Eur Food Res Technol DOI 10.1007/s00217-011-1515-8
ORIGINAL PAPER
Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks Ayse C. Mutlu • Ismail Hakki Boyaci • Huseyin E. Genis Rahime Ozturk • Nese Basaran-Akgul • Turgay Sanal • Asuman Kaplan Evlice
•
Received: 5 December 2010 / Revised: 5 April 2011 / Accepted: 28 May 2011 Ó Springer-Verlag 2011
Abstract In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination (R2) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour. Keywords Near infrared reflectance Artificial neural network Wheat flour Quality parameters
A. C. Mutlu I. H. Boyaci (&) H. E. Genis R. Ozturk N. Basaran-Akgul Department of Food Engineering, Faculty of Engineering, Hacettepe University, Beytepe, Ankara 06800, Turkey e-mail:
[email protected] T. Sanal A. K. Evlice The Central Research Institute for Field Crops, Quality Control Research Center, The Ministry of Agriculture and Rural Affairs, Ivedik, Ankara, Turkey
Introduction Wheat is one of the basic foodstuffs in many parts of the world and is primary ingredient for many food products like bread, pasta, flat bread, noodles, cakes, and biscuits [1, 2]. Different wheat varieties have different physical, chemical, and rheological properties. Endosperm texture is the most important determinant for the end use quality of wheat [3]. Several parameters that are milling yield, kernel weight, protein content, moisture content, gluten, enzyme activity, and rheological properties are used for maintaining the quality of the wheat. Rheological properties can be measured by alveograph, farinograph, and mixograph [4]. Deformation energy (W), tenacity (P), and extensibility (L) can be estimated by alveograph, so baking performance of flour can easily be predicted [4]. Essentialities for functionality of wheat flour are elasticity and extensibility, and their properties are determined by the wheat grain protein [5]. Dough visco-elasticity is provided by wheat flour protein quantity and quality, so they are important factors for bread making [6]. The sedimentation index (Zeleny test), which measures the sedimentation volume of gluten in the flour dispersion is a function of its gluten content and the gluten quality. Thus, the sediment obtained is related to the swelling of glutenins, which are intimately associated with the bread making quality of flours [7]. Flour and end product are affected by water activity of dough besides protein [8]. Water absorption is important due to the producing dough of workable consistency [9]. Dough water absorption can be related to flour moisture; however, it also depends on mix formulation and other factors. Flour absorption is an important consideration in the production of all types of baked goods. Usually, high absorption values are desirable. Even if traditional methods have been successfully used for studying the traditional analysis of wheat and flour, they
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are expensive, slow, require a skilled person and are not suited to automation. Rapid and nondestructive methods to investigate the wheat and flour quality parameters have recently increased. Infrared spectroscopy appears to be one of the most powerful and convenient analytical tools that can be used for studying the wheat quality parameters in the spectral ranges, which can be related to the main chemical components of foods [10]. Near-infrared (NIR) spectroscopy was first used in agricultural applications by Norris [11] to measure moisture in grain. Since then, it has been used for rapid analysis of mainly moisture, protein, and fat content of a wide variety of agricultural and food products [12]. Spectral data can be analyzed under all wave bands and multiple wavelengths qualitatively and quantitatively by modern NIR spectroscopy analysis technique. Most common chemometric multivariate statistical procedures such as principal component analysis (PCA), principal component regression (PCR), and partial least squares regression (PLSR) are the most commonly used multivariate methods to analyze the NIR spectroscopy [13] as well as multiple linear regression (MLR). However, it has been shown that most of the multivariate statistical procedures were not able to handle the nonlinear trends in the data [14] especially for the nonlinear calibration [15, 16]. Therefore, instead of using these methods, artificial neural network (ANN) was used for calibrating nonlinear data [17–24]. ANN had become a popular technique in biological sciences due to their predictive quality and simplicity than process-based models [25, 26]. Classification and calibration with neural networks have many advantages over conventional statistical methods specifically, the ability to detect nonpredefined relations such as nonlinear effects and/or interactions. ANN has been widely used and successfully applied in food industry such as cereal grain classification using morphological features [27–29], thermal conductivity of bakery products [30], prediction of Zeleny sedimentation volume of wheat flour [31], color determination of edible oils [32, 33], and prediction of moisture, protein, and starch content in corn [34]. Nowadays, nondestructive, rapid, sensitive, easy, chemical-free analyze methods are preferred in most of the analytical applications. Analyzing NIR spectrum with chemometric methods and ANN has a big potential to overcome this requirement. Although so many studies were done in this field, number of the quality parameters that are predicted using wheat flour NIR spectrum is limited. The aim of this work was to investigate the ability of the ANN to predict some of the physical, rheological, and chemical quality parameters of wheat flour using NIR spectrum of the sample. Seventy-nine wheat flour samples were used in the study. Quality parameters (protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough
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softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight)) and NIR spectrum of the samples were obtained with standard methods. For each quality parameter, individual network was designed, trained and optimum network parameters were determined for high accuracy prediction of the parameter. Prediction capability of the network was investigated by comparing experimental data with the predicted data.
