Effects of Visual Cues on Consumer Expectation, Emotion and Wellness Responses, and Purchase Intent of Red Chili Powders.

The effects of visual cues on familiarity, expected heat intensity, liking of appearance, emotional and wellness responses, and purchase intent (PI) before and after disclosing information associated with red chili powders were determined using a 3-point scale, a 15-cm line scale, a 9-point hedonic scale, a 15-cm line scale, and a binomial scale, respectively. In this study, consumers only visually evaluated red chili powder samples without sniffing nor tasting. Eight chili powders were prepared according to a 23 factorial design: roasted (Ro) compared with unroasted (Un); whole pod with seeds (Wh) compared with seedless (Sl); coarsely (Cr) compared with finely ground (Gr). Thai consumers (N = 230) were generally more familiar with samples having coarse particles and more reddish color (lower hue angle and higher a* values) than samples having finely ground particles and less reddish/more yellowish color (higher hue angle and lower a* values). The expected heat intensity and liking scores for appearance were lower for samples with higher hue values, particularly RoWhGr and RoSIGr samples. All scores for emotion/wellness terms, except curious, were generally higher for samples with lower hue angle and higher a* values (redness). The consumer familiarity to the appearance of the samples influenced expected heat intensity, liking of appearance, and emotion/wellness responses. PI increased by >10% after presenting "organic," "aflatoxin free," and "organic and aflatoxin free" product statements to consumers. Results showed that familiarity, overall liking of appearance, color liking, fine particles liking, and healthy as well as wild terms were significant predictors for PI (odds ratio = 1.282, 1.519, 1.314, 1.158, 1.056, and 0.939, respectively) of red chili powders. PRACTICAL APPLICATION: This study showed that consumer familiarity to the appearance (visual cues) of red chili powder affected expected heat intensity, liking of appearance, and emotion/wellness responses, which, in turn, affected purchase intent (PI). Samples with more reddish color and coarse particles were perceived to be more familiar than those with more yellowish color and finer particles. Familiarity, overall liking of appearance, color liking, fine particles liking, and healthy and wild terms were significant predictors for PI of chili power. The results demonstrated the importance of visual cues on the consumers' expectation and PI of red chili powder, thus offering valuable information for manufacturers.

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), ß-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.

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.