22 Drying is crucial in the quality preservation of food materials. Physics -based models are 23 effective tools to optimally control the drying process. However, t hese models require accurate 24 thermo -physical properties ; unavailability or uncertainty in the values of these properties 25 increases the possibility of error . Property estimation methods are not standardized, and usually 26 involve the use of many instruments a nd are time-consuming. In this work, we have developed 27 an experimentally validated deep learning -based artificial neural network model that estimates 28 sensitive input parameters of food materials using temperature and moisture data from a set of 29 simple expe riments . This model predicts input parameters with error less than 1%. Further, 30 using input parameters, physics -based model predict s temperature and moisture to within 5% 31 accuracy of experiments . The proposed work when interfaced with food machinery could play 32 a significant role in process optimization in food processing industries. 33 Keywords: Drying, Food properties, Food parameters, Modelling, Artificial Neural Network, 34 Deep learning 35 Nomenclature 36 t time (s) ∞ ambient condition 𝜌 density (kg/m3) 𝑚𝑖𝑛 minimum value 𝜙 porosity (unit -less) 𝑚𝑎𝑥 maximum value 𝑆 saturation 𝑝𝑟𝑒𝑑 predicted value 𝑀 moisture content (dry basis, kg/kg) 𝑇 temperature (oC) superscripts * normalised parameter subscripts 𝑤 water Abbreviations s solid ANN artificial neural network 𝑜 initial RMSE root mean square error 𝑎𝑣 average 3 1. Introduction 37 Artificial neural network (ANN) is an effective tool for prediction modelling. In recent years, 38 it has been implemented in various aspects of food processing and handling such as food 39 quality, microbial growth, processing methodo logies, properties estimation, transport 40 processes etc. (Chen, Ramaswamy, & Marcotte, 2007; Huang, Kangas, & Rasco, 2007) . 41 Various quality meas ures such as ripeness of fruits (Brezmes, Llobet, Vilanova, Saiz, & 42 Correig, 2000) , shape attributes (Morimoto, Takeuchi, Miyata, & Hashimoto, 2000) , their 43 sugar contents (Kondo, Ahmad, Monta, & Murase, 2000) , sensory properties (Di Natale, 44 Macagnano, Martinelli, Proietti, et al., 2001; Haddi et al., 2014) , harvesting and post -harvesting 45 defects (Di Natale, Macagnano, Martinelli, Paolesse, et al., 2001; Guyer & Yang, 2000) have 46 been predicted quantitatively using different ANN techniques. Image -based classification 47 using artificial neural networks for cultivar detection (Liu, Zhao, Ji a, Ji, & Sun, 2019; Parpinello 48 et al., 2007) , quality inspection (Shrestha, Kang, Yu, & Baik, 2016) , size-based classification 49 (Shahin, Symons, & Poysa, 2006) , varietal classification (Kozłowski Michałand Górecki & 50 Szczypiński, 2019; Ni, Wang, Vinson, Holmes, & Tao, 2019) and colour and morphology - 51 based classification (Douik & Abdellaoui, 2010; Majumdar & Jayas, 2000a, 2000b) has also 52 been performed . Another application is microbial growth analysis , their acti vity and associated 53 food-safety assessments (Cheroutre -Vialette & Lebert, 2000; Jeyamkondan, Jayas, & Holley, 54 2001; Timsorn, Thoopboochagorn, Lertwattanasakul, & Wongchoosuk, 2016) . These models 55 also find their applications include food extrusion processes (Cubeddu, Rauh, & Delgado, 56 2014; Popescu, Popescu, Wilder, & Karwe, 2001) . Besides, artificial neural network tools for 57 predictin g transport phenomena and shrinkage behaviour have also been proposed (Hernández, 58 2009; Kerdpiboon, Kerr, & Devahastin, 2006; Lertworasirikul & Tipsuwan, 2 008; Mercier & 59 Uysal, 2018; Mittal & Zhang, 2000; Reza, Mohebbi, & Khodaiyan, 2014; Tripathy & Kumar, 60 4 2009; Turker & others, 2008) . Similarly, models to predict texture characteristics using 61 properties data have been developed (Fan et al., 2013) . 