Determination of developmental and ripening stages of whole tomato fruit using portable infrared spectroscopy and Chemometrics

Attenuated total reflection Fourier-transform infrared spectroscopy for the prediction of hormone concentrations in plants

Plant hormones are important in the control of physiological and developmental processes including seed germination, senescence, flowering, stomatal aperture, and ultimately the overall growth and yield of plants. Many currently available methods to quantify such growth regulators quickly and accurately require extensive sample purification using complex analytic techniques. Herein we used ultra-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) to create and validate the prediction of hormone concentrations made using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectral profiles of both freeze-dried ground leaf tissue and extracted xylem sap of Japanese knotweed (Reynoutria japonica) plants grown under different environmental conditions. In addition to these predictions made with partial least squares regression, further analysis of spectral data was performed using chemometric techniques, including principal component analysis, linear discriminant analysis, and support vector machines (SVM). Plants grown in different environments had sufficiently different biochemical profiles, including plant hormonal compounds, to allow successful differentiation by ATR-FTIR spectroscopy coupled with SVM. ATR-FTIR spectral biomarkers highlighted a range of biomolecules responsible for the differing spectral signatures between growth environments, such as triacylglycerol, proteins and amino acids, tannins, pectin, polysaccharides such as starch and cellulose, DNA and RNA. Using partial least squares regression, we show the potential for accurate prediction of plant hormone concentrations from ATR-FTIR spectral profiles, calibrated with hormonal data quantified by UHPLC-HRMS. The application of ATR-FTIR spectroscopy and chemometrics offers accurate prediction of hormone concentrations in plant samples, with advantages over existing approaches.

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition

Introduction

Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation.

Objectives

This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like.

Methods

A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data.

Results

The reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson's correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process.

Conclusion

The proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.

The Visibility of Changes in the Antioxidant Compound Profiles of Strawberry and Raspberry Fruits Subjected to Different Storage Conditions Using ATR-FTIR and Chemometrics

Strawberry cultivars Portola and Enduro, as well as raspberry cultivars Enrosadira and Kwazi, were evaluated for their antioxidant potential after treatment with gaseous ozone and different refrigeration storage conditions. Their antioxidant capacity was investigated with ABTS and DPPH methods, and the chemical composition was determined by measuring the total phenolic (TPC) and flavonoid (TFC) compounds. The classification of different samples of berry puree was influenced significantly by both the cultivars and the refrigeration storage method. Moreover, FTIR spectroscopy coupled with chemometrics was used as an alternative technique to conventional methods to determine the chemical composition of strawberries and raspberries. The chemometric discrimination of samples was achieved using principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-500 cm-1). The fingerprint range between 1500 and 500 cm-1, corresponding to deformation vibrations from polysaccharides, pectin and organic acid content, had a significant impact on the grouping of samples. The results obtained by PCA-LDA scores revealed a clear separation between four classes of samples and demonstrated a high overall classification rate of 97.5% in differentiating between the raspberry and strawberry cultivars.

Organ-specific responses during acclimation of mycorrhizal and non-mycorrhizal tomato plants to a mild water stress reveal differential local and systemic hormonal and nutritional adjustments

MAIN CONCLUSION

Tomato plant acclimation to a mild water stress implied tissue-specific hormonal and nutrient adjustments, being the root one of the main modulators of this response. Phytohormones are key regulators of plant acclimation to water stress. However, it is not yet clear if these hormonal responses follow specific patterns depending on the plant tissue. In this study, we evaluated the organ-specific physiological and hormonal responses to a 14 day-long mild water stress in tomato plants (Solanum lycopersicum cv. Moneymaker) in the presence or absence of the arbuscular mycorrhizal fungus Rhizoglomus irregulare, a frequently used microorganism in agriculture. Several physiological, production, and nutritional parameters were evaluated throughout the experiments. Additionally, endogenous hormone levels in roots, leaves, and fruits at different developmental stages were quantified by ultrahigh-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Water deficit drastically reduced shoot growth, while it did not affect fruit production. In contrast, fruit production was enhanced by mycorrhization regardless of the water treatment. The main tissue affected by water stress was the root system, where huge rearrangements in different nutrients and stress-related and growth hormones took place. Abscisic acid content increased in every tissue and fruit developmental stage, suggesting a systemic response to drought. On the other hand, jasmonate and cytokinin levels were generally reduced upon water stress, although this response was dependent on the tissue and the hormonal form. Finally, mycorrhization improved plant nutritional status content of certain macro and microelements, specially at the roots and ripe fruits, while it affected jasmonate response in the roots. Altogether, our results suggest a complex response to drought that consists in systemic and local combined hormonal and nutrient responses.

