000 02502nab a22003017a 4500
001 G90940
003 MX-TxCIM
005 20211006075115.0
008 121211b |||p||p||||||| |z||| |
040 _aMX-TxCIM
100 1 _aJinsong Bao
245 0 0 _aDetermination of apparent amylose content, pasting properties and gel texture of rice starch by near-infrared spectroscopy
260 _c2007
340 _aComputer File
500 _aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0022-5142
520 _aFor breeding rice with improved quality, apparent amylose content (AAC), rapid visco analyser (RVA) pasting viscosities and gel texture properties may be routinely measured. As a direct measurement is time-consuming and expensive, rapid predictive method based on near-infrared spectroscopy (NIRS) is useful for measurement of these quality parameters. In this study, calibration models were developed using modified partial least-squares regression with different mathematical treatments based on the grain and flour spectra of non-waxy rice alone or in combination with waxy rice. The results showed that calibration models built with flour spectra are more robust than those with grain spectra, and with total rice including waxy rice are superior to those with only non-waxy rice. Some starch quality parameters, such as AAC, setback viscosity (SB), pasting temperature (PT), hardness (HD) and cohesiveness (COH) could be predicted with sufficient accuracy by NIRS based on flour spectra, whereas only AAC and PT could be predicted with sufficient accuracy based on grain spectra. All the models reported here are usable for rough sample screening (cold paste viscosity and breakdown viscosity), sample screening (SB, PT and COH) and for most applications (AAC and HD) for routine screening of a large number of samples in the early generation selection in breeding programs. However, for accurate assay of the pasting viscosity and gel textural parameters, direct instrumental measurement should be employed in later generations.
546 _aEnglish
591 _aJohn Wiley
650 1 0 _aAmylose
650 1 0 _anear-infrared spectroscopy (NIRS)
650 1 7 _aRice
_gAGROVOC
_2
_91243
650 1 0 _aStarch
650 1 0 _aViscosity
700 1 _aYuefei Wang,
_ecoaut.
700 1 _aYun Shen,
_ecoaut.
773 0 _tJournal of the Science of Food and Agriculture
_gv. 87, no. 11, p. 2040-2048
942 _cJA
_2ddc
999 _c27171
_d27171