Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning

2021 ◽  
Vol 346 ◽  
pp. 128654
Author(s):  
Siti Nurhidayah Sharin ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Azwan Jaafar ◽  
Mohd Hafis Yuswan ◽  
Nur Kartinee Kassim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Physicochemical Properties ◽  
Volatile Compound ◽  
Stingless Bee

A Study on Host Tropism Determinants of Influenza Virus Using Machine Learning

2020 ◽  
Vol 15 (2) ◽  
pp. 121-134 ◽  
Author(s):  
Eunmi Kwon ◽  
Myeongji Cho ◽  
Hayeon Kim ◽  
Hyeon S. Son
Keyword(s):  
Machine Learning ◽  
Amino Acids ◽  
Influenza Virus ◽  
Random Forest ◽  
Physicochemical Properties ◽  
Protein Sequences ◽  
Influenza Viruses ◽  
Host Tropism ◽  
Post Hoc ◽  
Ha Protein

Background: The host tropism determinants of influenza virus, which cause changes in the host range and increase the likelihood of interaction with specific hosts, are critical for understanding the infection and propagation of the virus in diverse host species. Methods: Six types of protein sequences of influenza viral strains isolated from three classes of hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest neighbor algorithms were used for host classification. The Java language was used for sequence analysis programming and identifying host-specific position markers. Results: A machine learning technique was explored to derive the physicochemical properties of amino acids used in host classification and prediction. HA protein was found to play the most important role in determining host tropism of the influenza virus, and the random forest method yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific differences were also selected and verified, and they were found to be useful position markers for host classification. Finally, ANOVA analysis and post-hoc testing revealed that the physicochemical properties of amino acids, comprising protein sequences combined with position markers, differed significantly among hosts. Conclusion: The host tropism determinants and position markers described in this study can be used in related research to classify, identify, and predict the hosts of influenza viruses that are currently susceptible or likely to be infected in the future.

Pitting corrosion prediction from cathodic data: application of machine learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamed Nadir Boucherit ◽  
Fahd Arbaoui
Keyword(s):  
Machine Learning ◽  
Physicochemical Properties ◽  
Input Data ◽  
Surface Condition ◽  
Recurrent Network ◽  
Localized Corrosion ◽  
Potential Region ◽  
Pitting Potential ◽  
Content Type ◽  
Nuclear Installation

Purpose To constitute input data, the authors carried out electrochemical experiments. The authors performed voltammetric scans in a very cathodic potential region. The authors constituted an experimental table where for each experiment we note the current values recorded at a low polarization range and the pitting potential observed in the anodic region. This study aims to concern carbon steel used in a nuclear installation. The properties of the chemical solutions are close to that of the cooling fluid used in the circuit. Design/methodology/approach In a previous study, this paper demonstrated the effectiveness of machine learning in predicting the localized corrosion resistance of a material by considering as input data the physicochemical properties of its environment (Boucherit et al., 2019). With the present study, the authors improve the results by considering as input data, cathodic currents. The reason of such an approach is to have input data that integrate both the surface state of the material and the physicochemical properties of its environment. Findings The experimental table was submitted to two neural networks, namely, a recurrent network and a convolution network. The convolution network gives better pitting potential predictions. Results also prove that the prediction by observing cathodic currents is better than that obtained by considering the physicochemical properties of the solution. Originality/value The originality of the study lies in the use of cathodic currents as input data. These data contain implicit information on both the chemical environment of the material and its surface condition. This approach appears to be more efficient than considering the chemical composition of the solution as input data. The objective of this study remains, at the same time, to seek the optimal neuronal architectures and the best input data.

scholarly journals Radiation sterilization of Honey and Honey-Alginate Wound Dressing from Stingless Bee (Tetragonula biroi) Collected from Sta. Maria, Laguna, Philippines

2021 ◽  
Vol 11 (2) ◽  
pp. 217-223
Author(s):  
Davison Baldos ◽  
◽  
Joseph Puno ◽  
Levelyn Tolentino ◽  
Djowel Montefalcon ◽  
...  
Keyword(s):  
Electron Beam ◽  
Physicochemical Properties ◽  
Wound Dressing ◽  
Transmission Rate ◽  
Wound Care ◽  
Stingless Bee ◽  
Soluble Solids ◽  
Radiation Sterilization ◽  
Fluid Handling ◽  
Potential Use

This study was conducted to determine the effect of radiation sterilization on alginate wound dressing containing honey from the Philippine stingless bee, Tetragonula biroi. Our results show that a radiation dose of 30 kGy did not affect the antibacterial property of honey against Staphylococcus aureus. Electron-beam irradiation did not produce significant alterations in the physicochemical properties (pH, total soluble solids, and flavonoids); however, the total phenolics was significantly increased in honey with higher irradiation doses. Demonstrating that irradiation can be applied to honey with negligible physicochemical effects, honey was incorporated in alginate and exposed to a sterilization dose of 25 kGy using an electron beam facility. Irradiation did not affect the physicochemical properties (pH, moisture content, gel fraction, moisture vapor transmission rate (MVTR), and fluid handling capacity) of the honey alginate wound dressing (HAWD). The perspectives for the potential use of irradiated HAWD as a natural product-based substitute for commercial wound care products may be considered.

