Kazuhiro Nakano

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.

Development of Non-Destructive Detector for Abnormal Eggs

Abstract In the present study, we developed a device for the non-destructive detection of abnormal eggs (mainly eggs with cracked shells, eggs with leaking cracks, unclean eggs, and eggs with blood spots). This device incorporates color and monochrome CCD cameras. Determination of eggs with a cracked shell is done by processing the transmission image of the egg acquired using the color CCD camera. To determine eggs with blood spots, eggs are irradiated with light of the wavelength band that absorbs blood and the permeating light is captured with the monochrome CCD camera, after which the image data are processed. The results suggest that all types of abnormal eggs can be detected with a high precision of 95.0%~98.5%.

Physiological and physico-chemical changes in green papaya (Carica papaya L. ‘Kaek Noul’) shreds taken from the outer or inner mesocarp

ABSTRACT This study compared physiological and physico-chemical changes in shreds of green papaya (Carica papaya L. ‘Kaek Noul’), taken from inner and outer mesocarp tissues, during storage at 7ºC for up to 8 d. Reductions in the flesh firmness of shreds, microstructure, colouration, dry matter content (DMC), and fresh weight (FW) loss, and in the rates of respiration, ethylene production, and enzyme activities were measured. The rapid loss of firmness of green papaya ‘Kaek Noul’ shreds taken from the inner mesocarp was attributed to the larger and more loosely arranged cells of the inner mesocarp compared to the smaller and compact cells of the outer mesocarp. Shreds taken from the outer mesocarp had a higher DMC [6.21–6.77% (w/w)] than those from the inner mesocarp [5.83–6.34% (w/w)] during storage at 7ºC. FW loss was higher for shreds from the inner mesocarp than from the outer mesocarp (0.89–1.12% vs. 0.39–1.00%, respectively). Colour values (h°) were lower at the end of storage for shreds from the inner mesocarp than shreds from the outer mesocarp (104.38° and 111.94°, respectively). Moreover, scanning electron micrographs of shreds from inner mesocarp and outer mesocarp tissues showed that the slower loss of firmness in shreds from the outer mesocarp could be attributed to having smaller and more compact cells, as well as to lower ethylene production by the outer mesocarp. However, this was not related to cellulase activity. This study indicated why processors prefer to use shreds from the outer mesocarp of green papaya.

The Application of Neural Network Systems to the Evaluation of the Quality of Fruits

Abstract The possibility of substituting the neural network for “the empirical sense of grading with worker’s eyes” is discussed in the color grading of apples. The whole image data collecting system is developed to overcome the unequal light intensity. The grades; Super excellent Excellent, Good, Poor colored and Injured are separated with neural networks.