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In a bid to produce rice flour noodles with improved texture and reduced cooking time, rice flour-soy protein isolate noodles (RNS) were structurally enhanced by a combined treatment (COM) of microbial transglutaminase (MTG) with glucono-δ-lactone (GDL). The RNS-COM was either dried using superheated steam (SHS) to yield RNS-COM-SHS or steamed for 10 min (S10) before air drying to produce RNS-COM-S10 noodles. Control samples were SHS-dried rice flour (RN-SHS) and air-dried RN-S10 noodles. In general, textural and microstructural properties indicated higher textural properties and a more robust network in RNS-COM-SHS and RNS-COM-S10 than in other noodles. However, optimum cooking time (P < 0.5) was in the order; RN-SHS, RNS-COM-SHS < RN-S10 < RNS-COM-S10. As a result of the COM treatment, structurally enhanced noodles were more resistant to cooking. As applied in RNS-COM-SHS noodles, SHS was able to improve cooking quality, probably through the formation of bigger and evenly spread pores that had promoted faster gelatinisation of starch, with a high order of relative starch crystallinity. [ABSTRACT FROM AUTHOR]

Demands for high nutritional value-added food products and plant-based proteins have increased over the last decade, in line with the growth of the human population and consumer health awareness. The quality of the plant-based proteins depends on their digestibility, amino acid content, and residues of non-nutritive compounds, such as phenolic compounds, anti-nutritional compounds, antioxidants, and saponins. The presence of these non-nutritive compounds could have detrimental effects on the quality of the proteins. One of the solutions to address these shortcomings of plant-based proteins is fermentation, whereby enzymes that present naturally in microorganisms used during fermentation are responsible for the cleavage of the bonds between proteins and non-nutritive compounds. This mechanism has pronounced effects on the non-nutritive compounds, resulting in the enhancement of protein digestibility and functional properties of plant-based proteins. We assert that the types of plant-based proteins and microorganisms used during fermentation must be carefully addressed to truly enhance the quality, functional properties, and health functionalities of plant-based proteins. Supplemental data for this article is available online at here. show [ABSTRACT FROM AUTHOR]

This research is part of an effort to enhance fermented whey proteins' quality and nutritional value. In this research, we investigated the feasibility of using a natural fermentation approach on whey proteins. We determined the protein digestibility, non-nutritive compounds (including phenolic compounds and saponins), and microbiological quality of water kefir-fermented whey proteins. Due to the water kefir-induced fermentation of whey proteins, the protein digestibility improved from 88.48 to 94.33%. The protein quality of fermented whey proteins has been modified after the water kefir fermentation, as observed with the changes in the secondary protein structure components, especially α-helices (decreased from 10.58 to 8.52%) and random coil (increased from 12.25 to 17.33%). In addition, their water solubility, surface hydrophobicity, and surface charge changed significantly (P <.05) during the fermentation time; 88.44 to 94.11%, 387.23 to 272.7 a.u. and −30.06 to −34.93 mV, respectively. Furthermore, water kefir is an excellent source of fermenting microorganisms, including lactic and acetic acid bacteria and yeast. Thus, the water kefir fermentation approach could be an effective, practical, non-thermal approach for altering whey proteins. [ABSTRACT FROM AUTHOR]

The application of protein-protein interaction (PPI) has been widely used in various industries, such as food, nutraceutical, and pharmaceutical. A deeper understanding of PPI is needed, and the molecular forces governing proteins and their interaction must be explained. The design of new structures with improved functional properties, e.g., solubility, emulsion, and gelation, has been fueled by the development of structural and colloidal building blocks. In this review, the molecular forces of protein structures are discussed, followed by the relationship between molecular force and structure, ways of a bind of proteins together in solution or at the interface, and functional properties. A more detailed look is thus taken at the relationship between the various influencing factors on molecular forces involved in PPI. These factors include protein properties, such as types, concentration, and mixing ratio, and solvent conditions, such as ionic strength and pH. This review also summarizes methods tha1t are capable of identifying molecular forces in protein and PPI, as well as characterizing protein structure. [ABSTRACT FROM AUTHOR]

Multivariate statistical techniques such as factor analysis (FA) and Discriminant analysis (DA), were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two rivers (Juru and Jejawi) in Malaysia, monitoring 10 parameters at 10 different sites each. Factor analysis resulted in two factors explaining more than 82% of the total variance in water quality data set. The factors indicate that the possible variances in water quality may be due to either sources of anthropogenic origin or due to different biochemical processes that are taking place in the system. The first factor called pseudo anthropogenic factor explained 59.29% of the total variance. The second factor called anthropogenic explained 23.03%. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers affording 100 % correct assignations. This study illustrates the benefit of multivariate statistical techniques for analyzing and interpretation of complex data sets, and to plan for future studies. Keywords: Discriminant analysis, Factor analysis, Water quality, Multivariate, 1. Introduction The rivers are the important sources of surface water and boon of nature to the human beings. They are the inseparable component of ecology on the earth. A [...]

Two statistical techniques; discriminant analysis (DA) and logistic regression model were used to analyze the concentration of arsenic and heavy metal contents in cockles (Anadara granosa) from two estuaries in the state of Penang, Malaysia. This study was undertaken to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status. Arsenic (As), chromium (cr), cadmium (cd), zinc (zn), copper (cu) and lead (pb) were analyzed using a graphite flame atomic absorption spectrophotometer (GF-AAS) whilst mercury (Hg) was analyzed using a cold vapor atomic absorption spectrophotometer (CV-AAS). Logistic regression model showed that only two explanatory variables Zn (p < 0.01) and Cd (p Keywords: Heavy metals, Arsenic, Estuary, Logistic regression model, Discriminant analysis, 1. Introduction The discharge of effluents and associated toxic compounds into aquatic systems represents an ongoing environmental problem due to their possible impact on communities in the receiving aquatic water [...]