Mario Shibata

Visualization of Gluten and Starch Distributions in Dough by Fluorescence Fingerprint Imaging

A novel method combining imaging techniques and fluorescence fingerprint (FF) data measurement was developed to visualize the distributions of gluten and starch in dough without any preprocessing. Fluorescence images of thin sections of gluten, starch, and dough were acquired under 63 different combinations of excitation and emission wavelengths, resulting in a set of data consisting of the FF data for each pixel. Cosine similarity values between the FF of each pixel in the dough and those of gluten and starch were calculated. Each pixel was colored according to the cosine similarity value to obtain a pseudo-color image showing the distributions of gluten and starch. The dough sample was then fluorescently stained for gluten and starch. The stained image showed patterns similar to the pseudo-color FF image, validating the effectiveness of the FF imaging method. The method proved to be a powerful visualization tool, applicable in fields other than food technology.

Development of a Quantitative Visualization Technique for Gluten in Dough Using Fluorescence Fingerprint Imaging

The distribution of constituents in food affects its end qualities such as texture, and there is a growing demand to develop a method for studying this distribution easily, accurately, and nondestructively. The objective of this study was to develop an imaging method that visualizes the precise quantity of constituents, using the fluorescence fingerprint (FF). The FF is a set of fluorescence spectra acquired at consecutive excitation wavelengths, and its pattern contains abundant information on the constituents of the sample measured. In this study, the target for visualization was the distribution of gluten in dough samples. Dough samples were prepared with different ratios of gluten, starch, and water, and fluorescence images at multiple combinations of excitation and emission wavelengths were acquired. The fluorescence intensities of a pixel at these different wavelengths constructed its FF, reflecting the constituents of the corresponding point in the sample. A partial least squares regression (PLSR) model was built using the average FFs of the samples and the corresponding gluten ratios as the explanatory and objective variables, respectively. The importance of each wavelength in the PLSR model was assessed using the selectivity ratio, and optimum wavelengths for the accurate prediction of gluten ratio were selected. Finally, the gluten ratio of each pixel was predicted with the PLSR model using the selected wavelengths, and each pixel was colored according to the predicted gluten ratio. The imaging method developed enables the distribution of constituents to be visualized with colors corresponding to their actual quantities or ratios.

Predicting the Buckwheat Flour Ratio for Commercial Dried Buckwheat Noodles Based on the Fluorescence Fingerprint

A rapid method for predicting the buckwheat flour ratio of dried buckwheat noodles was developed by using the fluorescence fingerprint and partial least squares regression. Fitting the calibration model to validation datasets showed R 2=0.78 and SEP=12.4%. The model was refined for a better fit by deleting several samples containing additional ingredients. The best fit was finally obtained (R 2=0.84 and SEP=10.4%) by deleting the samples containing vinegar, green tea, seaweed, polysaccharide thickener, and yam. This result demonstrates that a calibration model with high accuracy could be constructed based on samples similar in material composition. The developed methodology requires no complex preprocessing, enables rapid measurement with a small sample amount, and would thus be suitable for practical application to the food industry.

Changes in the Texture and Viscoelastic Properties of Bread Containing Rice Porridge during Storage

The objective of this study was to investigate the effects of rice porridge on the texture and viscoelastic properties of bread during storage. Three types of bread, wheat flour bread, 15% rice flour bread, and 15% rice porridge bread, were prepared. After baking and storing the bread for 24 h, 48 h, and 72 h at room temperature, we measured the texture and viscoelastic properties of the bread crumbs by texture profile analysis (TPA) and creep test. The 15% rice porridge bread showed a significantly higher specific volume and maintained softer crumbs than the other two types (p<0.05). It also had a slightly stickier texture than the others. It can be concluded that rice porridge improves the specific volume, texture, and viscoelastic properties of bread crumbs during storage.

Method of determining the optimal dilution ratio for fluorescence fingerprint of food constituents.

Quantitative determination by fluorescence spectroscopy is possible because of the linear relationship between the intensity of emitted fluorescence and the fluorophore concentration. However, concentration quenching may cause the relationship to become nonlinear, and thus, the optimal dilution ratio has to be determined. In the case of fluorescence fingerprint (FF) measurement, fluorescence is measured under multiple wavelength conditions and a method of determining the optimal dilution ratio for multivariate data such as FFs has not been reported. In this study, the FFs of mixed solutions of tryptophan and epicatechin of different concentrations and composition ratios were measured. Principal component analysis was applied, and the resulting loading plots were found to contain useful information about each constituent. The optimal concentration ranges could be determined by identifying the linear region of the PC score plotted against total concentration. The plot of PC score against total concentration can be used to identify an optimal concentration range, represented by linear region, for quantitative determination by FF.

Image Analysis of Bread Crumb Structure in Relation to Mechanical Properties

Abstract To correlate the mechanical properties with the crumb structure of bread, a simple and objective method of measuring air bubbles of crumb samples was developed using an image scanner and digital image processing. Four images of the sample scanned in four orthogonal directions were aligned and combined to obtain an enhanced image in which air bubble regions were emphasized by min-operation, selecting the minimum gray level among the four images for each pixel. Next, Otsu’s method was applied to threshold each sub-image of the enhanced image in order to quantify the geometries of the air bubbles precisely, and then the black regions of the image were found to be air-bubbles. As a result, the four air-bubble parameters of the bread samples were determined to be mean bubble area, mean bubble perimeter, number of bubbles, and bubble area ratio. In addition, the viscoelastic properties of the samples were measured by the creep test and determined to significantly correlate with the bubble area ratio (r > 0.59, p < 0.05). This indicates that with increasing air-bubble area, crumb hardness increases. The proposed method is inexpensive and easy to operate, and thus is considered to be applicable to the quality assessment in food factories.

Detection of Food Hazards using Fluorescence Fingerprint

Abstract Fluorescence fingerprint technique was applied to detect food hazards quantitatively. It is nondestructive and quick measurement. The acquired three dimensional volume data can be analyzed by multivariate analysis. As examples of food hazards, detection of mycotoxin in wheat flour and prediction of aerobic bacteria population on beef surface are shown. The merit of fluorescence is sensitivity. It could be measured ppm or ppb order for mycotoxin and APC was calibrated between 10 2 and 10 8 CFU/cm 2 .