Thongchai Suwonsichon

Optimizing time and temperature of enzymatic conversion of isoflavone glucosides to aglycones in soy germ flour.

Five factors (enzyme concentration, substrate concentration, pH, incubation temperature, and incubation time) were initially screened for the conversion of isoflavone glucosides to aglycones in soy germ flour. The incubation temperature/time most significantly affected aglycone yield; subsequently, a full 5 (35, 40, 45, 50, and 55 °C) × 6 (1, 2, 3, 4, 5, and 6 h) factorial design and response surface methodology were employed to attain an optimal incubation time/temperature condition. The optimum condition producing soy germ flour with a high concentration of daidzein, glycitein, and genistein was as follows: soy germ flour:deionized water (1:5, w/v), β-glucosidase at 1 unit/g of soy germ flour, pH 5, and incubation temperature/time of 45 °C/5 h. Under this optimal condition, most isoflavone glucosides were converted to aglycones with daidzein, glycitein, and genistein of ≥ 15.4, ≥ 6.16, and ≥ 4.147 μmol/g, respectively. In contrast, the control soy germ flour contained 13.82 μmol/g daidzin, 7.11 μmol/g glycitin, 4.40 μmol/g genistin, 1.56 μmol/g daidzein, 0.52 μmol/g glycitein, and 0.46 μmol/g genistein.

Ethnic food awareness and perceptions of consumers in Thailand and the United States.

Purpose – The purpose of this paper is to determine the awareness towards ethnic foods made with traditional spices among US and Thai consumers.Design/methodology/approach – Data on the awareness and use of ethnic cuisines and spices were collected from 100 consumers in a Midwestern university town in the USA and 100 consumers in Bangkok, Thailand. Consumers were also asked to identify ethnic spice blends by tasting spice blends in rice and they ranked the perceived health benefits of 15 ethnic cuisines.Findings – The results showed that more Thai consumers (94 percent) shop at ethnic grocery stores than US consumers (55 percent). Also, Thai consumers are more likely to consume ethnic foods from nearby countries, but US consumers have tried a greater variety of ethnic foods. Neither group generally was able to identify a cuisine based only on the spice flavors, although Thai consumers were slightly better at doing so. Asian cuisines were perceived to have the greatest health benefits to both American and ...

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.

Pasting properties by near-infrared reflectance analysis of whole grain paddy rice samples

A rapid predictive method based on near-infrared reflectance spectroscopy (NIRS) of paddy rice was developed to measure the pasting properties of rice. The paddy rice samples were scanned by a near-infrared reflectance spectrometer in the wavelength region of 1400–2400 nm and preprocessed by mathematical pretreatments prior to pasting properties analysis using a rapid visco-analyzer (RVA). The results indicated that the developed models of setback (SB), peak viscosity (PV), breakdown (BD) and consistency (CS) provided good prediction results with relatively high correlation coefficients (0.81–0.96). In addition, the validity of the calibration models was statistically tested. Standard error of prediction (SEP) and bias were small enough without any significance at 95% confidence interval. Nonetheless, this study proved that the use of NIRS for predicting pasting properties was feasible in paddy rice and could be applied in commercial trade and research.

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.