Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.

近红外光谱结合SIMCA法溯源羊肉产地的初步研究

产地溯源是食品安全追溯制度的重要组成部分。该文采用近红外光谱结合簇类独立软模式法(SIMCA)建立了羊肉产地溯源模型。结果表明,在11995～3999cm-1波长范围内,光谱经5点平滑(Smooth)与多元散射校正(MSC)预处理,山东济宁市、河北大厂县、内蒙临河市、宁夏银川市4个产地模型的主成分数分别为5、6、5、6时,采用SIMCA模式识别方法可以建立稳健的羊肉产地溯源模型;在1%的显著水平下,4个产地校正集模型对未知样本的识别率分别为95%、100%、100%、100%,拒绝率均为100%;其验证集模型的识别率分别为100%、83%、100%、92%,拒绝率均为100%。该研究表明,近红外光谱技术作为一种羊肉产地的溯源方法切实可行。

基于SIMCA模型的纸浆种类近红外光谱鉴别

研究了近红外光谱分析与SIMCA模式识别技术相结合鉴别纸浆种类的方法。收集了桉木浆、麦草浆(漂白与未漂白)、棉浆、湿地松浆等4种纸浆共90个样品,抄成不同定量纸样并采集其近红外光谱,其中部分桉木浆添加化学助剂。选择60个样品作为训练集建立SIMCA类模型,剩余30个样品用于模型检验。研究结果表明,建立的模型能完全正确识别各类纸浆,且不受抄纸定量和添加化学助剂因素的影响,为快速无损鉴别纸浆种类提供了一种准确可靠的方法。