Determining farming methods and geographical origin of chinese rice using NIR combined with chemometrics methods

This study was conducted to develop fast and nondestructive models for the discrimination of different farming methods and to determine the geographical origin of rice samples from different administrative regions in China using near-infrared (NIR) spectroscopy. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to build the NIR spectral models. Norris smoothing derivative (NSD) and multiplicative scatter correction (MSC) were used as preprocessing methods to reduce the spectral noise and enhance effective information. The results show that it was difficult to distinguish the farming methods with the original spectra plots and PCA score plots except for the rice samples from Heilongjiang Province. In addition, a PLS-DA model combined with NSD preprocessing provided the optimal predictive accuracy of 89.7% for the identification of different farming methods. NSD or MSC preprocessing combined with PLS-DA models provided the best discrimination of the origin traceability. The total accuracy of Northeast China rice samples was 100%, and of the South, East, Central and Southwest China rice samples was 98.2%. The total accuracy of Heilongjiang, Anhui, Jiangsu, Hubei, and Sichuan Provinces were 100%, 98.8%, 95.3%, 95.3%, and 93.6%, respectively. These indicate that NIR combined with PLS-DA and NSD or MSC preprocessing can provide a powerful method to distinguish the different farming methods and geographical origin of Chinese rice.

Automated summary

This study developed fast and nondestructive models using near-infrared spectroscopy to discriminate different farming methods and determine the geographical origin of rice samples from different regions in China. The results showed that NIR combined with PLS-DA and NSD or MSC preprocessing can effectively distinguish the geographical origin and farming methods of Chinese rice.