Application of Artificial Neural Networks to Predict the Oxidation of ...

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Food Bioprocess Technol DOI 10.1007/s11947-010-0386-5

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Application of Artificial Neural Networks to Predict the Oxidation of Menhaden Fish Oil Obtained from Fourier Transform Infrared Spectroscopy Method Wanwimol Klaypradit & Soraya Kerdpiboon & Rakesh K. Singh

Received: 20 October 2009 / Accepted: 20 May 2010 # Springer Science+Business Media, LLC 2010

Abstract Oxidation is a major cause of deterioration in fish oil, leading to considerable losses of quality and nutritional value. To date, the available methods to monitor lipid oxidation in foods are based on chemical analysis. Fourier transform infrared spectroscopy (FTIR) is an alternative technique for the study of molecular structure and compositional changes in a wide range of foods. The objectives of this study were to use attenuated total reflectance-FTIR for evaluating oxidative quality and application of artificial neural network analysis (ANN), a mathematical model, to predict the oxidative values of Menhaden fish oil. The oil was stored in the presence of light at room temperature. The oxidation was measured for primary and secondary oxidative change; peroxide value (PV) and anisidine value (AnV), respectively, using FTIR were compared with Sources of funding support The authors thank then Office of the Higher Education Commission, Ministry of Education, Thailand, and Department of Food Science and Technology, University of Georgia, USA, for financial support. Meeting presentation (1) 14th World Congress of Food Science and Technology, Shanghai, China, October 19–24, 2008, and (2) IFT 2009 Annual Meeting and Food Expo, California, USA, June 6–9, 2009. W. Klaypradit (*) Department of Fishery Products, Faculty of Fisheries, Kasetsart University, Bangkok 10900, Thailand e-mail: [email protected] S. Kerdpiboon Department of Food Technology, Kasetsart University, Sakon Nakhon Province Campus, Sakon Nakhon, Thailand R. K. Singh Department of Food Science and Technology, University of Georgia, Athens, GA, USA

chemical analysis each day during the 3 weeks of storage. The wavenumber and absorbance values of FTIR spectra were applied to predict the oxidative values of the oil by using ANN. Inputs consisted of wavenumber and absorbance outputs were composed of PV and AnV. It was found that changes in the region between 3,500 and 1,700 cm-1 and absorbance were related to PV and AnV of the chemical analysis (R2 >0.85). FTIR spectroscopy with the aid of ANN demonstrates its potential as an alternative and rapid technique rather than a conventional method for prediction of food lipids oxidation. Keywords Menhaden fish oil . Oxidation . Fourier transform infrared spectroscopy . Artificial neural networks . Lipid . Predictive modeling

Introduction The oxidative stability is an important indicator for the quality of oil. There are various methods available for measurement of lipid oxidation in oil foods. Up to date, the available methods to monitor lipid oxidation in foods are based on chemical analysis involving several reagents and sample preparation steps, which may be divided into two groups: primary and secondary oxidative changes. The peroxide value (PV) is normally used as the method for quantitative of hydroperoxides, which were measured to determine the primary oxidative change because they are generally accepted as the first product formed by oxidation. The anisidine value (AnV) is utilized to determine the secondary oxidative products, which are derived from decomposition of hydroperoxides (Shahidi & Wanasundara, 2002). Such methods are rather costly and time-consuming, and therefore, an alternative rapid and reliable method should be considered.

