电子鼻检测烤后烟叶挥发性组分的方法研究

本研究采用电子鼻系统对不同形态烤后烟样进行测定,以便初步建立一个烤后烟叶挥发性组分判别检测的方法。结果表明,不同样品形态、同一样品不同放置时间均对电子鼻检测效果有一定影响。对于烟丝样品,放置4h检测为宜;对于烟末样品,放置30 min检测为宜;采用烟丝样品检测效果优于烟末样品。结果分析表明,线性判别分析（LDA）比主成分分析（PCA）更能有效的区分不同样品,幵且LDA分析的结果更能代表样品的整体特征。传感器Loadings分析表明,烟丝样品检测时贡献率较大的传感器是2、7、8、9;烟末样品检测时贡献率较大的传感器是2、7、9。利用电子鼻技术在合适的条件下,可以对不同品种不同部位的烟样进行区分和鉴别。     

Calibration Transfer from Micro NIR Spectrometer to Hyperspectral Imaging: a Case Study on Predicting Soluble Solids Content of Bananito Fruit (<Emphasis Type="Italic">Musa acuminata</Emphasis>)

Calibration transfer from a handheld micro NIR spectrometer (NIR-point, 939–1602 nm, 6.2 nm) to a desktop hyperspectral imaging (NIR-HSI) for predicting soluble solids content (SSC) of bananito flesh was investigated in the study. Different spectral pre-processing and standardization methods were employed for correcting spectra so as to minimise spectral differences between NIR-point and NIR-HSI. Results show that application of standard normal variate (SNV) reduced spectral differences from 31.49 to 8.96%. The best standardization method was developed based on piecewise direct standardization (PDS) algorithm using ten transfer samples. The developed PLS model yielded a high prediction performance (R ² _p = 0.922 and RMSEP = 1.451%) for predicting SSC of validation samples using the NIR-point spectra. After SNV and standardization, the model was successfully transferred to NIR-HSI data, giving a comparable prediction accuracy of R ² _p = 0.925 and RMSEP = 1.592%. The results illustrated the potential of transferring calibration models from a simple and easy-available micro NIR spectrometer to a more expensive and sophisticated hyperspectral imaging system, when the spatial distribution of quality information is required.