Corner coil heating mode improves the matrix uniformity of cooked rice in an induction heating cooker.

Nowadays, an increasing number of people worldwide use induction heating cookers to cook rice for consumption. This work reveals the influence of induction heating cooker heating modes on the quality attributes of cooked rice. Three heating modes, including bottom coil heating mode (mode 1), corner coil heating mode (mode 2), and side coil heating (mode 3), were used. Among the three modes, mode 2 allowed for an intermediate heating rate during rice cooking. For mode 2, the minimized temperature difference between the upper layer (including the central upper layer and peripheral upper layer) and the lower layer (including the central lower layer and peripheral lower layer) can reduce the effect of water absorption time difference on rice quality. Consequently, the rice cooked using mode 2 exhibited improved matrix uniformity, as indicated by the similar moisture content (59.92-61.89%), hardness (15.87-18.24 N), and water mobility (the relaxation time and peak area of the third relaxation peak) of rice samples at four different positions in the pot. The rice cooked by mode 2 showed better texture appearance and a more uniform porous microstructure. Consistently, the cooked rice samples by mode 2 at different positions did not show substantial differences in their starch digestion features.

Quantitative study of starch swelling capacity during gelatinization with an efficient automatic segmentation methodology.

A novel image segmentation methodology combined with optical microscopy observation was developed for qualifying starch swelling. Starch granules in the micrograph were successfully segmented based on high-precision edges extraction achieved by Canny edge detection together with mathematical morphology operation. Granules were automatically identified by computer vision and characterized by giving quantifiable area of these granules. The evolved swelling process could be generally divided into two phases. During the first phase, starch granules were only swollen up by 2.56 %, which is hard to be identified by conventional naked eye. During the following narrow temperature interval (60-66 ℃), these starch granules were detected to swell up significantly by 9.08 %. Through the granule area variable, swelling capacity was high-throughput characterized, which allows for the whole evaluation to be completed within a couple of minutes. The proposed methodology showed a high accuracy and potential as a novel technique for characterizing gelatinization.

A new characterization methodology for starch gelatinization.

A gelatinization degree control system, with a combination of Artificial Neural Networks (ANNs) and computer vision, was successfully developed. An intelligent measurement framework was purposely designed to achieve a precise investigation on phase transition and morphology change of starch in real time, as well as a process control during gelatinization. Base on a variation of birefringence number, the degree of gelatinization (DG) control system provided a direct and fast methodology without subjective uncertainty in studying starch gelatinization. In the course, the whole system was a cascade structure with the hot-stage temperature chosen as the inner-loop parameter, thus the granule morphology and birefringence at different DG could be easily observed and compared in real time, and the relative transition temperature was simultaneously calculated.