62 An important application of artificial neural networks in the field of food processing is 63 properties estimation. Various researchers have analysed change in different engineering 64 properties such as thermal conductivity (Mohamed Azlan Hussain & Rahman, 1999) , porosity 65 (M A Hussain, Rahman, & Ng, 2002) , and diffusivity (Jena & Sahoo, 2013) for various food 66 materials for their raw state and during drying. However, most of these studies focus on 67 estimation of one or two pr operties and thus , exhibit limited contribution to physics -based 68 modelling. Further, since the predictions are mostly based on experiments, the datasets used in 69 model development are small leading to performance limitations. The importance and 70 applicabilit y of these properties’ predictions in physics -based modelling is not well addressed 71 and artificial neural network studies that focus on comprehensive modelling of thermal drying 72 processes are limited. 73 Physics -based models have emerged as powerful tool in prediction of thermo -physical 74 behaviour of food materials under different operating conditions. However, the prediction 75 accuracy of these models is limited by the accuracy of input parameters. Due to organic nature 76 of food materials, input properties such as moisture content, wate r saturation, porosity, etc. 77 vary among different varieties of the same cultivar owing to the variation in geographical 78 locations, climatic conditions and harvesting technique s and time etc. leading to erroneous 79 predictions by phy sics-based models. We have found a well-defined methodology to predict 80 all sensitive input parameters for drying modelling thus far . Further, t he present work proposes 81 a novel hybrid modelling approach that combines the advantages of experiments, artificia l 82 neural network model and physics -based model to provide an effective prediction tool for 83 drying process. 84 5 The objective of the present work is to develop a framework for predicti ve modelling of food 85 drying process utilizing artificial neural network. A methodology is proposed that predicts 86 characteristic input parame ters for food materials using an artificial neural network model 87 applied over a developed control experiment. The n, the predicted parameters are implemented 88 in physics based models for predic tion of transport behaviour of food materials and the results 89 are validated with experiments. 90 2. Modelling methodology 91 In order to train neural network model, a large data set of input vectors and corresponding 92 output is needed. Since carrying out such large number of experiments is not practical , an 93 experimentally validated physics -based model taken from our earlier work (Sinha & Bhargav, 94 2019) was used for data generation. Fig. 1 reproduces the modelling framework. This model 95 also helped in focusing attention on a subset of sensitive parameters, which were determined 96 through a sensitivity analysis. These sensitive para meters include initial porosity (∅𝑤𝑜), initial 97 water content (𝑆𝑤𝑜) and solid density ( 𝜌𝑠). These parameters are referred hereafter as 98 characteristic input parameters. Here ∅𝑤𝑜 is the ratio of initial void volume to the initial total 99 volume, 𝑆𝑤𝑜 is the ratio of initial water volume to the initial total volume and 𝜌𝑠 is the ratio of 100 mass of solids to volume of solids. 101 It is worth noting that v ariation in geographic location, climatic condition , method and time of 102 harvest s etc. impacts the sensitive parameters values and consequently alter the transport and 103 deformation behaviour. This was accounted by generating various combination of these 104 characteristic input parameters. Each combination would result in a different transport - 105 deformation behaviour. 