摘要
根据废旧纺织品所含成分对它们做分类回收和处理可节省大量纺织原材料。目前,在废旧纺织品的回收过程中往往使用人工分拣方法。这种方法成本高且效率低。近红外光谱分析是21世纪发展最迅速的技术之一,可以在不破坏样本的情况下快速测定样本的成分及每种成分的含量。利用该技术对废旧纺织品进行分析,预先判断废旧纺织品所含的成分及各种成分的含量,可为废旧纺织品的大规模精细分类回收提供帮助。多模型方法通过将各子模型的预测值做加权平均得到最终的预测值,用该方法建立的近红外光谱分析模型一般具有较好的稳定性。以废旧纺织品样本的锦纶含量为例,先用多模型方法建立了锦纶含量的近红外光谱分析模型。方法如下:将反射率向量按照波长划分为15组。用每组数据建立一个近红外光谱分析子模型。对子模型的预测值做加权平均得出锦纶含量的最终预测值。然后在多模型方法基础上,根据锦纶含量预测值与实验值之间的近似线性关系,通过用变量代替常量并对变量做标准化处理,给出了一种便于优化的预测锦纶含量的近红外光谱分析新模型。优化后的每个子模型中的参数比优化前减少了6个,这样可防止模型过拟合。将上述两个模型与常见的用偏最小二乘法建立的模型进行了对比。交叉验证的结果表明:(优化后的)新模型的拟合优度的平均值为0.8207,单纯使用多模型方法所建模型的拟合优度的平均值为0.7691,用偏最小二乘法建立的模型的拟合优度的平均值为0.7467。因此,使用多模型方法建立的模型的预测效果好于用偏最小二乘法建立的模型的预测效果。新模型的预测效果明显好于其他两个模型的预测效果。该研究主要创新之处是新模型的建立和优化。文中建模方法有望用于废旧纺织品样本其他成分的含量预测。
If the waste textiles are classified,recycled and disposed of according to their components,many textile raw materials can be saved.At present,the manual sorting method is often used in the recycling process of waste textiles.This method is costly and inefficient.Near-infrared spectroscopy analysis is one of the most rapidly developing technologies in the 21 st century.It can quickly determine the components of the sample and the content of each component without destroying the sample.Using this technology to analyze the waste textiles and prejudge the components and contents of various components of waste textiles can be helpful for the large-scale fine classification and recycling of waste textiles.In the multi-model method,the final predicted value is obtained by a weighted average of the predicted values of each sub-model.The near-infrared spectroscopy analysis model established by this method generally has good stability.In this paper,taking the nylon content of waste textile samples as an example,a near-infrared spectral analysis model for predicting the nylon content is first established using the multi-model method.The method is as follows:The reflectance vectors are divided into 15 groups according to their wavelengths.A sub-model of near-infrared spectral analysis is established with each data group.The final predicted value of the nylon content is obtained by a weighted average of the predicted values of sub-models.Then,based on the multi-model method,according to the approximately linear relationship between the predicted values and the experimental values of the nylon content,by replacing constants with variables and by standardizing the variables,a new model for predicting the nylon content by near-infrared spectral analysis is presented,and the model is convenient for optimization.After optimization,the parameters of each sub-model are reduced by 6.This can prevent overfitting of the model.The above two models are compared with the common model established by the partial least squares method.The re
作者
韩松辰
刘胜
HAN Song-chen;LIU Sheng(College of Science,Beijing Forestry University,Beijing 100083,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2022年第8期2477-2481,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(61571002)资助。
关键词
近红外光谱
定量分析
多模型方法
新模型
Near-infrared spectroscopy
Quantitative analysis
Multi-model method
New model
