直链淀粉、蛋白质、脂肪、水分含量是大米重要营养与储藏品质指标，这些指标的检测方法目前主要依赖于国标法，过程繁琐，且不能多指标同时检测。本研究以产自江苏省的126份粳米、糯米和籼米为建模样本，利用近红外光谱结合化学计量学，通过 5种光谱预处理方法和筛选波段建立了大米中直链淀粉、蛋白质、脂肪、水分含量的偏最小二乘模型。对脂肪和直链淀粉模型均采用 Savitzky-Golay滤波平滑对光谱进行处理，rc分别为0.8110和0.6671；蛋白质模型采用标准正态变化预处理，rc为0.9713；对于水分的检测，采用一阶导数光谱预处理方法较好，rc为0.9663。波长筛选后以验证集评估建模，直链淀粉、蛋白质、脂肪、水分模型的验证集相关系数rp分别为0.8030、0.9429、0.8331和0.9421。结果表明，利用近红外光谱可以实现对大米中直链淀粉、蛋白质、脂肪、水分含量同时快速无损的检测。
Amylose, protein, fat and moisture content are important factors of rice nutritional and storage quality. However, the mostly used method for determination of these indexes is the National Standards, which is tedious and cannot detect multiple indexes simultaneously. Herein, 126 samples of japonica, glutinous and indica rice from Jiangsu province were taken as modeling samples. After the selection of spectral pretreatment and optimal spectral range, the partial least square （PLS） models of rice amylose, protein, fat and moisture content were established by combining near-infrared spectroscopy and chemometrics. For the detection of fat and amylose models, savitzky-golay filter spectral pretreatment method was better, rc was 0.8110 and 0.6671, respectively; protein model was pretreated with standard normal variation, rc was 0.9713; the spectra were processed by first derivative spectral pretreatment method for moisture content, rc was 0.9663. Furthermore, optimized models were evaluated with prediction sets after selection of wavelength. For amylose, protein, fat and moisture content, the correlation coefficient of prediction set were 0.8030, 0.9429, 0.8331 and 0.9421. Accordingly, near infrared spectroscopy could achieve the simultaneous, rapid and nondestructive detection of amylose, protein, fat and moisture content in rice.
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