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Multi-parameter Analysis of Corn Using Near-infrared Reflectance Spectroscopy and Chemometrics
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 2 (2015) 949 – 953
5th International Conference on Perspectives in Vibrational Spectroscopy
Multi-parameter analysis of corn using near-infrared reflectance spectroscopy and chemometrics P. Praveen Samuela,b*, T. Chinnuc, Madan Kumar Lakshmananc a
Post Graduate Student, College of Engineering, Guindy, Chennai-600 025, Tamil Nadu, India Student-Trainee, CSIR-CEERI, CSIR Madras Complex, Chennai-600 113, Tamil Nadu, India c CSIR-Central Electronics Engineering Research Institute, CSIR Madras Complex, Chennai-600 113, Tamil Nadu b
Abstract The objective of the paper is to develop a multi-parameter chemometric model to determine the starch, oil, moisture and protein contents of corn using Near-Infrared Reflectance Spectroscopy (NIRS) data. The model is developed using Partial-Least Squares Regression (PLSR) algorithm in LabVIEW 2013. The corn data set used in the study was obtained from http://www.eigenvector.com/data/Corn where it is freely available for download. The study data set consisted of NIR absorbance spectra for 80 different corn samples along with the true values of moisture, protein, oil and starch contents of these samples obtained using laboratory analysis. The spectral band considered is 1100-2498 nm at 2 nm intervals (a total of 700 wavelengths). Out of this, 60 samples were used for calibration and the parameters for the remaining 20 samples were predicted. The following correlation coefficients were achieved between the actual value and the predicted value (Moisture - 0.999, Starch - 0.984, Oil 0.989, Protein-0.967). The results of this study show that the proposed method determines the parameters of interest accurately. © 2014 The Authors. Elsevier Ltd. All rights reserved. © 2015 Elsevier Ltd. All rights reserved. Selection Peer-review under responsibility of theCommittee Conference Committee the 5th International Conference on Selection andand Peer-review under responsibility of the Conference Members of the 5thMembers InternationalofConference on Perspectives in Vibrational Spectroscopy. Perspectives in Vibrational Spectroscopy. Keywords: Near-Infrared Reflectance Spectroscopy (NIRS); Partial-Least Squares Regression (PLSR); Corn; LabVIEW
*Corresponding author. Tel.: +91-900-391-8736; E-mail address: [email protected]
2214-7853 © 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Conference Committee Members of the 5th International Conference on Perspectives in Vibrational Spectroscopy. doi:10.1016/j.matpr.2015.06.014
950
P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
1. Introduction Corn is considered as a rich source of energy and it is called as the “king of energy”. The by-products of corn have enormous use. Corn oil is used for cooking and ethanol is used as fuel. Corn is also used as a livestock feed. Determination of the parameters such as starch, moisture, oil and protein content are important for the following reasons: • Corn intended for ethanol production require higher starch content and lower protein content [1]. • The low moisture content in corn would increase the cooking time in the dry milling process [2]. • High-oil corn is required for the livestock feed as it gives more energy [3]. There are many other uses apart from the above mentioned. Hence, measuring these parameters prior to processing would give more yields. Partial-Least Squares Regression algorithm can be used for instantaneous measurement of these parameters. The PLSR model is developed in system design and development environment, LabVIEW 2013 [4] and the results are presented in this paper. 2. Sample data The corn data set used in the study was obtained from http://www.eigenvector.com/data/Corn[5] where it is freely available for download. The study data set consisted of NIR absorbance spectra for 80 different corn samples along with the true values of moisture, protein, oil and starch contents of these samples obtained using laboratory analysis. The spectral band considered is 1100-2498 nm at 2 nm intervals (a total of 700 channels). The mean and standard deviation of the various study parameters of the data used are given in the Table 1. Table.1. Mean and Standard Deviation (SD) of the sample parameters used for calibration and prediction
No.of samples Parameter Moisture Oil Starch Protein
Calibration 60 Mean SD 10.18 0.34 3.52 0.16 64.67 0.83 8.71 0.49
Prediction 20 Mean SD 10.39 0.45 3.42 0.2 64.77 0.8 8.54 0.53
Out of the 80 samples, 60 samples were used for calibration and the remaining 20 for validation of the developed method. The raw data is first mean-centered to ensure that the criterion for choosing successive factors is based on how much variation they explain. The Partial Least Squares Regression (PLSR) algorithm [6] is used to develop the chemometric model to correlate the mean-centered NIRS spectra with the true laboratory values. The PLSR model is then used to predict the process parameters in the remaining 20 samples. 3. Results and Discussion 3.1. Impact of the number of PLS components The input is given in the form of a spread sheet and the output scores and loadings are also obtained in spread sheet. The number of components was varied from 1 to 10 and the PLSR model was used to predict the moisture content in corn. The graphs in Fig.1 show the comparison between the true (laboratory) and predicted (PLS model) values for different number of PLS components.
P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
Fig.1. Actual (using laboratory analysis) and predicted (using PLS model) values of the moisture content in the corn samples – (a) 1 PLS Component; (b) 2 PLS components; (c) 3 PLS components; (d) 4 PLS Components; (e) 5 PLS Components; (f)10 PLS Components.
