近红外光谱检测鲜枣酵母菌的动力学模型

酵母菌是引起鲜枣发酵的主要微生物。以室温(20 ℃)贮藏的鲜枣为研究对象，应用近红外光谱，建立了检测鲜枣内酵母菌的动力学模型，从而预测室温贮藏鲜枣的保鲜期，以确保鲜枣的品质安全。通过对近红外光谱预处理方法和特征波数的优选，分别建立了室温贮藏下鲜枣内酵母菌的近红外光谱定量检测模型和反映其变化规律的动力学模型。结果表明，在全光谱范围内，采用多元散射校正光谱预处理方法，通过多元线性回归，建立的鲜枣内酵母菌菌落总数的近红外光谱模型预测效果最好，其中校正集的相关系数为0.950，均方根误差为2.560，预测集的相关系数为0.863，均方根误差为2.477。结合鲜枣的近红外光谱，其零级反应动力学模型可以较好地描述酵母菌的变化情况，鲜枣光谱吸光度值与贮藏时间的动力学模型为B_t=171.395-124.445x₁-109.945x₂-32.763x₃-7.899x₄-1.426x₅-4.857x₆+0.045t，其相关系数为0.996，标准偏差为2.561。酵母菌安全限量为100 000 cfu·g-1，当酵母菌菌落总数初始值小于等于10 cfu·g-1时，预测鲜枣在室温下的贮藏时间为8 d，也可根据鲜枣中的酵母菌菌落总数初始值的不同实现实时监测鲜枣内部酵母菌菌落总数信息及其安全的贮藏时间。

Kinetic Models for Determination of Yeast in Fresh Jujube Using Near Infrared Spectroscopy

The objectives of this study were: (1) to optimize a near-infrared (NIR) spectroscopy model for fresh jujube stored at room temperature to predict the quality change (yeast growth), (2) to establish a kinetic model of yeast growth for fresh jujubes at room temperature according to NIR spectroscopy data and storage time, and (3) to predict the shelf life of fresh jujube at room temperature. The Lizao samples of fresh jujubes were adopted as the research object in the study. The NIR spectral data were achieved before yeast infection level measured. In order to optimize the NIR model, the pretreatment techniques such as Savitzky-Golay smoothing (S-G smoothing), multiplicative scatter correction (MSC), first derivative (1-Der) and second derivative (2-Der) were compared with the raw spectra by using a statistical software package (Unscrambler 9.8), and the regression coefficient (RC) method was used to choose the characteristic wavenumber. Multiple linear regression (MLR) was applied as NIR modeling method. According to the predicted yeast infection level using NIR model, the chemical kinetic models of spectral data and storage time at room temperature with zero-order and first-order reaction were established by using a statistical software package (SPSS 18). The shelf life could be predicted based on the chemical kinetic model. The results showed that the characteristic wave numbers of 10 300, 8 330, 6 900, 5 666, 5 150 and 4 060 cm-1 in the whole near-infrared range with MSC technique could be chosen to model the quality deterioration of fresh jujube at room temperature. The NIR model that produced the best prediction had the form of B=320.027-233.920x₁-206.663x₂-61.584x₃-14.847x₄-2.680x₅-9.131x₆, where B is yeast value (lg/cfu·g-1), x₁～x₆ are absorbance value of characteristic wavenumber. The correlation coefficient of calibration (R_c) was 0.950, the root mean square error of calibration (RMSEC) was 2.560, the correlation coefficient of prediction (R_p) was 0.863, and the root mean square error of prediction (RMSEP) was 2.447.The zero-order reaction kinetic model performed better than the first-order model. The zero-order reaction kinetic model of yeast growth with storage time was predicted by B_t=171.395-124.445x₁-109.945x₂-32.763x₃-7.899x₄-1.426x₅-4.857x₆+0.045t with a correlation coefficient of 0.996. Based on the linear correlation between the NIR measurement and storage time, the shelf life of fresh jujube at room temperature was predicted to be 8 days for the yeast infection level less than 10 cfu·g-1. The study showed that the NIR when combed with kinetic models could be used as a non-destructive, rapid method to detect the yeast growth in fresh jujube, and to predict the shelf life and ensure the quality and safety of fresh jujube.