Near infrared spectroscopy (NIR) is a weak signal, and its spectral quality is easily disturbed by the state of the measured object and various external factors. Specifically, the spectral quality in the qualitative analysis of NIR is mainly affected by the state change of measuring instrument, wrong operation, and the interference of singular samples. The robustness and applicability of the model are easily affected by the incorporation of poor quality spectra, so spectral quality determination is of vital importance to ensure the model prediction ability. At present, there are many studies on the determination of spectral quality for quantitative analysis, but few studies on the determination of spectral quality for qualitative analysis. In this paper, a method for the determination of spectral quality for near-infrared qualitative analysis based on data description of support vector is proposed. A self-made diffuse reflectance NIR acquisition device is used to collect the spectra of single-grain maize as an experimental object, and under normal conditions, the diffuse transmission spectra of a maize single grain were collected as normal samples, while the collected spectra were used as abnormal spectra under the conditions of artificial light leakage, near infrared detector window covering maize epidermis debris, intensity change of light source, distance change between light source and tested maize grain, and mixture of similar maize seeds. On this basis, the determination based on support vector data description (SVDD) was studied. The principle and method of establishing spectral quality judgment model were analyzed. Because the parameters of kernel function and regularization have important influence on the performance of spectral quality judgment model based on SVDD, the combination of grid search and cross validation was used to optimize the parameters of kernel function and regularization, and the optimal parameters of Gauss kernel were determined through experiments. Then, the SVDD method was compared with other spectral quality determination methods such as Mahalanobis distance and local anomaly factor. The average of correct recognition rate of normal samples and correct rejection rate of abnormal samples were used as evaluation criteria. The experimental results show that the spectral quality determination method based on support vector data description has the best performance. In near infrared qualitative analysis, this method can be used as a means of eliminating abnormal spectra before feature extraction and pattern classification, and the spectra quality determination step based on SVDD can effectively improve the reliability of the qualitative analysis.