Hyperspectral imaging and multivariate accelerated shelf life testing (MASLT) approach for determining shelf life of rocket leaves

Amodio, Maria Luisa; Amodio, María Luisa; Babellahi, Farahmand; Chiara, Maria Lucia Valeria de; Amigo, José Manuel; Colelli, G.

doi:10.1016/j.jfoodeng.2018.06.017

Cited by 37 publications

(19 citation statements)

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“…Since light reflectance profiles are indicative of a range of biochemical and physiological aspects [22][23][24][25][26], it was hypothesized that they may be employed to provide an indication of storage period. This hypothesis was tested by analyzing either leaf side, as well as the flower (top view).…”

Section: Leaf and Flower Reflectance Were Not Indicative Of Storage Periodmentioning

confidence: 99%

“…More importantly, a continuous supply of cut flowers with long vase life would benefit the horticultural industry by stimulating the cut flower popularity to consumers [16,21]. Such a candidate is the light reflectance properties, which are affected by both biochemical properties, and physiological condition [22][23][24][25][26]. The changes in cut flower reflectance during storage have not previously been evaluated, and their potential in distinguishing batches stored for different periods has not been currently addressed.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Water Relations and Oxidative Stress in the Vase Life Response to Prolonged Storage: A Case Study in Chrysanthemum

et al. 2022

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Long storage periods have been associated with decreased vase life. In this study, the processes underlying the vase life response to prolonged storage were investigated, along with the potential of light reflectance profiles to estimate storage duration. Three cut chrysanthemum cultivars were exposed to four cold (5 °C) storage periods (0, 7, 14, and 21 d). Stomata were present on the leaves (either side) and stem, but not on petals. As compared to the leaves, stomata on the stem were non-functional, smaller, and less dense. Floral transpiration was a small portion of the whole-cut flower transpiration, with the major contributor being the leaves or stem depending on the cut flower hydration. Storage duration linearly decreased vase life, with the rate of decrease being cultivar specific. Storage duration (0–21 d) did not affect leaf stomatal functioning, non-leaf tissue transpiration, or the relative contribution of each organ to the whole-cut flower transpiration. Cut flower hydration was generally enhanced by storage, while water uptake restoration ability was not impaired. Membrane lipid oxidation increased in response to storage duration owing to enhanced H2O2 accumulation. A strong correlation between membrane lipid oxidation level and the vase life response to cold storage was apparent. By examining the light reflectance profiles (400–1050 nm) of leaves (either side) and flowers (top view), an indication of the storage period could not be deduced. In conclusion, cultivar differences in vase life response to cold storage were attributed to variation in oxidative state, whereas cut flower water relations are clearly not involved.

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Section: Leaf and Flower Reflectance Were Not Indicative Of Storage Periodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of Water Relations and Oxidative Stress in the Vase Life Response to Prolonged Storage: A Case Study in Chrysanthemum

et al. 2022

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“…The differences found between the light emitted by the lamps and the radiation reflected by the samples allows certain attributes related to the composition or the quality to be estimated. Examples are found in vegetables, such as pepper (Schmilovitch et al, 2014), tomato (Liu, Liu, Chen, Yang, Zheng, 2015) or rocket leaves (Chaudhry et al, 2018), cereals, like maize (Williams & Kucheryavskiy, 2016), or rice (Kong, Zhang, Liu, Nie, He, 2013), and fruits such as bananas (Rajkumar, Wang, EImasry, Raghavan, Gariepy, 2012), pears (Li et al, 2016), grapes (Baiano, Terracone, Peri, Romaniello, 2012), strawberries (Zhang et al, 2016) or apples (Baranowski, Mazurek, Pastuszka-Wozniak, 2013). In the case of stone fruit, Herrero-Langreo, Lunadei, Lleó, Diezma and Ruiz-Altisent (2011) assessed the ripeness of peaches by using multispectral indexes; Lu and Peng (2006) assessed the firmness of peaches; Zhu, Lin, Nie, Wu and Chen (2016) obtained firmness distribution maps inside the peach pulp.…”

Section: Introductionmentioning

confidence: 99%

Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines

Munera

Blasco

Amigo

et al. 2019

Biosystems Engineering

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The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. 'Big Top' (yellow flesh) and 'Magique' (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630−900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split 1 pit of the 'Big Top' cultivar. Accuracies of 95.7 % and 94.6 % were achieved in the classification of the 'Big Top' and 'Magique' cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R 2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.

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“…However, the reflectance values increased with an increase in drying time due to degradation of total carotenoids and reduction of moisture content. This is due to the fact that both regions correspond to total carotenoids and moisture content absorption peaks (Chaudhry et al, 2018;Liu et al, 2016).…”

Section: Analysis Of Spectral Reflectance and Wavelength Selection Ba...mentioning

confidence: 99%

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

Saleh

Kulig

Arefi

et al. 2022

Food Processing Preservation

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The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot‐air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non‐invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non‐invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non‐invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.

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Hyperspectral imaging and multivariate accelerated shelf life testing (MASLT) approach for determining shelf life of rocket leaves

Cited by 37 publications

References 50 publications

The Role of Water Relations and Oxidative Stress in the Vase Life Response to Prolonged Storage: A Case Study in Chrysanthemum

The Role of Water Relations and Oxidative Stress in the Vase Life Response to Prolonged Storage: A Case Study in Chrysanthemum

Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

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