Extensions of the distributed lag non-linear model (DLNM) to account for cumulative mortality.

The effects of meteorological factors on health outcomes have gained popularity due to climate change, resulting in a general rise in temperature and abnormal climatic extremes. Instead of the conventional cross-sectional analysis that focuses on the association between a predictor and the single dependent variable, the distributed lag non-linear model (DLNM) has been widely adopted to examine the effect of multiple lag environmental factors health outcome. We propose several novel strategies to model mortality with the effects of distributed lag temperature measures and the delayed effect of mortality. Several attempts are derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and modeling the offset in the DLNM. Five strategies are evaluated by simulation studies based on permutation techniques. The longitudinal climate and daily mortality data in Taipei, Taiwan, from 2012 to 2016 were implemented to generate the null distribution. According to simulation results, only one strategy, named MVDLNM, could yield valid type I errors, while the other four strategies demonstrated much more inflated type I errors. With a real-life application, the MVDLNM that incorporates both the current and lag mortalities revealed a more significant association than the conventional model that only fits the current mortality. The results suggest that, in public health or environmental research, not only the exposure may post a delayed effect but also the outcome of interest could provide the lag association signals. The joint modeling of the lag exposure and the delayed outcome enhances the power to discover such a complex association structure. The new approach MVDLNM models lag outcomes within 10 days and lag exposures up to 1 month and provide valid results.

[Determination of taste quality of green tea using FT-NIR spectroscopy and variable selection methods].

The present paper was attempted to study the feasibility to determine the taste quality of green tea using FT-NIR spectroscopy combined with variable selection methods. Chemistry evaluation, as the reference measurement, was used to measure the total taste scores of green tea infusion. First, synergy interval PLS (siPLS) was implemented to select efficient spectral regions from SNV preprocessed spectra; then, optimal variables were selected using genetic algorithm (GA) from these selected spectral regions by siPLS, and the optimal model was achieved with Rp = 0.8908, RMSEP = 4.66 in the prediction set when 38 variables and 6 PLS factors were included. Experimental results showed that the performance of siPLS-GA model was superior to those of others. This study demonstrated that NIR spectra could be used successfully to measure taste quality of green tea and siPLS-GA algorithm has superiority to other algorithm in developing NIR spectral regression model.

Study on classification of soy sauce by electronic tongue technique combined with artificial neural network.

Electronic tongue as an analytical tool coupled with pattern recognition was attempted to classify 4 different brands and 2 categories (produced by different processes) of Chinese soy sauce. An electronic tongue system was used for data acquisition of the samples. Some effective variables were extracted from electronic tongue data by principal component analysis (PCA). Backpropagation artificial neural network (BP-ANN) was applied to build identification models. PCA score plots show an obvious cluster trend of different brands and different categories of soy sauce in the 2-dimensional space. The optimal BP-ANN model for different brands was achieved when principal components (PCs) were 2, and the identification rate of the discrimination model was 100% in both the calibration set and the prediction set, and the optimal BP-ANN model for different categories had the same result. This work demonstrates that electronic tongue technology combined with a suitable pattern recognition method can be successfully used in the classification of different brands and categories of soy sauce.