Food Bioprocess Technol

Fourier transform infrared spectroscopy (FTIR) is an analytical technique, which measures the infrared intensity versus wavenumber of light. The resulting spectrum is characteristic of the organic molecules, which absorb infrared energy at specific frequencies so that the basic structure of compounds can be determined by the spectral locations of their IR absorptions. Numerous reports have shown that the FTIR has been applied to evaluate the freshness of virgin olive oils in combination with multivariate analysis (Sinelli et al., 2007), to determine fatty acid profile and PV of virgin olive oil (Maggio et al., 2009), to analyze the free fatty acid content of Atlantic salmon (Salmo salar) skin lipids (Aryee et al., 2009), also to determine cis-unsaturation and trans-fatty acids together with free fatty acids during degradation of edible oils by heating process (Moros et al., 2009). FTIR can be used to investigate lipid oxidation of oil and many other applications as mentioned earlier. However, very few researches predict a relationship between the FTIR and the oxidative value changes of oil using artificial neural network (ANN). Therefore, the application of ANN analysis to predict oxidation from infrared intensity and wavenumber of light can be approached. The ANN is made up of a group of interconnected artificial neurons. It consists of input, hidden, and output layers. Each layer is also composed of neurons. Each neuron transforms input and sends output to other neurons to which it is connected. Weights and bias are determined from the receiving neurons. The network is trained with a subset or dataset of observations and optimized based on its ability to predict a set of known outcomes. ANN has been applied for modeling, optimization, predicting, and process control of complex problems (Kerdpiboon et al., 2006; Boyaci et al., 2009; Fathi et al., 2009; Argyri et al., 2010). The purpose of this study were (a) to use attenuated total reflectance (ATR)-FTIR spectroscopy as an alternative method compared with chemical method for evaluating oxidative quality of Menhaden fish oil (MO) and (b) to apply ANN analysis to predict the oxidative values of the oil stored in the presence of light.

Materials and Methods Sample MO purchased from Sigma-Aldrich Company (St. Louis, MO) was used for oxidation determination by chemical and instrumental methods. Upon arrival, the oil was immediately determined for the oxidative values; PV and p-AnV, which were 0 milliequivalents peroxide/kilogram (meq/kg) and 0, respectively.

Oxidation Determination by Chemical Methods The MO samples were placed in screw-cap test tubes and stored at 4 °C (control) and at room temperature (25 °C) in the presence of two fluorescent tube lights (each rated at 34 W) for 3 weeks. The tubes were about 1 m far from the light. Samples were collected periodically to determine any oxidative changes. Peroxide values (PVs) were determined following AOCS Official Method Cd 8b-90 (AOCS, 2005), while the panisidine values (AnV) were determined according to the AOCS Official Method Cd 18-90 (AOCS, 1998). Oxidation Determination by FTIR FTIR equipment (model Nicolet 6700; OMNIC Thermo Electron Corporation, Madison, WI) coupled with an ATR accessory was used to investigate the oxidation of MO samples stored at the conditions as previous mentioned. Each oil sample (1 mL) was applied on the flat crystal chamber of the ATR accessory. The background spectrum was obtained by measuring the empty chamber. A 4-cm-1 resolution was used, and the ATR spectra were averaged on 32 scans with wavenumber range 4,000–600 cm-1. The FTIR spectra of the nonoxidized and oxidized MO are shown in Fig. 1. ANN Training ANNs were simulated based on a multilayer feed forward neural network. The input layer, hidden layers, and output layer are shown in Fig. 2. The mathematical algorithm for feed forward and backpropagation processing was followed by Argyri et al. (2010). A backpropagation algorithm was used to implement supervised training of the network. During training, weighting functions for the inputs to each ANN were determined, such that the predicted output best matched the actual output from the data set. There were 120 data points in each frequency, 60 data points were used in testing and training and other 60 data points were used in the validation. The number of data used in testing and training was pointed at 1, 3, 5, 7,…, 119, and the number of data used in validation was pointed at 2, 4, 6, 8, …, 120, respectively. Weights were randomly assigned at the beginning of the training phase, according to the backpropagation algorithm. A hyperbolic tangent was used as the transfer function in each hidden layer, and a linear transfer function was used in the output layer. Minimization of error was accomplished using the Levenberg–Marquardt (LM) algorithm. Training was finished when the MSE converged and was less than 0.001. If the MSE did not go below 0.001, training was completed after 1,000,000 epochs, where an epoch represents on complete sweep through all the data in the training set.

Food Bioprocess Technol Fig. 1 FTIR spectra of the nonoxidized MO (a) and oxidized MO (b)

Data sets used to train the ANN consisted of two inputs, which were wavenumber range and absorbance, and two outputs, which were PV (0–73.5 meq/kg) and AnV (0– 48.9). The number of hidden layers and number of neurons in each hidden layer were varied from 1 to 10. Training was done for 30 trial configurations in each hidden layer/neuron combination, in order to find the combination of hidden layers and neurons that produced the minimum error. The ANN optimization process was performed using a trial and error technique (Kerdpiboon et al., 2006).

