Classification of alfalfa hay based on infrared spectroscopy

Abstract

Alfalfa hay plays a decisive role in the quality and safety of livestock products. Chemical analytical methods for alfalfa hays are laborious, time-consuming, and costly. Therefore, suitable methods are required for rapid and accurate detection of alfalfa hay. This study evaluated the feasibility of infrared spectroscopy (IR) in identifying different alfalfa hays. 105 alfalfa hay samples under three different drying methods were analysed. Results indicated that the full spectra model constructed through standard normal variable transformation (SNV), first-derivative (FD), and second-derivative (SD) preprocessing by BP and SVM had the best performance. The accuracies were all up to 100%. Under the same preprocessing method, the accuracy of BP neural networks was better than that of support vector machine models in most cases. The characteristic wavelength-based SNV-SD-SPA by BP exhibited better performance than the other pretreatment methods, such as: SNV-SPA, SNV-FD-SPA, and SNV-GA, etc. The classification accuracy of moldy-dried alfalfa, sun-dried alfalfa, and shade-dried alfalfa in the training set were 100%, 100%, and 99.5%, respectively, and the accuracy of the prediction set reached 100%, 97.6%, and 97.4%, respectively. Thus, a better theoretical basis was obtained for the grading and online monitoring of alfalfa hay.