Mochen Liu

Dr. Mochen Liu

orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 December 2021 in Agronomy
Verified
Journal Article
Open access
PDF file
ZSZhanhua Song
Zhanhua Song
JMJunxiang Ma
Junxiang Ma
QPQian Peng
Qian Peng
BLBaoji Liu
Baoji Liu
FLFade Li
Fade Li
YYYinfa Yan
Yinfa Yan
When seeds are treated with a high-voltage electric field (HVEF) to improve seed vigor, due to the large differences in the biological electromagnetic effects on different types of seeds, the methods of variance analysis and regression analysis based on data statistics are generally used to construct the optimal electric field dose prediction model; however, the generalization performance of the prediction model tends to be poor. To solve this problem, the electric intensity, frequency and treatment time were taken as the input variables for hybrid support vector regression (SVR) analysis to establish the prediction model of the seed comprehensive germination index. The whale optimization algorithm (WOA) was used to optimize the kernel parameters of the SVR. The optimized hybrid WOA–SVR model predicted the optimal comprehensive germination index of aged cotton (Gossypium spp.) seeds to be 329, the optimal HVEF dosage was 3.64 kV/cm × 99 s, and the frequency was 1.4 Hz. The aged cotton seeds were treated with the optimal HVEF and the germination test was carried out. Compared with the check (CK), the comprehensive germination index of seeds increased by 48%. The research results provided a new method and new idea for the optimal design of parameters for seed treatment with HVEF.

18 June 2026 in Computers and Electronics in Agriculture
Verified
Journal Article
Closed access
ZLZhenghao Li
Zhenghao Li
YYYinfa Yan
Yinfa Yan
JZJunjie Zhao
Junjie Zhao
MSMingrui Shang
Mingrui Shang
ZSZhanhua Song
Zhanhua Song
MLMochen Liu
Mochen Liu

19 April 2026 in Smart Agricultural Technology
Verified
Journal Article
Open access
GYGuang Yang
Guang Yang
YYYinfa Yan
Yinfa Yan
XLXiusong Li
Xiusong Li
ZSZhanhua Song
Zhanhua Song
FTFuyang Tian
Fuyang Tian
MLMochen Liu
Mochen Liu

7 September 2026 in Foods
Verified
Journal Article
Open access
PDF file
XQXinge Quan
Xinge Quan
QJQingqing Jiao
Qingqing Jiao
YLYao Lu
Yao Lu
MLMochen Liu
Mochen Liu
JWJing Wang
Jing Wang
YYYinfa Yan
Yinfa Yan
Mulberry (Morus spp.) leaves, fruits, branches, and root bark are rich in bioactive compounds, including 1-deoxynojirimycin (1-DNJ) and γ-aminobutyric acid (GABA), supporting their potential use in food, medicinal, and feed applications. Their high moisture content, however, makes fresh materials highly susceptible to postharvest quality deterioration, making drying essential for stabilization and high-value utilization. Drying technologies involve trade-offs among efficiency, energy consumption, sensory quality, rehydration, and bioactive-compound retention. This review provides a comprehensive overview of pretreatment and drying technologies for mulberry materials, with particular attention to differences in raw-material characteristics, processing conditions, analytical methods, and reporting bases that limit direct comparisons among studies. Current evidence suggests that low-temperature, low-oxygen, or short-duration technologies, including vacuum freeze-drying, microwave drying, and microwave-vacuum drying, may better preserve quality in certain thermosensitive products, although their benefits remain product- and process-dependent. Hot-air, solar, infrared, heat-pump, and hybrid drying remain practical options for bulk products but require optimization to balance quality, energy efficiency, and scalability. For juice and functional powders, carrier selection, powder properties, and bioaccessibility require further study. Overall, the available evidence is heterogeneous, and some conclusions rely on limited mulberry-specific data or extrapolation from related plant matrices. Future research should emphasize standardized quality evaluation, harmonized reporting, kinetic modeling, multi-objective optimization, online monitoring, energy and carbon-footprint assessment, and industrial-scale validation.