Fruit Classification Research Articles

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

AbstractImage-based fruit classification offers many useful applications in industrial production and daily life, such as self-checkout in the supermarket, automatic fruit sorting and dietary guidance. However, fruit classification task will have different data distributions due to different application scenarios. One feasible solution to solve this problem is to use domain adaptation that adapts knowledge from the original training data (source domain) to the new testing data (target domain). In this paper, we propose a novel deep learning-based unsupervised domain adaptation method for cross-domain fruit classification. A hybrid attention module is proposed and added to MobileNet V3 to construct the HAM-MobileNet that can suppress the impact of complex backgrounds and extract more discriminative features. A hybrid loss function combining subdomain alignment and implicit distribution metrics is used to reduce domain discrepancy during model training and improve model classification performance. Two fruit classification datasets covering several domains are established to simulate common industrial and daily life application scenarios. We validate the proposed method on our constructed grape classification dataset and general fruit classification dataset. The experimental results show that the proposed method achieves an average accuracy of 95.0% and 93.2% on the two datasets, respectively. The classification model after domain adaptation can well overcome the domain discrepancy brought by different fruit classification scenarios. Meanwhile, the proposed datasets and method can serve as a benchmark for future cross-domain fruit classification research.

Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (Phoenix dactylifera L.)

The aim of this study was to develop the procedure for the varietal discrimination of date palm fruit using image analysis and traditional machine learning techniques. The fruit images of ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, and ‘Bousthammi’ date varieties, converted to individual color channels, were processed to extract the texture parameters. After performing the attribute selection, the textures were used to build models intended for the discrimination of different varieties of date palm fruit using machine learning algorithms from Functions, Bayes, Lazy, Meta, and Trees groups. Models were developed for combining image textures selected from a set of all color channels and for sets of textures selected for individual color spaces and color channels. The models, including combined textures selected from all color channels, distinguished all five varieties with an average accuracy reaching 98%, and ‘Bousthammi’ and ‘Mejhoul’ were completely correctly discriminated for the SMO (Functions) and IBk (Lazy) machine learning algorithms. By reducing the number of varieties, the correctness of the date palm fruit classification increased. The models developed for the three most different date palm fruit varieties ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ revealed an average discrimination accuracy of 100% for each algorithm used (SMO, Naive Bayes (Bayes), IBk, LogitBoost (Meta), and LMT (Trees)). In the case of individual color spaces and channels, the accuracies were lower, reaching 97.3% for color space RGB and SMO and LMT algorithms for all five varieties and 99.63% for Naive Bayes and IBk for the ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ date palm fruits. The results can be used in practice to develop vision systems for sorting and distinguishing the varieties of date palm fruit to authenticate the variety of the fruit intended for further processing.

Automatic Identification of banana quality with Deep Neural Network Classification (DNN)

The industrialization of agriculture has gradually taken place in Colombia, because of the need to optimize the time and effort required for the processes involved from growing fruits and vegetables to their distribution and commercialization, whether they are meant for exportation or commercialization within the country. Fruit classification is a very important process before they are being taken out as a final product, since predetermined guidelines issued by regulatory entities that define the quality of the harvested product must be followed. The authors propose a low-cost prototype for the automatic classification of bananas according to the NTC 1190 standard (Colombian normative), using a convolutional neural network (CNN) of MobileNetV2 architecture trained through transfer learning and implemented in a Raspberry Pi 3B+ with a camera to monitor the specimens and an easy interface for interaction with the user, as well as a case designed to contain the hardware and allow access to its ports in the most compact way possible. The datasets utilized in this work for training, validation, and testing, consists of images taken from two free access fruit database and others acquired by the researchers. The achieved precision is 87 %, enough to ensure reliability and low computational cost.

