Antimicrobial and Functional Properties of Duckweed (Wolffia globosa) Protein and Peptide Extracts Prepared by Ultrasound-Assisted Extraction.

Wolffia globosa is an interesting alternative plant-based protein source containing up to 40% protein dry weight. Dried duckweed protein extract (PE) was obtained using ultrasound-assisted extraction (UAE) before isoelectric precipitation (pH 3.5) to yield protein concentrate (PC) and protein solution (PS). The PC was hydrolyzed using Alcalase enzyme to obtain protein concentrate hydrolysate (PCH). Among all fractions, PCH exhibited antimicrobial properties by decreasing populations of Vibrio parahaemolyticus and Candida albicans at 0.43 ± 1.31 log reduction (66.21%) and 3.70 ± 0.11 log reduction (99.98%), respectively. The PE and PS also showed high solubilities at pH 8 of 90.49% and 86.84%, respectively. The PE demonstrated the highest emulsifying capacity (EC) (71.29%) at pH 4, while the highest emulsifying stability (ES) (~98%) was obtained from the PE and PS at pH 6 and pH 2, respectively. The major molecular weights (Mw) of the PE, PC, PCH and PS were observed at 25, 45, 63 and 100 kDa, with a decrease in the Mw of the PCH (<5 kDa). The PCH contained the highest total amino acids, with aspartic acid and glutamic acid being the major components. The results revealed the antimicrobial and functional properties of duckweed protein and hydrolysate for the first time and showed their potential for further development as functional food ingredients.

Non-staining visualization of embryogenesis and energy metabolism in medaka fish eggs using near-infrared spectroscopy and imaging.

The energy metabolism and embryogenesis of fertilized Japanese medaka eggs were investigated in vivo at the molecular level using near-infrared (NIR) spectroscopy and imaging. Changes in chemical components, such as proteins and lipids, in yolk sphere and embryonic body were studied over the course of embryonic development. Metabolic changes that represent variations in the concentrations and molecular compositions of proteins and lipids in the yolk part, particularly on the 1st day after fertilization and the day just before hatching, were successfully identified in the 4900-4000 cm-1 wavenumber region. The yolk components were shown to have specific functions at the very early and final stages of the embryonic development. Proteins with α-helix- or ß-sheet-rich structures clearly showed the different variation patterns within the developing egg. Furthermore, the distribution of lipids could be selectively visualized using data from the higher wavenumber region. Detailed embryonic structures were clearly depicted in the NIR images using the data from the 6400-5500 cm-1 region in which the embryo parts had some characteristic peaks due to unsaturated fatty acids. It was made clear that yolk and embryo parts had different components especially lipid components. The present study provides new insights into material variations in the fertilized egg during its growth. NIR imaging proved to be valuable in investigating the embryogenesis in vivo at the molecular level in terms of changes in biomolecular concentrations and compositions, metabolic differentiation, and detailed information about embryonic structures without the need for staining.

Anti-oxidative assays as markers for anti-inflammatory activity of flavonoids.

The complexity of in vitro anti-inflammatory assays, the cost and time consumed, and the necessary skills can be a hurdle to apply to promising compounds in a high throughput setting. In this study, several antioxidative assays i.e. DPPH, ABTS, ORAC and xanthine oxidase (XO) were used to examine the antioxidative activity of three sub groups of flavonoids: (i) flavonol: quercetin, myricetin, (ii) flavanone: eriodictyol, naringenin (iii) flavone: luteolin, apigenin. A range of flavonoid concentrations was tested for their antioxidative activities and were found to be dose-dependent. However, the flavonoid concentrations over 50ppm were found to be toxic to the THP-1 monocytes. Therefore, 10, 20 and 50ppm of flavonoid concentrations were tested for their anti-inflammatory activity in lipopolysaccharide (LPS)-stimulated THP-1 monocytes. Expression of inflammatory genes, IL-1ß, IL-6, IL-8, IL-10 and TNF-α was found to be sequentially decreased when flavonoid concentration increased. Principle component analysis (PCA) was used to investigate the relationship between the data sets of antioxidative assays and the expression of inflammatory genes. The results showed that DPPH, ABTS and ORAC assays have an opposite correlation with the reduction of inflammatory genes. Pearson correlation exhibited a relationship between the ABTS assay and the expression of three out of five analyzed genes; IL-1ß, IL-6 and IL-8. Our findings indicate that ABTS assay can potentially be an assay marker for anti-inflammatory activity of flavonoids.

Kernel analysis of partial least squares (PLS) regression models.

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Non-destructive and rapid analysis of chemical compositions in Thai steamed pork sausages by near-infrared spectroscopy.

