基于激光诱导击穿光谱（LIBS）技术，结合偏最小二乘回归（PLSR），建立了废钢中铬、镍、铜、硅、锰、钒、碳、钛等8种元素的定量分析模型。采用便携激光诱导击穿光谱废钢成分检测仪对12个钢铁标准样品进行扫描，对光谱数据进行剔除、平均、基线校正、归一化等预处理，以美国国家标准与技术研究院发射谱线数据库为参考依据，以筛选出的各元素建模波段的光谱数据作为模型输入，采用十折交叉验证方法，确定铬、镍、铜、硅、锰、钒、碳、钛的潜变量数分别为16，24，18，22，25，14，9，22，在优化的参数下分别对8种元素进行建模。结果显示：8种元素模型的相关决定系数为0.957 1~0.999 6，均方根误差为0.003 4~0.070 6，平均百分比误差为6.676 4~75.645，残差平方和为0.002 0~0.653 2；除碳元素外，其余7种元素测定值的相对标准偏差（n=5）均不大于7.0%；模型用于预测实际废钢样品中锰、硅、铜元素的含量，所得结果与火花直读光谱法的基本一致。

The quantitative analysis models of chromium, nickel, copper, silicon, manganese, vanadium, carbon and titanium in scrap steel were established based on laser induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR). Twelve steel standard samples were selected and scanned by a portable laser induced breakdown spectrometer for scrap steel composition. The spectral data were preprocessed with elimination, average, baseline correction and normalization. Based on the emission spectral line database of National Institute of Standards and Technology, the spectral data of modeling band of each element were selected as the model input, and the numbers of latent variable of chromium, nickel, copper, silicon, manganese, vanadium, carbon and titanium were 16, 24, 18, 22, 25, 14, 9 and 22, respectively, with the ten-fold cross-validation method. Eight elements were modeled under the optimized parameters. The results showed that the correlation coefficients of determination of the models of 8 elements were in the range of 0.957 1-0.999 6, with the root mean square errors in the range of 0.003 4-0.070 6, the average percentage errors in the range of 6.676 4-75.645, and the residual sum of squares in the range of 0.002 0-0.653 2. Except for carbon, RSD (n=5) of the determined values of the other 7 elements were not more than 7.0%. The models were used to predict manganese, silicon and copper in actual scrap steel samples, and the results were in good agreement with those obtained by spark direct reading spectrometry.