Paper No: 200000mohtar/IET2007/073057.doc · Web viewPrincipal component analysis (PCA) and artificial neural networks using back-propagation algorithm (BP-ANN) were used to establish

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Author(s)

First Name Middle Name Surname Role Email

Xiaping Fu [email protected]

Yibin (or initial) Ying ASABE Member [email protected]

Huirong Xu [email protected]

Ying Zhou [email protected]

Affiliation

Organization Address Country

College of Biosystems Engineering and Food Science, Zhejiang University

268 Kaixuan St. Hangzhou, 310029,

China

Publication Information

Pub ID Pub Date

073057 2007 ASABE Annual Meeting Paper

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An ASABE Meeting Presentation

Paper Number: 073057

Principal Components-Artificial Neural Networks for Predicting SSC and Firmness of Fruits based on Near

Infrared Spectroscopy

Xiaping Fu, PH.D CandidateCollege of Biosystems Engineering and Food Science, Zhejiang University,

268 Kaixuan St. Hangzhou, 310029, China, [email protected]

Yibin Ying*, ProfessorCollege of Biosystems Engineering and Food Science, Zhejiang University,


Huirong Xu, PH.D CandidateCollege of Biosystems Engineering and Food Science, Zhejiang University,


Ying Zhou, Graduate studentCollege of Biosystems Engineering and Food Science, Zhejiang University,


Written for presentation at the2007 ASABE Annual International Meeting

Sponsored by ASABEMinneapolis Convention Center

Minneapolis, Minnesota17 - 20 June 2007

Mention any other presentations of this paper here, or delete this line.






Abstract. The use of near infrared (NIR) spectroscopy was proved to be a useful tool for components analysis of many materials. Principal component analysis (PCA) and artificial neural networks using back-propagation algorithm (BP-ANN) were used to establish nonlinear model for the prediction of soluble solid content (SSC) and firmness of peach and loquat fruits from NIR spectral data. The first ten principal components extracted from original spectra and spectra after multiplicative scattering correction (MSC) were used as input nodes of BP-ANN. TANSIG and LOGSIG transfer functions and two to nine neurons were considered for the hidden layer of the network. For peaches, the best results were R train=0.940 and R test= 0.900 for SSC; R train=0.701 and R test =0.453 for firmness. For loquats, the best results were R train=0.962 and R test= 0.893 for SSC; R train=0.812 and R test =0.624 for firmness. The results of this study show that combination of PCA and BP-ANN is feasible for predicting fruit quality based on NIRS. For further researches, factors such as the number of neurons for input layer, the number of hidden layers, other learning algorithms and so on could be studied to improve modeling performance and predicting accuracy.

Keywords. NIRS, PCA, BP-ANN, SSC, firmness

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IntroductionNowadays, non-destructive, simple and credible methods for fruit internal quality measurements are required by both fruit planters and sellers. Near infrared spectroscopy (NIRS) technique has gained wide application in food quality analysis mainly due to its suitability for acquiring the spectra of different sample states at low cost without pretreatment, which lead to its non-destructive and rapid characters (Sáiz-Abajo et al., 2004). However, NIR spectra typically consist of broad, weak, non-specific, extensively overlapped bands (Fidêncio et al., 2002). These characteristics hindered expansion of the NIR technique until multivariate calibration methods became widely available and accepted (Blanco et al., 2000).

There are numerous multivariate calibration methods for quantitative analysis. The most widely used linear approaches were multiple linear regression (MLR), principal component regression (PCR) and partial least-squares regression (PLSR), which assume a linear relationship between the measured parameters of the samples and spectra characteristics (Xu et al., 2004). These methods were applied both for analyzing the concentrations of specific chemicals and sample properties such as color, firmness or viscosity, which produce a spectral response.

