Mature-ripe tomato spectral classification according to lycopene content and fruit type by visible, NIR reflectance and intrinsic fluorescence

Clément, Alain; Bacon, R.; Sirois, Stéphane; Dorais, Martine

doi:10.3920/qas2014.0521

Cited by 9 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The profiles present broad but identifiable bands, ascribable to the contributions of the main constituents of the food matrix such as water and sugar. Reflectance value is below 0.1 from 400 to 575 nm as previously detected by ElMasry and Sun ( 52 ), including an intense absorption peak between 450 and 475 nm as found by Ciaccheri et al ( 53 ). Above 560 nm, reflectance values rose sharply because of the red coloration of ripened fruits ( 54 ).…”

Section: Resultssupporting

confidence: 75%

See 1 more Smart Citation

Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy

et al. 2022

View full text Add to dashboard Cite

Tomato-based products are significant components of vegetable consumption. The processing tomato industry is unquestionably in need of a rapid definition method for measuring soluble solids content (SSC) and lycopene content. The objective was to find the best chemometric method for the estimation of SSC and lycopene content from visible and near-infrared (Vis-NIR) absorbance and reflectance data so that they could be determined without the use of chemicals in the process. A total of 326 Vis-NIR absorbance and reflectance spectra and reference measurements were available to calibrate and validate prediction models. The obtained spectra can be manipulated using different preprocessing methods and multivariate data analysis techniques to develop prediction models for these two main quality attributes of tomato fruits. Eight different method combinations were compared in homogenized and intact fruit samples. For SSC prediction, the results showed that the best root mean squared error of cross-validation (RMSECV) originated from raw absorbance (0.58) data and with multiplicative scatter correction (MSC) (0.59) of intact fruit in Vis-NIR, and first derivatives of reflectance (R2 = 0.41) for homogenate in the short-wave infrared (SWIR) region. The best predictive ability for lycopene content of homogenate in the SWIR range (R2 = 0.47; RMSECV = 17.95 mg kg–1) was slightly lower than that of Vis-NIR (R2 = 0.68; 15.07 mg kg–1). This study reports the suitability of two Vis-NIR spectrometers, absorbance/reflectance spectra, preprocessing methods, and partial least square (PLS) regression to predict SSC and lycopene content of intact tomato fruit and its homogenate.

show abstract

Section: Resultssupporting

confidence: 75%

“…The variability of spectra was the highest in the Vis-NIR region between 650 and 930 nm. The reflectance maximum was measured between 700 and 705 nm, as found by Clément et al (52). In the NIR, there was a local absorption maximum at 976 nm.…”

Section: Spectral Acquisitionmentioning

confidence: 54%

Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…A comparison of our results with those from other nondestructive spectroscopic and chemometric techniques reported in the literature is difficult to make, given the variability in the measuring devices, elaboration of data, cultivars, and growing conditions of the plants, as well as in the chemical analysis of the lycopene. Moreover, no studies have been reported on the seasonal effects on lycopene prediction models, and only a single study concerning intercultivar lycopene prediction models has been found.…”

Section: Results and Discussionmentioning

confidence: 99%

Prediction Models for Assessing Lycopene in Open-Field Cultivated Tomatoes by Means of a Portable Reflectance Sensor: Cultivar and Growing-Season Effects

Ciaccheri

Tuccio

Mencaglia

et al. 2018

J. Agric. Food Chem.

View full text Add to dashboard Cite

Reflectance spectroscopy represents a useful tool for the nondestructive assessment of tomato lycopene, even in the field. For this reason, a compact, low-cost, light emitting diode-based sensor has been developed to measure reflectance in the 400-750 nm spectral range. It was calibrated against wet chemistry and evaluated by partial least squares (PLS) regression analyses. The lycopene prediction models were defined for two open-field cultivated red-tomato varieties: the processing oblong tomatoes of the cv. Calista (average weight: 76 g) and the fresh-consumption round tomatoes of the cv. Volna (average weight: 130 g), over a period of two consecutive years. The lycopene prediction models were dependent on both cultivar and season. The lycopene root mean square error of prediction produced by the 2014 single-cultivar calibrations validated on the 2015 samples was large (33 mg kg) in the Calista tomatoes and acceptable (9.5 mg kg) in the Volna tomatoes. A more general bicultivar and biyear model could still explain almost 80% of the predicted lycopene variance, with a relative error in red tomatoes of less than 20%. In 2016, the in-field applications of the multiseasonal prediction models, built with the 2014 and 2015 data, showed significant ( P < 0.001) differences in the average lycopene estimated in the crop on two sampling dates that were 20 days apart: on August 19 and September 7, 2016, the lycopene was 98.9 ± 9.3 and 92.2 ± 10.8 mg kg FW for cv. Calista and 54.6 ± 13.2 and 60.8 ± 6.8 mg kg FW for cv. Volna. The sensor was also able to monitor the temporal evolution of lycopene accumulation on the very same fruits attached to the plants. These results indicated that a simple, compact reflectance device and PLS analysis could provide adequately precise and robust (through-seasons) models for the nondestructive assessment of lycopene in whole tomatoes. This technique could guarantee tomatoes with the highest nutraceutical value from the production, during storage and distribution, and finally to consumers.

show abstract

“…Vitamin C content was estimated with a R 2 of 0.67 (Azadshahraki et al, 2018), total acidity with R 2 of 0.66-0.94 (Ding et al, 2016) and R 2 of 0.91-0.98 (Najjar and Abu-Khalaf, 2021), and firmness exhibited R 2 of 0.70-0.72 (Najjar and Abu-Khalaf, 2021). The analysis of b-carotene revealed R 2 values of 0.77-0.88 (Tilahun et al, 2018), phenols showed R 2 values of 0.76-0.98 (Ding et al, 2016), malic acid with R 2 of 0.27-0.42 (Torres et al, 2015), citric acid with R 2 of 0.66-0.94 (Ding et al, 2016) and lycopene with R 2 values ranging from 0.45-0.75 (Cleḿent et al, 2015), 0.73-0.83 (Ciaccheri et al, 2018) and0.85-0.89 (Tilahun et al, 2018). However, tomato exhibits differences in the structural and biochemical characteristics of different tissues, which leads to significant ramifications in the absorption and scattering of light inside the tomato fruit (Skolik et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Tosin,

Cunha,

Monteiro-Silva

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionPrecision 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.ObjectivesThis paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like.MethodsA 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.ResultsThe 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.ConclusionThe 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.

show abstract

Mature-ripe tomato spectral classification according to lycopene content and fruit type by visible, NIR reflectance and intrinsic fluorescence

Cited by 9 publications

References 33 publications

Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy

Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy

Prediction Models for Assessing Lycopene in Open-Field Cultivated Tomatoes by Means of a Portable Reflectance Sensor: Cultivar and Growing-Season Effects

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

Contact Info

Product

Resources

About