# Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods

^{1}

^{2}

^{3}

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## Abstract

**:**

^{−9}to 8.11 × 10

^{−9}m

^{2}/s), energy efficiency (from 0.89% to 15.23%) and dryer efficiency (from 2.11% to 21.2%). Compared to ultrasonic and blanching, microwave pretreatment increased the energy and drying efficiency; while the variations in the color and shrinkage were the lowest in the ultrasonic pretreatment. The optimal condition involved the temperature of 70 °C and sample thickness of 2 mm with the desirability above 0.89. The ANFIS model also managed to predict the response variables with R

^{2}> 0.96.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Turnip Preparation

#### 2.2. Pretreatments

#### 2.2.1. Blanching

#### 2.2.2. Ultrasound Pre-Treatment

#### 2.2.3. Microwave Pre-Treatment

#### 2.3. Hybrid Convective-Infrared (HCIR) Dryer

#### 2.4. Moisture Ratio

#### 2.5. Effective Moisture Diffusivity

#### 2.6. Specific Energy Consumption (SEC), Energy and Drying Efficiency

#### 2.7. Shrinkage Measurement

_{t}denotes the apparent volume of the dried sample (cm

^{3}) after the time of t and V

_{0}represents the volume of the raw samples (cm

^{3}). The apparent volume of the samples was measured by the toluene displacement method using a glass pycnometer (50 mL) in this method, the samples with determined weight were transferred into a semi-filled pycnometer containing toluene. The remaining volume of the pycnometer was then closely filled with the solvent and its weight was measured. The apparent volume of the samples (V) can be determined by the following equations [36]:

#### 2.8. Color Difference

#### 2.9. Response Surface Methodology (RSM)

^{2}/s), SEC (Mj/kg), energy efficiency (%), drying efficiency (%), shrinkage (%), and color difference) was evaluated for the samples pretreated by microwave, ultrasound, and blanching.

_{k}is the predicted response and ${\epsilon}_{1}$, ${\epsilon}_{2}$ and ${\epsilon}_{3}$ denote the natural (independent) variables. The second-order response surface equations are also presented in Equation (14) [25]:

_{j}also denotes the coded input variables. Design-expert software was used for fitting the response surfaces and optimize the drying process through solving a multiple regression equation (Equation (14)) using historical data and RSM. The mathematical models of each response were assessed by multiple linear regression analysis. The statistical significance of the independent variables for the response variables was explored at the confidence level of 95% (p < 0.05). Only the significant variables were included in the proposed regression equation. Finally, the optimal point of the process was determined according to the boundary conditions and the target functions as shown in Table 1.

#### 2.10. Adaptive Neuro-Fuzzy Inference System (ANFIS)

^{2}), and mean square root error (MSE).

## 3. Results and Discussion

#### 3.1. Drying Time

#### 3.2. Effective Moisture Diffusivity Coefficient (D_{eff})

_{eff}results for various pretreatments at the studied temperature and thicknesses. R

^{2}was larger than 0.6 indicating that the demonstrated models were the best models for predicting the value of D

_{eff}. According to Table 3, D

_{eff}showed a linear and significant variation in different pretreatments (p < 0.05).

_{eff}for an HCIR dryer with various pretreatments. The highest D

_{eff}value (8.11 × 10

^{−9}m

^{2}/s) was observed for the microwave-pretreated samples dried at the temperature of 70 °C and thickness of 2 mm (Figure 2b); while the lowest D

_{eff}(1.007 × 10

^{−9}m

^{2}/s) was recorded for the control samples with the thickness of 6 mm dried at 50 °C (Figure 2a). Other researchers reported the effective moisture diffusivity in the range of 5.47 × 10

^{−10}to 4.82 × 10

^{−9}m

^{2}/s [1,41]. Based on Figure 3, an increase in the input air temperature and a decline in the sample’s thickness can raise D

