Biology subjects

Huang, C.-y.

Publications and source records attributed to Huang, C.-y..

4 recordsLinked to original sources

bioRxiv2024

Spectroscopic assessment of flavor-related chemical compounds in fresh tea shoots using deep learning

This study employs a deep-learning method, Y-Net, to estimate 10 tea flavor-related chemical compounds (TFCC), including gallic acid, caffeine and eight catechin isomers, using fresh tea shoot reflectance and transmittance. The unique aspect of Y-Net lies in its utilization of dual inputs, reflectance and transmittance, which are seamlessly integrated within the Y-Net architecture. This architecture harnesses the power of a convolutional neural network-based residual network to fuse tea shoot spectra effectively. This strategic combination enhances the capacity of the model to discern intricate patterns in the optical characteristics of fresh tea shoots, providing a comprehensive framework for TFCC estimation. In this study, we destructively sampled tea shoots from tea farms in Alishan (Ali-Mountain) in Central Taiwan within the elevation range of 879-1552 m a.s.l. Tea shoot reflectance and transmittance data (n = 2032) within the optical region (400-2500 nm) were measured using a portable spectroradiometer and pre-processed using an algorithm; corresponding TFCC were qualified using the high-performance liquid chromatography analysis. To enhance the robustness and performance of Y-Net, we employed data augmentation techniques for model training. We compared the performances of Y-Net and seven other commonly utilized statistical, machine-/deep-learning models (partial least squared regression, Gaussian process, cubist, random forests and three feedforward neural networks) using root-mean-square error (RMSE). Furthermore, we assessed the prediction accuracies of Y-Net and Y-Net using spectra within the visible and near-infrared (VNIR) regions (for higher energy throughput and low-cost instruments) and reflectance only (for airborne and spaceborne remote sensing applications). The results showed that overall Y-Net (mean RMSE {+/-} standard deviation [SD] = 2.51 {+/-} 2.20 mg g-1) outperformed the other statistical, machine- and deep-learning models ([≥] 2.59 {+/-} 2.64 mg g-1), demonstrating its superiority in predicting TFCC. In addition, this original Y-Net also yielded slightly lower mean RMSE ({+/-} SD) compared with VNIR (2.76 {+/-} 2.41 mg g-1) and reflectance-only (2.68 {+/-} 2.74 mg g-1) Y-Nets using validation data. This study highlights the feasibility of using spectroscopy and Y-Net to assess minor biochemical components in fresh tea shoots and sheds light on the potential of the proposed approach for effective regional monitoring of tea shoot quality.

biochemistry↗

bioRxiv2024

Reconstructing fresh green leaf spectra in the SWIR-2 region (2001-2500 nm) collected in a humid environment by referring to publicly available green leaf spectral databases