For the reason of the presence of substantial non-linearity, which arise from scattered light or intrinsic non-linearity in the absorption bands, methods for correcting non-linearity were applied, such as mathematical processing of spectra or alternative non-linear calibration methods. Mathematical processing such as spectrum derivation, the standard normal variate (SNV) and multiplicative scattering correction (MSC) can minimize or avoid some of the non-linearity (Chu et al., 2004). Another way to solve this problem is non-linear calibration methods, artificial neural networks (ANNs) for instance. The wide range of applications of ANNs is due largely to their ability to deal with complex functions, enabling the modeling of non-linear relationships. (Cirovic, 1997).

ANNs imitate the structure and functioning of the human nervous system, to build parallel, distributed and adaptive information-processing systems, able to display a degree of intelligent behavior (Perez-Marin et al., 2007). ANNs are typically organized in layers where these layers are made up of a number of interconnected nodes which contain an activation function. Input vectors are presented to the network via the input layer which communicates to one or more “hidden layers” where the actual processing is done via a system of weighted “connections”. Most ANNs contain some form of “learning rule” which modifies the weights of the connections according to input patterns that it is presented with (Ramadan, 2004). Feed-forward networks based on back-propagation algorithm (BP-ANN) are among the mostly used networks. These networks transfer the information on the input layer to one or several hidden layers; subsequently, the information held by the neurons in the hidden layers is combined via non-linear functions - frequently of the sigmoidal type - in order to obtain the output data, i.e. the target parameter(s). This type of algorithm is highly suitable as it lends itself readily to supervised learning, i.e. to learn from data with known responses and to use the acquired knowledge to predict the answers for other problems (Blanco et al., 1999). It has wide application in NIR spectra modeling for many research objectives, such as pharmacy (Dou et al., 2005, 2006; Deng et al., 2005), soil (Ramadan, 2004), beer (Inon et al., 2006), chemical compounds (Dorn et al., 2003),dairy (Shao et al., 2007), grains (Liu et al., 2004; Lin et al., 2004; Ji et al., 1999) and so on. Most of these research concluded that ANNs approach obtained a better result than those of other linear approaches. And some of the researchers pointed out that it is very essential for the networks to use pressed variables from pretreated spectral data as input (Dou et al., 2006; Shao et al., 2007).

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In this study, principal components analysis (PCA) and BP-ANN were applied on fruit NIR spectra for determining soluble solid content (SSC) and firmness. The goal of this study was to predict fruit internal quality using non-linear BP-ANN model based on spectra pretreatments (including derivative and MSC) and data reduction.

Materials and Methods

Fruit Samples

White peaches were purchased from Jinhua County, Zhejiang Province in June, 2006. Loquats were picked from Tangxi, Zhejiang province in May, 2006. Samples were transported to the laboratory immediately and kept at cold storage (about 4 °C). 24 hours prior to measurements, samples were taken out from refrigerator and maintained at 25 °C and 60% RH to equilibrate to room condition. Samples with surface defects were removed from the samples set. Finally, a total of 120 peach samples and 100 loquat samples were used in this study. For each kind of fruit, three fourths of the samples were used for the training the network and the remaining one fourth were used for testing. For each sample, spectral acquisition, firmness and soluble solid content measurements were carried out in two hours.

Apparatus and software

NIR diffuse reflectance spectra of intact the fruit samples were acquired using a commercial FT-NIR spectrometer (Thermo Electron Corp., USA) with a bifurcated optical fiber cable. The fiber optic probe was enclosed in a 16 mm diameter stainless steel cylindrical tube. Both light source beams and receptor beams were enclosed in it randomly. The spectrometer consisted of a wide-band light source (50W quartz halogen lamp), an interferometer and an InGaAs detector with a spectral range of 800-2500 nm. Considering the physiological properties of fruit samples and the performance of the spectrometer, the parameters for spectra acquisition were set up as follows: resolution of 16 cm-1 and mirror velocity of 0.9494 cm s-1. A standard Teflon block was used as reference.

