Vichai Haruthaithanasan

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.

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.

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.

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.