光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2015年
5期
1392-1397
,共6页
黎文兵%药林桃%刘木华%黄林%姚明印%陈添兵%何秀文%杨平%胡慧琴%聂江辉
黎文兵%藥林桃%劉木華%黃林%姚明印%陳添兵%何秀文%楊平%鬍慧琴%聶江輝
려문병%약림도%류목화%황림%요명인%진첨병%하수문%양평%호혜금%섭강휘
激光诱导击穿光谱%PLS%数据前处理%定量模型
激光誘導擊穿光譜%PLS%數據前處理%定量模型
격광유도격천광보%PLS%수거전처리%정량모형
Laser induced breakdown spectroscopy%PLS%Data pretreatment%Quantitative model
应用激光诱导击穿光谱（LIBS）对脐橙中Cu元素进行快速检测，并结合偏最小二乘法（PLS）进行定量分析，探索光谱数据预处理方法对模型检测精度的影响。针对实验室污染处理后的52个赣南脐橙样品的光谱数据，进行不同数据平滑、均值中心化和标准正态变量变换三种预处理方法。然后选择包含Cu特征谱线的319～338 nm波段进行PLS建模，对比分析了模型的主要评价指标回归系数（r）、交互验证均方根误差（RMSECV）和预测均方根误差（RMSEP）。采用13点平滑、均值中心化的 PLS模型3个指标分别达到了0.9928，3.43和3.4，模型的平均预测相对误差仅为5.55％，即采用该前处理方法模型的校准质量和预测效果都最好。选择合适的数据前处理方法能有效提高LIBS检测果蔬产品PLS定量模型的预测精度，为果蔬产品L IBS快速精准检测提供了新方法。
應用激光誘導擊穿光譜（LIBS）對臍橙中Cu元素進行快速檢測，併結閤偏最小二乘法（PLS）進行定量分析，探索光譜數據預處理方法對模型檢測精度的影響。針對實驗室汙染處理後的52箇贛南臍橙樣品的光譜數據，進行不同數據平滑、均值中心化和標準正態變量變換三種預處理方法。然後選擇包含Cu特徵譜線的319～338 nm波段進行PLS建模，對比分析瞭模型的主要評價指標迴歸繫數（r）、交互驗證均方根誤差（RMSECV）和預測均方根誤差（RMSEP）。採用13點平滑、均值中心化的 PLS模型3箇指標分彆達到瞭0.9928，3.43和3.4，模型的平均預測相對誤差僅為5.55％，即採用該前處理方法模型的校準質量和預測效果都最好。選擇閤適的數據前處理方法能有效提高LIBS檢測果蔬產品PLS定量模型的預測精度，為果蔬產品L IBS快速精準檢測提供瞭新方法。
응용격광유도격천광보（LIBS）대제등중Cu원소진행쾌속검측，병결합편최소이승법（PLS）진행정량분석，탐색광보수거예처리방법대모형검측정도적영향。침대실험실오염처리후적52개공남제등양품적광보수거，진행불동수거평활、균치중심화화표준정태변량변환삼충예처리방법。연후선택포함Cu특정보선적319～338 nm파단진행PLS건모，대비분석료모형적주요평개지표회귀계수（r）、교호험증균방근오차（RMSECV）화예측균방근오차（RMSEP）。채용13점평활、균치중심화적 PLS모형3개지표분별체도료0.9928，3.43화3.4，모형적평균예측상대오차부위5.55％，즉채용해전처리방법모형적교준질량화예측효과도최호。선택합괄적수거전처리방법능유효제고LIBS검측과소산품PLS정량모형적예측정도，위과소산품L IBS쾌속정준검측제공료신방법。
Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis ,then the effect on the detection accuracy of the model with different spectral data pre-treatment methods was explored .Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoot-hing ,mean centralized and standard normal variable transform .Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models ,the main evaluation indexes of models such as regression coefficient (r) , root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed .Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0.992 8 ,3.43 and 3.4 respectively ,the average relative error of prediction model is only 5.55% ,and in one word ,the quality of calibration and prediction of this model are the best results .The results show that selecting the appropriate data pre-process-ing method ,the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effec-tively ,providing a new method for fast and accurate detection of fruits and vegetables by LIBS .