106 6 107 Fig. 1- Schematic representation of pathway followed in setting up the deep neural network model 108 Potato was selected as the subject of study. For each sensitive parameter, a range of variation 109 was identified (Table 1). In total, 12 values were selected through random ized selection from 110 a uniform distribution in the specified range . Then, all the possible combinations were created 111 leading to the development of a total of 1728 (12^3) pseudo -varieties of potato. The values 112 selected using uniform distribution are shown in Fig. 2. Physics -based model was used to 113 simulate the transport behaviour of each of the developed variety for a particular set of 114 operating conditions as discussed in the later sections . 115 Table 1 Sensitive system parameters and their range 116 Property Symbol Value range Initial porosity ∅𝑜 0.68-0.88 Initial water saturation 𝑆𝑤𝑜 0.49-0.99 Density of solids 𝜌𝑠 1100 -2700 kg/m3 117 7 118 Fig. 2 - Selected dataset for characteristic input parameters 119 120 3. Artificial n eural network (ANN) model 121 3.1 Input and output vectors 122 The objective of the neural network is to predict characteristic input parameters of any given 123 potato sample. These characteristic input parameters form the output vector of neural network 124 model. The input vector for the neural network needs to be some in-situ measurable 125 characteristics for the utility of model. Therefore, temporal variation of surface temperature, 126 centre temperature and average moisture content measure at a pre -defined set of operating 127 condition was used as the input for the neural network model. This set of operating condition 128 is hereafter referred as the control experiment (discussed in detail in section 4). For the 129 considered control experiment, the maximum gradient in temperature and moisture content was 130 observed in first 20 min ute of process . Therefore , only 20 -minute data of temperature and 131 moisture is sufficient to be fed as input for neural network. 132 All the input parameters (centre temperature, surface temperature and average moisture 133 content) were normalised using equation 1 and 2. A concatenated matrix of these vectors is 134 then generated, in which each row corresponds to a time step. The matrix is then transposed 135 and then finally converted to a column vector. The column vector is then fed as an input vector 136 for the neural netwo rk model. The corresponding output comprises of a column vector with 137 normalised initial porosity, initial water saturation and solid density as its elements. These 138 properties were then converted to dimensional form using equation 3, 4 and 5 respectively. 139 8 Equations for normalisation : 140 𝑇∗=𝑇−𝑇𝑂 𝑇∞−𝑇𝑜 (1) 𝑀𝑎𝑣∗=𝑀𝑎𝑣 𝑀𝑜 (2) 𝜌𝑠∗=𝜌𝑠−𝜌𝑠,𝑚𝑖𝑛 𝜌𝑠,𝑚𝑎𝑥−𝜌𝑠,𝑚𝑖𝑛 (3) 𝜙𝑜∗=𝜙𝑂 (4) 𝑆𝑤𝑜∗=𝑆𝑤𝑂 (5) 3.1.1 Input and output vectors formulation 141 142 143 144 145 146 147 148 149 here 𝑡 is the number of time -steps and equals to 21 for the present study . 150 3.2 Mode l development 151 The neural network model was developed using deep learning toolbox of a commercial 152 software MATLAB R2017 a. The curve fitting model was implemented for developing the 153 prediction model. Levenberg -Marquardt optimization algorithm was used for backpropag ation 154 while training the model and tan -sigmoid and linear functions were used as transfer functions 155 in the neural network. The data fed to the model was divided into traini ng, validation and testing 156 in the ratio 65:20:15. Data division was performed randomly. 157 Further details of the toolbox are given in its user -guide (Beale, Hagan, & Demuth, 1992) 158 whereas det ails about neural networks, Levenberg -Marquardt optimization algorithm and 159 backpropagation technique can be referred in the literature (Demuth, Beale, De Jess, & Hagan, 160 2014 ) 161 ANN input 𝑇𝑐∗ 𝑡×1 𝑇𝑠∗ 𝑡×1 𝑀𝑎𝑣∗ 𝑡×1 3𝑡×1 𝑇𝑐∗ 𝑇𝑠∗ 𝑀𝑎𝑣∗ 3𝑡×1 𝑇𝑐 𝑡×1 𝑇𝑠 𝑡×1 𝑀𝑎𝑣 𝑡×1 ∅𝑜 𝑆𝑤𝑜 𝜌𝑠 3×1 Neural network input: Neural network output: ∅𝑜∗ 𝑆𝑤𝑜∗ 𝜌𝑜∗ 3×1 ANN output Actual properties Actual data 9 The accuracy of the model was very poor for single layer . Therefore, additional modifications 162 in the code were performed to add hidden layers so as to implement deep learning in the neural 163 network. In order to improve accuracy, various combinations of number of neurons and number 164 of hidden layers were tried. For each tried case, model was run 15 times so as to ensure that 165 accuracy sustains and the most suitable neural network was selected (s hown in Fig. 3). 