## 1 Introduction
[PERSON] et al., (2018) succeeded in using the HSI (874-1734 nm, 975.01-1645.82 nm) with a radial basis function neural network algorithm for maize seed variety classification at an accuracy of 91.0% [13]. This HSI system was also used to discriminate the varieties of grape seed, where the highest accuracy of 88.7% was achieved in the prediction set [14]. [PERSON] et al., (2018) carried out research to recognize the varieties of mung beans using the HSI (380-1023 nm) system, where the extreme learning machine algorithm performed with accuracies ranging from 99.17% to 100% [10]. [PERSON] et al., (2018) utilized two HSI systems with different spectral ranges (380-1030 nm and 874-1734 nm) to identify rice seed varieties. Three modeling methods, including the K-nearest neighbor (KNN), SVM, and convolutional neural network, were tested. They came to the conclusion that long-wave spectra (874-1734 nm) could result in a higher accuracy than that of short-wave spectra (380-1030 nm) among three modeling methods [15]. This research demonstrated that spectral features identified by HSI are useful for cultivar classification. However, the HSI technique used in the above research can only show the features in the short-wave spectral range. All these HSI systems had a spectral range no greater than 1750 nm because of the limited capability of the detector sensor in the HSI system. The spectral range above 1750 nm may include more information and should also be studied to optimize cultivar classification. The Fourier transform near-infrared (FT-NIR) technique is an emerging method that can easily measure spectral responses in a wide wavelength range of near-infrared (NIR). The FT-NIR technique is also characterized by high-resolution and accurate frequency determination. It has been widely studied in seed quality detection because of its advantages of a high speed and low cost, and its non-contact detection features give it considerable potential for online detection. Thus far, FT-NIR spectroscopy has been studied in detecting the composition contents [16; 17; 18], viability [19; 20; 21; 22; 23], and contaminations [24; 25; 26] in various varieties of grains or seeds. [PERSON] et al., (2011) applied FT-NIR spectroscopy to classify five varieties of rice and achieved a high accuracy of 99.22% using the partial least-squares discriminant analysis (PLS-DA) modeling method [27]. [PERSON] et al., (2016) showed that a reasonable accuracy can be obtained while using Fourier transform mid-infrared (FT-MIR) and FT-NIR spectroscopy to classify species of sorghum seeds [28]. [PERSON] el at. (2018) established models based on FT-NIR data to identify maize varieties, and they obtained an average accuracy greater than 90% [9]. However, most of the studies could achieve applicable results using a batch of seeds as a sample. It can still be a challenge to measure the spectrum of single seeds for cultivar detection, especially when the contents of compositions inside the seed samples are highly similar. This study was carried out to classify two similar sweet corn seed cultivars using FT-NIR spectroscopy, with a wavelength range of 1000-2500 nm. It was aimed at developing a rapid, economical, and non-destructive method for ensuring the purity of seeds. The GA method was used to identify feature wavelengths to simplify the discriminant models. Given that previous studies [11; 15] have shown that different modeling methods may achieve unequal accuracies, four classification algorithms, namely KNN, the soft independent method of class analogy (SIMCA), PLS-DA, and support vector machine discriminant analysis (SVM-DA), were tested on the full-range and feature wavelengths to obtain the optimal classification model. Three validation methods, namely, cross-validation, the independent test, and the permutation test, were applied for optimizing and validating the reliabilities of all models. The performances of all models were compared and discussed in this study.