Materials and methods Raw materials The initial data set composed by 79 wheat flour samples was collected from different regions in the year 2006 in Turkey and measured by NIR spectroscopy. The samples were obtained from the Central Research Institute for Field Crops, Quality Control Research Center, The Ministry of Agriculture and Rural Affairs, Ivedik, Ankara, Turkey. Milling process Flour samples were prepared with Chopin CD1 Laboratory Size Flour Mill (Chopin Groupe Tripette and Renaud, Villeneuve la Garenne, France). The milling process was performed in order to obtain extraction rates of approximately 65–70%. Analytical methods All the experiments were carried out according to the Standard Methods of the International Association for Cereal Science and Technology (ICC) [35] and the American Association of Cereal Chemistry (AACC) [36]. These standard methods were as follows: moisture content (AACC 44-19), total protein content (ICC 105/2), Zeleny sedimentation value (ICC 116/1), rheological properties of dough (AACC 54-21) (Farinograph ((Brabender Farinograph (Brabender GmbH & Co. KG, Detmold, Germany)) (water absorption or percentage of water required to yield a dough consistency of 500 Brabender units (BU), dough development time (DDT) (time to reach the maximum consistency), stability (STA) (time during which the dough consistency is at 500 BU) (ICC 115/1), the Chopin alveograph test (AACC 54-30A) (deformation energy (W in 10-4 J), tenacity (P in mm), extensibility (L in mm), dough swelling (G in cm3), elasticity index (Ie)), bread making (AACC 10-11.01), and bread quality analysis (weight, volume (AACC 10-10B) (determined by seed displacement in a loaf volume meter)). The curves and parameters were computed with Alveolink apparatus (Tripette & Renaud, 92396 Villeneuve la Garenne, France) (ICC 121). The P/G
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ratio and Ie were also calculated from the alveograph curve (Ie = P200/P where P200 is the pressure 4 cm from the start of the curve). P is the maximum overpressure, L is the average length at rupture, G is the square root of the volume of air necessary to inflate the dough bubble until it ruptures, and W indicates the energy necessary to inflate the dough bubble to the point of rupture. All experiments were carried out in duplicate. NIR spectrometer The NIR spectral data set of the 79 wheat flour samples was obtained right after milling process in reflectance mode using an International, LLC FOSS NIRSystem 6500 Spectrophotometer (NIRSystems, Silver Spring, MD) with ISIscan software version 2.80 (Infrasoft International (ISI, Port Matilda, PA) equipped with a monochromator and a quartz sample cell, in the 400–2,498 nm range and with a 2 nm resolution. Absorbance values were recorded as log 1/R, where R is the sample reflectance. The measurements were repeated twice for each sample. ANN software and optimal network architecture The development of trained neural network involves; the generation of data required for training/testing stages, building of the ANN architecture, the training/testing of the ANN, and the evaluation of the ANN leading to the selection of an optimal trained network. The summarized architecture of the ANN model is shown in Fig. 1. Primarily, the data were generated by measuring the absorbance values for 79 wheat flour samples with the NIR spectroscopy in the range from 406 to 2,486 nm for every 32 nm. Fifty-eight of the 79 data were used as training data, the rest of the 79 data was used as testing data for developing an ANN with Neuro-Solutions Release 4.0 (NeuroDimension, Gainesville, FL). The calibration models were performed using generalized feed forward and multilayer perceptron network structures in Neuro Solutions Program. Transfer function (tanhAxon), number of neuron (1–10) in hidden layer and number of epoch (100–10.000) were investigated to minimize the mean square error (MSE) between targets and outputs. The optimal network which had minimum MSE and maximum coefficient of determination (R2) values between experimental and estimated data was selected for further applications.
