本文利用近红外光谱（Near infrared spectrum，NIR）技术，建立了一种衣物材质和含量检测模型。选择8种衣物样品与棉-涤混纺织物作为测试对象，通过对数据进行预处理、特征提取、分类识别和回归分析等步骤，建立了衣物材质和含量的检测模型。实验结果表明，最小二乘向量机所建立的定性判别模型准确性较高，运算时间更短。经二阶导数平滑与预处理后的棉-涤混合面料近红外光谱数据结合偏最小二乘法算法建立的定量分析模型有较好的精准度与稳定性，预测均方根误差达到了0.0019，满足了近红外快速检测的准确度要求。本文所建立的近红外检测方法具有环保、耗时短、成本低等优点，在衣物制造、质保和质检等领域都有一定应用价值。本研究为衣物材质和含量的检测提供了一种新的方法和思路，具有一定的理论和实际应用价值。

In this paper, Near infrared spectrum (NIR) technology is used to establish a clothing material and content detection model. Selecting 8 kinds of clothing samples and cotton-polyester blended fabrics as test objects, the detection model of clothing material and content was established through data preprocessing, feature extraction, classification recognition and regression analysis. The experimental results show that the qualitative discriminant model established by the least square vector machine has higher accuracy and shorter operation time. The quantitative analysis model based on the NIR data of the cotton-polyester mixed fabric after the second derivative smoothing and pretreatment combined with the partial least square algorithm has good accuracy and stability, and the predicted root-mean-square error reaches 0.0019, which meets the accuracy requirements of the NIR rapid detection. The near infrared detection method established in this paper has the advantages of environmental protection, short time and low cost, and has certain application value in the fields of clothing manufacturing, quality assurance and quality inspection. This study provides a new method and idea for the detection of fabric material and content, which has certain theoretical and practical application value.