_{eff}. At high temperatures, the free water of the sample can be evaporated rapidly, hence dramatically reducing the drying time and increasing D

_{eff}. The use of microwave pretreatment is also enhanced, compared to the other pretreatments. By polarizing the water molecules, the microwave increased the internal temperature of the product. Moreover, it destroyed the product texture and formed channels with larger diameters, thus preventing the surface from hardening, hence accelerating the free water evaporation. D

_{eff}will decrease as a result of a decline in the drying time [33]. Similar results were reported by other researchers for cranberry snacks [17], blackberry [30], and okra [42]. They declared that the use of different pretreatments can increase the moisture diffusivity coefficient compared to the control samples.

_{eff}was higher in the ultrasonic pretreatment as compared with the blanching as ultrasonic treatment could open capillary paths due to the dispersion of the surface species; giving rise to longer microscopic channels as a result of the deformation of the cell. Therefore, ultrasonic pretreatment can deform and destroy the cell walls and accelerate moisture evaporation [38]. These results are in line with the previous reports by other researchers [30,39].

#### 3.3. Specific Energy Consumption (SEC)

^{2}was larger than 0.84, indicating the suitability of this linear model for predicting the value of SEC. It must be noted that only the coefficients with significant (p < 0.05) impact on SEC are included in the equation.

#### 3.4. Energy (${\eta}_{e}$) and Dryer (${\eta}_{d}$) Efficiency

^{2}was above 0.89 for the energy efficiency and above 0.8 for the dryer efficiency indicating that these models can predict the energy and dryer efficiencies well. Under the ultrasound pretreatment, the influence of the input air temperature and sample thickness was significant (p < 0.05) through a second-order equation; while for the other pretreatment, these effects were linear and significant (p < 0.05). The variations in the dryer efficiency followed a second-order equation for the microwave and control samples; whereas the other pretreatments showed linear significant variation trends (p < 0.05).

#### 3.5. Shrinkage

^{2}, adj-R

^{2}, Pre-R

^{2}, and CV values. Regarding high R

^{2}values (above 0.97), the presented model is the best one for predicting the shrinkage level of the samples.

#### 3.6. Color Difference (∆E)

^{2}, adj-R

^{2}, and Pre-R

^{2}values of ∆E index were above 0.97, above 0.96, and above 0.92. Therefore, the presented equations can well fit the experimental data.

#### 3.7. Optimization

_{eff}(2.15 × 10

^{−9}m

^{2}/s) energy efficiency (6.64%) and dryer efficiency (9.13) showed their maximal levels. Other researchers also used the RSM method to optimize the drying process of various crops including apricots [45], lavender leaves [25], sunflower seeds [21], and pistachio [47].

#### 3.8. ANIFIS

_{eff}, SEC, energy and dryer efficiencies, shrinkage, and color variation of the dried turnip samples using an HCIR dryer. To measure the performance of the model, developed equations and two statistical functions, root mean square error (RMSE) and determination coefficient (R

^{2}), were used. In this table the lowest RMSE and highest R

^{2}are presented. According to Table 7, R

^{2}of prediction of drying time, D

_{eff}, SEC, energy efficiency, dryer efficiency, shrinkage, and color were 0.9965, 0.989, 0.000, 0.9993, 0.9989, and 0.9990, respectively (other pretreatments are shown in Table 9). According to Table 9, it can be concluded that the ANFIS model offered higher accuracy for all the studied parameters as compared with the RSM model. By drying almonds [20] and blackberry [30], the researchers have shown that the ANFIS model can successfully predict the drying properties of the products.