Laboratory and Field Trials to Identify Reduced-Risk Insecticides for the Control of the Golden Twin-Spot Moth Chrysodeixis chalcites (Esper) (Lepidoptera: Noctuidae) in Banana Plantations

The golden twin-spot moth (Chrysodeixis chalcites) is one of the most important pests in banana production on the Canary Islands (Spain). The efficacy of different biorational insecticides based on bioenzyme complexes (Intruder®), plant extracts: Rutaceae and Piperaceae (Avenger®), Rutaceae and Lauraceae (BioKnock®), cinnamon, citronella, and Menta (Cinamite®), Alliaceae and Solanacea (Garlitrol-Forte®), citrus (Prevam®), and neem oil (Indasol®) was assessed against C. chalcites. Laboratory assays included: choice (repellent effect), no choice, and contact toxicity on C. chalcites 2nd instar larvae. The highest repellent effect was observed with Prevam® (85.19 ± 1.7%), followed by Garlitrol® (68.44 ± 5.7%) and Intruder® (67.54 ± 4.3%). In no choice assays, Prevam® (0.92 ± 0.4%), Indasol® (0.98 ± 0.33%), and Intruder® (2.7 ± 0.33%) had the lowest leaf consumption. The contact toxicity assays showed the highest mortality with Intruder® both at 1 day and 7 days post-application (20.22 ± 2.98% and 77.77 ± 5.7%, respectively). In the screenhouse trial, the best results for C. chalcites larvae mortality, fruit damage, and fruit classification in quality categories 7 days after application of the bioinsecticide were obtained with Intruder®, Prevam®, and Indasol®. An economic analysis of biorational treatments was also performed. The results of this study provide successful alternatives to chemical pesticides for the control of C. chalcites on banana plants in the Canary Islands.

Reliable classification of nutmeg fruits from some geographical origins using SWNIR spectroscopy associated with PLS-DA and SVM methods: Case Study in Aceh Province, Indonesia

Essential oils of nutmeg (Myristica fragrans) can have different qualities due to the soil and industrial process, as Indonesia’s main nutmeg production areas are enormous, especially in Aceh province. The lack of information on detecting chemical constituents and classifying nutmeg from Aceh province makes the price of nutmeg from Aceh unable to compete. Therefore, this study aims to reliably classify nutmeg fruits of some geographical origins in Aceh Province using shortwave near-infrared (SWNIR) spectroscopy associated with partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM). The SWNIR used in this study has a wavelength of 381 to 1065 nm at a resolution of 3 nm and a diffuse reflectance mode. The fruit comes from four geographical areas of origin in the province of Aceh. A total of forty SWNIR spectral data were further classified using two algorithms, namely PLS-DA and SVM. The results show that SWNIR can correctly classify nutmeg from some sub-districts in Aceh province with the help of the PLS-DA and SVM algorithms. From this study, the following SVM algorithm is recommended to be used to classify nutmeg from the province of Aceh with an outstanding level of precision.

Tunicate Henry gas solubility optimization‐based deep residual network for fruit ripeness classification

SummaryFruits are considered a significant component to offer humans adequate nutrients, like carbohydrates, vitamins, and dietary fiber but the ripening of fruits is a major issue as the ripening of fruits is detected considering more or less time based on microclimate conditions. This article proposes a new technique to classify fruit ripening using the deep model. Here, the input images like mango, apple, and banana are fed to preprocessing phase wherein Gaussian filtering is adapted to eliminate unwanted distortions. Features like color histogram, histogram of gradients, statistical features, local Gabor binary patterns, significant local binary pattern, and convolution neural network (CNN) features are mined to classify fruit. The DCNN training is done with the adopted tunicate‐based Henry gas solubility optimization algorithm (THGSO) obtained by integrating the tunicate swarm algorithm and Henry gas solubility optimization (HGSO). Then, ripening classification is done with deep residual network (DRN). The DRN training is done using the adopted THGSO. The adopted THGSO‐based DRN presented developed fruit classification performance with elevated accuracy 91.4%, sensitivity 92.5%, and specificity 87.5%. The adopted THGSO‐based DRN provided improved fruit ripening performance with maximum accuracy 92.5%, sensitivity 93.5%, and specificity 90.5%.