166 167 Fig. 3 - Selected deep learning -based neural network 168 Further to ensure the accuracy of the model for future runs, following selection criteria for 169 output prediction were added in the neural network code: 170 1. Additional validation samples: While training the neural network model, an additional 171 validation was performed on the unseen datasets that were not fed to the n eural network 172 model. 173 2. Termination criteria: While training, t he developed neural network was made to run till the 174 root mean square error of the predicted output for the fed data as well as additional 175 validation data became less than 1%. 176 𝑅𝑀𝑆𝐸<1% (6) 3. Range of predicted output : While training and application of model, the developed code 177 checks if the predicted output meet following conditions: 178 0<𝜙𝑜<1 (7) 0<𝑆𝑤𝑜<1 (8) 4. Initial moisture content criteria: While training and application of model, it is ensured that 179 the predicted output values are consistent with the experimental value of initial moisture 180 63-10-20-10-3 neural network 10 content . This condition is put because initial moisture content is a combination of the 181 characteristic input parameters and thus transport and def ormation phenomena are sensitive 182 to it. 183 ∅𝑜𝑆𝑤𝑜𝜌𝑤 (1−∅𝑜)𝜌𝑠=𝑀𝑜,𝑝𝑟𝑒𝑑≈𝑀𝑜,𝑒𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡 (9) 3.3 User approach 184 The developed neural network along with physics -based model could be implemented for 185 prediction of drying process . The methodology is shown in Fig. 4. Firstly, a control experiment 186 is performed for obtaining the neural network input data. Details of the control expe riment is 187 given in section 4. Neural net work model predicts the characteristic input parameters which 188 are then fed to physics -based model. The physics -based model then could be run for any desired 189 condition by providing appropriate initial and boundary con ditions. 190 191 Fig. 4 - User approach for the model implementation 192 193 4. Materials and methods (Experiments’ details) 194 In order to implement the developed neural network, a control experiment has to be performed 195 first. The details of the controlled experiment performed during the validation of model is given 196 11 below. For the application of the developed neural network in any commercial space or for 197 research purpose , the s imilar control experiment is required to be perfor med. 198 Potato ( Solanum tuberosum ) were bought from a local market and were kept in cold storage. 199 For each of the experiments, potatoes were bought to room temperature, washed, peeled and 200 cut into cubes with each side measuring 14 mm. The excess moisture on t he surface was wiped 201 out gently using dry paper napkins. 202 Initial moisture content was measured using gravimetric method. The initial weight of the 203 sample was measured and then it was dried in the oven at a temperature of 103oC for 24 hours 204 and then weighed again. Dry basis moisture content was measured as the ratio of difference of 205 initial and final weight to the final weight of the sample. The experiment was repeated thrice 206 so as to ensure replicability and the mean value was us ed as initial moisture content. 207 For conducting drying experiment (control experiment), the convection oven was maintained 208 at a temp erature of 60oC by passing d ry hot air (zero relative humidity) at a velocity of 1 m s- 209 1. 210 For measuring variation in temperature during drying , sample was kept in convection oven and 211 thermocouples were attached at the surface and centre of the sample. The drying was performed 212 for 20 minute and surface and centre temperature were recorded. The experiment was repeated 213 thrice, each time with a fresh sample. 