Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques
Keywords:
cup lump rubber, dry rubber content, spectroscopic measurement, machine learning, partial least squares regression, least-squares support vector machineAbstract
Dry rubber content (DRC) is an important factor to be considered in evaluating the quality of cup lump rubber. The DRC analysis requires prolonged laboratory validation. To develop fast and effective DRC determination methods, this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement approaches. This involved a complete fundamental analysis leading to an efficient measurement method based on either point-based measurement using NIR reflectance spectrometer or area-based measurement using hyperspectral imaging. A dataset was prepared that 120 samples were randomly divided into a calibration set of 90 samples and a validation set of 30 samples. To obtain an average spectrum to represent a cup lump rubber sample, the spectral data were collected by locating and scanning for point-based and area-based measurement, respectively. The spectral data were calibrated using partial least squares regression (PLSR) and the least-squares support vector machine (LS-SVM) methods against the reference values. The experiments showed that the area-based measurement approach with both algorithms performed outstandingly in predicting the DRC of cup lump rubber and was clearly better than the point-based measurement approach. The best predictions of PLSR represented by the coefficient of determination (R2), the root mean square error of prediction (RMSEP) and the residual predictive deviation (RPD) were 0.99, 0.72% and 15.17, while the best prediction of LS-SVM were 0.99, 0.64% and 16.83, respectively. In summary, the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber. Keywords: cup lump rubber, dry rubber content, spectroscopic measurement, machine learning, partial least squares regression, least-squares support vector machine DOI: 10.25165/j.ijabe.20211403.6298 Citation: Puttipipatkajorn A, Puttipipatkajorn A. Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques. Int J Agric & Biol Eng, 2021; 14(3): 207–213.References
[1] Office of Agricultural Economics (OAE). Rubber Research Institute Department of Agriculture. Available: http://www.oae.go.th/view/1/ Information/EN-US. Accessed on [2020-06-06].
[2] Norris K H. Design and development of a new moisture meter. Agric. Eng., 1964; 45(7): 370–372.
[3] Maneewan W, Limpiti S, Theanjumpol P. The application of near infrared reflectance spectroscopy to determine the moisture and protein content in soybean seeds. The 14th International Conference of NIR Spectroscopy, Bangkok, Thailand, 2009.
[4] Rittiron R, Seehalak W. Moisture content in raw rubber sheet analyzed by transflectance near infrared spectroscopy. J. Innov. Opt. Health Sci., 2014; 7(4): 1350068. doi: 10.1142/S1793545813500685.
[5] Suchata S, Theanjumpolb P, Karrilaa S. Rapid moisture determination for cup lump natural rubber by near infrared spectroscopy. Industrial Crops and Products, 2015; 76: 772–780.
[6] Sirisomboon P, Kaewkuptong A, Williams P. Feasibility study on the evaluation of the dry rubber content of field and concentrated latex of Para rubber by diffuse reflectance near infrared spectroscopy. Journal of near infrared spectroscopy, 2013; 21: 81–88.
[7] Davies A M, Grant A. Review: near-infra-red analysis of food. Int. J. Food Sci. Technol., 1987; 22: 191–207.
[8] Lu R F, Chen Y R. Hyperspectral imaging for safety inspection of food and agricultural products. In Proceedings of SPIE Conference on Pathogen Detection and Remediation for Safe Eating, Boston, US, 1998.
[9] Park B. Quality inspection of poultry carcasses. In D.W. Sun (Ed.). Computer vision technology for food quality evaluation. Amsterdam: Academic Press, 2008; pp. 157–187.
[10] Barbin D, ElMasry G, Sun D W, Allen P. Near infrared hyperspectral imaging for grading and classification of pork. Meat Science, 2012; 90(1): 259–268.
[11] Ariana D, Guyer D E, Shrestha B. Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture, 2016; 50(2): 148–161.
[12] AOAC. Official Methods of Analysis of AOAC International. 2000. Available: http://www.aoac.org/aoac_prod_imis/AOAC/Publications/ Official_Methods_of_Analysis/AOAC_Member/Pubs/OMA/AOAC_Official_Methods_of_Analysis.aspx. Accessed on [2020-04-01].
[13] Nawi N M, Chen G, Jensen T, Mehdizadeh S A. Prediction and classification of sugar content of sugarcane based on skin scanning using visible and shortwave near infrared. Biosystems Engineering, 2013; 115: 154–161.
[14] Barnes R J, Dhanoa M S, Lister S J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc., 1989; 43: 772–777.
[15] Dos Santos C A T, Lopo M, Páscoa R N M J, Lopes J A. A review on the applications of portable near-infrared spectrometers in the agro-food industry. Appl. Spectrosc., 2013; 67: 1215–1233.
[16] Martens H, Naes T. Multivariate calibration (2nd edition). Chichester: John Wiley & Sons. 1989.
