Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord 88186-34141, Iran; Bakhtar Higher Education Institution, Ilam 69313-83638, Iran.
Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord 88186-34141, Iran.
Food Chem. 2023 Oct 30;424:136411. doi: 10.1016/j.foodchem.2023.136411. Epub 2023 May 19.
The aim of this study is to evaluate a previousely developed photoacoustic spectroscopy system with light sources of visible to short-wave near infrared (Vis-SWNIR, 395-940 nm) for detection of adulterations in cow's milk including formalin, urea, hydrogen peroxide, starch, sodium hypochlorite, and detergent powder. The results of principal component analysis (PCA) showed a very good visual differentiation of different adulterations. The artificial neural networks (ANN) showed the highest classification accuracy (97.6 %) in detection of adulteration type and adulteration level (nearly 100 %). It can be generally concluded that the Vis-SWNIR photoacoustic spectroscopy system is a reliable and potent instrument for detecting various types of milk adulterations. Further studies are suggested with including cow's milk of different sources with probable variations in composition to generalize the findings of the present study. With the extension of the light sources to the range of long-wave NIR, the system can be applied as a diagnostic tool for quality evaluation of other liquid foods.
本研究旨在评估先前开发的一种光声光谱系统，该系统采用可见到短波近红外（Vis-SWNIR，395-940nm）光源，用于检测牛奶中的掺假物，包括福尔马林、尿素、过氧化氢、淀粉、次氯酸钠和洗衣粉。主成分分析（PCA）的结果表明，不同掺假物的视觉区分非常好。人工神经网络（ANN）在检测掺假类型和掺假水平（接近 100%）方面表现出最高的分类准确性（97.6%）。总的来说，可以得出结论，Vis-SWNIR 光声光谱系统是一种可靠且有效的工具，可用于检测各种类型的牛奶掺假。建议进一步研究，包括不同来源的牛奶，可能存在成分差异，以推广本研究的结果。随着光源扩展到长波近红外范围，该系统可作为其他液体食品质量评估的诊断工具。