214 For average moisture content measurement, the sample was weighed and then put in convection 215 oven for 1 min ute. The sample was then taken out and weighed again. On the basis of initial 216 moisture content and di fference in sample weight, the moisture content of the dried sample was 217 estimated. The experiment was repeated thrice. Same set of experiment is then repeated with 218 an interval of 1 minute (i.e. drying for 2 minutes, 3 minutes etc.) for drying time up to 20 219 minutes. 220 12 In each of the above -mentioned experiments, sample once used was discarded and not used for 221 other trial or other set of experiment. 222 5. Results and discussions 223 Results are presented in the following order. First, the performance of developed deep ANN 224 model is discussed. Secondly, the developed neural network is further validated with new 225 datasets that were not used in model development. Effect of changing neural network input 226 variables and number of training datasets on the model performance is al so described. Finally, 227 the developed neural network is validated with experiments and the associated methodology 228 and obtained results are discussed. 229 5.1 Performance of the developed deep neural network 230 231 Fig. 5 show s mean square error and error histogram . Error of less than 1e -2 for test data and 232 less than 1e -6 for training and validation data was achieved in 250 epochs (Fig. 5(a)). Similarly, 233 error histogram ( Fig. 5(b)) shows that for maximum number of instances, difference between 234 target and obtained output values was less than 0.0005. Thus, the model is in well agreement 235 with the actual data. 236 5.1.1 Effect of number of datasets: 237 238 In order to analyse the effect of number of modelling datasets on the performance of model, 239 another model with only 64 datasets (4 values for e ach of the 3 parameters) was developed. 240 Fig. 6 show mean square error and error histogram for 64 datasets based model . Comparison 241 of the plots show that though error while training is very less for small datasets, the model 242 doesn’t achieve high accuracy during validation and test with small dataset. This is due to the 243 overfitting that occurs when neural network model is developed with small datasets. The error 244 increases further when the model is tested with data completely unseen to the model (not t used 245 in training, validation or test while model development). This is further explained in the next 246 section. 247 13 248 Fig. 5 - Performance plots for the neural network model with 1728 modelling dataset: (a) Mean square 249 error (b) error histogram 250 251 Fig. 6 - Performance plots for neural network model with 64 modelling datasets: (a) Mean square error 252 (b) error histogram 253 5.2 Post-modelling tests 254 Six datasets were used to test the developed neural network model. None of the se datasets were 255 used during model development. Table 2 shows the error obtained for each of the fed input 256 dataset. The model fits well for these unknown datasets as error less than 1% is observed for 257 each case. Further, these six datasets were the n tested on model developed with 64 datasets as 258 described in previous section. The root mean square in this case increased up to 6% whereas 259 true error increased up to 8% approximately as shown in Table 3. It is important to note that 260 while the performance plots developed during the model development depicted very less error 261 even for small datasets , the error actually amplified when tested for unknown datasets. This 262 14 might happen due to the overfitting . Since a large number of independent variables (called as 263 weights) are involved in neural network models , neural network tend to overfit the data for 264 small datasets (Huang et al., 2007; Knight, 1990; Wythoff, 1993) . 