The spectrometer is connected to a computer, and specific software OMNIC6.1a (Thermo Electron Corp., USA) was available for spectra acquisition, pretreatments and saving. The diffuse reflectance spectra of samples were saved as log(1/R). Each spectrum was the average of 64 successive scans. For each sample, several replicate were taken and the mean spectrum was calculated.

SSC and firmness measurements

According to the China standard for SSC measurement in fruit (China standard, 1990a), SSC was measured by a digital refractometer (ATAGO PR-101, Tokyo, Japan).Firmness were conducted using a standard 6 mm MT probe mounted in an Instron universal testing machine (Instron 5543, USA) with a loading rate of 20 mm/min, interfaced to a computer to obtain continuous force-deformation curves. SSC and firmness measurements were taken from the corresponding locations where NIR diffuse reflectance spectra were acquired on each fruit.

Data Analysis

Spectra from the OMNIC software were exported in suitable format and analyzed using the TQ Analyst software (Thermo Electron Corp., USA) and Matlab software. Nowadays, multiple developed neural network structures are available. In this study, a feed-forward type neural

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network structure with error back-propagation learning rule was selected based on graphical user interface (GUI) toolbox in Matlab software.

Input data for the network can be either the original/preprocessed spectra or the result of any data reduction procedure based on the original/preprocessed spectra (Blanco et al., 2000). Spectra preprocessing of derivation and multiplicative scattering correction were applied on the original spectra. Because the number of wavelengths of the spectral data were too large (including 1102 points in each spectrum) and the power of calculation for training process of the neural network, it is impossible to use the original variables directly as input layer in artificial neural networks. For establishing and optimizing neural network models, the original independent variables (wavelengths) were compressed by principle component analysis in order to reduce the dimensionality of the spectral data. The new variables (calculated principle components (PCs)) can describe the original data matrix by retaining as much information as possible (Mouazen et al., 2006; Otsuka, 2004). The scores of such PCs were used as input of the ANN.

The network leading to the highest correlation coefficient and the lowest error was adopted as optimal. The calculation of root mean square error (RMSE) of training indicated how well the model fits the calibration data. And the parameter of RMSE of testing was reported as a indication of the model’s prediction capability.

Table1 Statistics of SSC and firmness for fruit in training and testing data sets.

variety parametersTraining Testing

SSC (°Brix) Firmness (N) SSC (°Brix) Firmness (N)

peach

No. of samples 90 30

Mean 11.11 23.08 11.08 23.26

S.D. 1.58 4.38 1.59 4.30

Max. 14.43 34.10 14.05 33.11

Min. 7.30 8.93 7.87 14.05

loquat

No. of samples 75 25

Mean 14.04 17.88 14.15 18.09

S.D. 2.64 4.37 2.54 4.54

Max. 19.80 31.58 19.1 29.74

Min. 5.70 9.41 9.00 10.95

Results and Discussion

SSC and firmness statistics

Statistics of SSC and firmness for fruit in both training and testing data sets measured by standard destructive methods are summarized in Table1. As table1 shown, it’s clear that the SSC and firmness of samples in both training set and testing set are appropriately distributed. The mean value and standard deviation (S.D.) of both SSC and firmness in the two sets are very close to each other.

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Spectral Analysis

0.20

0.40

0.60

0.80

1.00

1.20

24982094180315831411127211581063983914853801wavelength / nm

Log(

1/R

) ave

rage

peach mean spectraloquat mean spectra

Figure1 Mean spectra of peach and loquat fruits

Mean spectra of peach and loquat fruit used in this study were shown in Figure1. Absorbance, described as log(1/R), was recorded at 1102 points in the spectral range of 800-2500 nm. The average spectra of the two kinds of fruits are similar in shape and have absorption bands around 970 nm, 1190 nm, 1450 nm, 1790 nm and 1940 nm. These bands are mainly related to O-H or C-H functional vibrations and overtones of water, sugars, cellulose, or substance contained hydroxyl, etc (Yan et al., 2005).

