Nondestructive testing of the quality of different grades of creamy strawberries based on Laida algorithm

Abstract

Rapid nondestructive testing of fruit quality is one of the hotspots in food industry research. This paper studied the quality detection of different grades of cream strawberries. Preprocessing methods such as detrending, moving average smoothing (MA), and standard normal variables (SNV) were used to eliminate spectral data errors. The competitive adaptive reweighted sampling algorithm (CARS), successive projection algorithm (SPA), the combination of the above two algorithms (CARS+SPA), and the self-programming algorithm based on the Laida criterion (collectively referred to as the Laida algorithm) were used to reduce the dimension of data, and a partial least squares regression prediction model was established. The results show that when the Laida algorithm predicted the soluble solid content (SSC), total acid (TA), and vitamin C (VC) content of strawberries, the prediction set correlation coefficients were 0.919, 0.931, and 0.907, respectively, the values of RPD were 3.15, 4.00, and 3.44, respectively, the relative errors of the predicted values and the measured values were 3.93%, 5.74%, and 3.69%, respectively. Practical applications This shows that it is feasible to use the Laida algorithm to extract the characteristic wavelengths and establish a prediction model for the SSC, TA, and VC content in cream strawberries. This study can provide a theoretical basis for the development of a rapid detection instrument for the quality of creamy strawberries.