Characterising Colour Feature Descriptors for Ficus carica L. Ripeness Classification Based on Artificial Neural Network (ANN)

Excessive feature dimensions impact the effectiveness of machine learning, computationally expensive and the analysis of feature correlations in the engineering area. This paper uses the colour descriptor to get the most optimal feature to improve time consumption and efficiency. This study investigated Ficus carica L. (figs) with three classification stages. The ripening classification of fig was examined using colour features descriptor with two different colour models, RGB and HSV. In addition, the machine learning classification model based on Artificial Neural Network (ANN) that utilised the Feed-Forward Neural Network (FFNN) model to classify the ripeness of fig is considered in this characterisation. Five different numbers of binning were characterised for RGB and HSV. Both colour feature descriptors were compared in terms of accuracy, sensitivity, precision, and time consumption to identify the dimension of the optimal feature. Based on the result, reducing the size of images will improve the time consumption with comparable accuracy. Moreover, the reduction of features dimension cannot be too small or too big due to inequitable enough to differentiate the ripeness stages and lead to a false error state. The optimal features dimension in binning for RGB was 8 (R/G/B) bins with 96.7% accuracy. Meanwhile, 96.7% accuracy for HSV at 15, 5, and 5 (H, S, V) bins as optimal colour features.

Tomato Cultivar Nyagous Fruit Surface Metabolite Changes during Ripening Affect Salmonella Newport

ABSTRACT

Tomatoes are a valuable crop consumed year-round. Ripe fruit is picked for local sale, whereas tomatoes intended for transit may be harvested at late mature green or breaker stages when fruit firmness preserves quality. In this study, we evaluated Solanum lycopersicum cv. BHN602 association with three Salmonella serotypes and S. lycopersicum cv. Nyagous with Salmonella Newport using fruit at two ripeness stages. Counts of Salmonella Javiana and Typhimurium were higher from red ripe fruit surfaces of BHN602, and counts of Salmonella Newport were higher from ripe Nyagous fruit than from mature green fruit (P < 0.05). Aqueous fruit washes containing fruit surface compounds collected from ripe Nyagous fruit supported more Salmonella Newport growth than green fruit washes (P < 0.05). Growth curve analysis showed that between 2 and 6 h, Salmonella Newport grew at a rate of 0.25 log CFU/h in red fruit wash compared with 0.17 log CFU/h in green fruit wash (P < 0.05). The parallel trend in Salmonella interaction between fruit and wash suggested that surface metabolite differences between unripe and ripe fruit affect Salmonella dynamics. Untargeted phytochemical profiling of tomato fruit surface washes with gas chromatography time-of-flight mass spectrometry showed that ripe fruit had threefold-lower amino acid and fourfold-higher sugar (fructose, glucose, and xylose) levels than green fruit. Green fruit had higher levels of lauric, palmitic, margaric, and arachidic acids, whereas red fruit had more capric acid. The phenolics ferulic, chlorogenic, and vanillic acid, as well as tyrosol, also decreased with ripening. Although limitations of this study preclude conclusions on how specific compounds affect Salmonella, our study highlights the complexity of the plant niche for foodborne pathogens and the importance of understanding the metabolite landscape Salmonella encounters on fresh produce. Fruit surface phytochemical profiling generated testable hypotheses for future studies exploring the differential Salmonella interactions with tomato varieties and fruit at various ripeness stages.