[17] Shao Y, Gao C, Xuan G, Gao X, Chen Y, Hu Z. Determination of damaged wheat kernels with hyperspectral imaging analysis. Agricultural and Biological Engineering, 2020; 13(5): 194–198.
[18] Moghimi A, Aghkhani M H, Sazgarnia A, Sarmad M. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering, 2010; 106:
295–302.
[19] Zhao X B, Zhao J W, Huang X Y, Li Y X. Use of FTNIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of “Fuji” apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems, 2007; 87: 43–51.
[20] Vapnik V, Lerner A. Pattern recognition using generalized portrait method. Automation and Remote Control, 1963; 24: 774–780.
[21] Suykens J A K, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J, Van Gestel T. Least squares support vector machine (Vol. 4). Singapore: World Scientific, 2002.
[22] Sun T, Lin H, Xu H, Ying Y. Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biol. Technol., 2009; 51(1): 86–90.
[23] Yua H Y, Niua X Y, Lina H J, Yinga Y B, Lib B B, Panc X X. A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines. Food Chem., 2009; 113(1): 291–296.
[24] Morellosa A, Pantazia X E, Moshou D, Alexandridisc T, Whettonb R, Tziotziosa G, et al. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Eng., 2016; 152: 104–116.
[25] Han Z, Cai S, Zhang X, Qian Q, Huang Y, Dai F, et al. Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy. Food Chem., 2017; 227: 342–348.
[26] Chen J H, Sun D W. Data fusion and hyperspectral imaging in tandem with least squares-support vector machine for prediction of sensory quality index scores of fish fillet. Food Sci. Technol., 2015; 63(2): 892–898.
[27] Kamruzzaman M, Makino Y, Oshita S. Hyperspectral imaging for real-time monitoring of water holding capacity in red meat. Food Sci. Technol., 2016; 66: 685–691.
[28] Yang Q, Sun D W, Cheng W. Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process. Food Eng., 2017; 192: 53–60.
[29] De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K. LS-SVM toolbox user’s guide. ESAT-SISTA Technical Report, 2011; 10–146.
[30] Abu-Khalaf N, Hmidat M. Visible/Near Infrared (VIS/NIR) spectroscopy as an optical sensor for evaluating olive oil quality. Computer and Electronics in Agriculture, 2020; 173: 105445. doi: 10.1016/ j.compag.2020.105445.
[31] Williams P. The RPD statistic: a tutorial note. NIR News, 2014; 25(1): 22.
[32] Esbensen K H, Geladi P, Larsen A. The RPD myth. NIR News, 2014; 25(5): 24.
[33] Redaellia R, Alfieria M, Cabassi G. Development of a NIRS calibration for total antioxidant capacity in maize germplasm. Talanta, 2016; 154: 164–168.
[34] Ye M, Gao Z, Li Z, Yuan Y, Yue T. Rapid detection of volatile compounds in apple wines using FT-NIR spectroscopy. Food Chemistry, 2016; 190: 701–708.
[35] Hans G, Leblon B, Cooper P, Rocque A L, Nader J. Determination of moisture content and basic specific gravity of Populus tremuloides (Michx.) and Populus balsamifera (L.) logs using a portable near-infrared spectrometer. J. Wood Mater Sci Eng., 2015; 10: 3–16.
[2] Norris K H. Design and development of a new moisture meter. Agric. Eng., 1964; 45(7): 370–372.
[3] Maneewan W, Limpiti S, Theanjumpol P. The application of near infrared reflectance spectroscopy to determine the moisture and protein content in soybean seeds. The 14th International Conference of NIR Spectroscopy, Bangkok, Thailand, 2009.
[4] Rittiron R, Seehalak W. Moisture content in raw rubber sheet analyzed by transflectance near infrared spectroscopy. J. Innov. Opt. Health Sci., 2014; 7(4): 1350068. doi: 10.1142/S1793545813500685.
[5] Suchata S, Theanjumpolb P, Karrilaa S. Rapid moisture determination for cup lump natural rubber by near infrared spectroscopy. Industrial Crops and Products, 2015; 76: 772–780.
[6] Sirisomboon P, Kaewkuptong A, Williams P. Feasibility study on the evaluation of the dry rubber content of field and concentrated latex of Para rubber by diffuse reflectance near infrared spectroscopy. Journal of near infrared spectroscopy, 2013; 21: 81–88.
[7] Davies A M, Grant A. Review: near-infra-red analysis of food. Int. J. Food Sci. Technol., 1987; 22: 191–207.
[8] Lu R F, Chen Y R. Hyperspectral imaging for safety inspection of food and agricultural products. In Proceedings of SPIE Conference on Pathogen Detection and Remediation for Safe Eating, Boston, US, 1998.
[9] Park B. Quality inspection of poultry carcasses. In D.W. Sun (Ed.). Computer vision technology for food quality evaluation. Amsterdam: Academic Press, 2008; pp. 157–187.