265 Table 2 Post-model ling test for the model trained with 1728 datasets , input variables: average moisture 266 content, centre temp erature and surface temperature , drying time: 20 min 267 268 Table 3 Post-model ling test for the model trained with 64 datasets, input variables: average moisture 269 content, centre temperature and surface temperature , drying time: 20min 270 Dataset ∅𝒐 𝝆𝒔 𝑺𝒘𝒐 % RMSE actual value ANN prediction %error actual value ANN prediction %error actual value ANN prediction %error 1 0.49 0.48 2.75 2200.00 2210.96 -0.50 0.60 0.60 -0.44 4.16 2 0.55 0.55 -0.16 1600.00 1706.46 -6.65 0.85 0.85 -0.09 2.54 3 0.73 0.72 0.80 2200.00 2296.72 -4.40 0.78 0.78 -0.48 2.07 4 0.99 1.00 -0.82 1600.00 1655.53 -3.47 0.85 0.84 1.36 4.56 5 0.99 0.90 9.39 2700.00 2490.88 7.75 0.60 0.60 -0.71 5.46 6 0.55 0.55 0.43 1100.00 1090.45 0.87 0.78 0.77 1.07 0.67 271 5.2.1 Effect of input variables 272 When instead of average moisture content, centre and surface moisture content was used as 273 input variable along with centre and surface temperature; then the observed error decreases. 274 For model trained with larger datasets, the error reduced from 0.54 % to 0.15 % as shown in 275 Table 4. Similarly, for smaller datasets, it reduced from 5.46 % to 3.86%. This is shown in 276 Table 5. It is important to note that while using large datasets, the RMSE is below 1% for both 277 the cases. . Besides, techniques for measuring surface and centre moisture during drying 278 experiments are not readily available. Therefore, model having average moistur e content, 279 Dataset ∅𝒐 𝝆𝒔 𝑺𝒘𝒐 % RMSE actual value ANN prediction %error actual value ANN prediction %error actual value ANN prediction %error 1 0.40 0.40 -0.91 1896.00 1893.88 0.11 0.65 0.65 0.19 0.54 2 0.88 0.88 0.01 1896.00 1895.25 0.04 0.66 0.66 -0.03 0.03 3 0.96 0.96 -0.03 2380.00 2380.85 -0.04 0.75 0.75 -0.03 0.03 4 0.74 0.74 0.00 1896.00 1896.08 0.00 0.78 0.78 0.00 0.00 5 0.94 0.94 -0.04 2700.00 2700.56 -0.02 0.89 0.89 -0.05 0.04 6 0.99 0.99 -0.05 1896.00 1897.03 -0.05 0.65 0.65 0.01 0.04 15 centre temperature and surface temperature as input variables when trained with large datasets 280 serve as suitable models for predicting characteristic parameters. 281 5.2.2 Effect of drying time 282 As mentioned in the modelling section, it was observed that maximum variation in 283 temperature and moisture was observed within first 20 minutes of drying. In order to further 284 validate that 20 -minute data suffice for the development of neural network model, a 20 - 285 minute data -based model ling was compared with modelling using 100 min data. Post- 286 modelling tests results are shown in Table 5 and 287 Table 6 respectively. It is evident that the order of error for both the cases remain same. To 288 further ensure this, the model was run multiple times and no significant variation in the order 289 for both the cases model was observed. Although, using 100 min drying data increases the 290 number of variables in the model and thus often led to overfitting and thus error incurred in 291 post modelling test was higher. 292 Table 4 Post-model ling test for the model trained with 1728 datasets, input variables: centre moisture 293 content, surface moisture content, centre temperature and surface temperature , drying time: 20min 294 295 Table 5 Post-model ling test for the model trained with 64 datasets , input variables: centre moisture content, 296 surface moisture content, centre temperature and surface temperature , drying time: 20min 297 Dataset ∅𝒐 𝝆𝒔 𝑺𝒘𝒐 % RMSE actual value ANN prediction %error actual value ANN prediction %error actual value ANN prediction %error 1 0.40 0.40 -0.13 1896.00 1894.72 0.07 0.65 0.65 0.21 0.15 2 0.88 0.88 0.08 1896.00 1895.92 0.00 0.76 0.76 0.00 0.04 3 0.96 0.96 -0.03 2380.00 2380.36 -0.02 0.75 0.75 0.03 0.03 4 0.74 0.74 0.00 1896.00 1895.78 0.01 0.78 0.78 -0.01 0.01 5 0.94 0.94 0.04 2700.00 2698.82 0.04 0.89 0.89 -0.03 0.04 6 0.99 0.99 -0.01 1896.00 1896.16 -0.01 0.65 0.65 -0.01 0.01 Dataset ∅𝒐 𝝆𝒔 𝑺𝒘𝒐 % RMSE actual value ANN prediction %error actual value ANN prediction %error actual value ANN prediction %error 1 0.49 0.49 -0.29 2200.00 2200.30 -0.01 0.60 0.59 0.85 0.52 2 0.55 0.55 -0.81 1600.00 1586.12 0.87 0.85 0.85 -0.11 0.69 3 0.73 0.73 0.18 2200.00 2229.61 -1.35 0.78 0.78 0.23 0.80 4 0.99 0.99 0.41 1600.00 1612.83 -0.80 0.85 0.85 -0.23 0.54 5 0.99 1.00 -1.42 2700.00 2719.78 -0.73 0.60 0.64 -6.50 3.86 6 0.55 0.57 -3.80 1100.00 1127.81 -2.53 0.78 0.80 -2.32 2.96 16 298 Table 6 Post-modelling test for the model trained with 64 datasets, input variables: centre moisture content, 299 surface moisture content, centre temperature and surface temperature, drying time: 20min 300 Dataset ∅𝒐 𝝆𝒔 𝑺𝒘 % RMSE actual value ANN prediction %error actual value ANN prediction %error actual value ANN prediction %error 1 0.49 0.48 3.00 1600.00 1571.21 1.80 0.85 0.86 -0.61 2.05 2 0.55 0.55 -0.29 2200.00 2140.75 2.69 0.60 0.60 -0.61 1.60 3 0.55 0.53 4.35 2700.00 2526.89 6.41 0.60 0.62 -3.22 4.84 4 0.55 0.55 0.56 2700.00 2669.00 1.15 0.78 0.78 -0.18 0.75 5 0.73 0.73 -0.13 2700.00 2620.11 2.96 0.90 0.89 1.09 1.82 6 0.99 1.01 -2.07 1100.00 1142.39 -3.85 0.60 0.62 -2.98 3.06 301 5.3 Experimental validation 302 303 The developed methodology was validated with experiments. Steps involved in the procedure 304 are shown in Fig. 7. Table 7 shows the estimated characteristic input parameters. In order to 305 check the accuracy of parameters, a physics -based simulation was performed using these 306 parameters. Comparison of the predicted behaviour and experimental observation s is shown in 307 Fig. 8. The observed absolute error was up to 15% whereas the overall root mean square error 308 was less than 5%. Various possible sources of error in the experiments include moisture 309 susceptibility while taking out and weighing the sample, displacement of the thermocouple due 310 to sample shrinkage, varied initial temperature, temperature fluctuations in conve ction oven, 311 instrumentation error and other human error. 312 Table 7 Estimated characteristic input parameters 313 Property Symbol Value Initial porosity ∅0 0.909 Initial water saturation 𝑆𝑤0 0.9538 Density of solids 𝜌𝑠 2995.84 kg/m3 314 315 17 316 Fig. 7 - Schematic representation of steps involved in validation of the ANN model 317 318 319 Fig. 8 - Prediction of transport behaviour using ANN and physics -based model and its comparison with 320 experimental results: (a) variation in surface temperature with time (b) variation in centre temperature 321 with time (c ) variation in average moisture 322 323 324 325 18 6. Conclusions 326 327 A novel methodology for estimati ng sensitive input parameters for food materials has been 328 developed and its utility in physics -based modelling is discussed . The major conclusions from 329 the study are: 330 1. Neural network models could serve as an effective indirect method of properties estimation 331 in food materials. 332 2. A small initial period data of a controlled drying experiment is sufficient to predict 333 characteristic parameters accurately. 334 3. In case of the availability of large datasets, surface temperature, centre temperature and 335 average mo isture content are sufficient model inputs for neural network model 336 development. 337 4. Artificial neural network when coupled with physics -based modelling altogether could 338 serve as a promising methodology for prediction modelling of food processes. 339 This work cou ld be extended further to develop a commercial interface for properties prediction 340 as well prediction and optimization of transport and deformation behaviour in food materials, 341 while processing. The applicability of neural network modelling in other aspects of food 342 engineering could also be explored in future. 343 Funding 344 Ankita Sinha receives stipend support from the Ministry of Human Resource Development 345 (MHRD), Govt. of India. This research did not receive any specific grant from funding agencies 346 in the commercial, public or not -for-profit sectors. 347 Conflict of Interest 348 The authors declare that they do not have any conflict of interest . 349 19