다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Near infrared spectroscopy: A mature analytical technique with new perspectives - A review.

In recent years, hyperspectral imaging has gained a wide recognition as a non-destructive and fast quality and safety analysis and assessment method for a wide range of food products. As the second part of this review, applications in quality and safety determination for food products are presented to illustrate the capability of this technique in the food industry for classification and grading, defect and disease detection, distribution visualization of chemical attributes, and evaluations of overall quality of meat, fish, fruits, vegetables, and other food products. The state of the art of hyperspectral imaging for each of the categories was summarized in the aspects of the investigated quality and safety attributes, the used systems (wavelength range, acquisition mode), the data analysis methods (feature extraction, multivariate calibration, variables selection), and the performance (correlation, error, visualization). With its success in different applications of food quality and safety analysis and assessment, it is evident that hyperspectral imaging can automate a variety of routine inspection tasks. Industrial relevance It is anticipated that real-time food monitoring systems with this technique can be expected to meet the requirements of the modern industrial control and sorting systems in the near future.

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Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications

Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data

During the past decade, hyperspectral imaging (HSI) has been rapidly developing and widely applied in the food industry by virtue of the use of chemometric techniques in which wavelength selection methods play an important role. This paper is a review of such variable selection methods and their limitations, describing the basic taxonomy of the methods and their respective advantages and disadvantages. Special attention is paid to recent developments in wavelength selection techniques for HSI in the field of food quality and safety evaluations. Typical and commonly used methods in HSI, such as partial least squares regression, stepwise regression and spectrum analysis, are described in detail. Some sophisticated methods, such as successive projections algorithm, uninformative variable elimination, simulated annealing, artificial neural network and genetic algorithm methods, are also discussed. Finally, new methods not currently used but that could have substantial impact on the field are presented. In short, this review provides an overview of wavelength selection methods in food-related areas and offers a thoughtful perspective on future potentials and challenges in the development of HSI systems.

Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry

Variables selection methods in near-infrared spectroscopy.

In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging

Independent component analysis in information extraction from visible/near-infrared hyperspectral imaging data of cucumber leaves

Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A method based on a genetic algorithm interval partial least squares regression (GAiPLS) combined successive projections algorithm (SPA) was proposed for variable selection in NIR spectroscopy. GAiPLS was used to select informative interval regions among the spectrum, and then SPA was employed to select the most informative variables and to minimize collinearity between those variables in the model. The performance of the proposed method was compared with the full-spectrum model, conventional interval partial least squares regression (iPLS), and backward interval partial least squares regression (BiPLS) for modeling the NIR data sets of pigments in cucumber leaf samples. The multiple linear regression (MLR) model was obtained with eight variables for chlorophylls and five variables for carotenoids selected by SPA. When the SPA model was applied to the prediction of the validation set, the correlation coefficients of the predicted value by MLR and the measured value for the validation data set (rp) of chlorophylls and carotenoids were 0.917 and 0.932, respectively. Results show that the proposed method was able to select important wavelengths from the NIR spectra and makes the prediction more robust and accurate in quantitative analysis.

Genetic algorithm interval partial least squares regression combined successive projections algorithm for variable selection in near-infrared quantitative analysis of pigment in cucumber leaves.

Objective to optimize the isolation condition of Epidermal Cell Protoplasts within the Allium cepa L. Methods According to orthogonal experiment design L 25 (56) of enzyme, osmotic pressure stabilizer(D-Mannitol), pH, enzymolysis time, we got the optimal isolation condition by the number of protoplasts. Results The optimal isolation condition is showed in results: enzymolysis time 2h, pH 5.5, D-Mannitol 0.6 mol/L, 1.5% cellulose and 0.1% pectolyase and 0.1%BSA, temperature 28°C. It also means that optimal isolation condition is D 5 B 3 C 4 A 2 . Conclusion The optimal isolation condition is viable.

Mao Hanpin

Papers

Variables selection methods in near-infrared spectroscopy.

In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging

Independent component analysis in information extraction from visible/near-infrared hyperspectral imaging data of cucumber leaves

Genetic algorithm interval partial least squares regression combined successive projections algorithm for variable selection in near-infrared quantitative analysis of pigment in cucumber leaves.

Optimum parameters of preparation of Epidermal Cell Protoplasts within the Allium cepa L