本文選題：激光誘導擊穿光譜 + 咖啡豆��； 參考：《光譜學與光譜分析》2017年07期

【摘要】：應用激光誘導擊穿光譜(LIBS)技術研究了快速檢測咖啡豆中咖啡因含量的可行性。將咖啡豆磨粉壓成片狀作為采集LIBS光譜數(shù)據(jù)的樣本,應用原子吸收分光光度計測量每個樣本中咖啡因的含量。應用基線校正,小波變換和歸一化等數(shù)據(jù)預處理方法;針對基于全部變量的偏最小二乘(PLS)模型會出現(xiàn)過擬合,分別應用回歸系數(shù)和主成分分析(PCA)選擇特征變量,并建立了基于特征變量的PLS和BP神經(jīng)網(wǎng)絡模型。結果表明:基于回歸系數(shù)所選特征變量的PLS模型中,建模集相關系數(shù)Rc=0.96,預測集Rp=0.91;基于PCA提取特征變量的PLS模型中,Rc=0.94,Rp=0.90;基于PCA所選特征變量的BP神經(jīng)網(wǎng)絡模型中,Rc=0.96,Rp=0.96。兩種方法所提取特征變量均對應C,H,O,N,Na,Mn,Mg,Ca和Fe,且基于上述兩種方法所選特征變量的PLS模型均對預測集樣本有較好的預測結果,說明上述元素與咖啡因含量存在聯(lián)系,應用回歸系數(shù)和PCA選擇的特征變量是有效的,但是咖啡豆內C,H,O,N,Na,Mn,Mg,Ca,Fe與咖啡因含量的確切關系需要進一步研究。基于PCA所選特征變量的BP神經(jīng)網(wǎng)絡模型有更優(yōu)的預測結果,說明所選特征變量適用于不同的建模方法。研究表明LIBS技術結合化學計量學方法可以實現(xiàn)咖啡豆中咖啡因含量的快速檢測。
[Abstract]:The feasibility of rapid determination of caffeine in coffee beans was studied by laser induced breakdown spectroscopy (LIBS). The coffee bean grinding powder was pressed into sheets as the sample to collect the Libs spectral data. The caffeine content in each sample was measured by atomic absorption spectrophotometer (AAS). The content of caffeine in each sample was measured by atomic absorption spectrophotometer (AAS). Based on baseline correction, wavelet transform and normalization, the partial least squares (PLS) model based on all variables is overfitted, and regression coefficients and principal component analysis (PCA) are used to select feature variables, respectively. The PLS and BP neural network models based on characteristic variables are established. The results show that in the PLS model based on the characteristic variables selected by the regression coefficient, the correlation coefficient of the modeling set is 0.96, the prediction set is Rp0.91; the PLS model based on PCA is used to extract the feature variables, and the Rc0.96Rp0.96 is found in the BP neural network model based on the feature variables selected by PCA. The characteristic variables extracted by the two methods correspond to Ca and Fe. the PLS models based on the above two methods have good prediction results for the predicted set samples, indicating that the above elements are related to the caffeine content. The regression coefficient and the characteristic variables selected by PCA are effective, but the exact relationship between the content of caffeine and the content of caffeine in coffee bean needs further study. The BP neural network model based on PCA selected feature variables has better prediction results, which shows that the selected feature variables are suitable for different modeling methods. The results showed that LIBS combined with chemometrics could be used to detect caffeine in coffee beans.
【作者單位】： 浙江大學生物系統(tǒng)工程與食品科學學院;
【基金】：國家科技支撐計劃項目(2014BAD10B02) 浙江省自然科學基金項目(LY15C130003)資助
【分類號】：O657.319;TS273

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