Results and discussion Seventy-nine flour samples milled from wheat harvested in year 2006 from different regions of Turkey were used to predict the quality parameters (protein content, moisture
Fig. 1 Structure of generalized feed forward and multilayer perceptron ANN for calculating the wheat quality parameters
content, Zeleny sedimentation, ABS, DDT, STA, DS, P, L, P/G, W, Ie, and baking test (loaf volume and loaf weight)) based on NIR spectrum using ANN. The wheat flour quality parameters were analyzed chemically, and results of the range, mean, and standard deviation (SD) were summarized in Table 1. Each quality parameter varied widely among all samples.
Table 1 The results of the chemical analysis of wheat flour quality parameters Parameter
Unit
Moisture
%
11.7–16.9
14.7
Protein
%
11.7–19.2
14.5
1.79
P
BU
3.2–14.3
7.2
2.23
Zeleny
mL
19–63
33.9
8.64
1.2–12.3
3.9
2.17
50.0–66.6
58.9
6.82
3.3 68.2
0.99 37.35
P/G
BU /cm
ABS
%
DDT DS
min BU
Range
3
1.4–5.1 5.4–170.4
Mean
SD 1.15
STB
min
1–15.5
8.2
3.95
W
10-4 J
72–312
164.9
52.37
L
mm
2.2–20.3
IE
0–58.3
Loaf volume
mL
Loaf weight
g
8.6
3.99
40
13.58
340–680
455. 8
50.67
130.5–149.1
138.8
4.14
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Eur Food Res Technol 0.90 a
b
c
d
Moisture (%)
13.9
14.2
15.2
13.9
Protein (%)
16.8
11.9
16.0
19.2
P (BU)
9.6
10.0
5.9
4.7
Zeleny (ml)
33.0
21.0
42.0
63.0
Parameters/Samples
0.80 0.70
log 1/R
0.60 0.50 0.40
P/G (BU/cm3)
4.5
8.5
2.2
2.3
ABS (%)
62.6
59.9
58.1
56.5
DDT (min)
4.5
2.4
3.0
4.8
DS (BU)
45.8
58.1
28.1
126.5
STB (dk)
11.4
4.8
15.4
8.3
W (10-4 J)
110.0
248.0
115.0
217.0
L (mm)
9.0
2.8
14.2
8.3
IE
46.2
0.0
48.1
41.5
Loaf volume (ml)
375.0
435.0
510.0
405.0
Loaf weight (g)
137.8
136.3
139.9
133.3
a b c d
0.30 0.20 0.10 0.00 406
666
926
1186
1446
1706
1966
2226
2486
2746
Wavelength (nm)
Fig. 2 NIR spectra of randomly selected wheat flour samples
Differences in the varieties of the chemical composition cause differences in the spectral characteristics of wheat varieties. Figure 2 shows the spectra of randomly selected flour samples in the spectral region between 406 and 2,486 nm for every 32 nm. The collected NIR data and the results of the chemical analysis were used to train and test the ANN. After, we tried to establish a relationship between the results obtained with classical methods and the results received from NIR spectroscopy using ANN. The networks which had minimum MSE and maximum R2 (coefficient of determination) values between experimental and estimated data were selected as the best trained networks. Then, the best trained network was used for the prediction of each quality parameter of the flour sample with an ANN program. The results are summarized in Table 2. The optimized network of the NIR data for estimation of 14 quality parameters was network (generalized feed forward and multiplayer perceptron), transfer function (linear tanhAxon), number of hidden layer (1–2), number of neuron in hidden layer (4–20), and number of epoch (1,000–10,000). Multilayer ANN has been used effectively in various kinds of applications including classification of different types of cereal grains [28]. In this study, the generalized feed forward and multilayer perceptron models were trained and tested for varying number of iterations keeping the number of neurons in the hidden layer constant. Generally, the training trials were achieved in about 6,000 iterations. Doing more iteration did not improve the performance of the ANN significantly. However, keeping the number of neurons constant for every parameter did not give the best performance. The performance of the ANN remained more or less stable as long as the number of neurons in the hidden layer was between 4 and 20. Based on the R2 values, it was considered that whether the ANN model used to predict the flour quality parameter
