigated. The mass transfer kinetic coefficients for moisture and fat were 0.0086 and 0.1137, respectively. Frying time affects the oil and water content as well as the texture of the product, so it is essential to control the frying time to reduce the oil intake. This study explored the storage characteristics and deep-frying process of flour-coated tilapia cakes and provided some theoretical basis for the industrialized production of tilapia. In conclusion, this study investigated the storage characteristics and shelf-life of DFBTNs and analyzed the changes in moisture and fat content during frying. The study's results can guide the production process of fried tilapia products and provide an experimental basis for the industrialized production of tilapia. The following research will provide a comprehensive and in-depth understanding of the effects of different frying conditions on the quality and flavor of DFBTNs to produce high-quality processed tilapia that meet the needs of consumers.

Ethical statement

The study was approved by the College of Food Science and Technology, Guangdong Ocean University, China. Experiments related to sensory evaluation were conducted in accordance with established ethical guidelines, which require each panelist to provide informed written consent prior to participation in the study. It should be noted that there are no strict ethical approval requirements for sensory experiments of conventional aquatic foods or conventional animal foods in China.

Data availability

Data will be made available on request.

CRediT authorship contribution statement

Shouchun Liu: Writing – original draft, Funding acquisition, Conceptualization. Luyao Zhang: Writing – review & editing, Writing – original draft, Visualization, Formal analysis. Yongjia Guo: Validation, Investigation, Data curation. Minjie Wang: Validation, Investigation. Hongying Cai: Software, Data curation. Pengzhi Hong: Supervision, Funding acquisition. Saiyi Zhong: Project administration, Methodology, Funding acquisition. Jiayong Lin: Resources.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Acknowledgements

This work was financially supported by This work was financially supported by Zhanjiang Modern Marine Ranching Industry Talent Revitalization Plan Project (CYRC002 ), the Guangdong Ocean University Research Initiation Project (060302042311 ); the Innovative Team Program of High Education of Guangdong Province (2021KCXTD021 ); Research and Development Project of Maoming Tilapia Advantageous and Characteristic Industrial Cluster (22282109-1 ).

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36528.
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