## 4. Conclusions

_{eff}, SEC, energy efficiency, drying efficiency, color, and shrinkage of the turnip samples dried by an HCIR dryer were evaluated under various pretreatments (microwave, ultrasonic, and blanching). The following results were obtained:

- The lowest drying time (40 min), D
_{eff}(1.007 × 10^{−9}m^{2}/s), and SEC (21.57 Mj/kg) were observed in the microwave pretreatment. - Energy and dryer efficiencies of 0.89–15.23% and 2.11–21.20% were recorded for the microwave-pretreated samples with a thickness of 2 mm which were dried at 70 °C.
- In the HCIR dryer, SEC declined by increasing the temperature and reducing the thickness, microwave power, and blanching temperature; the energy and dryer efficiencies were increased.
- The ultrasonic pretreatment led to the lowest shrinkage (19.28%) and color variation (11.12) moreover, an increase in the temperature and sample thickness enhanced the shrinkage and color variations for all the pretreatments.
- The optimal condition for the lowest SEC and the highest energy and dryer efficiencies involved the air temperature of 70 °C and sample thickness of 2 mm which led to the desirability of over 89% for all the pretreatments.
- A comparison of the parameter prediction by RSM and ANFIS models indicated that the RSM model exhibited very good performance in modeling and optimizing the process; while the ANFIS method did not have this capability. ANFIS, however, showed better performance in predicting the dependent variables.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclatures

${D}_{eff}$ | Effective moisture diffusion coefficient (m^{2}/s) |

${E}_{t}$ | Total energy input to dryer (MJ) |

$EU$ | Total energy consumption |

${E}_{evap}$ | Energy consumed to evaporate moisture from drying samples (kJ) |

${E}_{heating}$ | Energy for the material heating (kJ) |

M | weight of the sample (g) |

Mf | weight of pycnometer (g), |

${M}_{b}$ | Initial moisture content (kg _{water}/kg _{dry matter}) |

${M}_{e}$ | Equilibrium moisture content (kg _{water}/kg _{dry matter}) |

${M}_{t}$ | Moisture content at any time (kg _{water}/kg _{dry matter}) |

$MR$ | Moisture ratio |

Msf | weight of the toluene for filling the pycnometer (g) |

Mt+s | weight of pycnometer plus the weights of the sample and toluene (g) |

$N$ | Number of data values |

R^{2} | determination coefficient |

${S}_{b}$ | Shrinkage (%) |

$SEC$ | Specific energy consumption (MJ/kg) |

${S}_{k}$ | Predict data |

$t$ | Drying time (min) |

${T}_{k}$ | Experimental data |

${T}_{m}$ | average predicted values |

Vf | pycnometer volume (cm^{3}) |

${V}_{o}$ | Final volume (cm^{3}) |

${V}_{t}$ | Initial volume (cm^{3}) |

$\Delta E$ | Total color change |

$\Delta {L}^{\ast}$,$\Delta {b}^{\ast}$,$\Delta {a}^{\ast}$ | Differences between the color of the fresh and dried sample |

ρs | density of toluene (0.87 g/cm^{3} at 20 °C) |

${\eta}_{d}$ | Drying efficiency (%) |

${\eta}_{e}$ | Energy efficiency (%) |

${S}_{a}$ | Shrinkage (cm^{3}) |

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**Figure 1.**Effect of the drying temperature and sample thickness on the drying time (min) of the turnip slices dried under an hybrid convective-infrared (HCIR) dryer with various pretreatments (

**a**) control, (

**b**) microwave, (

**c**) ultrasound, and (

**d**) blanching.

**Figure 2.**Effect of the drying temperature and sample thickness on the effective moisture diffusivity coefficient (D

_{eff}) (m

^{2}/s) of the turnip slices dried under an HCIR dryer with various pretreatments (

**a**) control, (

**b**) microwave, (

**c**) ultrasound, and (

**d**) blanching.

**Figure 3.**Effect of the drying temperature and sample thickness on the specific energy consumption (SEC, MJ/kg) of an HCIR dryer with various pretreatments (

**a**) control, (

**b**) microwave, (

**c**) ultrasound, and (

**d**) blanching.

**Figure 4.**Effect of the drying temperature and sample thickness on the energy efficiency (%) of an HCIR dryer with various pretreatments (

