Fast identification of soybean varieties using Raman spectroscopy

Fast and accurate identification of soybean varieties is important for determining seed quality, protecting the interests of soybean growers and ensuring food safety. A combination of Raman spectroscopy with chemometric methods has been widely applied in the identification of agricultural products such as grains, fruit, edible oils, and honey; however, the identification methods of soybean varieties mainly focuses on infrared spectroscopy and hyperspectral imagery. Therefore, a partial least squares (PLS) identification model was established by Raman spectroscopy combined with characteristic wavenumber extraction method to rapidly identify four high-protein soybean varieties (Heinong 88, Heinong 98, Suinong 71, and Suinong 76) that are extensively planted in Heilongjiang Province, China. Owing to the high-dimensional and high correlation data obtained from Raman spectroscopy, a full-spectrum prediction mode will lead to high complexity and poor stability. Therefore, first, the optimal combinations of spectral subintervals were selected by backward interval PLS (BIPLS) and synergy interval PLS (SIPLS); however, redundant variables still existed in the selected continuous band and strong collinearity was observed between adjacent wavenumbers. Consequently, ElasticNet was used to re-filter the characteristic spectral regions of BIPLS and SIPLS, which effectively reduced the collinearity between the selected wavenumbers. The performance of different algorithms, i.e., full-spectrum and five characteristic wavenumber selection algorithms—BIPLS, SIPLS, ElasticNet, BIPLS-ElasticNet, and SIPLS-ElasticNet —were compared. In the BIPLS-ElasticNet algorithm, 1113 effective wavenumbers were extracted, which accounted for approximately 35% of the total wavenumbers. Moreover, the values of root mean square error, determination coefficient, and identification accuracy of the prediction set were determined to be 0.2298,0.9568 and 100%, respectively. Therefore, the proposed BIPLS-ElasticNet demonstrated the best performance among the PLS models and can be used for the fast and accurate identification of soybean varieties; moreover, it can also provide a reference for the rapid identification of other crop seeds.