HIGHLIGHTS

Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.

Utilizing laser spectrochemical analytical methods for assessing the ripening progress of tomato

To meet market demands and minimize losses, the tomato crop (Solanum Lycopersicum L.) requires a simple, rapid, and cost-effective method to distinguish between different maturity stages with high accuracy. This study aimed at evaluating two spectrochemical analytical techniques, namely laser-induced fluorescence (LIF) and laser-induced breakdown spectroscopy (LIBS), to discriminate three different maturity stages of tomato fruit (‘Green/Breaker’; ‘Turning/Pink’; and ‘Light-red/Red’). The simple linear regression confirmed the obtained LIF results with chlorophyll content (mg/100 g), hue angle (h°), and firmness (kg/cm2) of the different maturity stages (measured by conventional methods). Furthermore, the findings showed that the peak intensities of LIF spectra decreased with the chlorophyll content depletion during ripening. Moreover, the data exposed a reasonably good association between LIF spectra and chlorophyll content with a regression coefficient of 0.85. On the other hand, firmness and skin hue have shown an excellent predictor for the spectra with a high regression coefficient of 0.94. For LIBS spectra of each maturity stage, the ratios of Ca’s ionic-to-atomic spectral lines intensities have followed the same trend as conventionally measured firmness. The results demonstrated that LIF and LIBS are accurate, easy, and fast techniques used to define tomatoes’ different ripening stages. Both methods are useable in situ without any prior laboratory work.

Know your enemy: Application of ATR-FTIR spectroscopy to invasive species control

Extreme weather and globalisation leave our climate vulnerable to invasion by alien species, which have negative impacts on the economy, biodiversity, and ecosystem services. Rapid and accurate identification is key to the control of invasive alien species. However, visually similar species hinder conservation efforts, for example hybrids within the Japanese Knotweed complex.We applied the novel method of ATR-FTIR spectroscopy combined with chemometrics (mathematics applied to chemical data) to historic herbarium samples, taking 1580 spectra in total. Samples included five species from within the interbreeding Japanese Knotweed complex (including three varieties of Japanese Knotweed), six hybrids and five species from the wider Polygonaceae family. Spectral data from herbarium specimens were analysed with several chemometric techniques: support vector machines (SVM) for differentiation between plant types, supported by ploidy levels; principal component analysis loadings and spectral biomarkers to explore differences between the highly invasive Reynoutria japonica var. japonica and its non-invasive counterpart Reynoutria japonica var. compacta; hierarchical cluster analysis (HCA) to investigate the relationship between plants within the Polygonaceae family, of the Fallopia, Reynoutria, Rumex and Fagopyrum genera.ATR-FTIR spectroscopy coupled with SVM successfully differentiated between plant type, leaf surface and geographical location, even in herbarium samples of varying age. Differences between Reynoutria japonica var. japonica and Reynoutria japonica var. compacta included the presence of two polysaccharides, glucomannan and xyloglucan, at higher concentrations in Reynoutria japonica var. japonica than Reynoutria japonica var. compacta. HCA analysis indicated that potential genetic linkages are sometimes masked by environmental factors; an effect that can either be reduced or encouraged by altering the input parameters. Entering the absorbance values for key wavenumbers, previously highlighted by principal component analysis loadings, favours linkages in the resultant HCA dendrogram corresponding to expected genetic relationships, whilst environmental associations are encouraged using the spectral fingerprint region.The ability to distinguish between closely related interbreeding species and hybrids, based on their spectral signature, raises the possibility of using this approach for determining the origin of Japanese knotweed infestations in legal cases where the clonal nature of plants currently makes this difficult and for the targeted control of species and hybrids. These techniques also provide a new method for supporting biogeographical studies.

