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• W2941862275 abstract "Abstract BACKGROUND Tomato taste is defined by the accumulation of sugars and organic acids. Individual analyses of these compounds using high‐performance liquid chromatography (HPLC) or capillary zone electrophoresis (CZE) are expensive, time‐consuming and are not feasible for large number of samples, justifying the interest of spectroscopic methods such as Fourier‐transform mid‐infrared (FT‐MIR). This work analyzed the performance of FT‐MIR models to determine the accumulation of sugars and acids, considering the efficiency of models obtained with different ranges of variation. RESULTS FT‐MIR spectra (five‐bounce attenuated total reflectance, ATR) were used to obtain partial least squares (PLS) models to predict sugar and acid contents in specific sample sets representing different varietal types. A general model was also developed, obtaining R 2 values for prediction higher than 0.84 for main components (soluble solids content, fructose, glucose, and citric acid). Root mean squared error of prediction (RMSEP) for these components were lower than 15% of the mean contents and lower than 6% of the highest contents. Even more, the model sensitivity and specificity for those variables with a 10% selection pressure was 100%. That means that all samples with the 10% highest content were correctly identified. The model was applied to an external assay and it exhibited, for main components, high sensitivities (> 70%) and specificities (> 96%). RMSEP values for main compounds were lower than 21% and 13% of the mean and maximum content respectively. CONCLUSION The models obtained confirm the effectiveness of FT‐MIR models to select samples with high contents of taste‐related compounds, even when the calibration has not been performed within the same assay. © 2019 Society of Chemical Industry" @default.
• W2941862275 created "2019-05-03" @default.
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• W2941862275 date "2019-05-24" @default.
• W2941862275 modified "2023-09-29" @default.
• W2941862275 title "FT‐MIR determination of taste‐related compounds in tomato: a high throughput phenotyping analysis for selection programs" @default.
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• W2941862275 doi "https://doi.org/10.1002/jsfa.9760" @default.
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