采用可见/近红外光谱技术结合化学计量学方法对油茶籽油三元体系掺假进行定量检测研究。 将菜籽油和花生油按不同比例掺入纯油茶籽油中, 获得掺假样本。 采集纯油茶籽油及掺假样本在350~1 800 nm范围内的可见/近红外光谱数据, 随机分为校正集和预测集, 并从不同建模波段、 预处理方法及建模方法角度对掺假预测模型进行优化。 研究结果表明, 菜籽油、 花生油和总掺伪量的最优建模波段及预处理方法分别为750~1 770, 900~1 770 , 870~1 770 nm和多元散射校正(MSC)、 标准归一化处理(SNV)和二阶微分, 而最优的建模方法均为最小二乘支持向量机(LSSVM)。 对于最优掺假模型, 菜籽油、 花生油和总掺伪量的预测集相关系数(Rp)和预测均方根误差(RMSEP)分别为0.963, 0.982, 0.993和2.1%, 1.5%, 1.8%。 由此可见, 可见/近红外光谱技术结合化学计量学方法可以用于油茶籽油的三元体系掺假定量检测。

Visible/near infrared spectroscopy combined with chemometrics methods was used to detect ternary system adulteration in camellia oil quantificationally. In order to get adulterated samples, rapeseed oil and peanut oil were added to pure camellia oil in different proportion. Visible/near infrared spectroscopy data of pure and adulterated camellia oil samples were acquired in the wavelength range of 350~1800nm, and samples were randomly divided into calibration set and prediction set. The adulteration models were optimized by comparing different wavelength ranges, pretreatment methods and calibration methods The results show that the optimal modeling wavelength ranges and pretreatment methods for the prediction models of rapeseed oil, peanut oil and total adulteration amount are 750~1 770, 900~1 770, 870~1 770 nm and Multiple scattering correction (MSC), Standard normal variate (SNV) and second order differentia, and the best modeling method is Least square support vector machine (LSSVM). The correlation coefficient (RP) in prediction set and the root mean square error predictions(RMSEPs) of optimal adulteration models for rapeseed oil, peanut oil and total adulteration are 0.963, 0.982, 0.993 and 2.1%, 1.5%, 1.8%, respectively. Thus it can be seen that visible /near infrared spectroscopy combined with chemometrics methods can be used for quantitative ternary system adulteration detection in camellia oil.