PCA

Table2 Cumulated contributions of the first ten PCs for spectral data of peach and loquat

PC1 PC2 PC3 PC4 PC5

Peach

Original spectra 75.168% 97.354% 99.187% 99.641% 99.773%

MSC spectra 75.186% 87.167% 94.041% 96.178% 97.354%

1st derivative spectra 20.411% 34.297% 40.694% 46.789% 51.355%

Loquat


MSC spectra 82.503% 89.714% 95.144% 97.049% 97.836%


PC6 PC7 PC8 PC9 PC10

Peach


MSC spectra 97.847% 98.138% 98.369% 98.526% 98.671%


Loquat


MSC spectra 98.251% 98.485% 98.650% 98.775% 98.887%


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PCA was performed on each data set for modeling to reducing the number of original independent variables (1102 wavelengths) into a less number of new orthogonal variables (principal components). Examination of the contributions of the principal component weights can give some indication of the variance relating to each component (Lister et al., 2000). The cumulated contributions of the first ten PCs of the original spectra, MSC spectra, and 1st derivative spectra for the two fruit sets are shown in table2. It is clear that contributions of the PCs extracted from original spectra are much larger than those from MSC spectra and 1st derivative spectra. For both peach and loquat original spectra, the first 10 PCs account for more than 99.9% of the total variance; and for MSC spectra, the cumulated contribution of the first 10 PCs have exceeded 98.6%. It indicates that these PCs can be used to replace the original data, and the corresponding results could be believable. However, for 1st derivative spectra, the contributions of the PC1 to PC10 were too low to replace the original spectra. Therefore, only PCs extracted from original spectra and MSC spectra were used for ANN modeling.

BP-ANN

In BP-ANN models, many factors must be considered for developing calibration models, such as the number of extracted components, the number of the nodes in each layer, and the type of transfer function, etc. In this study, the first 10 principal components were derived from spectral data to ensure that all variability is considered by the analysis, so the number of neurons in the input layer was ten. The number of the hidden layers was fixed to one in this study. Regarding transfer function, TANSIG and LOGSIG functions were considered for the hidden layer. SSC and firmness were predicted using two independent models, so the output layer has only one neuron. And PURELIN function was used for the output layer. For network structures with one hidden layer, the number of neurons in the hidden layer should less than the number of neurons in the input layer and more than that in the output layer (Lin et al., 2004). Therefore, two to nine neurons were considered for the hidden layer.

Figure2 and figure4 show the correlation coefficients of the BP-ANN models corresponding to different neurons (2～9) in the hidden layer for peaches and loquats, respectively. Plot (A) and (B) are results of SSC; plot (C) and (D) are results of firmness. Plot (A) and (C) are related to original spectra; and plot (B) and (D) are related to MSC spectra. Results of the training set are in blue color and those of the testing set are in red color. The broad-lines are related to models using LOGSIG transfer function, and filaments are related to models using TANSIG transfer function.

Considering the correlation coefficients (R) and RMSE for both training and testing, the best model for peach SSC evaluation was based on original spectra using LOGSIG transfer function and with nine neurons in the hidden layer, R = 0.940 and RMSE = 0.536 °Brix in training process, R = 0.900 and RMSE = 0.691 °Brix in testing process. For peach firmness, the best model was based on MSC spectra using LOGSIG transfer function and with 4 neurons in the hidden layer, R = 0.701 and RMSE = 3.086 N in training process, R = 0.453 and RMSE = 3.844 N in testing process. Figure3 shows the relationship between measured and predicted values of SSC and firmness for peaches. The results for loquats are shown in Figure5. The best model for loquat SSC evaluation was based on MSC spectra using TANSIG transfer function and with 9 neurons in the hidden layer, R = 0.962 and RMSE = 0.723 °Brix in training process, R = 0.893 and RMSE = 1.242 °Brix in testing process. For loquat firmness, the best model was based on MSC spectra using LOGSIG transfer function and with 5 neurons in the hidden layer, R = 0.812 and RMSE = 3.723 N in training process, R = 0.624 and RMSE = 4.169 N in testing process.

