Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90–100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.

中文翻译：

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使用光谱技术结合化学计量学对杏仁粉中的杂质进行非目标检测。

结合有针对性的技术和化学计量学来分析食品真伪的方法只能促进对预定义或已知掺假物的检测，而使用此类方法无法检测到未知掺假物。因此，目前非常需要对食品中的掺假物进行非目标检测。在这项研究中，将FT-IR和FT-NIR光谱技术与非目标化学计量学方法结合使用，例如一类偏最小二乘（OCPLS）和数据驱动的类比性软独立建模（DD-SIMCA） ，用于检测掺有杏仁和花生粉的杏仁粉中的掺假品。收集来自两个不同品种（每个50个）的100个纯杏仁粉样品的反射光谱，以基于每种光谱技术建立校正模型。然后评估每个模型的四个独立套，分别掺入不同浓度的杏粉和花生粉的两种杏仁粉样品。使用这两种技术的分类都是高度敏感的，使用两种光谱技术的OCPLS方法在不同种类的样品中均可产生90-100％的准确度，而与FT-IR结合使用时，DD-SIMCA方法可达到100％的最高准确度验证集。此外，DD-SIMCA与FT-NIR结合使用，对于不同的验证集实现了91％到100％的检测精度，而错误分类的样本属于5％和7％掺假集。这些结果表明，结合一类分类器的光谱技术可以有效地用于杏仁粉中潜在掺假物的高通量筛选。