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Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

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Journal of Innovative Optical Health Sciences 2020年第2期13卷 81-92页

作者： amorndej puttipipatkajorn Amornrit puttipipatkajorn Department of Food Engineering Faculty of Engineering at Kamphaeng Saen Kasetsart University Nakorn Pathom 73140Thailand Department of Computer Engineering Faculty of Engineering at Kamphaeng Saen Kasetsart University Nakorn Pathom 73140Thailand

Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber *** quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside,but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair ***,we developed a rapid,robust and nondestructive near-infrared spectroscopy(NIRS)-based method for moisture content determination in rubber sheets.A set of 300 rubber sheets were divided into a calibration(200 samples)and prediction groups(100 samples).The calibration set was used to develop NIRS calibration equation using different calibration models,Partial Least Square Regression(PLSR),Least Square Support Vector Machine(LS-SVM)and Articial Neural Network(ANN).Among the models investigated,the ANN model with therst derivative of spectral preprocessing presented the best prediction with a coe±cient of determination(R^(2)_(P))of 0.993,root mean square error of calibration(RMSEC)of 0.126%and root mean square error of prediction(RMSEP)of 0.179%.The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

关键词： NIR spectroscopy rubber sheet moisture content partial least squares regression arti¯cial neural network least squares support vector machine

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Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques

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International Journal of Agricultural and Biological Engineering 2021年第3期14卷 207-213页

作者： amorndej puttipipatkajorn Amornrit puttipipatkajorn Department of Food Engineering Faculty of Engineering at Kamphaeng SaenKasetsart UniversityNakhon Pathom73140Thailand Department of Computer Engineering Faculty of Engineering at Kamphaeng SaenKasetsart UniversityNakhon Pathom73140Thailand

Dry rubber content(DRC)is an important factor to be considered in evaluating the quality of cup lump *** DRC analysis requires prolonged laboratory *** develop fast and effective DRC determination methods,this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement *** 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 *** 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,*** spectral data were calibrated using partial least squares regression(PLSR)and the least-squares support vector machine(LS-SVM)methods against the reference *** 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 *** 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,*** summary,the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber.

关键词： cup lump rubber dry rubber content spectroscopic measurement machine learning partial least squares regression least-squares support vector machine

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