Development of methods for determining free fatty acid contents in red colored oils

Food Science and Biotechnology - Buah merah oil and red palm oil are red colored and unrefined edible oils. Because of this color characteristic, measuring acid value by titration method can be...     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.     

Classification of geographical origin of kimchi by volatile compounds analysis using an electronic nose

Food Science and Biotechnology - Food authenticity is one of the largest concerns in recent days. As kimchi has been a global food, its production origin has been important issue, particularly due...     

Quantitative determination of kokumi compounds, γ-glutamyl peptides,in Korean traditional fermented foods,ganjang and doenjang,by LC-MS/MS

Food Science and Biotechnology - Recent studies have shown that γ-glutamyl peptides (GGPs) are recognized by the calcium-sensing receptor and induce kokumi taste. The contents of GGP have been...     

Top-down preparation of Ni–Pd–P@graphitic carbon core-shell nanostructure as a non-Pt catalyst for enhanced electrocatalytic reactions

Electrochemical reactions such as the oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and methanol oxidation reaction (MOR) are essential for energy conversion applications such as water electrolysis and fuel cells. Furthermore, Pt or Ir-related materials have been extensively utilized as electrocatalysts for the OER, ORR, and MOR. To reduce the utilization of precious metals, innovative catalyst structures should be proposed. Herein, we report a bi-metallic phosphide (Ni₂P and PdP₂) structure surrounded by graphitic carbon (Ni–Pd–P/C) with an enhanced electrochemical activity as compared to conventional electrocatalysts. Despite the low Pd content of 3 at%, Ni–Pd–P/C exhibits a low overpotential of 330 mV at 10 mA cm^?2 in the OER, high specific activity (2.82 mA cm^?2 at 0.8 V) for the ORR, and a high current density of 1.101 A mg^?1 for the MOR. The superior electrochemical performance of Ni–Pd–P/C may be attributed to the synergistic effect of the bi-metallic phosphide structure and core-shell structure formed by graphitic carbon.     

Prophylactic effects of probiotics on respiratory viruses including COVID-19: a review

Food Science and Biotechnology - Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is mainly transmitted through respiratory droplets....