Abstract 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; R2T = 0.88; RMSET = 0.43%) and firmness (RPDT = 2.54; R2T = 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.

中文翻译：

---

使用高光谱成像和多变量统计对甜樱桃的硬度和总可溶性固体进行不确定性评估

摘要 樱桃果实质量参数的量化对于在整个供应链中保持高品质产品至关重要，因为它会影响消费者对产品的信心。高光谱成像作为一种非破坏性的快速分析工具具有很高的潜力，可用于估计不同食品中的各种质量参数。该研究的目的是研究高光谱成像对新鲜樱桃果实质量（总可溶性固体浓度、TSS 和果肉硬度、FF）评估的潜力。偏最小二乘回归（PLSR）和高斯过程回归（GPR）用于评估预测性能和预测不确定性。测试数据集结果突出显示 GPR 可用于预测 TSS（RPDT = 3.04；R2T = 0.88；RMSET = 0.43%）和硬度（RPDT = 2.54；R2T = 0.60；RMSE = 0。38 N) 樱桃果实的高精度。此外，探地雷达模型显示出较低的不确定性，预测区间覆盖概率 (PICP) 为 0.90–0.97。总体而言，高光谱成像与使用 GPR 的多变量数据分析相结合，可用作估算樱桃果实质量参数的强大而可靠的工具。