Pitiporn Ritthiruangdej

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-1β, 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-1β, IL-6 and IL-8. Our findings indicate that ABTS assay can potentially be an assay marker for anti-inflammatory activity of flavonoids.

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.

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.

Temperature compensation for determination of moisture and reducing sugar of longan honey by near infrared spectroscopy

Near infrared spectra of honeys are affected by sample temperature variation, mainly due to a change in hydrogen bonding of water. The aim of this study was to develop robust and powerful calibration models which can compensate for a variation of sample temperature for the determination of moisture and reducing sugar content in honey using near infrared spectroscopy. Partial least squares regression with the aid of standard normal variate transformation was used to develop three calibration models at constant temperature (25, 35 and 45℃) and a robust calibration model with temperature compensation. All the developed models for moisture and reducing sugar content showed high performance of prediction with coefficient of determination (r2) and residual prediction deviation values greater than 0.95 and 3.8, respectively. The results show that the temperature compensation model can be considered as a robust calibration model for near infrared determination of moisture and reducing sugar in the honey when sample temperature is varied.

Determination of sulfur dioxide content in osmotically dehydrated papaya and its classification by near infrared spectroscopy

Sulfur dioxide (SO2) is used as a preservative in osmotically dehydrated papaya to improve product quality and extend shelf-life. The potential of near infrared spectroscopy, as a rapid method, was investigated to determine sulfur dioxide in osmotically dehydrated papaya. Commercial and laboratory osmotically dehydrated papaya samples were selected to determine the sulfur dioxide content using the Monier–Williams method. From the total of 350 samples, subsets were selected randomly for the calibration set (n=250) and validation set (n = 100). Near infrared spectra in the region 800–2400 nm were measured on the samples of osmotically dehydrated papaya. Quantitative analyses of sulfur dioxide in the osmotically dehydrated papaya and their qualitative analyses were carried out using multivariate analysis. Before developing models, a second derivative spectral pretreatment was applied to the original spectral data. Subsequently, two wavelength interval selection methods, namely moving window partial least squares regression (MWPLSR) and searching combination moving window partial least squares (SCMWPLS), were applied to determine the suitable input wavelength variables. For quantitative analysis, three linear models (partial least squares regression, MWPLSR and SCMWPLS) and a non-linear artificial neural network model were applied to develop predictive models. The results showed that the artificial neural network model produced the best performance, with correlation coefficient (R) and root mean square error of prediction values of 0.937 and 114.53 mg SO2 kg−1, respectively. Qualitative models were developed using partial least squares-discriminant analysis and soft independent modeling of class analogy (SIMCA) for the optimized combination of informative regions of the near infrared spectra to classify osmotically dehydrated papaya into three groups based on sulfur dioxide. The SIMCA in combination with SCMWPLS model had the highest correct classification rate (96%). The study demonstrated that near infrared spectroscopy combined with SCMWPLS is a powerful procedure for both quantitative and qualitative analyses of osmotically dehydrated papaya. Therefore, it was demonstrated that near infrared spectroscopy could be effective tools for food quality and safety evaluation in food industry.

Rapid analysis of chemical composition in intact and milled rice cookies using near infrared spectroscopy

This study has investigated the potential of near infrared (NIR) spectroscopy to predict the content of moisture, protein, fat and gluten in rice cookies in different sample forms (intact and milled samples). Gluten-free (n = 48) and gluten (n = 48) rice cookies were formulated with brown and white rice flours in which butter was substituted with fat replacer at 0, 15, 30 and 45%. With regard to gluten cookies, rice flour was substituted with wheat gluten at 1, 3 and 5%. Partial least squares regression modeling produced models with coefficient of determination (R2) values greater than 0.88 from NIR spectra of intact samples and greater than 0.92 for milled samples. These models were able to predict the four components with a ratio of prediction to deviation greater than 2.7 and 3.8 in intact and milled samples, respectively. The results suggest that the models obtained from the intact samples can be successfully applied for chemical composition of rice cookies and are reliable enough use for potential quality control programs.

Rapid and nondestructive analysis of deep-fried taro chip qualities using near infrared spectroscopy:

The objective of this study was to use near infrared spectroscopy to determine the moisture and fat content, color properties and maximum force of break of deep-fried taro chips as a rapid and non-destructive technique. Near infrared spectra were recorded on intact taro chips in the wavelength range of 1100–2500 nm collected using a near infrared spectrometer, followed by quality attribute measurements. The near infrared calibration models were developed individually using partial least square regression. The partial least square calibration models were found to have coefficients of determination (R2) between 0.85 and 0.97 and for independent samples the ratio of prediction to deviation ranged from 2.0 to 4.9. The results indicated that near infrared spectroscopy offers a fast, simple, accurate and nondestructive method to determine the quality of intact, deep-fried taro chips. Therefore, it can be used in-line or at-line for the quality control of the deep-fried process and for better monitoring of changes in the chemical and physical properties of fried products during processing.