用近红外光谱快速评价天然胶乳及其制品的性能

P. Sirisomboon, C. H. Lim
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引用次数: 2

摘要

干胶含量(DRC)、总固形物含量(TSC)、挥发性脂肪酸(VFA)数、碱度(氨含量)、氢氧化钾(KOH)数等理化性能参数和粘度、交联密度等物理性能参数对天然胶乳及其制品的贸易和加工具有重要意义。传统上,现场和浓缩乳胶及其产品(包括薄膜和厚膜)的这些特性是用化学品和耗时的方法以及技术专家的需要来评估的。近红外光谱技术是一种无损、快速、准确、环保的技术。它不需要化学物质。介绍了近红外光谱技术在天然胶乳及其制品性能评价中的应用。利用原始和预处理的吸光度光谱建立了PLSR模型。在700 ~ 950 nm波长范围内对光谱进行平滑和距离归一化处理的模型对DRC的预测精度最好,而在相同波长范围内使用原始光谱的模型对TSC的预测精度最好。DRC预测的斜率、偏移量、r 2、SEP和偏差分别为1.0154、- 0.6286、0.992、1.1092和0.0321%,TSC预测的斜率、偏移量、r 2、SEP和偏差分别为1.0084、- 0.2332、0.991、1.3611和0.1456%。对DRC和TSC分别使用50个和35个样本的新未知样本集验证了最佳模型。模型的r 2、SEP、RPD和偏倚分别为0.988和0.974,1.4296和2.1255,10.0和6.2,- 0.6191和0.5476%。本研究表明,SW-NIR光谱法在评价TSC质量保证过程中可用于乳胶工厂的控制
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid Evaluation of the Properties of Natural Rubber Latex and Its Products Using Near-Infrared Spectroscopy
The parameters including physico-chemical properties such as dry rubber content (DRC), total solids content (TSC), volatile fatty acid (VFA) number, alkalinity (ammonia content) and potassium hydroxide (KOH) number and physical properties such as viscosity and cross-link density are important to the trading and processing of natural rubber latex and its products. Traditionally these properties of field and concentrated latices and their products including thin and thick films are evaluated with chemicals and time-consuming methods and with the need of technical experts. The near-infrared (NIR) spectroscopy is a nondestructive, rapid, accurate and environment-friendly technique. It needs no chemical. This chapter describes the application of NIR spectroscopy on evaluation of the properties of natural rubber latex and its products. latex The PLSR models were developed using original and pretreated absorbance spectra. The model developed from smoothing and range normalization pretreated spectra in the wavelength range of 700–950 nm the best prediction accuracy for DRC, and the models using raw spectra in the same wavelength range gave the best results for TSC. The slope, offset, r 2 , SEP and bias were 1.0154, − 0.6286, 0.992, 1.1092 and 0.0321%, respectively, for the DRC prediction and 1.0084, − 0.2332, 0.991, 1.3611 and 0.1456%, respectively, for the TSC prediction. The best models were validated using new unknown sample sets of 50 and 35 samples for DRC and TSC, respectively. The models provided an r 2 , SEP, RPD and bias of 0.988 and 0.974, of 1.4296 and 2.1255, of 10.0 and 6.2 and of − 0.6191 and 0.5476% for DRC and TSC, respectively. This work showed that the SW-NIR spectroscopy in the evaluation TSC for quality assurance process control latex the factory
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