Rapid quantification of rice (Oryza sativa) qualities based on adaptive near infrared spectroscopy

Abstract

Determination of rice quality parameters is the key factor affecting sustainable agriculture practices. The main purpose of this present study is to develop prediction models based on adaptive near infrared spectroscopy (NIRS) for rapid quantification of rice qualities in form of protein content. Rice samples were obtained from several paddy field in Aceh province with different cultivars. Near infrared spectral data of rice samples were acquired and in wavelength range from 1000 to 2500 nm and recorded as diffuse reflectance spectrum. Prediction models were established using principal component analysis (PCA), principal component analysis (PCR) and partial least square regression (PLSR). The results showed that NIRS combined with PCA can classify rice samples based on their cultivars. Moreover, this approach with PCR and PLSR can also predicted and determined protein contents with satisfactory performance achieving maximum correlation coefficient (r) of 0.81 and ratio prediction to deviation (RPD) index of 2.84 for PCR and r of 0.90 and RPD of 3.19 for PLSR respectively. Based on achieved results, it may conclude that adaptive NIRS approach can be used to quantify rice qualities rapidly and non-destructively.