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Figure2 Correlation coefficients corresponding to different neurons in the hidden layer of the BP-ANN models for peaches (Broad-lines relate to LOGSIG transfer function, and filaments relate to TANSIG transfer function; blue lines relate to training set and red lines relate to testing set)

Figure3 Best modeling results for peach SSC (original spectra, LOGSIG transfer function, and 9 neurons in the hidden layer) and firmness (MSC spectra, LOGSIG transfer function, and 4

neurons in the hidden layer)

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Figure4 Correlation coefficients corresponding to different neurons in the hidden layer of the BP-ANN models for loquats (Broad-lines relate to LOGSIG transfer function, and filaments relate to

TANSIG transfer function; blue lines relate to training set and red lines relate to testing set)

Figure5 Best modeling results for loquat SSC (MSC spectra, TANSIG transfer function, and 9 neurons in the hidden layer) and firmness (MSC spectra, LOGSIG transfer function, and 5

neurons in the hidden layer)

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For both peaches and loquats, the results of SSC are much better than those of firmness. For the data of this study, the results show that the more neurons of the hidden layer are needed for SSC evaluation than for firmness evaluation. And for firmness, MSC spectra and LOGSIG transfer function of the hidden layer are more effective for ANN modeling. However, for SSC, it is hard to conclude that whether original spectra are more suitable for ANN modeling than pretreated spectra or not. And it’s also hard to conclude that whether LOGSIG transfer function is more effective than TANGSIG transfer function or not.

ConclusionPCA and BP-ANN were used to estimate SSC and firmness of peach and loquat fruits based on NIRS. Principal component analysis is an effective way for reducing the dimension of the vast spectra data. For both peach and loquat original spectra, the first 10 PCs account for more than 99.9% of the total variance; and for MSC spectra, the cumulated contribution of the first 10 PCs have exceeded 98.6%. It indicates that these PCs can be used to replace the original data, and the corresponding results could be believable. When establishing BP-ANN models, TANSIG and LOGSIG transfer functions and two to nine neurons were considered for the hidden layer. The results for SSC evaluation of both peach and loquat were much better than those for firmness evaluation. For peaches, the best results were R train=0.940 and R test= 0.900 for SSC; R train=0.701 and R test =0.453 for firmness. For loquats, the best results were R train=0.962 and R

test= 0.893 for SSC; R train=0.812 and R test =0.624 for firmness. The results of this study show that combination of PCA and BP-ANN is feasible for predicting fruit quality based on NIRS. In this study, only transfer function and number of neurons of the hidden layer were considered for BP-ANN modeling. In further researches, factors such as the number of neurons for input layer, the number of hidden layers, other learning algorithms and so on could be studied to improve modeling performance and predicting accuracy.

Acknowledgements

The authors gratefully acknowledge the financial support provided by National Natural Science Foundation of China (No. 30671197) and Program for New Century Excellent Talents in University (No. NCET-04-0524).

ReferencesBlanco, M., J. Coello, H. Iturriaga, S. Maspoch, and J. Pages. 1999. Calibration in non-linear

near infrared reflectance spectroscopy: a comparison of several methods. Analytica Chimica Acta 384: 207-214.

Blanco, M., J. Coello, H. Iturriaga, S. Maspoch, and J. Pages. 2000. NIR calibration in nonlinear systems: different PLS approaches and artificial neural networks. Chemometrics and Intelligent Laboratory Systems 50: 75-82.

Chu, X., H. Yuan, and W. Lu. 2004. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique. Progress in Chemistry 16(4): 528-542.

