Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics

Quantifying cherry fruit quality parameters is essential to maintaining high quality produce throughout the supply chain as it influences consumer confidence in the product. Hyperspectral imaging offers high potential as a non-destructive and fast analytical tool for estimating various quality parameters in different food products. The objective of the study is to investigate the potential of hyperspectral imaging for quality (total soluble solids concentration, TSS and flesh firmness, FF) assessment in fresh cherry fruits. Partial least squares regression (PLSR) and Gaussian process regression (GPR) was used to evaluate the prediction performance and predictive uncertainty. Test dataset results highlight that GPR can be used to predict TSS (RPDT = 3.04; R-T(2) = 0.88; RMSET = 0.43%) and firmness (RPDT = 2.54; R-T(2) = 0.60; RMSE = 0.38 N) of cherry fruits with high accuracy. In addition, GPR models showed lower uncertainty with a prediction interval coverage probability (PICP) of 0.90-0.97. Overall, hyperspectral imaging combined with multivariate data analysis using GPR can be used as a robust and reliable tool to estimate cherry fruit quality parameters.

Automated summary

This study explores the potential of hyperspectral imaging for quality assessment in fresh cherry fruits, showing that Gaussian process regression can accurately predict important quality parameters of cherry fruits. Additionally, the predictive models exhibit lower uncertainty, indicating high reliability.