Fast and massive characterization of quality attributes in tomatoes is a necessary step toward its improvement; for sensory attributes this process is time-consuming and very expensive, which causes its absence in routine phenotpying. We aimed to assess the feasibility of near infrared (NIR) spectroscopy as a fast and economical tool to predict both the chemical and sensory properties of tomatoes. We built partial least squares models from spectra recorded from tomato puree and juice in 53 genetically diverse varieties grown in two environments. Samples were divided in calibration (210 samples for chemical traits, 45 samples for sensory traits) and validation sets (60 and 10, respectively) using the Kennard Stone algorithm. Models from puree spectra gave validation r² values higher than 0.97 for fructose, glucose, soluble solids content, and dry matter (relative standard error of prediction, RSEP% ranged 3.5–5.8), while r² values for sensory properties were lower (ranging 0.702–0.917 for taste-related traits (RSEP%: 9.1–20.0), and 0.009–0.849 for texture related traits (RSEP%: 3.6–72.1)). For sensory traits such as explosiveness, juiciness, sweetness, acidity, taste intensity, aroma intensity, and mealiness, NIR spectroscopy is potentially useful for scanning large collections of samples to identify likely candidates to select for tomato quality.

对番茄的质量属性进行快速和大规模的表征是改进番茄的必要步骤；对于感官属性，这个过程既耗时又非常昂贵，这导致它在常规表型分析中缺失。我们旨在评估近红外 (NIR) 光谱作为一种快速且经济的工具来预测西红柿的化学和感官特性的可行性。我们根据在两种环境中生长的 53 个遗传多样性品种的番茄泥和番茄汁记录的光谱建立了偏最小二乘模型。使用 Kennard Stone 算法将样本分为校准（210 个化学性状样本，45 个感官性状样本）和验证集（分别为 60 和 10 个）。来自果泥光谱的模型给出了验证 r ²果糖、葡萄糖、可溶性固形物含量和干物质的值高于 0.97（预测的相对标准误差，RSEP% 范围为 3.5-5.8），而感官特性的r ²值较低（味觉相关性状的范围为 0.702-0.917 （RSEP%：9.1-20.0），质地相关性状为 0.009-0.849（RSEP%：3.6-72.1））。对于诸如爆炸性、多汁性、甜度、酸度、味道强度、香气强度和粉度等感官特征，NIR 光谱对于扫描大量样品以识别可能的番茄品质候选物可能很有用。