African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6578

Full Length Research Paper

Study temperature influence in the application of near-infrared reflectance spectroscopy (NIRS) to estimate amylose content of rice

Xingang Xie1 and Lijuan Shi2*
1College of Engineering, Huazhong Agriculture University, 430070, Wuhan, China. 2College of Basic Sciences, Huazhong Agriculture University, 430070, Wuhan, China.
Email: [email protected] ,[email protected]

  •  Accepted: 22 April 2013
  •  Published: 04 July 2013

Abstract

In the application of near-infrared reflectance spectroscopy (NIRS) to estimate amylose content of rice, analysis was performed based on the relationship between absorbance and amylose content. But absorbance is sensitive to environmental temperature. In this study, characteristics of rice spectra collected at different temperatures were studied by comparing the average spectra and standard deviation spectra of rice with same amylose content at the temperature of 5, 10, 15 and 20°C. The results showed that spectra collected at different temperatures had significant difference in absorbance, and spectra acquired at different temperatures had different stabilities. When the environment temperature was 15°C, stability of spectra was better than when the temperature was 5, 10 and 20°C within the range consisting of abundant information. Accuracies of four linear models on calibration set acquired at four temperatures built by partial least square (PLS) were investigated by using validation sets got at four different temperatures to perform cross validation. The results showed that the relation between spectra and amylose content was not linear in the result of temperature’s influence, and non-linear model methodology least square support vector machine (LS-SVM) method was used to establish calibration model on the calibration set acquired at 15°C. The validation results indicated that the non-linear model could predict the amylose content of samples collected at 15°C with high accuracy, and the root mean square error of prediction was 0.62. Meanwhile, non-linear model showed good prediction ability for samples whose spectra were collected at other temperatures.

 

Key words: Amylose content, near-infrared spectroscopy, temperature influence.