## 1 Introduction  
The intrinsic quality characteristics of seeds, such as longevity and vigor, depend on the maturation stage of seeds at harvest to a large extent, and they are dominant factors for successful crop production in the seed industry [1; 2]. Particularly, obtaining seeds of high quality requires timely harvest at physiological maturity [3; 4] before the seeds are fully ripe to ensure collection prior to dispersal [5] and to ensure maximum seed quality. During commercial seed production, maturity is usually estimated visually, relying on the experience of the growers [6]. Thus, two common scenarios often occur: harvesting too early may result in low yield and poor seed quality [7; 8], while delaying harvest increases the risk of the seeds either shattering or deteriorating in the field. Even when the date of harvest is the same, there still exist the seed-to-seed differences that can occur on a plant or within an infloresc, which will also result in physiological differences among seeds. Situations such as those described above contribute to the collection of a substantial proportion of seeds that are unsuitable for planting and long-term storage [9] due to their immaturity or low vigor. However, in terms of conventional grading methods based on seed physical properties that are great enough to separate \"good\" from \"inferior\" seed such as size, shape, weight, color, and specific density [10], it is very difficult to recognize the relatively small differences exhibited by seeds at different maturation stages. Moreover, there are few studies on how to identify and further classify the maturation stage of seeds in a nondestructive, precise, rapid, and inexpensive way, and that is in line with the demand of the seed industry for uniformly controlling seed performance to improve crop yield [11]. A heuristic method based on measuring the chlorophyll fluorescence signals of intact seeds has been used to determine seed maturity and seed quality [12]. However, bulk sample measurement of this type cannot effectively identify single-seed differences, and no discrimination is possible. Near-infrared spectroscopy (NIRS) analysis at the single-seed level followed by the use of a sorting mechanism could help increase sample uniformity [13] and has been successfully applied in grain, soybean, and corn [14; 15; 16]. It is widely known that near infrared spectral patterns, especially in the wavelength ranges of 1600-1800 nm and 2100-2200 nm, contain information about cis-unsaturation as well as about the carbon chain itself according to many reports [17; 18; 19]. Previous work has shown that the SK-NIRS approach coupled with chemometrics can be used to identify differences in viability in corn seeds [20]. Since the maturation stage of seeds at harvest can vary over a long time period, i.e., from 30 days after pollination (DAP) to 50 DAP, the differentiation of maturation stage results in different classes. Then, the challenge is whether the use of a combination of SK-NIRS and the chemometrics method can cope with this multiclass classification problem simultaneously. To date, this issue has been investigated with exploratory analysis; as far as we know, neither an available successful method nor satisfying classification results have been reported. Before diving into the specific model building process, it is often necessary and helpful to do an exploratory data analysis, which may help to uncover relationships in the data, identify rules from the data, and discover latent data patterns [21]. Clustering is one such method to visualize and group observations based on similarities of the overall set of variables of interest, such as scores plot of the principal components in a principal component analysis (PCA) model, which can help develop a fine intuition of the latent group structure that will enlighten the forthcoming classification problem.