## 1 Introduction  
With global warming, the global mean annual temperature will increase up to 0.3-4.8 \({}^{\circ}\)C by 2100, and the temperature in numerous regions is likely to be warmer than the global average temperature [1]. High temperatures are becoming more common and cause heat stress, which has the potential to decrease the yield of leafy vegetables in the near future [2]. Chinese cabbage is vulnerable to high temperatures. It grows best in cold (16-25 \({}^{\circ}\)C) and humid climates [3]. High temperatures often affect the formation of narrow and uneven leaves, reduce yields, delay the formation of heading, and cause an increase in sensitivity to contagious diseases, which leads to serious declines in the productivity and quality of Chinese cabbage [3, 4]. Therefore, studies on heat-tolerant genotypes have been performed for cabbage breeding based on microRNAs, long noncoding RNAs, and RNA sequencing technologies [4; 5; 6]. Phenotypic analyses have also been conducted for heat tolerance in Chinese cabbage [7; 8]. Despite these efforts, some Asian countries, such as the Republic of Korea, Japan, and China, still suffer from heat stress during summer, resulting in economic loss, lower yields, and even death [9; 10; 11]. The development of rapid non-destructive evaluation (NDE) techniques for heat stress screening of Chinese cabbage (Brassicaceae family) has attracted substantial attention in the fields of food quality and safety [12; 13; 14; 15]. In this study, we suggested an early screening technique for NDE of heat stress during the growth of Chinese cabbage using the snapshot-based multispectral imaging (SMI) method within the visible-near infrared (Vis/NIR) region because early screening methods of heat stress can prevent potential damage to leaves, productivity, and economic loss. Spectral imaging (i.e., hyperspectral and multispectral imaging) within the Vis/NIR region is a relatively new method for nondestructive inspection of various food and agricultural products [16], because in the Vis/NIR region, chemical bonds such as C-H, O-H, and N-H functional groups have relatively high absorbance peaks in specific frequencies, which reflects the stretching vibration of the overtones involved [17]. Combining conventional imaging with spectroscopy techniques can provide both spatial and spectral information from a target specimen [18; 19; 20]. Multispectral imaging can be used to obtain Vis/NIR spectra for all pixels of a target sample with 3-20 bands and hyperspectral imaging uses narrow bands (hundreds or thousands of bands, 10-20 nm) [21]. Due to this feature, spectral imagery can be used for the rapid identification and detection of a target. Spectral imaging is primarily conducted using scanning and snapshot imagers. Scanning spectral imagers employ point-scanning (wisk-broom) or line-scanning (push-broom), which acquire continuous one-dimensional (1D) or 2D measurements to produce 3D spectral data cubes [22; 23]. However, its image acquisition can be slow because the scanning imagers are mechanically controlled [23]. In contrast, snapshot spectral imagers can create a spectral data cube in a single detector integration time by acquiring spatial and spectral data with one detector [24]. In addition, its structure is simpler than that of a spectral imager, because mechanical moving components are not installed internally. Moreover, the data acquisition speed and spatial resolution are relatively higher than those of spectral imaging. Although snapshot imaging provides high spatial resolution with high imaging speed, its spectral resolution and signal-to-noise ratio are lower than those of spectral imaging [25]. To overcome the limitations of snapshot imaging and improve system accuracy, partial least squares-discriminant analysis (PLS-DA) and least-squares support-vector machine (LS-SVM) models have been utilized in the field of plant disease monitoring and food quality and safety [26; 21]. PLS-DA is a useful machine learning method for feature selection and data classification [27].