Measurement of cross link densities of prevulcanized natural rubber latex and latex products using low-cost near infrared spectrometer

Lim, Chin Hock; Sirisomboon, Panmanas

doi:10.1016/j.indcrop.2020.113016

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…The studies by Lim and Sirisomboon of crosslink density of natural rubber film developed from prevulcanized latex model which was created by PLSR using the spectra scanned by FT-NIR spectrometer [ 3 ], the natural rubber thin film model provided the r 2 , root mean square error of cross validation and bias of 0.65, 4.01%TSI and −0.028%TSI, respectively, using the wavenumber range of 6102–5446.3 cm −1 and 4428–4242.9 cm −1 (1639–1836 nm (included natural rubber absorption bands) and 2258–2357 nm), whereas for the natural rubber thick film model the r 2 , root mean square error of cross validation and bias were 0.70, 4.00%TSI and −0.006%TSI, respectively, using the wavenumber range of 6102–4597.7 cm −1 (1639–2175 nm (included natural rubber absorption bands)). By using the low-cost VIS/NIR diode array spectrometer in the wavelength range of 450–1000 nm using a fiber optic probe scanned on the thin and thick film, the models for crosslink density indicated by prevulcanisate relaxed modulus (PRM) had poor results, as indicated by the R 2 of calibration and RMSEC of 0.02 and 17.31 × 10 4 N/m 2 and 0.05 and 16.63 × 10 4 N/m 2 , respectively [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Jongyingcharoen,

Howimanporn,

Sitorus

et al. 2024

Polymers

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Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

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Section: Discussionmentioning

confidence: 99%

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Jongyingcharoen,

Howimanporn,

Sitorus

et al. 2024

Polymers

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“…Lim et al proposed to determine the densities of cross linking in latex by an innovative method based on portable NIR ( Lim et al, 2021 ).…”

Section: Applications To Wood Cellulose Lignin and Lignitementioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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“…NIR spectroscopy have been studied for feasibility determination of cross link density of rubber latex such as cross link density determined by prevulcanisate relaxed modulus (PRM) test [1] using NIR Fourier Transform spectrometer (FT-NIR) and [2] using low cost spectrometer (LCS) and toluene swell test [3] using FT-NIR, when reference methods has been used in rubber latex factory. For prevulcanized latex an effective model was developed using partial least squares regression with preprocessing (first derivative + straight line subtraction method).…”

Section: Introductionmentioning

confidence: 99%

“…The coefficient of determination (R 2 ), root mean square error of cross validation of the calibration set were 0.85 and 2.98% and coefficient of determination (r 2 ), root mean square error of cross validation and bias of the validation set were 0.71, 3.93% and -0.005%, respectively. By PRM test for PV 50% (prevulcanised latex of 50% total solids content), FT-NIR gave coefficient of determination of validation (r 2 ) and root mean square error of prediction (RMSEP) of 0.88 and 6.74×104 Nm -2 , respectively, while scanned PV50 latex in quartz cup [1] and LCS gave 0.70 and 8.13×104 Nm-2, respectively, while dipped probe in PV50 latex in glass beaker [2]. For FT-NIR spectrometer to be used in prevulcanized latex production, Williams et al [4] indicated this model was for screening and some other approximate calibration (r 2 is between 0.66-0.81).…”

Section: Introductionmentioning

confidence: 99%

Near infrared spectroscopy and machine learning classifier of crosslink density level of prevulcanized natural rubber latex

Sirisomboon

Lim²,

Posom

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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By toluene swell index for cross link density level of prevulcanized (PV) rubber latex knowledge, toluene swell of PV latex is measured for trading and production management. Therefore, aim of this research is to use the Fourier transform near infrared (FT-NIR) spectroscopy with machine learning to classify different cross link density levels by toluene swell index including, Unvulcanized (U) (> 160%swell), Lightly vulcanized (L) (100-160%swell), Moderately vulcanized (M) (80-100%swell), Fully vulcanized (F) (< 80%swell) of prevulcanized (PV) natural rubber latex of raw PV latex and 50% solids content PV latex (PV50). The result shows that toluene swell index of rubber prevulcanized latex could be 91.8% correct classified into L group and M group using PV50 MSC pretreated spectra with PLS-DA classifier. Unfortunately, sample obtained for this experiment were loss of U and F groups. In future, to develop the robust model, the sample of all crosslink density levels should be collected.

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Measurement of cross link densities of prevulcanized natural rubber latex and latex products using low-cost near infrared spectrometer

Cited by 7 publications

References 6 publications

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Portable NIR spectroscopy: the route to green analytical chemistry

Near infrared spectroscopy and machine learning classifier of crosslink density level of prevulcanized natural rubber latex

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