The objective of the present study was to evaluate the ability of near-infrared (NIR) spectroscopy to predict chemical compositions of Thai steamed pork sausages in relation to different types of sample presentation forms of NIR measurements (with and without plastic casing). NIR spectra of sausages were scanned to predict the chemical compositions, protein, fat, ash and carbohydrate non-destructively. NIR spectrum features of the sausage samples were strongly influenced by physical properties of the samples, such as the presence of plastic casing and inhomogeneous physical structure inside the samples, yielding significant baseline fluctuations. Thus, regression models were developed using partial least squares (PLS) regressions with two pretreatment methods, namely multiplicative scatter correction (MSC) and second derivative, which overcame the baseline problems. The prediction results suggest that the contents for the protein, fat and moisture can be estimated well with the proper selection of the pretreatment method.

Relationships between NIR spectra and sensory attributes of Thai commercial fish sauces.

Twenty Thai commercial fish sauces were characterized by sensory descriptive analysis and near-infrared (NIR) spectroscopy. The main objectives were i) to investigate the relationships between sensory attributes and NIR spectra of samples and ii) to characterize the sensory characteristics of fish sauces based on NIR data. A generic descriptive analysis with 12 trained panels was used to characterize the sensory attributes. These attributes consisted of 15 descriptors: brown color, 5 aromatics (sweet, caramelized, fermented, fishy, and musty), 4 tastes (sweet, salty, bitter, and umami), 3 aftertastes (sweet, salty and bitter) and 2 flavors (caramelized and fishy). The results showed that Thai fish sauce samples exhibited significant differences in all of sensory attribute values (p < 0.05). NIR transflectance spectra were obtained from 1100 to 2500 nm. Prior to investigation of the relationships between sensory attributes and NIR spectra, principal component analysis (PCA) was applied to reduce the dimensionality of the spectral data from 622 wavelengths to two uncorrelated components (NIR1 and NIR2) which explained 92 and 7% of the total variation, respectively. NIR1 was highly correlated with the wavelength regions of 1100 - 1544, 1774 - 2062, 2092 - 2308, and 2358 - 2440 nm, while NIR2 was highly correlated with the wavelength regions of 1742 - 1764, 2066 - 2088, and 2312 - 2354 nm. Subsequently, the relationships among these two components and all sensory attributes were also investigated by PCA. The results showed that the first three principal components (PCs) named as fishy flavor component (PC1), sweet component (PC2) and bitterness component (PC3), respectively, explained a total of 66.86% of the variation. NIR1 was mainly correlated to the sensory attributes of fishy aromatic, fishy flavor and sweet aftertaste on PC1. In addition, the PCA using only the factor loadings of NIR1 and NIR2 could be used to classify samples into three groups which showed high, medium and low degrees of fishy aromatic, fishy flavor and sweet aftertaste.

Determination of total nitrogen content, pH, density, refractive index, and brix in Thai fish sauces and their classification by near-infrared spectroscopy with searching combination moving window partial least squares.

Near-infrared (NIR) transflectance spectra in the region of 1100-2500 nm were measured for 100 Thai fish sauces. Quantitative analyses of total nitrogen (TN) content, pH, refractive index, density and brix in the Thai fish sauces and their qualitative analyses were carried out by multivariate analyses with the aid of wavelength interval selection method named searching combination moving window partial least squares (SCMWPLS). The optimized informative region for TN selected by SCMWPLS was the region of 2264-2428 nm. A PLS calibration model, which used this region, yielded the lowest root mean square error of prediction (RMSEP) of 0.100% w/v for the PLS factor of 5. This prediction result is significantly better than those obtained by using the whole spectral region or informative regions selected by moving window partial least squares regression (MWPLSR). As for pH, density, refractive index and brix, the 1698-1722, and 2222-2258 nm regions, the 1358-1438 nm region, the 1774-1846, and 2078-2114 nm regions, and the 1322-1442, and 2000-2076 nm regions were selected by SCMWPLS as the optimized regions. The best prediction results were always obtained by use of the optimized regions selected by SCMWPLS. The lowest RMSEP for pH, density, refractive index and brix were 0.170, 0.007 g cm(-3), 0.0079 and 0.435 degrees Brix, respectively. Qualitative models were developed by using four supervised pattern recognitions, linear discriminant analysis (LDA), factor analysis-linear discriminant analysis (FA-LDA), soft independent modeling of class analog (SIMCA), and K neareat neighbors (KNN) for the optimized combination of informative regions of the NIR spectra of fish sauces to classify fish sauces into three groups based on TN. All the developed models can potentially classify the fish sauces with the correct classification rate of more than 82%, and the KNN classified model has the highest correct classification rate (95%). The present study has demonstrated that NIR spectroscopy combined with SCMWPLS is powerful for both the quantitative and qualitative analyses of Thai fish sauces.