[1] Wang H P, Chen P, Dai J W et al. Recent advances of chemometric calibration methods in modern spectroscopy: algorithms, strategy, and related issues[J]. TrAC Trends in Analytical Chemistry, 153, 116648(2022).

[2] Li C B, Zhang H L, Wang S S et al. Application of near infrared spectroscopy in food quality[J]. Food Research and Development, 36, 98-101(2015).

[3] Rao M, Gui J X, Wang X J et al. Application prospect of near infrared spectroscopy rapid detection in port safety supervision[J]. Journal of Instrumental Analysis, 39, 1225-1230(2020).

[4] Yang J B. Application of infrared spectroscopy in environmental monitoring[J]. China Resources Comprehensive Utilization, 38, 148-150(2020).

[5] Hu J, Feng Y Z, Wang Y J et al. Detection of umami substances and umami intensity based on near-infrared spectroscopy[J]. Acta Optica Sinica, 42, 0130002(2022).

[6] Luo Z Y, Qin Y H, Wang S J et al. Application of improved auto-encoding network feature extraction method in near infrared spectral quantitative analysis[J]. Laser & Optoelectronics Progress, 60, 0630001(2023).

[7] Sun W P. Research on producing area recognition of tobacco leaf based on near infrared spectroscopy[D](2016).

[8] Kong Q Q, Ding X Q, Gong H L et al. Research on application of feature selection algorithm based on combination of random forest and game theory in near infrared spectroscopy[J]. Journal of Instrumental Analysis, 36, 1203-1207(2017).

[9] Geng Y R, Shen H C, Ni H F et al. Support vector machine optimized by near-infrared spectroscopic technique combined with grey wolf optimizer algorithm to realize rapid identification of tobacco origin[J]. Spectroscopy and Spectral Analysis, 42, 2830-2835(2022).

[10] Zheng Z S, Liu B, Lu P et al. Spectral classification and characteristic spectral analysis of nearshore aquatic plants based on AlexNet[J]. Chinese Journal of Lasers, 50, 0211001(2023).

[11] Lu M Y, Yang K, Song P F et al. The study of classification modeling method for near infrared spectroscopy of tobacco leaves based on convolution neural network[J]. Spectroscopy and Spectral Analysis, 38, 3724-3728(2018).

[12] Zheng A B, Yang H H, Pan X P et al. Identifying multi-class drugs by using near-infrared spectroscopy and variational auto-encoding modeling[J]. Spectroscopy and Spectral Analysis, 40, 3946-3952(2020).

[13] Ding J G, Li X R, Zhao L Y. CDFormer: a hyperspectral image change detection method based on transformer encoders[J]. IEEE Geoscience and Remote Sensing Letters, 19, 6015405(2022).

[14] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2022).

[15] Zhang Y C, Zheng X T, Lu X Q. Hyperspectral image classification method based on hierarchical Transformer network[J]. Acta Geodaetica et Cartographica Sinica, 52, 1139-1147(2023).

[16] Li J H, Li X R, Cao Z Y et al. ROBYOL: random-occlusion-based BYOL for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 19, 6014405(2022).

[17] Duan M M, Jin C. Time series classification network based on transformer feature fusion[J/OL]. Computer Science, 1-11. http://kns.cnki.net/kcms/detail/50.1075.TP.20230913.1222.018.html

[18] Chen Q, Tang B, Long Z R et al. Water quality classification using convolution neural network based on UV-vis spectroscopy[J]. Spectroscopy and Spectral Analysis, 43, 731-736(2023).

[19] Xiao B, Chen J B, Bi X L et al. A method of heart sound analysis based on one-dimensional convolutional neural network and recurrent neural network[J]. Acta Electronica Sinica, 50, 2425-2432(2022).

[20] Hu X, Yang W J, Wen H et al. A lightweight 1-D convolution augmented transformer with metric learning for hyperspectral image classification[J]. Sensors, 21, 1751(2021).

[21] Wei Z, Li W W, Lin M et al. Near-infrared spectroscopy detection of cotton/polyester content based on Dropout deep belief network[J]. Acta Optica Sinica, 41, 1730002(2021).