Data fusion based on near-infrared spectroscopy and hyperspectral imaging technology for rapid adulteration detection of Ganoderma lucidum spore powder

Abstract

As a function food resource with significant medicinal and economic value, Ganoderma lucidum spore powder (GLSP) is often adulterated with dyed starch by unscrupulous merchants in pursuit of higher profits. In this study, near-infrared spectroscopy (NIRS) and hyperspectral imaging techniques (HSI) were selected, and data fusion strategies were introduced to quickly detect the adulteration of GLSP. To enhance the model performance, 49 pretreatments and their combinations, as well as 5 feature variables selection techniques, were applied. The results showed that if only a single spectral technique was considered, NIRS was more advantageous than HSI in the adulteration identification and adulteration level prediction of GLSP. Mid-level fusion (MLF) strategy was more suitable for adulteration identification if data fusion strategy was introduced, with 100% accuracy, precision, recall and F1 score at random frog (RF) level. Therefore, NIRS and HSI data fusion can be used for quality evaluation of GLSP, which provides a new idea and potential method for the evaluation and detection of functional foods.