**a**) control, (

**b**) microwave, (

**c**) ultrasound, and (

**d**) blanching.

**Figure 5.**Effect of the drying temperature and sample thickness on the drying efficiency (%) of an HCIR dryer with various pretreatments (

**a**) control, (

**b**) microwave, (

**c**) ultrasound, and (

**d**) blanching.

**Figure 6.**Effect of the drying temperature and sample thickness on the shrinkage (%) of the turnip slices dried under an HCIR dryer with various pretreatments (

**a**) control, (

**b**) microwave, (

**c**) ultrasound, and (

**d**) blanching.

**Figure 7.**Effect of the drying temperature and sample thickness on the color difference of the turnip slices dried under an HCIR dryer with various pretreatments (

**a**) control, (

**b**) microwave, (

**c**) ultrasound, and (

**d**) blanching.

Variables | Goal | Lower Limits | Upper Limits | Importance |
---|---|---|---|---|

Drying air temperature (°C) | In range | 50 | 70 | 5 |

Sample thickness (mm) | In range | 2 | 6 | 5 |

Drying time (min) | Minimum | 40 | 250 | 5 |

Effective moisture diffusivity (m^{2}/s) | Maximum | 1.01 × 10^{−9} | 8.11 × 10^{−9} | 5 |

SEC (Mj/kg) | Minimum | 21.57596 | 168.98 | 5 |

Energy efficiency (%) | Maximum | 0.89 | 15.23 | 5 |

Drying efficiency (%) | Maximum | 2.11 | 21.2 | 5 |

Color difference | Minimum | 11.12 | 50.59 | 5 |

Shrinkage (%) | Minimum | 19.28 | 67.84 | 5 |

**Table 2.**Response surface method (RSM) modeling results for predicting the drying time under a hybrid convective-infrared (HCIR) dryer with various pretreatments.

Pretreatment | Equation | R^{2} | Adj R^{2} | Pred R^{2} | CV (%) |
---|---|---|---|---|---|

Control | 348.33 − 3.75 × A + 13.75 × B | 0.9830 | 0.9773 | 0.9567 | 3.43 |

Microwave | 87.77 − 0.833 × A + 0.40 × B − 0.50 × A × B | 0.9821 | 0.9713 | 0.9358 | 6.78 |

Ultrasonic | 270.55 − 2.91 × A + 6.66 × B | 0.9944 | 0.9925 | 0.9862 | 1.97 |

Blanching | 999.44 − 25.5 × A − 12.91 × B + 0.18 × A^{2} + 2.70 × B^{2} | 0.9924 | 0.9847 | 0.9613 | 3.14 |

^{2}: determination coefficient and CV: Coefficient of variation.

**Table 3.**Response surface method (RSM) modeling for predicting effective moisture diffusivity coefficient (D

_{eff}) under a hybrid convective-infrared (HCIR) dryer with various pretreatments.

Pretreatment | Equation | R^{2} | Adj R^{2} | Pred R^{2} | CV (%) |
---|---|---|---|---|---|

Control | −2.86 × 10^{−10} + 3.85 × 10^{−11}×A + 1.26 × 10^{−10} × B | 0.9447 | 0.9263 | 0.8776 | 7.34 |

Microwave | −4.03 × 10^{−9} + 1.82 × 10^{−10} × A −6.14 × 10^{−10} × B | 0.9496 | 0.9328 | 0.8674 | 11.40 |

Ultrasonic | −7.51 × 10^{−10} + 6.38 × 10^{−11} × A −1.84 × 10^{−10} × B | 0.8594 | 0.8065 | 0.6041 | 12.99 |

Blanching | −7.34 × 10^{−10} + 5.16 × 10^{−11} × A −1.08 × 10^{−10} × B | 0.8996 | 0.8661 | 0.7817 | 9.71 |

^{2}: determination coefficient and CV: Coefficient of variation.