Leaf spectra (reflectance and transmittance) are key parameters for land surface physical and biogeochemical modeling and are commonly measured using a portable spectroradiometer and an integrating sphere or contact probe with an artificial light source. However, spectral data may be obscured mainly because of water vapor and low signal-to-noise ratios, especially in the shortwave infrared-2 region (SWIR-2, 2001-2500 nm). This erroneous pattern is particularly pronounced in humid conditions, such as in many tropical and subtropical regions, making data unusable in SWIR-2. In this study, we proposed a statistical/mathematical spectral reconstruction approach to retrieve noise-free SWIR-2 fresh green leaf spectra by referring to the available previously published quality-controlled fresh green leaf reflectance and transmittance reference databases. We processed 896 pairs of fresh tea (Camellia sinensis var. sinensis) leaf reflectance and transmittance data from Alishan in central Taiwan. The spectral data were acquired by a field spectroradiometer with an integrating sphere. We selected a subset (500-1900 nm) of the spectra in the visible, near-infrared, and SWIR-1 regions (VNS-1) that was relatively insensitive to atmospheric conditions. Then, we applied a Gaussian fitting function to smooth the spectral profile. We matched those spectra with publicly available, quality-controlled, and Gaussian fitting function smoothed reference green leaf spectral databases obtained from Italy (LOPEX), Panama (SLZ), and Puerto Rico (G-LiHT) (1694 reflectance and 997 transmittance samples) and selected the one that was most similar (yielding the highest correlation coefficient) to each smoothed Alishan VNS-1 spectrum. We then used multivariable linear regression, linear parameter multiplication, and spectral reversion to reconstruct SWIR-2 spectra based on VNS-1 spectra. To assess the validity of the proposed SWIR-2 reconstruction method, we acquired an independent set of green leaf spectral databases from France (Angers) with SWIR-2 of 2001- 2450 nm. We found that the performance of the SWIR-2 reconstruction approach was satisfactory, with mean ({+/-} standard deviation) root-mean-square errors (RMSEs) of 0.0041 {+/-} 0.0019 (reflectance, 3.0% of the mean SWIR-2 of the test data) and 0.0054 {+/-} 0.0027 (transmittance, 2.5%) for each spectrum and RMSEs of 0.0058 {+/-} 0.0027 (reflectance, 4.2%) and 0.0055 {+/-} 0.0043 (transmittance, 2.5%) for each SWIR-2 band. The proposed approach successfully modeled SWIR-2 of the test spectra, which could be further improved with the availability of a more comprehensive set of green leaf reference spectral databases.

biochemistry↗

bioRxiv2020

An allometric scaling approach to estimate epiphytic bryophyte biomass in tropical montane cloud forests

Epiphytic bryophytes (EB) are some of the most commonly found plant species in tropical montane cloud forests, and they play a disproportionate role in influencing the terrestrial hydrological and nutrient cycles. However, it is difficult to estimate the abundance of EB due to the nature of their "epiphytic" habitat. This study proposes an allometric scaling approach to measure EB biomass, implemented in 16,773 ha tropical montane cloud forests of northeastern Taiwan. A general allometry was developed to estimate EB biomass of 100 cm2 circular-shaped mats (n = 131) and their central depths. A point-intercept instrument was invented to measure the depths of EB along tree trunks (n = 210) below 3-m from the ground level (sampled stem surface area [SSA]) in twenty-one 30 x 30 m plots. Biomass of EB of each point measure was derived using the general allometry and was aggregated across each SSA, and its performance was evaluated. Total EB biomass of a tree was estimated by referring to an in-situ conversion model and was interpolated for all trees in the plots (n = 1451). Finally, we assessed EB biomass density at the plot scale and preliminarily estimated EB biomass of the study region. The general EB biomass-depth allometry showed that the depth of an EB mat was a salient variable for biomass estimation (R2 = 0.72, p < 0.001). The performance of upscaling from mats to SSA was satisfactory, which allowed us to further estimate mean ({+/-} standard deviation) EB biomass of the 21 plots (272 {+/-} 104 kg ha-1) and to provide preliminary estimation of the total EB biomass of 4562 Mg for the study region. Since a significant relationship between tree size and EB abundance is commonly found, regional EB biomass may be mapped by integrating our method and three-dimensional airborne data.

ecology↗

bioRxiv2017

Response to Qian et al (2017): Daily and seasonal climate variations are both critical in the evolution of species’ elevational range size

In their recent critique, Qian et al. (2017) claimed that the results of structural equation modeling analysis (SEM) in Chan et al. (2016) were flawed. Here, we show that the source of the difference in their re-analysis is that Qian et al. did not follow the standard, iterative process of SEM, which allows researchers to evaluate which model offers the best account of the data in both absolute and relative senses. Here, we provide step-by-step instructions to reproduce our published results. All of Qian et al.s concerns regarding SEM can be put to rest. Moreover, in our original paper we used three distinct statistical methods--hierarchical partitioning, SEM, and stationary bootstrap--to show that different temporal scales of environmental variability can differentially impact the elevational range size (ERS) of species. It is time to move on to probing the pressing issue of how and why climatic variability impacts ERS.

ecology↗