Cirovic, D. A. 1997. Feed-forward artificial neural networks: applications to spectroscopy. Trends in Analytical Chemistry 16(3): 148-155.

Deng B., Y. R. Zhou, Z. H. Liu, and P. X. Feng. 2006. Study on application of FT-NIR and Principal components analysis to the classing and distinguishing of fritillaria. Chinese Journal of Spectroscopy Laboratory 23(50: 1103-1106.

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Dorn, E. D., G. D. McDonald, M. C. Storrie-Lombardi, and K. H. Nealson. 2003. Principal component analysis and neural networks for detection of amino acid biosignatures. Icarus 166: 403-409.

Dou, Y., H. Mi, L. Zhao, Y. Ren, and Y. Ren. 2006. Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis. Analytical Biochemistry 351: 174-180.

Dou, Y., Y. Sun, Y. Ren, and Y. Ren. 2005. Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy. Analytica Chimica Acta 528: 56-61.

Fidêncio, P. H., R. J. Poppi, J. C. de Andrade. 2002. Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy. Analytica Chimica Acta 12: 125–134.

Inon, F. A., S. Garrigues, and M. Guardia. 2006. Combination of mid-and near-infrared spectroscopy for the determination of the quality properties of beers. Analytica Chimica Acta 571: 167-174.

Ji, H., and Y. Yan. 1999. Application of principal components-Artificial neural networks in near infrared spectroscopy quantitative analysis. Journal of Instrumental Analysis 18(3): 12-15.

Lin, M., and J. Lu. 2004. Determination on components of corns based on neural networks and near infrared spectrum. Infrared Technology 26(3): 78-81.

Lister, S. J., M. S. Dhanoa, J. L. Stewart, and M. Gill. 2000. Classification and comparison of Gliricidia provenances using near infrared. Animal Feed Science and Technology 86: 221-238.

Liu, J., S. Wu, and R. Fang. 2004. Determination of protein content of rice by near infrared spectroscopy based on neural networks. Journal of Jiangsu University (Natural Science Edition) 25(3): 196-198.

Mouazen, A. M., R. Karoui, J. De Baerdemaeker, and H. Ramon. 2006. Classification of soils into different moisture content levels based on VIS-NIR spectra. 2006 ASABE Annual International Meeting, Portland, Oregon, 9-12 July, paper number 061067.

Otsuka, M. 2004. Comparative particle size determination of phenacetin bulk powder by using Kubelka-Munk theory and principal component regression analysis based on near-infrared spectroscopy. Power technology 141(3): 244-250.

Perez-Marin, D., A. Garrido-Varo, and J. E. Guerrero. 2007. Non-linear regression methods in NIRs quantitative analysis. Talanta 72(1): 28-42.

Ramadan, Z., P. K. Hopke, M. J. Johnson, and K. M. Scow. 2004. Application of PLS and Back-Propagation Neural Networks for the estimation of soil properties, Chemometrics and Intellignet Laboratory Systems 75: 23-30.

Sáiz-Abajo, M. J., J. M. González-Sáiz, and C. Pizarro. 2004. Near infrared spectroscopy and pattern recognition methods applied to the classification of vinegar according to raw material and elaboration process. Journal of Near Infrared Spectroscopy 12: 207-219.

Shao, Y., Y. He, S. Feng. 2007. Measurement of yogurt internal quality through using Vis/NIR spectroscopy. Food Research International, in press, available online.

Xu, L., and X. Shao. 2004. Methods of Chemometrics 2nd ed. Bejing, China: Science Press.Yan, Y. L., L. L. Zhao, D. H. Han, and S. M. Yang. 2005. Basic and application of near infrared

spectral analysis. China Light Industry Press, Bejing.

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Paper No: 200000mohtar/IET2007/073057.doc · Web viewPrincipal component analysis (PCA) and artificial neural networks using back-propagation algorithm (BP-ANN) were used to establish