**Table 4.**Modeling results by the use of RSM for prediction of specific energy consumption (SEC) under an HCIR dryer with various pretreatments.

Pretreatment | Equation | R^{2} | Adj R^{2} | Pred R^{2} | CV (%) |
---|---|---|---|---|---|

Control | 85.75 − 0.60 × A + 45.78 × B − 0.53 × A × B | 0.9909 | 0.9853 | 0.9830 | 4.05 |

Microwave | 52.40 − 0.56 × A + 18.83 × B − 0.20 × A × B | 0.9954 | 0.9926 | 0.9777 | 3.25 |

Ultrasonic | 132.39 − 1.63 × A + 9.32 × B | 0.9329 | 0.9106 | 0.8484 | 9.29 |

Blanching | 44.38 − 0.06 × A + 33.97 × B − 0.46 × A × B | 0.9866 | 0.9786 | 0.9432 | 4.54 |

**Table 5.**RSM modeling of the energy and dryer efficiencies under an HCIR dryer with different pretreatments.

Variable | Pretreatment | Equation | R^{2} | Adj. R^{2} | Pred. R^{2} | CV (%) |
---|---|---|---|---|---|---|

${\eta}_{e}$ | Control | −6.04 + 0.19 × A − 0.54 × B | 0.9706 | 0.9608 | 0.9271 | 11.39 |

Microwave | −7.97 + 0.37×A + 1.26B − 0.04 × A × B | 0.9951 | 0.9921 | 0.9721 | 2.73 | |

Ultrasonic | 26.00 − 0.82 × A + 0.73B − 0.02 × A × B + 0.008 × A^{2} | 0.9893 | 0.9787 | 0.9276 | 3.58 | |

Blanching | −1.75 + 0.15 × A − 0.58 × B | 0.9618 | 0.9491 | 0.8999 | 7.63 | |

${\eta}_{d}$ | Control | 11.70 − 0.44 × A − 0.11 × B + 5.85 × A^{2} − 0.04 × B^{2} | 0.9991 | 0.9981 | 0.9952 | 1.94 |

Microwave | 82.80 − 2.49 × A − 1.42 × B + 0.02 × B^{2} | 0.9897 | 0.9834 | 0.9665 | 3.69 | |

Ultrasonic | 2.11 + 0.15 × A − 0.70 × B | 0.9790 | 0.9720 | 0.9520 | 3.55 | |

Blanching | −3.57 + 0.21 × A − 0.54 × B | 0.9314 | 0.9085 | 0.8067 | 9.24 |

**Table 6.**RSM modeling for predicting shrinkage of the turnip samples under an HCIR dryer with different pretreatments.

Pretreatment | Equation | R^{2} | Adj. R^{2} | Pred. R^{2} | CV (%) |
---|---|---|---|---|---|

Control | −14.24 + 0.90 × A + 2.75 × B | 0.9821 | 0.9761 | 0.9547 | 2.80 |

Microwave | −12.17 + 0.59A + 2.63 × B | 0.9768 | 0.9691 | 0.9367 | 2.09 |

Ultrasonic | −3.99 + 0.36×A + 0.07 × B + 0.04 × A × B | 0.9951 | 0.9922 | 0.9809 | 3.60 |

Blanching | −8.41 + 0.53 × A + 3.76 × B | 0.9840 | 0.9787 | 0.9692 | 3.02 |

**Table 7.**RSM modeling for predicting color difference (∆E) of the samples dried under an HCIR dryer with different pretreatments.

