[1] GIOVANELLI G, SINELLI N, BEGHI R, et al. NIR spectroscopy for the optimization of postharvest apple management[J]. Postharvest Biology and Technology, 2014, 87: 13-20. doi: 10.1016/j.postharvbio.2013.07.041
[2] FAN SH X, ZHANG B H, LI J B, et al. Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple[J]. Biosystems Engineering, 2016, 143: 9-19. doi: 10.1016/j.biosystemseng.2015.12.012
[3] MENDOZA F, LU R F, ARIANA D, et al. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content[J]. Postharvest Biology and Technology, 2011, 62(2): 149-160.
[4] 高升, 王巧华, 李庆旭, 等. 基于近红外光谱的红提维生素C含量、糖度及总酸含量无损检测方法[J]. 分析化学,2019,47(6):941-949.

GAO SH, WANG Q H, LI Q X, et al. Non-destructive detection of vitamin c, sugar content and total acidity of red globe grape based on near-infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2019, 47(6): 941-949. (in Chinese)
[5] 史云颖, 李敬岩, 褚小立. 多元校正模型传递方法的进展与应用[J]. 分析化学,2019,47(4):479-487.

SHI Y Y, LI J Y, CHU X L. Progress and applications of multivariate calibration model transfer methods[J]. Chinese Journal of Analytical Chemistry, 2019, 47(4): 479-487. (in Chinese)
[6] 王凡, 李永玉, 彭彦昆, 等. 基于可见/近红外透射光谱的番茄红素含量无损检测方法研究[J]. 分析化学,2018,46(9):1424-1431.

WANG F, LI Y Y, PENG Y K, et al. Nondestructive determination of lycopene content based on visible/near infrared transmission spectrum[J]. Chinese Journal of Analytical Chemistry, 2018, 46(9): 1424-1431. (in Chinese)
[7] 路皓翔, 徐明昌, 张卫东, 等. 基于压缩自编码融合极限学习机的柑橘黄龙病鉴别方法[J]. 分析化学,2019,47(5):652-660.

LU H X, XU M CH, ZHANG W D, et al. Identification of citrus huanglongbing based on contractive auto-encoder combined extreme learning manchine[J]. Chinese Journal of Analytical Chemistry, 2019, 47(5): 652-660. (in Chinese)
[8] 郭文川, 王铭海, 谷静思, 等. 近红外光谱结合极限学习机识别贮藏期的损伤猕猴桃[J]. 光学 精密工程,2013,21(10):2720-2727. doi: 10.3788/OPE.20132110.2720

GUO W CH, WANG M H, GU J S, et al. Identification of bruised kiwifruits during storage by near infrared spectroscopy and extreme learning machine[J]. Optics and Precision Engineering, 2013, 21(10): 2720-2727. (in Chinese) doi: 10.3788/OPE.20132110.2720
[9] 郭志明, 黄文倩, 彭彦昆, 等. 自适应蚁群优化算法的近红外光谱特征波长选择方法[J]. 分析化学,2014,42(4):513-518.

GUO ZH M, HUANG W Q, PENG Y K, et al. Adaptive ant colony optimization approach to characteristic wavelength selection of NIR spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2014, 42(4): 513-518. (in Chinese)
[10] ZHANG B H, HUANG W Q, GONG L, et al. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier[J]. Journal of Food Engineering, 2015, 146: 143-151. doi: 10.1016/j.jfoodeng.2014.08.024
[11] ZHANG B H, DAI D J, HUANG J CH, et al. Influence of physical and biological variability and solution methods in fruit and vegetable quality nondestructive inspection by using imaging and near-infrared spectroscopy techniques: a review[J]. Critical Reviews in Food Science and Nutrition, 2018, 58(12): 2099-2118. doi: 10.1080/10408398.2017.1300789
[12] 樊书祥, 黄文倩, 郭志明, 等. 苹果产地差异对可溶性固形物近红外光谱检测模型影响的研究[J]. 分析化学,2015,43(2):239-244.

FAN SH X, HUANG W Q, GUO ZH M, et al. Assessment of influence of origin variability on robustness of near infrared models for soluble solid content of apples[J]. Chinese Journal of Analytical Chemistry, 2015, 43(2): 239-244. (in Chinese)
[13] LI X N, HUANG J CH, XIONG Y J, et al. Determination of soluble solid content in multi-origin ‘Fuji’ apples by using FT-NIR spectroscopy and an origin discriminant strategy[J]. Computers and Electronics in Agriculture, 2018, 155: 23-31. doi: 10.1016/j.compag.2018.10.003
[14] JANNOK P, KAMITANI Y, HIRONAKA K, et al. Development of a near infrared calibration model with temperature compensation using common temperature-difference spectra for determining the Brix value of intact fruits[J]. Journal of Near Infrared Spectroscopy, 2017, 25(1): 26-35. doi: 10.1177/0967033516678516
[15] 王拓, 戴连奎, 马万武. 拉曼光谱结合后向间隔偏最小二乘法用于调和汽油辛烷值定量分析[J]. 分析化学,2018,46(4):623-629.

WANG T, DAI L K, MA W W. Quantitative analysis of blended gasoline octane number using raman spectroscopy with backward interval partial least squares method[J]. Chinese Journal of Analytical Chemistry, 2018, 46(4): 623-629. (in Chinese)
[16] 刘翠玲, 吴静珠, 孙晓荣. 近红外光谱技术在食品品质检测方法中的研究[M]. 北京: 机械工业出版社, 2016.

LIU C L, WU J ZH, SUN X R. Study on Near Infrared Spectroscopy in Food Quality Testing Methods[M]. Beijing: China Machine Press, 2016. (in Chinese)
[17] CHANG CH W, LAIRD D A, MAUSBACH M J, et al. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[J]. Soil Science Society of America Journal, 2001, 65(2): 480-490. doi: 10.2136/sssaj2001.652480x
[18] ZHANG D Y, XU L, WANG Q Y, et al. The optimal local model selection for robust and fast evaluation of soluble solid content in melon with thick peel and large size by Vis-NIR spectroscopy[J]. Food Analytical Methods, 2019, 12(1): 136-147. doi: 10.1007/s12161-018-1346-3
[19] YUAN L M, CAI J R, SUN L, et al. Nondestructive measurement of soluble solids content in apples by a portable fruit analyzer[J]. Food Analytical Methods, 2016, 9(3): 785-794. doi: 10.1007/s12161-015-0251-2
[20] YUN Y H, LI H D, DENG B CH, et al. An overview of variable selection methods in multivariate analysis of near-infrared spectra[J]. TrAC Trends in Analytical Chemistry, 2019, 113: 102-115. doi: 10.1016/j.trac.2019.01.018