以绍兴酒为研究对象, 利用近红外光谱分析技术和化学计量学分析方法, 开展了光程变化对基于近红外光谱的黄酒品质检测影响的研究, 建立了黄酒酒精度、 糖度和pH值３个品质指标的分析模型。 通过比较0.5, 1, 1.5, 2, 2.5和3 mm 6个不同光程, 并比较原始光谱、 一阶微分光谱和二阶微分光谱三种光谱预处理方式对模型性能的影响, 确定了这３个指标的最佳检测方式。 黄酒酒精度和pH的最佳检测方式为： 光程2 mm, 采用偏最小二乘回归结合原始光谱； 糖度的最佳检测方式为光程1 mm, 采用偏最小二乘回归结合原始光谱。 这一研究表明光程变化对基于近红外光谱的黄酒品质检测有影响, 选择合适的光程能够有效地提高检测精度。

Near infrared (NIR) spectroscopy and chemometrics were applied to determine the effect of pathlength variation on the spectra of the Chinese rice wine and the consequences of the prediction precision of calibration models developed for measuring alcoholic degree, sugar content, and pH was investigated in the present research. Samples were scanned in transmission mode using a commercial FT-NIR spectrometer and a demountable liquid cell for versatile path length liquid sampling. By comparing the results of performance between models based on different optical pathlength (0.5, 1, 1.5, 2, 2.5, and 3 mm), the best indicators of optical pathlength were identified. Based on the optimum pathlength, the models for alcoholic degree, sugar content and pH were established. The best optical pathlength for the alcoholic degree was 2 mm, using partial least squares regression (PLSR) model with the original spectra, correlation coefficient (r) was 0.942, root mean standard error of calibration (RMSEC) and root mean standard error of cross-validation (RMESCV) were 0.256 (％,(φ)) and 0.292 (％,(φ)), respectively; the best optical pathlength for the sugar content was 1 mm, using PLSR model with the original spectra, r was 0.945, and RMSEC and RMESCV were 0.125％ and 0.149％, respectively; the best optical pathlength for the pH was 2 mm, using PLSR model with the original spectra, r was 0.947, and RMSEC and RMESCV were 0.018 and 0.039, respectively. This study showed that pathlength variation had influence on the performance of calibration models for Chinese rice wine, and a suitable pathlength could effectively improve detection accuracy.