Identification Of Japonica Rice Seeds Based On Spectral Technology

Rice is one of the most important food crops.As a major rice producing and consuming country,China consumes about 200 million tons of rice yearly,and rice is the main food for about two thirds of the population.The quality and characteristics of rice seeds dictate the growth,processing,yield and quality of subsequent rice.Seed identification technology is crucial for rice quality control,stability of rice market,and prosperity of the food industry.With the rapid development of spectroscopy,it plays an increasingly important role in the field of non-destructive food detection.Unique geographical factors make rice in specific regions possess high quality but low yield,leading to a regional rice brand.Unscrupulous traders often replace brand rice with inferior ones from neighboring regions,which is of similar appearance and hard to identify by naked eyes.In this paper,the japonica rice seeds were studied and analyzed using spectroscopic techniques.We aim to develop rapid,non-destructive detection and classification methods for rice seeds based on their ages and producing areas.Japonica rice seeds from different producing areas and different years in Jilin Province were systematically studied using fluorescence spectroscopy,Raman spectroscopy and hyperspectral imaging technology.The database containing spectral information for japonica rice seeds from 8 different producing areas was established.As fluorescence spectroscopy is fairly surface sensitive,three types of samples,including rice seeds with shells,brown rice after shelling,and ground brown rice powder,were included in the study.The spectral characteristics of essential ingredients such as riboflavin and chlorophyll were analyzed.Brown rice and ground rice powder showed larger spectral differences than rice seeds.The findings were consistent with the geographical information of producing areas.In Raman spectroscopy,the ground rice of different years were studied.First-principles calculations were performed to simulate Raman spectra for amylopectin,and the Raman signal from ground brown rice agree well with of amylopectin.To analyze the small differences in Raman spectra,different data processing methods and multiple machine learning based analysis models were combined.Support vector machine(SVM)model with characteristic wavelength as input works better than other models.We further studied the seeds of five different producing areas using hyperspectral imaging technology.Hyperspectral data of hundreds of seeds were obtained.Different data processing and analysis methods were compared.Identification models based on support vector machine(SVM)and extreme learning machine(ELM)was established.The results of this paper provide a fast and effective method for the identification of rice seeds from different producing areas and years,and pave the way to application of spectroscopic techniques for identification of other types of seeds.