Selection of Optimal ANN Configuration The optimized configurations from training for each neuron were selected from 30 configurations based on performance of neural network that gave the minimum error from the training process. The average mean square error (MAE), standard deviation of MAE (STDA), percentage of relative mean square error (%MRE), and standard deviation of %MRE (STDR) were used to compare the performances of various ANN models and were calculated

Food Bioprocess Technol 1st neuron

1st neuron h 11

w1

h 21

Wavenumber range w21 w3 wn1

n neuron 2nd

PV

n neuron h 12 2nd

h 22 w 22 2

w1 h 13

Absorbance

3rd neuron

w3

AnV

3rd neuronh 2

wn2 h1n h2n t neuron 10 th

Input layer

t neuron 10 th

Hidden layer

Output layer

Fig. 2 Schematic structure of the developed neural network. The input layer consists of wavenumber and absorbance. The output layer consists of PV and AnVof MO. wij: weights, with i representing the index of the input signal neuron and j representing the output signal neuron

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rP N 2 ð$PA $PA Þ i¼1 as: MAE ¼ N $PA , STDA ¼ , %MRE ¼ N 1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rP  N  i¼1 N 2 P ð$PR $PR Þ 1 i¼1 $PR  100 and STDR ¼ ,where N N 1 N P 1

i¼1

$PA ¼ jPP  PE j, $PR ¼ jðPP  PE Þ=PE j, PP is the predicted output and PE is the experimentally measured output.

Results and Discussion

manner. However, PV and AnV derived from the control remained unchanged throughout the experiment period. There is a band near 1746 cm-1, which represents the triglyceride ester groups and indicates the nonoxidized oil (Fig. 1a). During the oxidation process, the ester group changes and degrades into the secondary products (Fig. 1b), which is related to the increase of AnV with a decrease in the wavenumber for the increased absorbance (Table 1) throughout the oxidation process for 3 weeks. Similar result was found for the infrared spectrum near 3,012 cm-1, which shows the band of cis double bonds groups of nonoxidized MO with high unsaturated fatty acids (Fig. 1a). The disappearance of cis double bonds produced increased absorbance at lower wavenumber values throughout the oxidation process (Table 1). The other wavenumber shift of the oil during the oxidation process was found for the band near 3,455 cm-1. For the first period of storage, the wavenumber value corresponding to increase absorbance value decreased slowly due to the generation of hydroperoxides, and afterward, the appearance of a band at a new wavenumber occurred at approximately 3,530 cm -1 (Fig. 1b). The new band was related to alcohols and other compounds derived from hydroperoxides and also indicated higher values of AnV. These results are in agreement of Guillén & Cabo (2002), who observed the correlation between the changes in infrared spectra and oxidative stability of different edible oils. Sinelli et al. (2007) also demonstrated that the FTIR spectroscopy has the ability to be used to classify fresh and oxidized oil samples, and the FTIR spectra results are in agreement with oxidation data obtained by classical methods.

FTIR Spectra and Oxidative Stability Oxidation Determination by ANN The FTIR is a promising tool for providing the information on the functional groups of samples. Figure 1 shows the infrared spectra of the nonoxidized and oxidized MO that the spectra obtained when infrared radiation interacts with matter and causes the chemical bonds in the material to vibrate. Similar results were found for other nonoxidized oils because the functional groups tend to absorb infrared radiation in the same wavenumber range regardless of the structure of the rest of the molecule that their functional groups are in (Guillén & Cabo, 1999). The results were related with Flåtten et al. (2005), who indicated the spectra of fatty acid composition in pork fat by FTIR. The changes of PV and AnV were from 0 to 73.5 meq/kg and 0 to 48.9, respectively, as well as the changes of wavenumber and absorbance according to the oxidation process during storage of 3 weeks. PV of MO exposed to light showed fast increased in PV from the beginning of the oxidation experiment to reach a maximum value, after which a less pronounced decrease in PV was observed and the generation of secondary products, AnV, increased in a similar

