摘要： 糊化特性是小米的最重要加工特性之一，对小米的加工性能及产品质量有重要的影响。基于可见-近红外光谱特征信息，在不粉碎小米颗粒的状态下，提出了一种快速无损检测小米的糊化特性的方法。首先，获取小米在370~1 020 nm范围内漫反射光谱后，将小米粉碎成小米粉，使用RAV快速粘度分析仪测定小米粉的峰值粘度（PV）、最低粘度（TV）、衰减值（BD）、最终粘度（FV)、和回升值（SB）、糊化温度（GT）以及峰值时间（PT）等7个糊化特性指标。然后，对原始光谱进行Savitzkye-Golay（SG）平滑、多元散射校正（MSC）和一阶导数法(1-D)预处理。最后，结合三种处理光谱和小米糊化特性指标值，通过Sample set partitioning based on joint x-y distances（SPXY）方法确定样本的校正集和验证集；基于连续投影算法（SPA)选择了特征波长，利用特征波长反射光谱信号建立了小米糊化特性指标的多元线性回归（MLR）预测模型，并使用验证集样本验证MLR模型的预测准确性。糊化指标预测结果：对于粘度指标中的PV、TV和SB参数值，经过MSC预处理后光谱，分别选择了9，17和18个特征波长建立的MLR模型的预测结果最好，预测相关系数（Rp）分别为0.934 7，0.825 5和0.874 6，预测误差（SEP）分别为174.039 7，67.220 3和74.281 8；对于BD值，经过S-G预处理后选择了14个特征波长的MLR模型预测结果最好，Rp为 0.924 4，SEP为178.020 1；此外，对于FV参数值，经过1-D处理后选择了16个特征波长所建立MLR模型的预测相关系数Rp为0.853 1，SEP为132.166 7。研究结果表明，利用可见-近红外光谱结合SPXY和SPA算法在不粉碎小米的状态下对其糊化特性进行检测是可行的。本研究为小米产品相关企业在生产前期，通过快速测定小米原料糊化特性，进而评估产品加工品质提供一种新的技术手段，具有较强的实际应用潜力。
关键词：小米；糊化特性；可见-近红外反射光谱；SPXY算法；SPA算法
Abstract：Gelatinization is one of the most important processing properties of millet, which has an important influence on the processing performance and product quality of millet. In this study, a rapid non-destructive detection method for the gelatinization characteristics of millet was proposed based on visible/near infrared spectroscopy technology which was used to detect the gelatinization properties of millet without crushing millet granules. Firstly, the VIS/NIR diffuse reflectance spectrum of millet in the range of 370~1 020 nm was collected, and crushed the millet into millet flour subsequently, then seven gelatinization characteristics such as the peak viscosity (PV), trough viscosity (TV), breakdown(BD), final viscosity (FV), setback (SB), pasting temperature (GT) and peak time (PT) were determined by RAV rapid viscosity analyzer. After that, the reflectance spectrum was preprocessed by Savitzkye-Golay smoothing (SG), multivariate scattering correction (MSC) and first derivative method (1-D). Finally, combined different preprocessed spectra and millet gelatinization characteristics, the calibration sets and validations set of the samples were determined by Sample set partitioning based on joint x distances (SPXY), and then based on the Successive projections algorithm(SPA) the optimal wavelengths were selected, the multivariate linear regression (MLR) prediction models of millet gelatinization characteristic were established using optimal wavelength reflection spectral signal, and the validation set samples were used to verify the prediction accuracy of the MLR model. MLR models prediction results: for the parameters of PV, TV and SB, after MSC pretreatment, the MLR model established by 9, 17 and 18 optimal wavelengths was the best, the predicted correlation coefficients (Rp) were 0.934 7, 0.825 5 and 0.874 6 respectively, and the standard error of predicted residual (SEP) were 174.039 7, 67.220 3 and 74.281 8 respectively; for BD value, the MLR model with 14 optimal wavelengths after S-G pretreatment was the best with the Rp was 0.924 4, and the SEP was 178.020 1; in addition, for the FV parameters, after 1-D pretreatment, the prediction Rp and SEP of MLR model based on 16 optimal wavelengths were 0.853 1 and 132.166 7 respectively. The results show that it is feasible to detect the gelatinization properties of millet without crushing millet by using VIS/NIR diffuse reflectance spectroscopy combined with SPXY and SPA algorithm. This study provides a new technical means for millet products enterprises to determine the gelatinization characteristics of millet raw materials in the early stage of production rapidly and evaluate the processing quality of millet products, which has a high practical application potential.
[1] YAN Sha，YAN Hong，GAO Ya, et al(延 莎, 闫 虹, 高 雅, 等). The Food Industry(食品工业)，2017，42(10)：159.
[2] WU Li-gen, QU Ling-bo(吴立根，屈凌波). Food Research and Development (食品研究与开发), 2018, 39(15): 191.
[3] CHEN Xiang-dong, WU Xiao-jun, JIANG Xiao-ling, et al(陈向东, 吴晓军, 姜小苓, 等). Food Research and Development(食品研究与开发), 2018, 40(1): 163.
[4] LIU Ya-chao, LI Yong-yu, PENG Yan-kun, et al(刘亚超，李永玉，彭彦昆，等). Chinese Journal of Analytical Chemistry(分析化学), 2019, 47(5): 785.
[5] Chen H Z, Song Q Q, Tang G Q, et al. Journal of Cereal Science, 2014, 60: 595.
[6] XU Yi-ru, LIU Cui-ling, SUN Xiao-rong, et al(徐一茹, 刘翠玲, 孙晓荣, 等). Food Science(食品科学)，2014, 35(12): 128.
[7] ZOU Xiao-bo, CUI Xue-ping, SHI Ji-yong, et al(邹小波, 崔雪平, 石吉勇, 等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报), 2018, 49(3): 341.
[8] Wiedemair V, Mair D, Held C, et al. Talanta, 2019, 205: 120115.
[9] GB/T5009.3—2016 National Food Safety Standard-Determination of Moisture in Food(食品安全国家标准 食品中水分的测定).
[10] GB24852—2010 National Food Safety Standard-Determination of Pasting Properties of Rice-Rapid Visco Analyzer Method(食品安全国家标准 大米及米粉糊化特性测定快速粘度仪法).
[11] Tian H, Zhang L N, Li M, et al. Infrared Physics & Technology, 2018, 95: 88.
[12] ZHAN Xiao-ri，ZHU Xiang-rong, SHI Xin-yuan, et al(展晓日, 朱向荣, 史新元, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(4): 964.
[13] Soares S F C, Galvao R K H, Araujo M C U, et al. Analytica Chimica Acta, 2011, 689(1): 22.
[14] Liu F, He Y. Food Chemistry, 2009, 115(4): 1430.
