In this paper, a methodology based on characteristic spectral bands of near infrared spectroscopy (1000–2500 nm) and multivariate analysis was proposed to identify camellia oil adulteration with vegetable oils. Sunflower, peanut and corn oils were selected to conduct the test. Pure camellia oil and that adulterated with varying concentrations (1–10% with the gradient of 1%, 10–40% with the gradient of 5%, 40–100% with the gradient of 10%) of each type of the three vegetable oils were prepared, respectively. For each type of adulterated oil, full-spectrum partial least squares partial least squares (PLS) models and synergy interval partial least squares (SI-PLS) models were developed. Parameters of these models were optimized simultaneously by cross-validation. The SI-PLS models were proved to be better than the full-spectrum PLS models. In SI-PLS models, the correlation coe±cients of predition set (Rp) were 0.9992, 0.9998 and 0.9999 for adulteration with sunflower oil, peanut oil and corn oil seperately; the corresponding root mean square errors of prediction set (RMSEP) were 1.23, 0.66 and 0.37. Furthermore, a new generic PLS model was built based on the characteristic spectral regions selected from the intervals of the three SI-PLS models to identify the oil adulterants, regardless of the adultrated oil types. The model achieved with Rp 0.9988 and RMSEP=1.52. These results indicated that the characteristic near infrared spectral regions could determine the level of adulteration in the camellia oil.

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。

利用近红外光谱在不同光程下对山茶油中掺杂大豆油的掺伪量进行定量检测研究,着重分析光程对掺伪量检测精度的影响.将大豆油按一定质量分数掺入山茶油获取实验样本,掺伪质量分数范围为1%～50%.利用QualitySpec型光谱仪采集样本在不同光程(1,2,4,10 mm)下的透射光谱,通过对比不同建模方法、预处理方法及建模波段范围所建立的掺伪量定量预测模型,分析光程对掺伪量检测精度的影响.研究结果表明,光程由1 mm增加到4 mm时,掺伪量定量预测模型性能随着光程的增加而逐渐变好,检测精度逐步提高;光程由 4 mm增加到10 mm时,掺伪量定量预测模型性能变差,检测精度下降,4 mm为较优的光程.在1,2,4和10 mm下所建立的较优掺伪量定量预测模型的预测集决定系数(R2P)和预测均方根误差(RMSEP)分别为0.923,0.977,0.989,0.962和4.58%,2.54%,1.72%,3.20%。

为打击山茶油掺假，保障消费者的合法利益，利用近红外光谱和子窗口重排分析(SPA)对山茶油的复杂掺假(掺入大豆油、菜籽油、花生油及混合油)进行检测。采集85 个纯山茶油和315 个掺假山茶油样本的近红外光谱，利用SPA 变量选择方法对样本光谱的波长变量进行筛选，再由偏最小二乘-线性判别分析(PLS-LDA)建立山茶油掺假检测模型，并与竞争性自适应重加权算法(CARS)和无信息变量消除(UVE)变量选择方法的结果进行比较。研究结果表明，近红外光谱联合SPA 方法可以用于山茶油的复杂掺假检测，预测集样本的分类错误率、灵敏度及特异性分别为0、1和1。SPA 方法优于UVE 方法，与CARS方法相当，是一种有效的变量选择方法，能简化模型并提高模型的预测精度和稳定性。