Nondestructive Analysis of Sugar Content on Watermelon Using MRI Device Takashi MIKI, Kazuyoshi SAITO, Seiji HAYASHI, Hiroshi KAJIKAWA, Masao SHIMADA,

Size: px

Start display at page:

Download "Nondestructive Analysis of Sugar Content on Watermelon Using MRI Device Takashi MIKI, Kazuyoshi SAITO, Seiji HAYASHI, Hiroshi KAJIKAWA, Masao SHIMADA,"

ゆきさのたけ
5 years ago
Views:

1 Nondestructive Analysis of Sugar Content on Watermelon Using MRI Device Takashi MIKI, Kazuyoshi SAITO, Seiji HAYASHI, Hiroshi KAJIKAWA, Masao SHIMADA, Rikuo OGAWA, Yoshio KAWATE, Dai IKEGAYA,* Noriyuki KIMURA,* Kazuo TAKAEATAKE,* Takashi NISHIZAWA,,** Nobuhiro SUGIURA*** and Motofumi SUZUKI*** Electronics Research Laboratory, Kobe Steel Ltd., Takatsukadai, Nishi-ku, Kobe * Japan Magnet Technology Ltd., Takatsukadai, Nishi-ku, Kobe ** Chemical Research Laboratory, Kobe Steel Ltd., Takatsukadai, Nishi-ku, Kobe *** Maki Engineering Ltḍ, 630 Sasagase-cho, Hamamatsu 435 (Received December 22, 1995) Synopsis: So far, the use of superconducting magnets has been limited to chemical analysis and medical inspection. To develop a new use, we have tried to apply common MRI devices as nondes tructive sugar content detector of watermelon. To estimate the sugar content, a new technique using multiple regression analysis of two NMR relaxation times was developed. It was found that the coefficient of multiple regression at the center of the watermelon exceeded 0.9 and the standard error of prediction was around 0.5, and that the over-ripened part of flesh called 'nieka' in watermelon could be distinguished because its T2 was much longer than that of the oridinary part. An evaluation rate of sugar content could be below 6s per watermelon. It is concluded that multiple analysis of Tl and T2 on intact watermelon can be applied as a non-invasive, nondestructive indicator of sugar content. Keywords: NMR imaging, nondestructive testing machine, sugar content

2 Fig. 1 Typical measurement point on the cross section of watermelons, a1, a2: the center of the cross section (2cm ~2cm), b1, b2: the center of the cross section (3cm ~3cm), c1: the rim of the center area (which includes fibers), c2: the center of the cross section of a slightly north slice (2cm ~2cm), d: the rim of the center area (which does not im dude fibers), e: the outermost region of red tissue (which includes fibers), f: the outermost region of red tissue (which does not include fibers), g: others. Vol. 31 No. 5 (1996) 259

3 (b) Fig. 2 Pulse sequences used in the experiments (a) SE, (b) Localized STEAM. (b) Fig. 3 Typical NMR images of cross section parallel to the equator. (a1) T1-weighted NMR image of a watermelon (Tr=500, Te=40ms), (b1) T2-weighted NMR image of a watermelon (Tr=3,000, Te=220ms). The NMR image was acquired with 256 ~ 128 resolution. Table 1 Parameters used in the experiment.

4 Fig. 4 Relationship between measured relaxation times and the observed sugar content. (a) T1 and sugar content of watermelon from Ishikawa prefecture, (b) T1 and sugar content of watermelon from Nagano prefecture, (c) T2 and sugar content of waterm melon from Ishikawa prefecture, (d) T2 and sugar content of watermelon from Nagano prefecture. The solid line shows the regression line which is calculated with data except for position e, f (here N: no. of point used for regression, r: the correlation coefficient, S. E. P.: the standard error of the predicted sugar content). Vol. 31 No. 5 (1996) 261

5 24 Fig. 5 The photo image of the over-ripened sample.

6 Table 2 Result of the simple and multiple regression between the sugar content and T1, T2. r, SEP denote the coefficient of correlation and the standard error of prediction, respectively. The coefficient of the multiple regression exceeds that of the simple regression regardless of the field where they were grown. The positions which include fibres tend to have a better correla tion with the sugar content, Each measurement position is shown in Fig. 1. Vol. 31 No. 5 (1996) 263

7 (b) (c) Fig. 6 Result of the multiple regression be tween the sugar content and T1, T2, (a) Sample from Ishikawa prefecture, (b) Sam ple from Nagano prefecture, (c) Sample from Kumamoto prefecture.

8 Table 3 Results of the regression in Kumamoto Sample and the effect of temperature compensation. Vol. 31 No. 5 (1996) 265

9 1) B. Zion, P. Chen and M. J. McCarthy: ASAE/CSAE Meeting (1993) Paper ) N. Ishia, et al.: Agr. Biol. Chem. 53 (1989) ) M. E. Saltveit, Jr.: Postharv. Biol. Technol. 1 (1991) 153 4) P. Chen, M. J. McCarthy and R. Kauten: Trans. ASAE 32 (1989) ) N. Ishida, et al.: Anal. Sci, 7 (1991) 817 6) S. I. Cho, et al.: Trans. ASAE 32 (1989) ) V. Bellon, S. I. Cho, et al.: Food Control, January (1992) 45 8) J. Frahm, et al.: J. Magn. Reson. 72 (1987) ) S. Kawano, et al.: J. Jpn. Soc. Hort. Sci. 61 (1992) ) S. Kawano, et al.: J. Jpn. Soc. ort. Sci. 62 (1992) 465

304 Nippon Shokuhin Kagaku Kogaku Kaishi Vol. /., No.0, -. -3 (,**1) 58 * * : * : * Development of Sorting System Based on Potato Starch C

304 Nippon Shokuhin Kagaku Kogaku Kaishi Vol. /., No.0, -*. -*3 (,**1) 58 * ** *** : * : ** *** Development of Sorting System Based on Potato Starch Content Using Visible and Near-Infrared Spectroscopy