Maturity stages affect nutritional quality and storability of tomato cultivars

A comprehensive study from the micro- to the nanometric scale: Evaluation of chilling injury in tomato fruit (Solanum lycopersicum)

Tomato fruit is susceptible to chilling injury (CI) during its postharvest handling at low temperature. The symptoms caused by this physiological disorder have been commonly evaluated by visual inspection at a macro-observation scale on fruit surface; however, the structure at deeper scales is also affected by CI. This work aimed to propose a descriptive model of the CI development in tomato tissue under the micro-scale, micro-nano-scale and nano-scale approaches using fractal analysis. For that, quality and fractal parameters were determined. In this sense, light microscopy, Environmental Scanning Electron Microscopy (ESEM) and Atomic Force Microscopy (AFM) were applied to analyse micro-, micro-nano- and nano-scales, respectively. Results showed that the morphology of tomato tissue at the micro-scale level was properly described by the multifractal behaviour. Also, generalised fractal dimension (Dq=0) and texture fractal dimension (FD) of CI-damaged pericarp and cuticle were higher (1.659, 1.601 and 1.746, respectively) in comparison to non-chilled samples (1.606, 1.578 and 1.644, respectively); however, FD was unsuitable to detect morphological changes at the nano-scale. On the other hand, lacunarity represented an appropriate fractal parameter to detect CI symptoms at the nano-scale due to differences observed between damaged and regular ripe tissue (0.044 and 0.025, respectively). The proposed multi-scale approach could improve the understanding of CI as a complex disorder to the development of novel techniques to avoid this postharvest issue at different observation scales.

Artificial intelligence-based prediction of lycopene content in raw tomatoes using physicochemical attributes

INTRODUCTION

Lycopene consumption reduces risk and incidence of cancer and cardiovascular diseases. Tomatoes are a rich source of phytochemical compounds including lycopene as a major constituent. Lycopene estimation using high-performance liquid chromatography is time-consuming and expensive.

OBJECTIVE

To develop artificial intelligence models for prediction of lycopene in raw tomatoes using 14 different physicochemical parameters including salinity, total dissolved solids (TDS), electrical conductivity (EC), firmness, pH, total soluble solids (TSS), titratable acidity (TA), colour values on Hunter scale (L, a, b), total phenolic content (TPC), total flavonoid content (TFC) and antioxidant activity (AOA).

MATERIAL AND METHODS

The post-harvest data acquisition was collected through investigation for more than 100 raw tomatoes stored for 15 days. Linear multivariate regression (LMVR), principal component regression (PCR) and partial least squares regression (PLSR) models were developed by splitting data set into train and test datasets. The training of models was performed using 10-fold cross validation (CV).

RESULTS

Principal component analysis showed strong positive association between lycopene, colour value 'a', TPC, TFC and AOA. The R2 (CV), root mean square error (RMSE) (CV) and RMSE (Test) for best LMVR model was observed to be at 0.70, 8.48 and 9.69 respectively. The PCR model revealed R2 (CV) at 0.59, RMSE (CV) at 8.91 and RMSE (Test) at 10.17 while PLSR model revealed R2 (CV) at 0.60, RMSE (CV) at 9.10 and RMSE (Test) at 10.11.

CONCLUSION

Results of the present study show that epidemiological studies suggest fully ripened tomatoes are most beneficial for consumption to ensure recommended daily intake of lycopene content.

Functional and Nutraceutical Compounds of Tomatoes as Affected by Agronomic Practices, Postharvest Management, and Processing Methods: A Mini Review

Tomatoes and their by-products are indisputable sources of substances with antioxidants properties. Several factors limit the production and influence the nutritional and antioxidant quality of tomato fruit. However, consumers can benefit from the effects of environmental factors, such as water and hydric stress, UV radiation, agronomic practices, among others, which lead to changes in the content of secondary metabolites in tomatoes. Molecules as phenolic compounds, carotenoids, and biogenic amines are often formed in response to environmental adversities. In this way, the consumption of tomato fruits or their by-products with higher levels of antioxidants may be important adjuvants in the prevention or reduction of diseases. In this mini-review, we will present how pre- and postharvest conditions may influence the content of some bioactive compounds in tomatoes. Furthermore, we will present how some heat processing methods may change the antioxidant content, as well as, the functional and nutritional properties of the final product.

Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Nondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool. Regression coefficients (R) in estimated acidity, tissue firmness, and starch content properties were R=0.930±0.014, R=0.851±0.014, and R=0.974±0.006, respectively, using only the three most effective wavelengths from the acquired spectra.

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Automatic regression infrared imaging system for nondestructive estimation of physicochemical properties in apple fruit.

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Tissue firmness (kgf/cm), acidity (pH) and starch content (%) fruit properties estimated.

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Hardware used includes: spectrophotometer, photo-detector, light source, and optical fiber.

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System comprises both variable wavelength Vis-NIR ranges and fixed optimal NIR wavelength windows.

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Results: regression plots, regression (R) and determination (R²) coefficient boxplots, measured vs estimated value graphs.

Transcriptome Analysis of Pre-Storage 1-MCP and High CO2-Treated 'Madoka' Peach Fruit Explains the Reduction in Chilling Injury and Improvement of Storage Period by Delaying Ripening

Cold storage of peach fruit at low temperatures may induce chilling injury (CI). Pre-storage 1-MCP and high CO₂ treatments were reported among the methods to ameliorate CI and reduce softening of peach fruit. However, molecular data indicating the changes associated with pre-storage 1-MCP and high CO₂ treatments during cold storage of peach fruit are insufficient. In this study, a comparative analysis of the difference in gene expression and physico-chemical properties of fruit at commercial harvest vs. stored fruit for 12 days at 0 °C (cold-stored (CS), pre-storage 1-MCP+CS, and pre-storage high CO₂+CS) were used to evaluate the variation among treatments. Several genes were differentially expressed in 1-MCP+CS- and CO₂+CS-treated fruits as compared to CS. Moreover, the physico-chemical and sensory data indicated that 1-MCP+CS and CO₂+CS suppressed CI and delayed ripening than the CS, which could lead to a longer storage period. We also identified the list of genes that were expressed commonly and exclusively in the fruit treated by 1-MCP+CS and CO₂+CS and compared them to the fruit quality parameters. An attempt was also made to identify and categorize genes related to softening, physiological changes, and other ripening-related changes. Furthermore, the transcript levels of 12 selected representative genes from the differentially expressed genes (DEGs) in the transcriptome analysis were confirmed via quantitative real-time PCR (qRT-PCR). These results add information on the molecular mechanisms of the pre-storage treatments during cold storage of peach fruit. Understanding the genetic response of susceptible cultivars such as ‘Madoka’ to CI-reducing pre-storage treatments would help breeders release CI-resistant cultivars and could help postharvest technologists to develop more CI-reducing technologies.