Fruits Classification and Detection Application Using Deep Learning

Computer vision and image processing techniques are considered efficient tools for classifying various types of fruits and vegetables. In this paper, automated fruit classification and detection systems have been developed using deep learning algorithms. In this work, we used two datasets of colored fruit images. The first FIDS-30 dataset of 971 images with 30 distinct classes of fruits is publicly available. A major contribution of this work is to present a private dataset containing 761 images with eight categories of fruits, which have been collected and annotated by ourselves. In our work, the YOLOv3 deep learning object detection algorithm have been used for individual fruit detection across multiple classes, and ResNet50 and VGG16 techniques have been utilized for the final classification for the recognition of a single category of fruit in images. Next, we implemented the automatic fruit classification models with flask for the web framework. We got 86% and 85% accuracies from the public dataset with ResNet50 and VGG16, respectively. We achieved 99% accuracy with ResNet50 and 98% accuracy with the VGG16 model on the custom dataset. The domain adaptation approach is used in this work so that the proposed deep learning-based prediction model can cope with real-world problems of diverse domains. Finally, an Android smartphone application has been developed to classify and detect fruits with the camera in real-time. All the images uploaded from the Android device are automatically sent and consequently analyzed on the web, and finally, the processed data and results are returned to the smartphone. The custom dataset and implementation codes will be available after the manuscript has been accepted. The custom dataset can be found at: https://github.com/SumonAhmed334/dataset_fruit.

An empirical evaluation of feature extraction for Vietnamese fruit classification

In recent years, Vietnamese fruit production has marked significant progress in terms of scale and product structure. Viet Nam enjoys suitable climates for tropic, subtropical fruits, and some temperate fruits. Thanks to the diversified ecology, implementing an automatic classification system has received a significant concern. In this paper, we address the problem of Vietnamese fruit classification. However, the first requirement to explore this problem is the qualified dataset. To this end, we first introduced the UIT-VinaFruit20 dataset, a novel Vietnamese fruit image dataset that includes 63,541 images from 20 types of fruits from three regions of Viet Nam. The diversity in different fruits in distinct areas poses many new challenges. In addition, we further leverage the feature extraction from hand-crafted and deep learning features along with the SVM classification model to effectively classify Vietnamese fruits. The extensive experiments conducted on the UIT-VinaFruit20dataset provide a comprehensive evaluation and insightful analysis. An encouraging empirical success was obtained as the EfficientNetB0 feature achieved the best results of 85.465 % and 86.919 % in terms of Accuracy and Macro F1-score, respectively

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning.

Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.

Classification of Oil Palm Fresh Fruit Bunches Based on Their Maturity Using Thermal Imaging Technique

The maturity of oil palm Fresh Fruit Bunches (FFB) is considered to be a significant factor that affects the profitability and salability of palm oil FFB. Typical methods of grading FFB consist of physical grading of fresh fruit, which is time-consuming and expensive, and the results are prone to human error. Therefore, this research attempts to formulate a thermal imaging method to indicate the precise maturity of oil palm fruits. A total of 297 oil palm FFBs were collected. The samples were divided into three groups: under-ripe, ripe, and over-ripe. Afterward, all the samples were scanned using a thermal imaging camera to calculate the real temperature of each sample. In order to normalize the measurement, the difference between the average temperature of the palm bunch and the ambient temperature (∆Temp) was considered as the main parameter. The results indicated that the mean ∆Temp of oil palm FFBs decreased consistently from under-ripe to over-ripe. The results of the ANOVA test demonstrated that the observed significance value was less than 0.05 in terms of ∆Temp, so there is a statistically significant difference in the means of all three maturity categories. It can be concluded that ∆Temp is a reliable index to classify the FFBs of oil palm. The classification analysis was conducted using the ∆Temp of the FFBs and its application as an index in Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN), and Kernel Nearest Neighbor (KNN). The highest degrees of overall accuracy (99.1% and 92.5%) were obtained through the ANN method. This study concludes that thermal images can be used as an index of oil palm maturity classification.

Feature selection and classification improvement of Kinnow using SVM classifier

An approach to enhance the classification of the kinnow is proposed in this paper. The fruit images are captured in the proposed approach, and texture, color, shape, and size features are extracted and merged to generate a dataset. To cope with outliers and dominant features, the Pareto normalization method is used. A hybrid feature selection approach that combines neighborhood component analysis and ReliefF methods to select the optimal features is proposed to eliminate redundant and irrelevant features. The dataset is classified using the SVM machine learning algorithm following the feature selection. Utilizing the SVM classifier, the proposed approach chooses 54.20% of the features with an accuracy of 94.67%. This proves that the proposed approach is efficient and can be used for the classification of the other fruits.

Еволюційна й екологічна інтерпретація структури плодів однодольних рослин флори України

Background. Monocot plants in the flora of Ukraine embrace about 1000 species. The members of this group have various fruit types and ways of dissemination. In this review, the results of the evolutionary-morphological analysis of fruits in monocot plants of the flora of Ukraine and their structural adaptations to dispersal are presented. Gynoecium structure and morphogenetic fruit types in monocots. The features of the inner gynoecium structure and the relation between gynoecium and fruit structure were analyzed, and the main problems of fruit classification were detected. It was revealed that apocarpous fruits are often polymerous or oligomerous, composed of 3–6 one-seeded fruitlets. The species with inferior ovary often develop capsular fruits. One-seeded fruits represent the most reduced variant of the gynoecium structure and fruit wall anatomy, providing few structural traits for analysis. Ecological and functional features of fruits in monocots. The characteristics of dehiscent and indehiscent fruits, as well as adaptations to various ways of dispersal: myrmeco-, endozoo-, epizoo-, anemo-, hydro- and autochory are presented. Dehiscent fruits (follicle and capsule) reveal ventral, dorsal or double, dorsoventral dehiscence. One-seeded fruits and fruitlets are often adapted to hydrochory or anemochory. Trends of evolutionary changes of fruits in monocots. The most diverse fruits are found in low-species taxa of early monocots. Within the monocots, we can observe certain evolutionary trends: incomplete carpel fusion, the occurrence of superior one-seeded fruits and inferior many-seeded fruits. Each of these trends may be evoked by adaptations for pollination in gynoecium at the flowering stage. Conclusions. The present evolutionary morphological and ecological studies of fruits in monocot plants of the flora of Ukraine are impeded by controversial structural types of the gynoecium in some species and a scarcity of data on ways of dispersal of fruits and seeds. The precise carpological and carpoecological investigations of the monocots are desirable for many plants of the flora of Ukraine.

A Deep Learning based Model for Fruit Grading using DenseNet

Detecting the rotten fruits become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits is carried by humans is not effectual for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, this paper proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of rottenness. The proposed model classifies the fresh fruits and rotten fruits from the input fruit images. For this work, we have used three types of fruits, such as apple, banana, and oranges. A Convolutional Neural Network (CNN) is used for extracting the features from input fruit images, and Softmax is used to classify the images into fresh and rotten fruits. The performance of the proposed model is evaluated on a dataset that is downloaded from Kaggle and produces an accuracy of 97.82%. The results showed that the proposed CNN model can effectively classify the fresh fruits and rotten fruits.

A novel fuzzy pooling based modified ThinNet architecture for lemon fruit classification

Computer vision functions like object detection, image segmentation, and image classification were recently getting advance due to Convolutional Neural Networks (CNNs). In the food and agricultural industries, image classification plays a critical role in quality control. CNNs are made up of layers that alternate between convolutional, nonlinearity, and feature pooling. In this article, we proposed Fuzzy Pooling, a novel pooling approach that works based on fuzzy logic, that can increase the accuracy of the CNNs by replacing the conventional pooling layer. This proposed Fuzzy Pooling was put to the test with CIFAR-10 and SVHN data sets on single layer CNN, and it outperformed previous pooling strategies by achieving 92% and 97% classification accuracy. This proposed Fuzzy Pooling layer was replaced the Max Pooling layer in the ThinNet architecture, and it was trained using the back propagation method. It was demonstrated experimentally on the Lemon fruit data set to classify the fruits into three categories such as Good, Medium, and Poor. In order to classify the lemon fruit into three categories, the 1000 Fully Connected layer in ThinNet architecture was replaced with three Fully Connected layers. The Modified ThinNet architecture called ThinNet_FP was trained with a learning rate of 0.001 and achieved 97% accuracy in classifying the images and outperformed previous CNN architectures when trained on the same data set.

Classification of Prunus Genus by Botanical Origin and Harvest Year Based on Carbohydrates Profile.

The 1H-NMR carbohydrates profiling was used to discriminate fruits from Rosaceae family in terms of botanical origin and harvest year. The classification was possible by application of multivariate data analysis, such as principal component analysis (PCA), linear discriminant analysis (LDA) and Pearson analysis. Prior, a heat map was created based on 1H-NMR signals which offered an overview of the content of individual carbohydrates in plum, apricot, cherry and sour cherry, highlighting the similarities. Although, the PCA results were almost satisfactory, based only on carbohydrates signals, the LDA reached 94.39% and 100% classification of fruits according to their botanical origin and growing season, respectively. Additionally, a potential association with the relevant climatic data was explored by applying the Pearson analysis. These findings are intended to create an efficient NMR-based solution capable of differentiating fruit juices based on their basic sugar profile.

Tomato harvesting robotic system based on Deep-ToMaToS: Deep learning network using transformation loss for 6D pose estimation of maturity classified tomatoes with side-stem

This paper presents the development of an autonomous harvesting robot system for tomato, a representative crop cultivated in the facility horticulture smart farm. The automated harvesting work using a robotic system is very challenging because of the appearance, environmental features, such as the atypical directions of the peduncles or their growing form in a bunch. Also, the robot system should enable the harvesting of the target fruit only without damaging other fruits, stems, and branches. Hence, this paper presents a deep learning network pipeline, Deep-ToMaToS, capable of three-level maturity classification and 6D pose (3D translation + 3D rotation) estimation of the target fruit simultaneously. Due to the difficulties encountered in building a large-scale dataset to train and test the deep learning model for the 6D pose estimation in the real world, we presented an automatic data collection scheme based on a photo-realistic 3D simulator environment. The robotic harvesting system includes a harvesting motion control algorithm based on the result of the 6D pose estimation. The overall process of the motion control phase is described along with the decision way of the appropriate final posture of the harvesting module mounted at the end-effector of the robot manipulator via removal of invalid motions getting out of the valid workspace or redundant motions. We conducted experiments on the 6D pose estimation based on the Deep-ToMaToS and the harvesting motion control in virtual and real smart farm environments. The experimental results showed a 6D pose estimation accuracy of 96 % based on the ADD_S metric, and the proposed harvesting motion control algorithm achieves the harvesting success rate of 84.5 % on average. The experimental results reveal that the harvesting robot system has significant potential to extend to harvesting works for other fruits and crops.

Estimation of Soluble Solids for Stone Fruit Varieties Based on Near-Infrared Spectra Using Machine Learning Techniques.

The quality control for fruit maturity inspection is a key issue in fruit packaging and international trade. The quantification of Soluble Solids (SS) in fruits gives a good approximation of the total sugar concentration at the ripe stage, and on the other hand, SS alone or in combination with acidity is highly related to the acceptability of the fruit by consumers. The non-destructive analysis based on Visible (VIS) and Near-Infrared (NIR) spectroscopy has become a popular technique for the assessment of fruit quality. To improve the accuracy of fruit maturity inspection, VIS–NIR spectra models based on machine learning techniques are proposed for the non-destructive evaluation of soluble solids in considering a range of variations associated with varieties of stones fruit species (peach, nectarine, and plum). In this work, we propose a novel approach based on a Convolutional Neural Network (CNN) for the classification of the fruits into species and then a Feedforward Neural Network (FNN) to extract the information of VIS–NIR spectra to estimate the SS content of the fruit associated to several varieties. A classification accuracy of was obtained for the CNN classification model and a correlation coefficient of for the SS estimation of the FNN models was obtained. The results reported show the potential of this method for a fast and on-line classification of fruits and estimation of SS concentration.

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra.

The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is usually done every 10 to 14 days, and an FFB is deemed ready to be harvested if there are around 5 to 10 empty sockets on the fruit bunch. Technology aided by imaging techniques relies heavily on the color of the fruit bunch, which is highly dependent on the surrounding light intensities. In this study, Raman spectroscopy is used for ripeness classification of oil palm fruits, based on the molecular assignments extracted from the Raman bands between 1240 cm−1 and 1360 cm−1. The Raman spectra of 52 oil palm fruit samples which contain the fingerprints of different organic compounds were collected. Signal processing was applied to perform baseline correction and to reduce background noises. Characteristic data of the organic compounds were extracted through deconvolution and curve fitting processes. Subsequently, a correlation study between organic compounds was developed and eight hidden Raman peaks including protein, beta carotene, carotene, lipid, guanine/cytosine, chlorophyll-a, and tryptophan were successfully located. Through ANOVA statistical analysis, a total of six peak intensities from proteins through Amide III (β-sheet), beta-carotene, carotene, lipid, guanine/cytosine, and carotene and one peak location from lipid were found to be significant. An automated oil palm fruit ripeness classification system deployed with artificial neural network (ANN) using the seven signification features showed an overall performance of 97.9% accuracy. An efficient and accurate ripeness classification model which uses seven significant Raman peak features from the correlation analysis between organic compounds was successfully developed.

A Comparative Metabolomics Approach for Egyptian Mango Fruits Classification Based on UV and UPLC/MS and in Relation to Its Antioxidant Effect.

Mango (Mangifera indica L.) is a tropical climacteric fruit that encompasses a myriad of metabolites mediating for its nutritive value, unique taste, flavor, and medicinal uses. Egypt is among the top mango producers worldwide, albeit little characterization has been made toward its fruits’ chemical composition. This study aims to assess metabolites difference via comparative profiling and fingerprinting of Egyptian mango in context to its cultivar (cv.) type and/or growth province. To achieve such goal, hyphenated chromatographic techniques (UPLC/MS) and UV spectroscopy were employed and coupled to multivariate data analysis for Egyptian mango fruits’ classification for the first time. UPLC/MS led to the detection of a total of 47 peaks identified based on their elution times and MS data, belonging to tannins as gallic acid esters, flavonoids, xanthones, phenolic acids and oxylipids. UV/Vis spectra of mango fruits showed similar absorption patterns mostly attributed to the phenolic metabolites, i.e., gallic acid derivatives and phenolic acids showing λmax at ca. 240 and 270 nm. Modeling of both UPLC/MS and UV data sets revealed that cv. effect predominated over geographical origin in fruits segregation. Awees (AS) cv. showed the richest phenolic content and in agreement for its recognition as a premium cv. of mango in Egypt. Results of total phenolic content (TPC) assay revealed that AS was the richest in TPC at 179.1 mg GAE/g extract, while Langara from Ismailia (LI) showed the strongest antioxidant effect at 0.41 mg TE/g extract. Partial least square modeling of UV fingerprint with antioxidant action annotated gallates as potential contributor to antioxidant effect though without identification of exact moieties based on UPLC/MS. The study is considered the first-time investigation of Egyptian mango to aid unravel phytoconstituents responsible for fruits benefits using a metabolomics approach.

Fruit Classification and Grading

Fruit Classification and Grading