Variations of physicochemical properties of stingless bee honey from different botanical origin in state of Johor, Malaysia

2019 ◽  
Vol 269 ◽  
pp. 012028
Author(s):  
Mahani Majid ◽  
Mohd Fadzelly Abu Bakar ◽  
Zakbah Mian ◽  
Fahmiruddin Esa ◽  
You Kok Yeow
Keyword(s):  
Physicochemical Properties ◽  
Stingless Bee ◽  
Botanical Origin

Effect of 60Co γ-Irradiation on Physicochemical Properties of Adlay During Storage Period

2019 ◽  
Vol 15 (8) ◽  
Author(s):  
Guolin Li ◽  
Xiuyan Zheng ◽  
Daomei Huang ◽  
Xi Chen ◽  
Fanbo Meng ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Physicochemical Properties ◽  
Volatile Compound ◽  
Storage Period ◽  
Agricultural Products ◽  
Cool Temperature ◽  
Γ Irradiation ◽  
Treatment Groups ◽  
Fatty Acid Compositions

Abstractγ-Irradiation is applied to many agricultural products as a method for quality control. This study investigated the influence of γ-irradiation on physicochemical properties of adlay. Adlay samples were treated with 0 to 4.0 kGy 60Co γ-irradiation and subsequently stored at cool temperature (8 to 10 °C). Hardness of all treatment groups showed no marked changes at 0 kGy but exhibited variations at 2.0 and 4.0 kGy. Linoleic acid (C18:2) was the most sensitive to irradiation among 11 fatty-acid compositions. Saturated fatty-acid (SFA) content was increased, whereas unsaturated fatty acid was reduced by dose augmentation. Types of volatile compound increased from 15 to 21, and the major compound n-hexanol was increased by 80.41 % after 4-kGy irradiation. Odor changes caused by doses of irradiation were more remarkable than those caused by 12 months of storage. Hence, we conclude that 1.0 kGy irradiation barely affects physicochemical properties during storage; it could be an alternative way to control quality of adlay during storage.

scholarly journals Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties

2018 ◽  
Vol 5 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Matthew R. Findlay ◽  
Daniel N. Freitas ◽  
Maryam Mobed-Miremadi ◽  
Korin E. Wheeler
Keyword(s):  
Machine Learning ◽  
Surface Chemistry ◽  
Physicochemical Properties ◽  
Silver Nanoparticle ◽  
Protein Corona ◽  
Engineered Nanomaterials ◽  
Predictive Analysis ◽  
Environmental Systems ◽  
Corona Formation ◽  
P Proteins

Proteins encountered in biological and environmental systems bind to engineered nanomaterials (ENMs) to form a protein corona (PC) that alters the surface chemistry, reactivity, and fate of the ENMs.

scholarly journals The Effects of Roasting Time of Unfermented Cocoa Liquor Using the Oil Bath Methods on Physicochemical Properties and Volatile Compound Profiles

2019 ◽  
Vol 39 (1) ◽  
pp. 36
Author(s):  
Nurhayati Nurhayati ◽  
Francis Maria Constance Sigit Setyabudi ◽  
Djagal Wiseso Marseno ◽  
Supriyanto Supriyanto
Keyword(s):  
Physicochemical Properties ◽  
Volatile Compounds ◽  
Volatile Compound ◽  
L Value ◽  
Roasting Time

This study aimed to measure the effect of roasting time on physicochemical properties and volatile compounds of unfermented cocoa liquor roasted with an oil bath method. Physicochemical properties (pH, temperature, and color), flavor, and volatile compounds were analyzed. The results showed that the longer the roasting time the higher the unfermented cocoa liquor’s temperature, °Hue, and ΔE value, but lower pH and L value. There were 126 volatile compounds obtained by various roasting time, identified as pyrazines (12), aldehydes (16), esters (1), alcohols (31), acids (15), hydrocarbons (11), ketones (19), and others (21). At 15, 20, and 25 minutes of roasting time, 69, 74, and 67 volatile compounds, respectively, were identified. Volatile compounds’ profiles were indicated to be strongly influenced by roasting time. The largest area and highest number of compounds, such as pyrazines and aldehydes, were obtained at 20 minutes, which was also the only time the esters were identified. As well as the time showed a very strong flavor described by panelists.

scholarly journals The Optimum Storage Conditions on the Quality of the Stingless bee Honey

2021 ◽  
Vol 2049 (1) ◽  
pp. 012003
Author(s):  
Imron Meechai ◽  
Isma-ae Chelong ◽  
Romlee Chedoloh
Keyword(s):  
Moisture Content ◽  
Physicochemical Properties ◽  
Sugar Content ◽  
Storage Condition ◽  
Storage Conditions ◽  
Stingless Bee ◽  
Significant Difference ◽  
Storage Times ◽  
And Storage

Abstract Honey of stingless bee has a higher moisture content than bees. Long-term storage may cause fermentation processes to change the physicochemical properties and taste. Thus, the aim of this research was evaluation of the optimum storage condition on the quality of stingless bee honey. Stingless bee honey (Tetragonular larviceps) was contained in plastic bottle and kept at ambient temperature (30-35°C) and low temperature (4-8°C) for 0-45 days. Before and after storage honey were analyzed the physicochemical properties and sensory investigation for comparison of quality. The results showed that temperatures and storage times have affected on the reducing sugar content, pH, conductivity, color, moisture content with significant difference (p<0.05). While, temperatures and storage times have unaffected on the °Brix value (p>0.05). Additionally, the physicochemical properties of honey were according with previously quality report. The sensory investigation indicated that the smell natural flavor, consistency, taste and sourness were not significant difference (p>0.05). In contrast, the color and overall preference were significant difference (p<0.05). For honey quality, Thus, the honey might keep at 4-35°C for ≤45 day of this study.

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