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

With the advances in innovative instrumentation and various valuable applications, near-infrared (NIR) spectroscopy has become a mature analytical technique in various fields. Variable (wavelength) selection is a critical step in multivariate calibration of NIR spectra, which can improve the prediction performance, make the calibration reliable and provide simpler interpretation. During the last several decades, there have been a large number of variable selection methods proposed in NIR spectroscopy. In this paper, we generalize variable selection methods in a simple manner to introduce their classifications, merits and drawbacks, to provide a better understanding of their characteristics, similarities and differences. We also introduce some hybrid and modified methods, highlighting their improvements. Finally, we summarize the limitations of existing variable selection methods, providing our remarks and suggestions on the development of variable selection methods, to promote the development of NIR spectroscopy.

An overview of variable selection methods in multivariate analysis of near-infrared spectra

Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing

Background Taste is an important factor in evaluating the quality of tea. The sweet and umami tastes are usually well-accepted for consumers, whereas the bitter and astringent tastes are usually undesired, but they are important for providing the complex sensory perceptions of Camellia teas. The compounds responsible for the bitterness and astringency in tea leaves can be modified by processing, and subsequently endowed different taste of various tea types. Therefore, the taste of tea is closely related to the chemical constituents, and the taste mechanism is critical for improving tea quality. Scope and approach The aim of this review is to review and discuss the association between chemical composition of Camellia teas and their effects on bitterness, astringency, sweetness aftertaste and umami. Key findings and conclusions In the tea infusion, flavonol-O-glycosides, tannins and galloylated catechins are the main astringent compounds, caffeine and non-galloylated catechins enhance the tea bitterness. Furthermore, l -theanine, succinic acid, gallic acid and theogallin contribute to the umami taste. Sweetness aftertaste is a unique perception of green tea, which is attributed to the hydrolysis of galloylated catechins. T1R2 and T1R3 have been identified as sweet and umami taste receptors, while T2Rs functionas the bitter taste receptor.

Association between chemistry and taste of tea: A review

In the last decade, ion mobility spectrometry (IMS) has reemerged as an analytical separation technique, especially due to the commercialization of ion mobility mass spectrometers. Its applicability has been extended beyond classical applications such as the determination of chemical warfare agents and nowadays it is widely used for the characterization of biomolecules (e.g., proteins, glycans, lipids, etc.) and, more recently, of small molecules (e.g., metabolites, xenobiotics, etc.). Following this trend, the interest in this technique is growing among researchers from different fields including food science. Several advantages are attributed to IMS when integrated in traditional liquid chromatography (LC) and gas chromatography (GC) mass spectrometry (MS) workflows: (1) it improves method selectivity by providing an additional separation dimension that allows the separation of isobaric and isomeric compounds; (2) it increases method sensitivity by isolating the compounds of interest from background noise; (3) and it provides complementary information to mass spectra and retention time, the so-called collision cross section (CCS), so compounds can be identified with more confidence, either in targeted or non-targeted approaches. In this context, the number of applications focused on food analysis has increased exponentially in the last few years. This review provides an overview of the current status of IMS technology and its applicability in different areas of food analysis (i.e., food composition, process control, authentication, adulteration and safety).

Ion Mobility Spectrometry in Food Analysis: Principles, Current Applications and Future Trends

Widely targeted metabolomic analysis reveals dynamic changes in non-volatile and volatile metabolites during green tea processing.

Influence of fixation methods on the chestnut-like aroma of green tea and dynamics of key aroma substances

Fermentation is the key process to produce the special color of congou black tea. The machine vision technology is applied to detect the color space changes of black tea’s color in RGB, Lab and HSV, and to find out its relevance to black tea’s fermentation quality. And then the color feature parameter is used as input to establish physicochemical indexes (TFs, TRs, and TBs) and sensory features’ linear and non-linear quantitative evaluation model. Results reveal that color features are significantly correlated to quality indices. Compared with the other two color models (RGB and HSV), CIE Lab model can better reflect the dynamic variation features of quality indices and foliage color information of black tea. The predictability of non-linear models (RF and SVM) is superior to PLS linear model, while RF model presents a slight advantage over the classic SVM model since RF model can better represent the quantitative analytical relationship between image information and quality indices. This research has proved that computer image color features and non-linear method can be used to quantitatively evaluate the changes of quality indices (e.g. sensory quality) and the pigment during black tea’s fermentation. Besides, the test is simple, fast, and nondestructive.

Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods.

Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process.

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

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Haibo Yuan

Papers

Widely targeted metabolomic analysis reveals dynamic changes in non-volatile and volatile metabolites during green tea processing.

Influence of fixation methods on the chestnut-like aroma of green tea and dynamics of key aroma substances

Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods.

Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis