Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data

Abstract

Anthocyanins are widely used in the food industry as an additive, improving antioxidant capacity and strengthening the human immune system. However, rapid and nondestructive detection methods are lacking. This study aimed to develop a rapid and nondestructive method to detect anthocyanin content in fresh purple maize leaves using hyperspectral reflectance. Sensitivity bands were screened by analyzing the correlation between the spectrum and anthocyanin, chlorophyll, and moisture content in maize leaves with models constructed. Through a combination of the sensitivity bands of the three components, the interference of chlorophyll and moisture on the spectral detection of anthocyanin in fresh leaves was analyzed. The results showed that the anthocyanin sensitivity band was approximately 550 nm. The determination coefficient and root mean square error of the optimal hyperspectral model were 0.766 and 4.215 mg/g, respectively. After excluding chlorophyll and moisture interference, the anthocyanin content detection accuracy was improved by only 2% compared to that of the original. These results indicate that hyperspectral technology can be used to nondestructively detect anthocyanin content in fresh purple maize leaves with good accuracy. Chlorophyll and moisture in the leaves did not significantly influence anthocyanin content.