Pretreatment | Equation | R^{2} | Adj. R^{2} | Pred. R^{2} | CV (%) |
---|---|---|---|---|---|

Control | +7.31 + 0.41 × A + 2.3 × 6B | 0.9861 | 0.9815 | 0.9706 | 1.79 |

Microwave | −33.93 + 0.79 × A + 9.08 × B − 0.08 × A × B | 0.9880 | 0.9808 | 0.9580 | 3.72 |

Ultrasonic | −21.38 + 0.51×A + 3.09 × B | 0.9871 | 0.9829 | 0.9686 | 4.21 |

Blanching | −10.61 + 0.49 × A + 3.15 × B | 0.9720 | 0.9627 | 0.9269 | 4.31 |

**Table 8.**Optimization of the response parameters for turnip drying under an HCIR dryer with different pretreatments by RSM.

Pretreatment | Air Temperature (°C) | Thickness (mm) | Time (min) | D_{eff} (m^{2}/s) | SEC (MJ/kg) | ${\mathit{\eta}}_{\mathit{e}}$ (%) | ${\mathit{\eta}}_{\mathit{d}}$ (%) | Shrinkage (%) | Color Difference | Desirability |
---|---|---|---|---|---|---|---|---|---|---|

Control | 70 | 2 | 113.33 | 2.15 × 10^{−9} | 59.31 | 6.40 | 9.13 | 54.87 | 40.83 | 0.896 |

Microwave | 70 | 2 | 39.44 | 7.49 × 10^{−9} | 21.59 | 15.12 | 21.06 | 34.62 | 27.40 | 0.893 |

Ultrasonic | 70 | 2 | 79.72 | 3.34 × 10^{−9} | 36.59 | 9.67 | 11.64 | 28.43 | 20.75 | 0.892 |

Blanching | 70 | 2 | 97.77 | 2.66 × 10^{−9} | 53.30 | 7.66 | 10.20 | 36.87 | 30.07 | 0.911 |

**Table 9.**Prediction of the response parameters for turnip drying under an HCIR dryer with different pretreatments by ANFIS.

Pretreatment | Time (min) | Deff (m ^{2}/s) | SEC (Mj/kg) | ${\mathit{\eta}}_{\mathit{e}}$ (%) | ${\mathit{\eta}}_{\mathit{d}}$ (%) | Shrinkage (%) | Color | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

R^{2} | MSE | R^{2} | MSE | R^{2} | MSE | R^{2} | MSE | R^{2} | MSE | R^{2} | MSE | R^{2} | MSE | |

Control | 0.9975 | 0.0012 | 0.9690 | 0.0059 | 0.9995 | 0.0002 | 0.9859 | 0.0012 | 0.9994 | 0.0002 | 0.9968 | 0.0009 | 0.9979 | 0.0008 |

Microwave | 0.9965 | 0.0019 | 0.9890 | 0.0022 | 0.9990 | 0.0004 | 0.9993 | 0.0004 | 0.9989 | 0.0004 | 0.9869 | 0.0020 | 0.9990 | 0.0004 |

Ultrasonic | 0.9990 | 0.0004 | 0.9797 | 0.0048 | 0.9805 | 0.0017 | 0.9896 | 0.00011 | 0.9939 | 0.0010 | 0.9996 | 0.0002 | 0.9990 | 0.0004 |

Blanching | 0.9980 | 0.0008 | 0.9708 | 0.0054 | 0.9989 | 0.0004 | 0.9979 | 0.0008 | 0.9928 | 0.0011 | 0.9979 | 0.0008 | 0.9988 | 0.0004 |

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**MDPI and ACS Style**

Taghinezhad, E.; Kaveh, M.; Szumny, A.
Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods. *Foods* **2021**, *10*, 284.
https://doi.org/10.3390/foods10020284

**AMA Style**

Taghinezhad E, Kaveh M, Szumny A.
Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods. *Foods*. 2021; 10(2):284.
https://doi.org/10.3390/foods10020284

**Chicago/Turabian Style**

Taghinezhad, Ebrahim, Mohammad Kaveh, and Antoni Szumny.
2021. "Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods" *Foods* 10, no. 2: 284.
https://doi.org/10.3390/foods10020284