Near infrared (NIR) diffuse reflectance was used to predict the contents of taste-related compounds of tomato. Models were obtained for several varietal types including processing tomato, cherry and cocktail tomato, mid-sized tomato and tomato landraces, with a wide range of varieties. Good performance was obtained for the prediction of soluble solids, sugars and acids, considering a non-destructive methodology applied to fruits with different internal structure. Specific models averaged RMSEP (%mean) values lower than 6.1% for SSC, 13.3% for fructose, 14.1% for glucose, 12.7% for citric acid, 13.8% for malic acid and 21.9% for glutamic acid. The performance was dependent on varietal type. General models with a higher number of samples and variation did not improve the performance of specific models. The models obtained, either specific or general, couldn't be extrapolated to external assays and an internal calibration would be required for each assay in order to provide a reliable performance.Ibáñez, G., Cebolla-Cornejo, J., Martí, R., Roselló, S. and Valcárcel, M., 2019. Non-destructive determination of taste-related compounds in tomato using NIR spectra. Journal of Food Engineering, 263, pp.237-242For more detailed data contact the authorsAckowledgementsThis research was performed despite the lack of direct public funding for its development and thanks to the enthusiasm of the authors.The authors thank Dr. Lahoz and Dr. Campillo for providing processing tomato samples and Dr. Moreno for providing samples from tomato landraces. G. Ibañez thanks Universitat Jaume I for funding his pre-doctoral grant (PREDOC/2015/45).Link to publication:https://doi.org/10.1016/j.jfoodeng.2019.07.004Link to repository:http://hdl.handle.net/10234/183798

Near infrared (NIR) diffuse reflectance was used to predict the contents of taste-related compounds of tomato. Models were obtained for several varietal types including processing tomato, cherry and cocktail tomato, mid-sized tomato and tomato landraces, with a wide range of varieties. Good performance was obtained for the prediction of soluble solids, sugars and acids, considering a non-destructive methodology applied to fruits with different internal structure. Specific models averaged RMSEP (%mean) values lower than 6.1% for SSC, 13.3% for fructose, 14.1% for glucose, 12.7% for citric acid, 13.8% for malic acid and 21.9% for glutamic acid. The performance was dependent on varietal type. General models with a higher number of samples and variation did not improve the performance of specific models. The models obtained, either specific or general, couldn't be extrapolated to external assays and an internal calibration would be required for each assay in order to provide a reliable performance.Ibáñez, G., Cebolla-Cornejo, J., Martí, R., Roselló, S. and Valcárcel, M., 2019. Non-destructive determination of taste-related compounds in tomato using NIR spectra. Journal of Food Engineering, 263, pp.237-242For more detailed data contact the authorsAckowledgementsThis research was performed despite the lack of direct public funding for its development and thanks to the enthusiasm of the authors.The authors thank Dr. Lahoz and Dr. Campillo for providing processing tomato samples and Dr. Moreno for providing samples from tomato landraces. G. Ibañez thanks Universitat Jaume I for funding his pre-doctoral grant (PREDOC/2015/45).Link to publication:https://doi.org/10.1016/j.jfoodeng.2019.07.004Link to repository:http://hdl.handle.net/10234/183798

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 R2 values for prediction higher than 0.84 for main components (SSC, 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.Ibáñez, G., Valcárcel, M., Cebolla‐Cornejo, J., & Roselló, S. (2019). FT‐MIR determination of taste‐related compounds in tomato: a high throughput phenotyping analysis for selection programs. Journal of the Science of Food and Agriculture, 99(11), 5140-5148.   environments. Food chemistry, 239, pp.148-156. Link to publication:https://doi.org/10.1002/jsfa.9760Link to repository:http://hdl.handle.net/10234/183409 For more detailed data contact the authors ACKNOWLEDGEMENTSThis research was performed despite the lack of direct public funding for its development and thanks to the enthusiasm of the authors. The authors thank Dr Lahoz and Dr Campillo for providing samples of processing tomato. G. Ibañez thanks Universitat Jaume I for funding his pre-doctoral grant (PREDOC/2015/45).

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 R2 values for prediction higher than 0.84 for main components (SSC, 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.Ibáñez, G., Valcárcel, M., Cebolla‐Cornejo, J., & Roselló, S. (2019). FT‐MIR determination of taste‐related compounds in tomato: a high throughput phenotyping analysis for selection programs. Journal of the Science of Food and Agriculture, 99(11), 5140-5148.   environments. Food chemistry, 239, pp.148-156. Link to publication:https://doi.org/10.1002/jsfa.9760Link to repository:http://hdl.handle.net/10234/183409 For more detailed data contact the authors ACKNOWLEDGEMENTSThis research was performed despite the lack of direct public funding for its development and thanks to the enthusiasm of the authors. The authors thank Dr Lahoz and Dr Campillo for providing samples of processing tomato. G. Ibañez thanks Universitat Jaume I for funding his pre-doctoral grant (PREDOC/2015/45).

AcknowledgementsThis research was partially funded by the Spanish national government (INIA, RTA2011-00062), an Spanish regional government(Gobierno de Extremadura, GRU-10130) and the European Union (FEDER funds).Link to publication:https://doi.org/10.1016/j.foodchem.2018.01.098Link to repository:http://hdl.handle.net/10234/175130

AcknowledgementsThis research was partially funded by the Spanish national government (INIA, RTA2011-00062), an Spanish regional government(Gobierno de Extremadura, GRU-10130) and the European Union (FEDER funds).Link to publication:https://doi.org/10.1016/j.foodchem.2018.01.098Link to repository:http://hdl.handle.net/10234/175130