Regional differences in clonal Japanese knotweed revealed by chemometrics-linked attenuated total reflection Fourier-transform infrared spectroscopy

BACKGROUND

Japanese knotweed (R. japonica var japonica) is one of the world's 100 worst invasive species, causing crop losses, damage to infrastructure, and erosion of ecosystem services. In the UK, this species is an all-female clone, which spreads by vegetative reproduction. Despite this genetic continuity, Japanese knotweed can colonise a wide variety of environmental habitats. However, little is known about the phenotypic plasticity responsible for the ability of Japanese knotweed to invade and thrive in such diverse habitats. We have used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, in which the spectral fingerprint generated allows subtle differences in composition to be clearly visualized, to examine regional differences in clonal Japanese knotweed.

RESULTS

We have shown distinct differences in the spectral fingerprint region (1800-900 cm^- 1) of Japanese knotweed from three different regions in the UK that were sufficient to successfully identify plants from different geographical regions with high accuracy using support vector machine (SVM) chemometrics.

CONCLUSIONS

These differences were not correlated with environmental variations between regions, raising the possibility that epigenetic modifications may contribute to the phenotypic plasticity responsible for the ability of R. japonica to invade and thrive in such diverse habitats.

Non-Destructive Quality Measurement for Three Varieties of Tomato Using VIS/NIR Spectroscopy

The non-destructive visible/near-infrared (VIS/NIR) spectroscopy is a promising technique in determining the quality of agricultural commodities. Therefore, this study aimed to examine the ability of VIS/NIR spectroscopy (550–1100 nm) to distinguish between three different varieties of tomato (i.e., Ekram, Harver and Izmer), as well as to predict the quality parameters of tomato, such as soluble solids content (SSC), titratable acidity (TA), taste (SSC/TA) and firmness. Ninety intact samples from three tomato varieties were used. These samples were examined using VIS/NIR spectroscopy and quality parameters were also measured using traditional methods. Principal component analysis (PCA) and partial least square (PLS) were carried out. The results of PCA showed the ability of VIS/NIR spectroscopy to distinguish between the three varieties, where two PCs explained about 99% of the total variance in both calibration and validation sets. Moreover, PLS showed the possibility of modelling quality parameters. The correlation coefficient (R2) and the ratio of performance deviation (RPD) for all quality parameters (except for firmness) were found to be higher than 0.85 and 2.5, respectively. Thus, these results indicate that the VIS/NIR spectroscopy can be used to discriminate between different varieties of tomato and predict their quality parameters.

Suppression of N-glycan processing enzymes by deoxynojirimycin in tomato (Solanum lycopersicum) fruit

The present study investigated the potential of a small molecule inhibitor, 1-deoxynojirimycin (DNJ), to extend the shelf life of tomatoes. The optimum concentration of DNJ and the proper ripening stage for treatment were standardized using response surface methodology, following a central composite design. The concentration of DNJ used for the analysis was 0.15 mM, and 0.30 mM and the ripening stages of the tomato fruit analysed were immature green, mature green, breaker, ripen and over-ripen. Analysis of the influence of the DNJ treatment of the fruit using quadratic multiple regression models considering the factors colour, texture, and free sugars revealed significant responses. A DNJ concentration of 0.30 mM and fruit-ripening stage of mature green was found to be optimal for the treatment. DNJ-treatment maintained fruit firmness throughout ripening with a significant reduction in reducing sugar formation. Enzyme activity of the N-glycan processing enzymes involved in cell wall softening, α-mannosidase and β-d-N-acetylhexosaminidase revealed a significant reduction in their activity by 2 and 3.5-fold, respectively. Down-regulation of expression of important ripening-related and softening process-associated genes, aminocyclopropane carboxylic synthase-4, aminocyclopropane carboxylic oxidase, polygalacturonase and pectin methylesterases at 4, 5, 6 and 5-fold, respectively, was also observed. The present results showed that the treatment of mature green tomato fruit with DNJ at a concentration of 0.30 mM can delay the ripening of the tomato fruit by inhibiting cell wall and N-glycan processing enzymes.

Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space.