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. 2023 Nov 23;13(1):20568.
doi: 10.1038/s41598-023-47873-4.

Methodological pipeline for monitoring post-harvest quality of leafy vegetables

T C Tonto #  1 , S Cimini #  1 , S Grasso  2 , A Zompanti  3 , M Santonico  2 , L De Gara  1 , V Locato  4
Affiliations

Methodological pipeline for monitoring post-harvest quality of leafy vegetables

T C Tonto et al. Sci Rep. .

Abstract

Plants are primary source of nutrients for humans. However, the nutritional value of vegetables tends to decrease once organ and tissue sinks are detached from the plant. Minimal processing of leafy vegetables involves cutting and washing before packaging and storage. These processing procedures result in stressful conditions and post-harvest disorders senescence-related can also occur. The aim of this work is to define a methodological pipeline to evaluate the "quality" changes of fresh cut leafy vegetables over their shelf-life. At this purpose, intra-species variability has been investigated considering two varieties of Lactuca sativa (var. longifolia and capitata), showing different susceptibility to browning. Since browning mainly depends on phenol oxidation, redox parameters as well as the activity of the enzymes involved in phenol biosynthesis and oxidation have been monitored over storage time. At the same time, the metabolic changes of the lettuce leaves have been estimated as response patterns to chemical sensors. The obtained sensor outputs were predictive of browning-related biological features in a cultivar-dependent manner. The integration of the results obtained by this multivariate methodological approach allowed the identification of the most appropriate quality markers in lettuce leaves from different varieties. This methodological pipeline is proposed for the identification and subsequent monitoring of post-harvest quality of leafy vegetables.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Hue angle grade and browning index of butterhead lettuce (A,B) and romaine lettuce (C,D) during storage period under air conditions. Values are means ± SE of at least three biological replicates, each one with three technical replicates. Different letters indicate a statistical difference (p < 0.05) based on one-way ANOVA followed by Tukey test correction.
Figure 2
Figure 2
Photosynthetic pigments of butterhead lettuce (A) and romaine lettuce (B) during storage period under air and MA. Values are means ± SE of at least three biological replicates, each one with three technical replicates. Different letters indicate a statistical difference (p < 0.05) based on one-way ANOVA followed by Tukey test correction. 5* indicates the data referred to the samples collected at day 5 of storage under MA condition. Ca chlorophyll a, Cb chlorophyll b, Cx-c carotenoids.
Figure 3
Figure 3
Antioxidant capacity of butterhead lettuce (A–C) and romaine lettuce (D–F) over storage period under air and MA. (A) DPPH scavenging capacity of butterhead lettuce leaves; (B) FRAP scavenging capacity of butterhead lettuce leaves; (C) TEAC scavenging capacity of butterhead lettuce leaves; (D) DPPH scavenging capacity of romaine lettuce leaves; (E) FRAP scavenging capacity of romaine lettuce leaves; (F) TEAC scavenging capacity of romaine lettuce leaves. Values are means ± SE of at least three biological replicates, each one with three technical replicates. Different letters indicate a statistical difference (p < 0.05) based on one-way ANOVA followed by Tukey test correction. 5* indicates the data referred to the samples collected at day 5 of storage under MA condition.
Figure 4
Figure 4
Phenol metabolism of butterhead lettuce and romaine lettuce under air and MA storage conditions. Total Phenol Content (TPC) in butterhead lettuce (A) and in romaine lettuce (D). Polyphenol oxidase (PPO) activity in butterhead lettuce (B) and in romaine lettuce (E). Phenylalanine ammonia lyase (PAL) activity in butterhead lettuce (C). Values are means ± SE of at least three biological replicates, each one with three technical replicates. Different letters indicate a statistical difference (p < 0.05) based on one-way ANOVA followed by Tukey test correction. 5* indicates the data referred to the samples collected at day 5 of storage under MA condition.
Figure 5
Figure 5
Gas sensor array analysis of butterhead lettuce (A) and romaine lettuce (B) headspace during storage period under air and MA conditions. Values are means ± SE of at least three biological replicates, each one with three technical replicates. Different letters indicate a statistical difference (p < 0.05) based on one-way ANOVA followed by Tukey test correction.
Figure 6
Figure 6
Scores plot of a Principal Component Analysis (PCA) showing the distribution of butterhead lettuce (A) and romaine lettuce (B) over storage period along the main principal components (PC1, PC2 and PC3).
Figure 7
Figure 7
Calculated partial last square model for the prediction of photosynthetic pigments on butterhead lettuce (A–C) and romaine lettuce (D–F). RMSECV associated with the models are reported.
Figure 8
Figure 8
Calculated Partial Last Square model for the prediction of antioxidant capacity on butterhead lettuce (A–C) and romaine lettuce (D–F). PLS model for the prediction of (A) DPPH scavenging capacity of butterhead lettuce leaves; (B) FRAP scavenging capacity of butterhead lettuce leaves; (C) TEAC scavenging capacity of butterhead lettuce leaves; (D) DPPH scavenging capacity of romaine lettuce leaves; (E) FRAP scavenging capacity of romaine lettuce leaves; (F) TEAC scavenging capacity of romaine lettuce leaves. RMSECV associated with the models are reported.
Figure 9
Figure 9
Calculated PLS model for the prediction of phenol metabolism parameters on butterhead lettuce (A–C) and romaine lettuce (D,E). RMSECV associated with the models are reported.
Figure 10
Figure 10
Workflow of the methodological pipeline employed to assess and predict quality attributes of minimally processed plant food over shelf life period.
See this image and copyright information in PMC

References

    1. Massaglia S, et al. Consumer attitudes and preference exploration towards fresh-cut salads using best–worst scaling and latent class analysis. Foods. 2019;8:568. doi: 10.3390/foods8110568. - DOI - PMC - PubMed
    1. Arienzo A, Gallo V, Tomassetti F, Antonini G. Implication of sodium hypochlorit as a sanitizer in ready-to-eat salad processing and advantages of the use of alternative rapid bacterial detection methods. Foods. 2023;12:3021. doi: 10.3390/foods12163021. - DOI - PMC - PubMed
    1. Grasso S, et al. Microbiological risk assessment of ready-to-eat leafy green salads via a novel electrochemical sensor. Chemosensors. 2022;10:134. doi: 10.3390/chemosensors10040134. - DOI
    1. Murata M, et al. Food chemistry and biochemistry of enzymatic browning. Food Sci. Technol. Res. 2022;28:1–12. doi: 10.3136/fstr.FSTR-D-21-00130. - DOI
    1. Degl’Innocenti, E., Pardossi, A., Tognoni, F. & Guidi, L. Physiological basis of sensitivity to enzymatic browning in ‘lettuce’, ‘escarole’ and ‘rocket salad’ when stored as fresh-cut products. Food Chem. 104, 209–215 (2007).