Modeling of soluble solid content of PE-packaged blueberries based on near-infrared spectroscopy with back propagation neural network and partial least squares (BP-PLS) algorithm

Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near-infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)-packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky-Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR-based SSC prediction models for unpacked blueberries and PE-packaged blueberries were developed using partial least squares (PLS). The prediction model for PE-packaged samples (R-P(2)= 0.876, root mean square error of prediction [RMSEP] = 0.632) had less precision than the model for unpacked samples (R-P(2)= 0.953, RMSEP = 0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP-PLS algorithm based on the residual learning algorithm. The model of BP-PLS (R-P(2)= 0.947, RMSEP = 0.414) was successfully developed to improve the prediction accuracy of SSC for PE-packaged blueberries. The results suggested a promising way of using the BP-PLS method in tandem with NIR for the rapid detection of the SSC of PE-packaged blueberries.

Automated summary

This study developed a rapid method for detecting the properties of packaged blueberries using near-infrared spectroscopy. Various preprocessing and optimization techniques were used to establish prediction models for the soluble solid content. Combining BP neural network and PLS algorithm improved the prediction accuracy for PE-packaged blueberries.