The traditional dry sausage is highly appreciated in China because of its unique quality characteristics, but it contains high salt content. Hence, this study aimed to evaluate the effects of lactic acid bacteria (LAB) comprising Lactobacillus sakei (LS), Lactobacillus curvatus (LC), Lactobacillus plantarum (LP), and Weissella hellenica (WH) on the quality attributes of the reduced-salt dry fermented sausages. The results indicated that the LP sausage showed higher (P < 0.05) shear force value compared to the other sausages, while the LP, LC, and WH sausages had higher (P < 0.05) lightness values than the control sausages. Seventy-six volatile compounds were detected, and nine compounds were identified as the key differential volatile compounds among the sausages based on partial least squares discriminant analysis. Clustering analysis of electronic nose results corresponded well to the volatile compound results. The sensory evaluation confirmed that the inoculation with LC and LP had a positive impact on the aroma, color, and hardness attributes of the reduced-salt sausages. Besides, partial least squares regression analysis further indicated that L. curvatus and L. plantarum could be used as the potential starter cultures to manufacture reduced-salt dry fermented sausage having improved texture, color, and flavor. 

Application of lactic acid bacteria for improving the quality of reduced-salt dry fermented sausage: Texture, color, and flavor profiles

The traditional dry sausage is highly appreciated in China because of its unique quality characteristics, but it contains high salt content. Hence, this study aimed to evaluate the effects of lactic acid bacteria (LAB) comprising Lactobacillus sakei (LS), Lactobacillus curvatus (LC), Lactobacillus plantarum (LP), and Weissella hellenica (WH) on the quality attributes of the reduced-salt dry fermented sausages. The results indicated that the LP sausage showed higher (P

Unique physicochemical properties and rare reducing sugar trehalulose mandate new international regulation for stingless bee honey.

Unique physicochemical properties and rare reducing sugar trehalulose mandate new international regulation for stingless bee honey

The stingless bee has been gaining more attention in recent years due to the uniqueness and benefits of its products. Similar to the common honeybee, stingless bees also produce honey, propolis, and pollen, which offer superior benefits for direct or indirect consumption. However, reports on the benefits of stingless bee products are scarce. This article summarises recent reports on stingless bee products. The function and application of the properties of the products such as phenolic compounds, antioxidant properties, and chemical content are elucidated. The antimicrobial properties and anticancer potential of the products are also highlighted. Future trends, potential, and uniqueness of stingless bee products are discussed. Stingless bee honey is highlighted as a superfood that exceptionally has the potential to be an active ingredient in treating cancer. Stingless bee propolis has been extensively studied for its rich beneficial chemical compounds that contribute to its antioxidant properties. Though studies on stingless bee pollen are scarce, it has been reported that it also has the potential of being a functional food.

/pdf/a-comprehensive-review-of-stingless-bee-products-20u6c2ns.pdf

A Comprehensive Review of Stingless Bee Products: Phytochemical Composition and Beneficial Properties of Honey, Propolis, and Pollen

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

Lard adulteration in food products is undesirable particularly among people with certain diet preference or religious restrictions. Previous attempts on detecting lard in wheat-based biscuits using PCR-based method were inconsistent due to the minute amount of DNA present in lard and entrapment of DNA in the starch matrix. Hence, alternative method using fatty acid–based approach is necessary. The present study aimed to detect lard adulterated in wheat biscuit using chemometrics and machine learning–assisted GCMS. Oil was extracted from the laboratory-prepared wheat biscuits using Soxhlet extraction method, converted to fatty acid methyl ester and analysed using GCMS. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were able to cluster lard, wheat biscuits and lard-adulterated samples based on their fatty acid distribution. Random forest outperformed partial least squares-discriminant analysis (PLS-DA) in sample classification. Feature selection using random forest identified two fatty acids as potential biomarkers. C18:3n6 is proposed as the potential biomarker in discriminating pure wheat biscuits and lard-adulterated biscuits due to its dose-dependent composition with lard addition.

Detection of Lard Adulteration in Wheat Biscuits Using Chemometrics-Assisted GCMS and Random Forest

This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states. 

https://doi.org/10.1016/j.health.2022.100080

Siti Nurhidayah Sharin

Papers

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

Detection of Lard Adulteration in Wheat Biscuits Using Chemometrics-Assisted GCMS and Random Forest

A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia