Sustainable Valorization of Tomato By-Products to Obtain Bioactive Compounds: Their Potential in Inflammation and Cancer Management

Laranjeira, Tânia; Costa, Ana Margarida; Faria-Silva, Catarina; Ribeiro, Daniela; Oliveira, José Miguel P. Ferreira de; Simões, Sandra; Ascenso, Andreia

doi:10.3390/molecules27051701

Cited by 34 publications

(12 citation statements)

References 96 publications

(110 reference statements)

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“…In North America, this percentage is relatively high. A person consumes approximately forty-two (42) kilograms of tomatoes annually (Laranjeira et al, 2022). The high demand for tomatoes necessitates the development of early detection technologies for viruses, bacterial, and viral contaminations.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

et al. 2022

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Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images—using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

et al. 2022

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“…Fruit or vegetable waste products like peels, seeds, or stones can be successfully employed as a source of phytochemicals, antioxidants, or antimicrobials [8][9][10][11][12][13][14]. The majority of fruit waste had some antibacterial action and minor antifungal or yeast activity [15]. The fruit peels are high in minerals or phytochemicals and can be used as pharmaceuticals or as food additives [16].…”

Section: Introductionmentioning

confidence: 99%

Physical Characteristics, Mineral Content, and Antioxidant and Antibacterial Activities of Punica granatum or Citrus sinensis Peel Extracts and Their Applications to Improve Cake Quality

El-Beltagi

Eshak

Mohamed

et al. 2022

Plants

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One-third of all food produced for human use is discarded as waste, resulting in environmental pollution and impaired food security. Fruit peels have bioactive compounds that may be used as antimicrobials and antioxidants, and the use of fruit peels is considered an alternative way to reduce environmental problems and agro-industrial waste. The aim of this study was to evaluate the phytochemical, mineral, extraction yield, total phenolic, total flavonoids, antioxidant, and antibacterial activity of several peel fruits, including Citrus sinensis (orange) and Punica granatum (pomegranate). The results revealed that pomegranate peel powder contains the highest amounts of ash, fiber, total carbohydrates, Ca, Fe, Mg, and Cu, while orange peel contains the highest amounts of moisture, protein, crude fat, P, and K. Furthermore, the aqueous and methanolic pomegranate peel extracts yielded higher total phenolic and total flavonoids than the orange peel extract. The identification and quantification of polyphenol compounds belonging to different classes, such as tannins, phenolic acids, and flavonoids in pomegranate peel and flavonoid compounds in orange peel were performed using UPLC-MS/MS. In addition, GC−MS analysis of orange peel essential oil discovered that the predominant compound is D-Limonene (95.7%). The aqueous and methanolic extracts of pomegranate peel were proven to be efficient against both gram-positive and gram-negative bacteria linked to human infections. Sponge cake substituting wheat flour with 3% pomegranate peel and 10% orange peel powder had the highest total phenolic, flavonoid compounds, and antioxidant activity as compared to the control cake. Our results concluded that pomegranate and orange peel flour can be used in cake preparation and natural food preservers.

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“…Tomatoes ( Solanum lycopersicum L.) belong to the Solanaceae family, with approximately 2800 species and an annual global production of over 161 million tons, making it one of the most-consumed vegetable commercial crops [ 1 , 2 ]. Tomatoes are often used as fresh fruit, cooked food, and processed products due to a range of human health benefits, such as antimicrobial [ 3 ], antioxidant [ 4 ], and protective effects against cardiovascular disease and cancer [ 5 ]. The demand for quality vegetables and fruits also increased gradually with rising living standards and importance placed on health.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation of Effects on Water-Level Deficits on Tomato Polyphenol Composition, Nutritional Quality and Antioxidant Capacity

Jin

Wang

et al. 2022

Antioxidants

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Tomatoes have high nutritional value and abundant bioactive compounds. Moderate water deficit irrigation alters metabolic levels of fruits, improving composition and quality. We investigated the effects of water deficit (T1, T2, T3, and T4) treatments and adequate irrigation (CK) on tomato polyphenol composition, antioxidant capacity, and nutritional quality. Compared with CK, the total flavonoid content increased by 33.66% and 44.73% in T1 and T2, and total phenols increased by 57.64%, 72.22%, and 55.78% in T1, T2, and T3, respectively. The T2 treatment significantly enhanced antioxidant’ capacities (ABTS, HSRA, FRAP, and DPPH). There were multiple groups of significant or extremely significant positive correlations between polyphenol components and antioxidant activity. For polyphenols and antioxidant capacity, the classification models divided the treatments: CK and T4 and T1–T3. The contents of soluble solids, soluble protein, vitamin C, and soluble sugar of the treatment groups were higher than those of CK. The soluble sugar positively correlated with sugar–acid ratios. In the PCA-based model, T3 in the first quadrant indicated the best treatment in terms of nutritional quality. Overall, comprehensive rankings using principal component analysis (PCA) revealed T2 > T1 > T3 > T4 > CK. Therefore, the T2 treatment is a suitable for improving quality and antioxidant capacity. This study provides novel insights into improving water-use efficiency and quality in the context of water scarcity worldwide.

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Sustainable Valorization of Tomato By-Products to Obtain Bioactive Compounds: Their Potential in Inflammation and Cancer Management

Cited by 34 publications

References 96 publications

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Physical Characteristics, Mineral Content, and Antioxidant and Antibacterial Activities of Punica granatum or Citrus sinensis Peel Extracts and Their Applications to Improve Cake Quality

A Comprehensive Evaluation of Effects on Water-Level Deficits on Tomato Polyphenol Composition, Nutritional Quality and Antioxidant Capacity

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