Rapid detection of moisture content and shrinkage ratio of dried carrot slices by using a multispectral imaging system

Carrot has high nutritional value and health-promoting effects and is popular among consumers. Real-time quality detection of dried carrot slices allows producers to adjust the process parameters of the drying device in time, thereby ensuring the final product quality and realizing energy conservation. Traditional methods of detecting moisture content and shrinkage ratio usually require a long measurement time, which is difficult to meet the needs of practical application. This investigated a rapid method based on a novel multispectral imaging system for acquiring multispectral images of samples in 25 wavebands over the spectral region between 675 and 975 nm at one time to detect moisture content and shrinkage ratio of dried carrot slices. The multispectral images of 600 carrot slice samples, which were dried at different times, were acquired using the multispectral imaging system. After extracting the spectral and GLCM features of the samples, prediction models were developed based on partial-least squares regression (PLSR) and least squares-support vector machines (LS-SVM) by using different feature combinations. Compared with PLSR models, LS-SVM models achieved better detection accuracy for moisture content and shrinkage ratio. The LS-SVM model obtained the following best results: coefficient of determination in prediction (Rp) = 0.942, root mean square error of prediction (RMSEP) = 0.0808%, and residual predictive deviation (RPD) = 2.636 for shrinkage ratio as well as Rp = 0.953, RMSEP = 0.0902%, and RPD = 3.271 for moisture content under static condition (without movement). The detection accuracy decreased with increasing movement speed of the test sample. When the movement speed of the sample was lower than 30 mm/s, the moisture content detected achieved satisfactory accuracy, with Rp = 0.941, RMSEP = 0.0981%, and RPD = 3.001. The novel multispectral imaging system shows potential for real-time detection of moisture and shrinkage of products during drying.