Rapid identification of horse oil adulteration based on deep learning infrared spectroscopy detection method

As a natural oil, horse oil has unique biological activity ingredients and therapeutic characteristics, which has important application value and market potential in healthcare, food, skin care and other fields. However, fraud is rampant in the horse oil market, and traditional methods such as chemical analysis and physical property detection are time-consuming, costly, and have low accuracy in detecting adulteration. Excessive adulteration may cause health risks, skin problems, and economic losses. Therefore, it is urgent to establish a rapid method for identifying adulteration in horse oil. Infrared spectroscopy exhibits substantial potential within detection applications, attributable to its fast analysis speed, non-destructive, and easy operation. This study collected four types of samples: horse oil, butter, sheep oil, and lard, and mixed them in different proportions (5%, 10%, 20%, 30%, 40%, 50%). The infrared spectral data were enhanced by Gaussian white noise and preprocessed by Standard normal variable transformation and detrending (SNV-DT), and 591 × 3601 infrared spectral data were obtained for each adulteration ratio. In terms of model selection, by comparing CNN, RNN, Transformer, and ResNet, which are commonly used in foods, cosmetics and other fields, it is found that the fine-tuning ResNet can achieve the best results in identifying adulterated horse oil applications. For the first time, this study proposed a method for rapid detection of horse oil adulteration by combining infrared spectroscopy and deep learning, which reflected the significance of combining deep learning and infrared spectroscopy in the field of adulteration, and laid a foundation for qualitative detection in this field.