[10] Barbin D, ElMasry G, Sun D W, Allen P. Near infrared hyperspectral imaging for grading and classification of pork. Meat Science, 2012; 90(1): 259–268.
[11] Ariana D, Guyer D E, Shrestha B. Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture, 2016; 50(2): 148–161.
[12] AOAC. Official Methods of Analysis of AOAC International. 2000. Available: http://www.aoac.org/aoac_prod_imis/AOAC/Publications/ Official_Methods_of_Analysis/AOAC_Member/Pubs/OMA/AOAC_Official_Methods_of_Analysis.aspx. Accessed on [2020-04-01].
[13] Nawi N M, Chen G, Jensen T, Mehdizadeh S A. Prediction and classification of sugar content of sugarcane based on skin scanning using visible and shortwave near infrared. Biosystems Engineering, 2013; 115: 154–161.
[14] Barnes R J, Dhanoa M S, Lister S J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc., 1989; 43: 772–777.
[15] Dos Santos C A T, Lopo M, Páscoa R N M J, Lopes J A. A review on the applications of portable near-infrared spectrometers in the agro-food industry. Appl. Spectrosc., 2013; 67: 1215–1233.
[16] Martens H, Naes T. Multivariate calibration (2nd edition). Chichester: John Wiley & Sons. 1989.
[17] Shao Y, Gao C, Xuan G, Gao X, Chen Y, Hu Z. Determination of damaged wheat kernels with hyperspectral imaging analysis. Agricultural and Biological Engineering, 2020; 13(5): 194–198.
[18] Moghimi A, Aghkhani M H, Sazgarnia A, Sarmad M. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering, 2010; 106:
295–302.
[19] Zhao X B, Zhao J W, Huang X Y, Li Y X. Use of FTNIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of “Fuji” apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems, 2007; 87: 43–51.
[20] Vapnik V, Lerner A. Pattern recognition using generalized portrait method. Automation and Remote Control, 1963; 24: 774–780.
[21] Suykens J A K, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J, Van Gestel T. Least squares support vector machine (Vol. 4). Singapore: World Scientific, 2002.
[22] Sun T, Lin H, Xu H, Ying Y. Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biol. Technol., 2009; 51(1): 86–90.
[23] Yua H Y, Niua X Y, Lina H J, Yinga Y B, Lib B B, Panc X X. A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines. Food Chem., 2009; 113(1): 291–296.
[24] Morellosa A, Pantazia X E, Moshou D, Alexandridisc T, Whettonb R, Tziotziosa G, et al. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Eng., 2016; 152: 104–116.
[25] Han Z, Cai S, Zhang X, Qian Q, Huang Y, Dai F, et al. Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy. Food Chem., 2017; 227: 342–348.
[26] Chen J H, Sun D W. Data fusion and hyperspectral imaging in tandem with least squares-support vector machine for prediction of sensory quality index scores of fish fillet. Food Sci. Technol., 2015; 63(2): 892–898.
[27] Kamruzzaman M, Makino Y, Oshita S. Hyperspectral imaging for real-time monitoring of water holding capacity in red meat. Food Sci. Technol., 2016; 66: 685–691.
[28] Yang Q, Sun D W, Cheng W. Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process. Food Eng., 2017; 192: 53–60.
[29] De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K. LS-SVM toolbox user’s guide. ESAT-SISTA Technical Report, 2011; 10–146.
[30] Abu-Khalaf N, Hmidat M. Visible/Near Infrared (VIS/NIR) spectroscopy as an optical sensor for evaluating olive oil quality. Computer and Electronics in Agriculture, 2020; 173: 105445. doi: 10.1016/ j.compag.2020.105445.
[31] Williams P. The RPD statistic: a tutorial note. NIR News, 2014; 25(1): 22.
[32] Esbensen K H, Geladi P, Larsen A. The RPD myth. NIR News, 2014; 25(5): 24.
[33] Redaellia R, Alfieria M, Cabassi G. Development of a NIRS calibration for total antioxidant capacity in maize germplasm. Talanta, 2016; 154: 164–168.
[34] Ye M, Gao Z, Li Z, Yuan Y, Yue T. Rapid detection of volatile compounds in apple wines using FT-NIR spectroscopy. Food Chemistry, 2016; 190: 701–708.
[35] Hans G, Leblon B, Cooper P, Rocque A L, Nader J. Determination of moisture content and basic specific gravity of Populus tremuloides (Michx.) and Populus balsamifera (L.) logs using a portable near-infrared spectrometer. J. Wood Mater Sci Eng., 2015; 10: 3–16.
Downloads
Published
2021-06-11
How to Cite
Puttipipatkajorn, A., & Puttipipatkajorn, A. (2021). Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques. International Journal of Agricultural and Biological Engineering, 14(3), 207–213. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6298
Issue
Section
Information Technology, Sensors and Control Systems
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
