A calibration transfer optimized single kernel near-infrared spectroscopic method.

Single kernel near-infrared spectroscopy (SKNIRS) could aid in the quality screening of early-generation seeds, to improve the efficiency of seed breeding. However, the application of SKNIRS is limited due to the irregular physical characteristics, the heterogeneous constituent distributions of individual seeds, and the insufficient detection accuracy of the reference method. The reported near-infrared detection results of single seeds are often less accurate than those of dehusked seeds and seed flour. In this paper, a calibration transfer-optimized single kernel near-infrared spectroscopic method is proposed. This method aims to accurately detect the chemical composition of single seeds by using the calibration model of the corresponding dehusked seeds or seed flour. The proposed method was applied to the analysis of the protein content of a single rice kernel. The near-infrared transmission spectra of three forms of rice (single rice kernel (SRK), single brown rice kernel (SBK) and rice flour (RF)) of 201 individual rice seeds and the corresponding protein content values were obtained. By comparing different pretreatment methods and spectral ranges, the spectral range 950-1250 nm, the standard normal variate transformation (SNV) pretreatment, and 9 PLS factors were selected to construct the optimal partial least squares (PLS) regression models. Then, the protein content of single rice kernels were determined through two different methods: (i) the direct method, in which single rice kernels were analyzed using the single rice kernel model directly; and (ii) the proposed method, in which the spectra of single rice kernels were transferred into the spectra of single brown rice kernels and rice flours with a calibration transfer algorithm, spectral space transformation (SST), and were analyzed by the respective calibration models. The external validation coefficient correlation (R) value of the direct method was 0.971, and the R values of the proposed method were 0.962 (SBK) and 0.975 (RF). The root mean square error of prediction (RMSEP) value of the direct method was 0.423, and the RMSEP of the proposed method were 0.480 (SBK) and 0.401 (RF). In addition, the transfer results among the spectra of three forms of rice were compared. By comparison, the results of the proposed method are fairly close to the results of the direct method. The results indicate that the spectra generated from one individual rice seed can be transferred freely among the three forms by means of calibration transfer. The proposed method is a promising way to overcome the challenges associated with analyzing individual seeds and to improve SKNIRS.