At-site monitoring of multiple quality traits of processing tomato fruits using portable infrared technology

The application of high-throughput phenotyping techniques at various stages of the agricultural process can foster crop improvements when combined with genetic strategies. In this study, a portable infrared (IR) spectrometer was used for in-field determination of multiple important quality traits in processing tomato varieties. Tomato fruits, harvested from 2021 to 2023 seasons, were cut in half; one half was used to determine brix, pH and predicted paste Bostwick (PPB) of raw juice, while the other half was used to prepare hot-break juice (cooked juice) for determining brix, juice Bostwick (JB), kinematic viscosity of the supernatant from centrifuged tomato paste (KVost) and PPB of cooked samples. Duplicate spectra of each tomato juice sample were acquired in the field using a portable IR system operating in attenuated total reflectance (ATR) mode. Regression models were developed using Partial least squares regression (PLSR) and orthogonal PLSR (OPLSR). OPLSR models exhibited better calibration performance, although they showed comparable prediction performance to PLSR models when validated with an independent data set. The correlation coefficient for prediction in the PLSR models exceeded 0.77, with a low standard error of prediction (RMSEP = 0.04 to 0.70). Furthermore, the generated regression algorithms were integrated with the portable IR system for predicting the desired variables at-site. Analyzing the samples soon after harvesting from the fields and generating the calibration models at-site made the predictive models reliable and robust. The implementation of portable and high-throughput monitoring of phenotypic traits at-site allows for the simultaneous determination of multiple quality traits, facilitating cost-effective and rapid decision-making with minimal use of consumables compared to traditional analytical techniques that require laboratory facilities, skilled labor, and are expensive and time-consuming. • Portable FTIR rapidly predicts the quality traits in processing tomato varieties. • A diverse sample set (n > 1800) captured expected variations in composition. • Reference and spectral data were collected at-site. • OPLSR showed comparable prediction performance to PLSR models. • Portable MIR was deployed in-field for real-time monitoring of tomato quality.