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P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
The graphs in Fig.1 show that, as the number of components increases, the error decreases. The plot of the sum of |Error| of the 20 samples against the Number of components is shown in Fig. 2. This also depicts the same.
Fig.2. Sum of |Error| of the 20 samples vs. No.of Components
3.2. Results for other parameters The number of components was fixed to be 10 and then the same procedure was repeated for determining the oil, starch and protein contents in corn. The graphs in Fig.3 and 4 show the comparison between the true (laboratory) and predicted (PLS model) values.
Fig.3. Actual (using laboratory analysis) and predicted (using PLS model) values of the (a) oil; (b) starch contents in the corn samples
P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
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Fig.4 Actual (using laboratory analysis) and predicted (using PLS model) values of the protein content in the corn samples.
The maximum error value, the Standard Deviation (SD) of the errors between the actual and predicted values and the correlation coefficient between the actual and predicted values are given in Table 2. Table 2. Statistics of chemometric modeling and prediction- maximum error, Standard Deviation and correlation coefficient
Moisture (%)
Maximum Error 0.225
Standard Deviation 0.01
Correlation Coefficient 0.999
Oil (%)
-0.369
0.10
0.989
Parameter
Starch (%)
0.335
0.15
0.984
Protein (%)
-0.492
0.14
0.967
4. Conclusion From, the above analysis we can conclude that the proposed method determines the parameters of interest accurately. This model can also be used for other Agricultural samples and also for the determination of other parameters. This model can also be used for developing an online system for the instantaneous determination of the parameters. References [1] C.W. Smith, J. Betrán, Corn: Origin, History, Technology, and Production. In: L.W. Rooney, C.M. McDonough, R.D. Waniska (Eds.) The Corn Kernel, John Wiley & Sons, New York, 2004, P-295. [2] E.W. Lusas, L.W. Rooney, Snack Foods Processing, Florida, CRC Press, New York, 2001, P-61. [3] High-oil corn production questions and answers.http://www.ces.ncsu.edu/plymouth/cropsci/docs/high_oil_corn97.html. Retrieved 27-03-2014. [4] LabVIEW 2013 software http://www.ni.com/gate/gb/GB_EVALLV/US. Retrieved 17-03-2014 . [5] The NIRS data set of corn. http://www.eigenvector.com/data/Corn/index.html. Retrieved 17-03-2014. [6] P. Geladi, B.R. Kowalski, Analytica Chimica acta 185 (1986) 1-17.
ScienceDirect Materials Today: Proceedings 2 (2015) 949 – 953
5th International Conference on Perspectives in Vibrational Spectroscopy
Multi-parameter analysis of corn using near-infrared reflectance spectroscopy and chemometrics P. Praveen Samuela,b*, T. Chinnuc, Madan Kumar Lakshmananc a
Post Graduate Student, College of Engineering, Guindy, Chennai-600 025, Tamil Nadu, India Student-Trainee, CSIR-CEERI, CSIR Madras Complex, Chennai-600 113, Tamil Nadu, India c CSIR-Central Electronics Engineering Research Institute, CSIR Madras Complex, Chennai-600 113, Tamil Nadu b
Abstract The objective of the paper is to develop a multi-parameter chemometric model to determine the starch, oil, moisture and protein contents of corn using Near-Infrared Reflectance Spectroscopy (NIRS) data. The model is developed using Partial-Least Squares Regression (PLSR) algorithm in LabVIEW 2013. The corn data set used in the study was obtained from http://www.eigenvector.com/data/Corn where it is freely available for download. The study data set consisted of NIR absorbance spectra for 80 different corn samples along with the true values of moisture, protein, oil and starch contents of these samples obtained using laboratory analysis. The spectral band considered is 1100-2498 nm at 2 nm intervals (a total of 700 wavelengths). Out of this, 60 samples were used for calibration and the parameters for the remaining 20 samples were predicted. The following correlation coefficients were achieved between the actual value and the predicted value (Moisture - 0.999, Starch - 0.984, Oil 0.989, Protein-0.967). The results of this study show that the proposed method determines the parameters of interest accurately. © 2014 The Authors. Elsevier Ltd. All rights reserved. © 2015 Elsevier Ltd. All rights reserved. Selection Peer-review under responsibility of theCommittee Conference Committee the 5th International Conference on Selection andand Peer-review under responsibility of the Conference Members of the 5thMembers InternationalofConference on Perspectives in Vibrational Spectroscopy. Perspectives in Vibrational Spectroscopy. Keywords: Near-Infrared Reflectance Spectroscopy (NIRS); Partial-Least Squares Regression (PLSR); Corn; LabVIEW
*Corresponding author. Tel.: +91-900-391-8736; E-mail address: [email protected]
2214-7853 © 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Conference Committee Members of the 5th International Conference on Perspectives in Vibrational Spectroscopy. doi:10.1016/j.matpr.2015.06.014
950
P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
