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

• Published in: IOP conference series. Earth and environmental science Vol. 922; no. 1; pp. 12020 - 12027
• Main Authors: Hayati, R, Munawar, A A, Marliah, A
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.11.2021
• Subjects: Agricultural practices; Correlation coefficient; Correlation coefficients; Cultivars; Infrared spectroscopy; Near infrared radiation; Prediction models; Principal components analysis; Proteins; Rice; Spectrum analysis; Sustainable agriculture
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/922/1/012020

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.