Abstract Spatially-resolved spectroscopy (SRS) enables better interrogation of tissue properties at different depths, and it thus has the potential for enhancing quality assessment of horticultural products like tomato, which are heterogeneous in structure and chemical composition. This research was aimed at assessing quality of tomato fruit by using a newly developed SRS system with 30 detection optic fibers covering the wavelength range of 550–1650 nm and comparing its performance with two conventional single-point (SP) spectroscopic instruments covering the visible and shortwave near-infrared (Vis/SWNIR) (400–1100 nm) and near-infrared (NIR) (900–1300 nm) regions, respectively. Spatially-resolved (SR) spectra and SP interactance spectra were acquired for 600 ‘Sun Bright’ tomato fruit. Partial least squares (PLS) models for individual SR spectra and their combinations and for SP Vis/SWNIR and NIR spectra were developed for prediction of soluble solids content (SSC) and pH. Results showed that SSC and pH predictions by SRS varied depending on the light source-detector distance, with the correlation coefficient of prediction (rp) ranging 0.608–0.791 and 0.688–0.800, respectively. Combinations of two or more SR spectra resulted in better, more consistent SSC and pH predictions. SR predictions of pH (rp = 0.819) were better than for SP Vis/SWNIR (rp = 0.743) and NIR (rp = 0.741) predictions, whereas SR predictions of SSC (rp = 0.800) were comparable to SP NIR predictions (rp = 0.810) but better than SP Vis/SWNIR predictions (rp = 0.729). This research showed that owning to its ability of acquiring spatially-resolved spectral information, the SRS technique has advantages over conventional SP spectroscopy for enhancing quality assessment of tomatoes.

中文翻译：

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通过空间分辨和常规可见/近红外光谱法评估番茄可溶性固形物含量和 pH 值

摘要 空间分辨光谱 (SRS) 能够更好地了解不同深度的组织特性，因此它具有增强对番茄等结构和化学成分异质的园艺产品质量评估的潜力。本研究旨在通过使用新开发的 SRS 系统评估番茄果实的质量，该系统具有 30 根检测光纤，覆盖 550-1650 nm 的波长范围，并将其性能与两种覆盖可见光和波长范围的传统单点 (SP) 光谱仪器进行比较。分别是短波近红外 (Vis/SWNIR) (400–1100 nm) 和近红外 (NIR) (900–1300 nm) 区域。获得了 600 个“Sun Bright”番茄果实的空间分辨 (SR) 光谱和 SP 相互作用光谱。开发了用于单个 SR 光谱及其组合以及 SP Vis/SWNIR 和 NIR 光谱的偏最小二乘 (PLS) 模型，用于预测可溶性固体含量 (SSC) 和 pH。结果表明，SRS 对 SSC 和 pH 值的预测因光源-探测器距离而异，预测的相关系数 (rp) 分别为 0.608-0.791 和 0.688-0.800。两个或多个 SR 光谱的组合导致更好、更一致的 SSC 和 pH 预测。pH (rp = 0.819) 的 SR 预测优于 SP Vis/SWNIR (rp = 0.743) 和 NIR (rp = 0.741) 预测，而 SSC (rp = 0.800) 的 SR 预测与 SP NIR 预测 (rp = 0.810) 但优于 SP Vis/SWNIR 预测 (rp = 0.729)。