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was successful or not. The results of the optimal ANN model for 6 of 14 quality parameters showed relatively good agreement between the predicted and the actual values (Fig. 3). Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and ABS in particular gave a very good accuracy with R2 of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively (Table 2). On the other hand, there is no good correlation between the predicted and actual data for other quality parameters. Wheat quality parameters are very important for the use purposes of wheat [37]. Proteins are important in determining the nutritional value of wheat, for both human and animal consumption, and are the main factors of baking quality. Generally, the protein content of wheat varies between 10 and 20%; wheat for bread has 10–15%, for pasta 11–17% [38]. In this study, the protein content of the wheat flour had a range of 11.7–19.2%; 19% of the varieties had a range of 11.7–13%; and 81% of varieties contained more than 13.0% of protein. Bordes et al. [39] reported similar results of a larger range of grain protein content of 10.9–19.2%. Zeleny sedimentation values vary in the range of 3–70 mL. In this study, Zeleny value was measured between 19 and 63 mL, experimentially. And, they were predicted by trained network in the range of 24.5–56.0 mL. Wheat flour with more than 36 mL of Zeleny sedimentation value is considered as a good quality. In this study, 91% of the samples had more than 36 mL of Zeleny sedimentation value. Bread baking strength can be estimated more closely from the sedimentation value. The ABS varied from 50.0 to 66.6% for the wheat flour samples. P ranged from 3.2 to 14.3 BU, L ranged from 2.2 to 20.3 mm. Kahraman et al. [40] developed some bread wheat lines for breeding in their study for wheat with Zeleny sedimentation value of 44.25–60.25 ml, protein content of 12.0–20.0%. Aydin et al. [41] conducted a similar study, 12.4–13.3% protein and Zeleny sedimentation values ranged from 24.5 to 41.8 ml. Bayram et al. [42] conducted a study based on the breeding lines and they reported the protein content from 11.8 to 17.5%, the Zeleny sedimentation value from 31.9 to 63.4 ml, and energy (W) from 138 to 336 10-4 J. The results obtained in this study were in the range of the reported data in the literature. Earlier, ANN architectures were used to classify cereal grains [28], and it has been used for prediction of quality parameters such as Zeleny sedimentation as a function of total protein, wet gluten and hardness index [31], and dough rheological properties as a function of protein content, wet gluten, Zeleny sedimentation value, and falling number [43]. The determined R2 values were 0.868, 0.616, 0.891, 083, and 0.90 for Zeleny sedimentation, ABS, DDT, STA, and DS, respectively.