1. Introduction Corn is considered as a rich source of energy and it is called as the “king of energy”. The by-products of corn have enormous use. Corn oil is used for cooking and ethanol is used as fuel. Corn is also used as a livestock feed. Determination of the parameters such as starch, moisture, oil and protein content are important for the following reasons: • Corn intended for ethanol production require higher starch content and lower protein content [1]. • The low moisture content in corn would increase the cooking time in the dry milling process [2]. • High-oil corn is required for the livestock feed as it gives more energy [3]. There are many other uses apart from the above mentioned. Hence, measuring these parameters prior to processing would give more yields. Partial-Least Squares Regression algorithm can be used for instantaneous measurement of these parameters. The PLSR model is developed in system design and development environment, LabVIEW 2013 [4] and the results are presented in this paper. 2. Sample data The corn data set used in the study was obtained from http://www.eigenvector.com/data/Corn[5] where it is freely available for download. The study data set consisted of NIR absorbance spectra for 80 different corn samples along with the true values of moisture, protein, oil and starch contents of these samples obtained using laboratory analysis. The spectral band considered is 1100-2498 nm at 2 nm intervals (a total of 700 channels). The mean and standard deviation of the various study parameters of the data used are given in the Table 1. Table.1. Mean and Standard Deviation (SD) of the sample parameters used for calibration and prediction
No.of samples Parameter Moisture Oil Starch Protein
Calibration 60 Mean SD 10.18 0.34 3.52 0.16 64.67 0.83 8.71 0.49
Prediction 20 Mean SD 10.39 0.45 3.42 0.2 64.77 0.8 8.54 0.53
Out of the 80 samples, 60 samples were used for calibration and the remaining 20 for validation of the developed method. The raw data is first mean-centered to ensure that the criterion for choosing successive factors is based on how much variation they explain. The Partial Least Squares Regression (PLSR) algorithm [6] is used to develop the chemometric model to correlate the mean-centered NIRS spectra with the true laboratory values. The PLSR model is then used to predict the process parameters in the remaining 20 samples. 3. Results and Discussion 3.1. Impact of the number of PLS components The input is given in the form of a spread sheet and the output scores and loadings are also obtained in spread sheet. The number of components was varied from 1 to 10 and the PLSR model was used to predict the moisture content in corn. The graphs in Fig.1 show the comparison between the true (laboratory) and predicted (PLS model) values for different number of PLS components.
P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
Fig.1. Actual (using laboratory analysis) and predicted (using PLS model) values of the moisture content in the corn samples – (a) 1 PLS Component; (b) 2 PLS components; (c) 3 PLS components; (d) 4 PLS Components; (e) 5 PLS Components; (f)10 PLS Components.
951
952
P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
The graphs in Fig.1 show that, as the number of components increases, the error decreases. The plot of the sum of |Error| of the 20 samples against the Number of components is shown in Fig. 2. This also depicts the same.
Fig.2. Sum of |Error| of the 20 samples vs. No.of Components
3.2. Results for other parameters The number of components was fixed to be 10 and then the same procedure was repeated for determining the oil, starch and protein contents in corn. The graphs in Fig.3 and 4 show the comparison between the true (laboratory) and predicted (PLS model) values.
Fig.3. Actual (using laboratory analysis) and predicted (using PLS model) values of the (a) oil; (b) starch contents in the corn samples
P. Praveen Samuel et al. / Materials Today: Proceedings 2 (2015) 949 – 953
953
Fig.4 Actual (using laboratory analysis) and predicted (using PLS model) values of the protein content in the corn samples.
The maximum error value, the Standard Deviation (SD) of the errors between the actual and predicted values and the correlation coefficient between the actual and predicted values are given in Table 2. Table 2. Statistics of chemometric modeling and prediction- maximum error, Standard Deviation and correlation coefficient
Moisture (%)
Maximum Error 0.225
Standard Deviation 0.01
Correlation Coefficient 0.999
Oil (%)
-0.369
0.10
0.989
Parameter
Starch (%)
0.335
0.15
0.984
Protein (%)
-0.492
0.14
0.967
4. Conclusion From, the above analysis we can conclude that the proposed method determines the parameters of interest accurately. This model can also be used for other Agricultural samples and also for the determination of other parameters. This model can also be used for developing an online system for the instantaneous determination of the parameters. References [1] C.W. Smith, J. Betrán, Corn: Origin, History, Technology, and Production. In: L.W. Rooney, C.M. McDonough, R.D. Waniska (Eds.) The Corn Kernel, John Wiley & Sons, New York, 2004, P-295. [2] E.W. Lusas, L.W. Rooney, Snack Foods Processing, Florida, CRC Press, New York, 2001, P-61. [3] High-oil corn production questions and answers.http://www.ces.ncsu.edu/plymouth/cropsci/docs/high_oil_corn97.html. Retrieved 27-03-2014. [4] LabVIEW 2013 software http://www.ni.com/gate/gb/GB_EVALLV/US. Retrieved 17-03-2014 . [5] The NIRS data set of corn. http://www.eigenvector.com/data/Corn/index.html. Retrieved 17-03-2014. [6] P. Geladi, B.R. Kowalski, Analytica Chimica acta 185 (1986) 1-17.