Search

In order to achieve non-destructive measurement of varieties in persimmon, a fast discrimination method based on Vis ]> NIRS spectroscopy was put forward. A Field Spec 3 spectroradiometer was used for collecting 22 sample spectra data of the three kinds of persimmon separately. Then principal component analysis (PCA) was used to process the spectral data after pretreatment. The near infrared fingerprint of persimmon was acquired by principal component analysis(PCA), Cand support vector machine (SVM) methods were used to further identify the persimmon separately. The result of PCA indicated that the score map made by the scores of PCI, CPC2 and PC3 was used, and 8 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99. 888%.51 persimmon samples were used for calibration and the remaining 15 persimmon samples were used for validation. A one-against- all multi-class SVM model was built, and the result showed that SVM possessing with the RBF kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the mixed algorithm method of principal component analysis( PCA) and support vector Machine(SVM) has a good identification effect, and can work as a new method for quick, efficient and correct identification of persimmon separately. [ABSTRACT FROM AUTHOR]

In order to achieve non-destructive of mature vinegar varieties, a fast discrimination method was put forward based on Visible_near infrared reflectance (NIR) spectroscopy. The FieldSpec3 spectrometer was used for collecting 20 sample spectra data of the three kinds of mature vinegar separately. Then principal component analysis (PCA) was used to process the spectral data after pretreatment using average smoothing method and multiplicative scatter correction (MSC) method, and principal components(PCs) were selected based on accumulative reliabilities. A total of 60 mature vinegar samples were divided into calibration sets and validation sets randomly, the calibration sets had 45 samples and the validation sets had 15 samples. The stepwise discriminant analysis was trained with five PCs in calibration sets as the inputs,and mature vinegar varieties as the outputs. The stepwise discriminant analysis model was built for discrimination of mature vinegar variety ,and the model contains 15 samples in the validation sets. The result showed that a 100% recognition ration was achieved.The BP-ANN model for discrimination of mature vinegar varieties were built based on PCA and the stepwise discriminant analysis, then the model was tested with the 15 sample in the validation sets. The result showed that a 100% recognition ration was achieved with the threshold predictive error ±0.027. It based on five principal components had a higher prediction accuracy and efficiency more than the BP neural network model. It could be concluded that PCA combined with stepwise discriminant analysis and BP-ANN was an available method for varieties recognition of mature vinegar based on NIR spectroscopy. [ABSTRACT FROM AUTHOR]

The mass customization can make customized products with the mass products efficiency, which will become mainstream manufacturing mode in the 21 century. The design for mass customization is the most important factor in the mass customization, on the base of extracting the excellences of the modulization design and the variant design, the article brings forward an idea of module-based variant design. Combining with the technologies of module coding, database, case-based design and CAD, The idea has been used in the customized design of the equipment of pushing and measuring automobile chair track, some equipments have been designed and manufactured successfully. [ABSTRACT FROM AUTHOR]