Wavenumber and absorbance values of FTIR spectra were used to predict the oxidation of MO. Data range of inputs and outputs used to train ANN are shown in Table 1. The minimum MRE for prediction of PV and AnV was found to have different numbers of hidden layers and neurons in each hidden layer (not shown). However, using of number of hidden layers more than 10 layers did not find the low MRE. The number of hidden layers in this work was set as 1–10 hidden layers. The minimum MRE for region range of 1,736–1,746 cm-1 was found with two hidden layers and eight neurons per layer for PV, and one hidden layer and eight neurons per layer for AnV. For the region range of 3,455–3,530 cm-1, the minimum MRE was found with two hidden layers and two neurons per layer for PV, and one hidden layer and four neurons per layer for AnV. The minimum MRE for the bands range of 3,002–3,012 cm-1 was found with two hidden layers and six neurons per layer for PV, and one hidden layer and ten neurons per layer for AnV. A large number of hidden

Food Bioprocess Technol Table 1 Errors in the prediction of PV and AnV with different number of layers and neurons per layer for oxidized MO, which differ in wavenumber and absorbance values of FTIR spectra Wavenumber range (cm-1)

1,746–1,736 3,012–3,002 3,455–3,530

Absorbance

PV

1.632–2.109 0.196–0.433 0.088–0.298

AnV

MAE

STDA

%MRE

STDR

MAE

STDA

%MRE

STDR

4.33 7.44 6.46

6.36 11.93 0.91

2.99 11.93 4.74

3.29 25.15 5.53

2.58 2.95 2.28

2.77 2.80 1.69

2.78 4.41 2.07

3.33 5.31 2.40

layers do not necessarily lower the error if there are enough numbers of neurons in each hidden layer (Torrecilla et al., 2005). The best prediction in most of data sets contained two hidden layers for PV and one hidden layer for AnV. The results showed that wavenumber and absorbance values of FTIR spectra were quite related with PV and AnV (Table 1). The experimental and predicted values of PV and AnV of MO for the frequency range 1,736–1,746 are shown in Fig. 3. The correlation of experiment and predicted oxidation of other frequency ranges were greater than 0.873 in all cases (not shown). The region range of 1,736– 1,746 cm-1, R2 =0.926 and 0.952 for prediction of PV and AnV, respectively, were obtained. The wavenumber range of 3,455–3,530 cm-1 gave R2 =0.884 for predicted values of PV and 0.933 for AnV. For the region range of 3,002–

Conclusion The oxidation of MO obtained by FTIR spectra with the aid of ANN exhibited results in a similar fashion to that did by chemical method for PV and AnV. The FTIR coupled with ANN model demonstrated its potential to be used as an alternative rapid method without sample preparation for prediction of routine oxidation determination for MO. The procedure can be adapted for prediction of oxidation in other food lipids.

80 2 hidden layers 8 neurons per hidden layer

60

Predicted PV

Predicted PV

80

40 20

2 hidden layers 8 neurons per hidden layers

60 40 20

Testing and Training dataset 2 R = 0.8963

Validation dataset 2

R = 0.9255

0

0 20

0

40

60

80

0

20

60

Predicted AnV

40 30 20 Testing and Training dataset 2 R = 0.9493

10 0 0

10

20 30 40 50 Experimental AnV

60

80

60

2 hidden layers 8 neurons per hidden layer

50

40 Experimental PV

Experimental PV

Predicted AnV

Fig. 3 Correlation of experimental and predicted oxidation of MO with testing and training data sets, as well as validation data set for the first wavenumber range 1,736–1,746 using the optimal ANN

3,012 cm-1, R2 =0.873 and 0.939 for predicted values of PV and AnV, respectively, were found. The ability to predict AnV was better than that for PV, because the oxidized oil had higher secondary products than primary products.

60

50 2 hidden layers 8 neurons per hidden layer 40 30 20 Validation dataset 10

2

R = 0.952

0 0

10

20 30 40 Experimental AnV

50

60

Food Bioprocess Technol Acknowledgment This work was, in part, financially supported by the Office of the Higher Education Commission, Ministry of Education, Thailand.

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