Eur Food Res Technol Table 2 The optimum network for prediction of each wheat flour quality parameter Parameter
Network
Hidden layer
Moisture
Generalized feed forward
2
MSE
R2
7,000
0.976
0.920
TanhAxon
15,000
0.991
0.952
TanhAxon
1,000
1.040
0.948
TanhAxon
9,500
0.934
0.917
TanhAxon
10,000
1.104
0.933
TanhAxon
3,000
0.998
0.832
Processing element
Transfer function
20
TanhAxon
Epoch number
15 Protein
Generalized feed
2
10
2
10
forward P
Generalized feed
5
forward Zeleny
Generalized feed
5 2
forward P/G
Generalized feed
10 5
2
10
2
10 5
forward
4
ABS
Generalized feed forward
DDT
Multilayer perceptron
1
10
TanhAxon
7,000
0.904
0.174
DS
Multilayer
2
4
TanhAxon
5,000
0.782
0.034
2
10
TanhAxon
10,000
1.053
0.009
TanhAxon
2,000
0.972
0.049
TanhAxon
8,000
0.944
0.365
TanhAxon
3,000
1.010
0.000
TanhAxon
1,100
0.978
0.687
TanhAxon
5,000
1.002
0.714
perceptron STB
Multilayer
4
perceptron W
Generalized feed
5 2
forward L
Generalized feed
2
forward Ie
Generalized feed Multilayer
2
Generalized feed forward
4 4
2
perceptron Loaf weight
10 6
forward Loaf volume
8 4
10 10
2
10 5
Miralbes [4] studied the prediction of the chemical composition and alveogram parameters (W, P, and P/L) of whole wheat grown in different countries around the world were analyzed using ANN based on NIR transmittance spectroscopy data. The determined R2 values using modified partial least squares (MPLS) were 0.99, 0.99, 0.67, 0.54, and 0.84 for protein, moisture, P, P/L, and W, respectively. In another study by Miralbes [44], wheat flour samples were characterized by in terms of protein, moisture, dry gluten, wet gluten, starch damage, ash content, and wheat flour dough rheological properties using MPLS on NIR transmittance spectroscopy data. The reported R2 values were 0.97, 0.95, and 0.88 for ABS, W, and STA, respectively. Kashaninejad et al. [2] used multilayer perceptron (MLP) neural network and radial basis function (RBF) network to estimate the moisture ratio of wheat kernel during soaking. The determined results for R2 and MSE were, 0.99, 0.0003 for MLP, 0.97, and 0.0012 for RBF, respectively. Jirsa et al. [37] studied to predict the Zeleny sedimentation, P, W, and loaf volume using PLS based on
NIR data, and the R2 values of this study were reported as 0.732, 0.823, 0.578, and 0.627, respectively.
Conclusion Near-infrared spectroscopy is based on molecular overtone and combination vibrations. It is possible to develop a qualitative and/or quantitative analytical method using NIR spectrum. The principle of the method is the absorption or reflection of different wavelengths of incident radiation, which depends on the chemical composition of the analyzed sample. Although NIR spectrum contains so much information about the sample, it is hard to determine the relation between NIR spectrum and quality parameters of the samples. Wheat is one of the main foodstuffs in the world, and physical, chemical, and rheological properties of the wheat have to be known to determine economical value and also characteristics of final product. In this study, it was investigated whether NIR spectroscopy could be
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Eur Food Res Technol Fig. 3 Comparison of quality parameters of wheat determined by ANN and by chemical method; a moisture content, b protein content, c P, d Zeleny sedimentation, e P/G, f absorption
applied to perform quantitative analyzes of 14 wheat quality parameters or not. Based on our knowledge, it is the first study which the NIR spectrum was related with so much quality parameters. We succeeded to predict 6 of the 14 quality parameters with high accuracy by using ANN. The results indicate that this technique could be considered as a valuable tool for wheat flour quality prediction, demonstrating a high level of accuracy for moisture content, protein content, P, P/G, Zeleny sedimentation, and water absorption. Especially, Zeleny sedimentation value is predicted with high accuracy than the other studies in the literature. Compared with traditional methods for the
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determination of these parameters, the developed method in the study is rapid, simple, and repeatable. Also, it is possible to perform simultaneous analysis of these parameters without needing of any solvent and/or chemical, trained person, and other expensive equipment.
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