Fruit Quality Evaluation Research Articles

Metabolomics analyses provide insights into the nutritional quality profiling in 95 avocado germplasms grown in China

Avocado is widely grown in tropical and subtropical regions of China. Diverse germplasms have been generated through natural hybridization and selective breeding. Here, to screen high-quality avocado germplasms, we characterized the nutritional quality of 95 avocado germplasms grown in China based on metabolomics. The oil content of the 95 avocado germplasms was 2.18 %–16.60 % and followed a normal distribution. We further profiled nine fatty acids, 16 hydrolyzed amino acids, and eight mineral elements in avocado fruit, which varied widely among different germplasms. Correlation analysis revealed significant positive correlations between fatty acid components, as well as between amino acid components and between mineral elements. Clustering analysis and evaluation of the 95 avocado germplasms identified 14 germplasms with high nutritional quality. These findings provide a basis for evaluation of avocado fruit quality and utilization of high-quality avocado germplasms, as well as important implications for the breeding of avocado.

Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach.

The current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time-consuming, and prone to human errors. Consequently, there have been emerging trends and needs for non-destructive methods to evaluate fruit quality accurately and practically. This research aimed to develop a novel approach for predicting the physicochemical properties of papayas using a convolutional neural network (CNN) model that combines image analysis and weight assessment. This study involved capturing images of papayas at different ripening stages, measuring papaya weights, and determining various physicochemical properties such as texture, pH, total soluble solids, and seed weight. A total of 532 images were obtained from 132 papayas, and an additional 1064 images were generated through image augmentation. The dataset was divided into three sets with an 8:1:1 ratio for training, validation, and testing. The CNN model was trained using papaya images and weights as input values to predict and estimate the physicochemical property values. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R2) as metrics. The CNN model, integrated with image processing, could predict the diverse physicochemical properties of papayas with high accuracy. The MSE values estimated for the training and validation sets were 0.0284 and 0.1729, respectively. The R2 values for the test dataset ranged from 0.71 to 0.94. These findings demonstrate that CNN-based models could provide detailed and quantitative insights, facilitating improved understanding and management of papaya quality while enhancing predictive modeling accuracy in agriculture. PRACTICAL APPLICATION: This research introduces a new method for accurately predicting the quality of papayas using a computer model. Instead of relying on manual inspection, which can be slow and prone to errors, this model uses images of papayas and their weights to predict properties, including texture, pH, total soluble solids, and seed weight. This can help manage papaya quality better while also improving agricultural production and transportation processes.

A novel stone fruit quality non-destructive evaluation method based on PVR by LF-NMRI

For stone fruits, the pulp ratio, indicating the proportion of pulp to the total fruit, serves as a vital metric in assessing stone fruit quality. Traditionally, this ratio is computed by measuring the mass of each component through destructive sampling, and acquired results remain controversial. In order to obtain this ratio non-destructively and more accurately, a novel evaluation method for stone fruit quality based on pulp volume ratio (PVR) by low-field nuclear magnetic resonance imaging (LF-NMRI) is proposed. This approach integrates the SwinUnet segmentation network to differentiating the fruit pulp and core structure, thereby precisely computing the volume proportion of pulp. Experimental results reveal that SwinUnet exhibits better accuracy and robustness in the LF-NMRI data compared to the other networks. Based on the segmentation results, the disparities between the PVR values computed using the ellipse fitting method and the integral method, compared to the ground truth (GT), are less than 1% and 0.5% respectively. This paper provides a new reference for selecting premium stone fruits and enriches the fruit quality evaluation system.

Prediction of Mango Quality During Ripening Stage Using MQ-based Electronic Nose and Multiple Linear Regression

In recent years, consumers have shown interest in non-destructive methods to assess the fruit's internal quality during ripening. The objective of this study is to construct and evaluate an E-Nose system using low-cost MQ sensors to evaluate fruit quality, specifically soluble sugar content (SSC) and hardness of mango during ripening. The correlation test was performed to compare sensor readings with SSC and hardness, and multiple linear regression (MLR) was used to establish linear equations for mango quality indices based on sensor variation. Over the storage period, the hardness of mango was decreased from the value of 15.4 kgf/cm² to 12.25 kgf/cm². Similarly, the SSC for mangoes increased from 19.7%Brix to a final value of 24.66%Brix. The sensor values also showed positive correlation with SSC and negative correlation with hardness of mango, respectively. Using the MLR analysis, the hardness and SSC of mango during the ripening stage, the correlation coefficient (r) of 0.847, standard error of 1.49 kgf/cm2 and 0.815, standard error of 1.696 % Brix for hardness and SSC prediction, respectively. These results indicate that MQ-based E-nose is the rapid and non-destructive method for predicting mango qualities during ripening stages.

Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables.

Light penetration depth, as a characteristic parameter reflecting light attenuation and transmission in biological tissues, has been applied in nondestructive detection of fruits and vegetables. Recently, with emergence of new optical detection technologies, researchers have begun to explore methods evaluating optical properties of double-layer or even multilayer fruit and vegetable tissues due to the differences between peel and pulp in the chemical composition and physical properties, which has gradually promoted studies on light penetration depth. A series of demonstrated research on light penetration depth could ensure the accuracy of the optical information obtained from each layer of tissue, which is beneficial to enhance detection accuracy for quality assessment of fruits and vegetables. Therefore, the aim of this review is to give detailed outlines about the theory and principle of light penetration depth based on several emerging optical detection technologies and to focus primarily on its applications in the field of quality evaluation of fruits and vegetables, its future applicability in fruits and vegetables and the challenges it may face in the future.

Impact of microbial-based biopreparations on soil quality, plant health, and fruit chemistry in raspberry cultivation

Application of microbial-based biopreparations as a pre-harvest strategy offers a method to obtain sustainable agricultural practices and could be an important approach for advancing food science, promoting sustainability, and meeting global food market demands.The impact of a bacterial-fungal biopreparation mixture on soil-plant-microbe interactions, fruit chemical composition and yield of 7 raspberry clones was investigated by examining the structural and functional profiles of microbial communities within leaves, fruits, and soil. Biopreparation addition caused the enhancement of the microbiological utilization of specific compounds, such as d-mannitol, relevant in plant-pathogen interactions and overall plant health. The biopreparation treatment positively affected the nitrogen availability in soil (9–160%).The analysis of plant stress marker enzymes combined with the evaluation of fruit quality and chemical properties highlight changes inducted by the pre-harvest biopreparation application. Chemical analyses highlight biopreparations' role in soil and fruit quality improvement, promoting sustainable agriculture. This effect was dependent on tested clones, showing increase of soluble solid content in fruits, concentration of polyphenols or the sensory quality of the fruits. The results of the next-generation sequencing indicated increase in the effective number of bacterial species after biopreparation treatment. The network analysis showed stimulating effect of biopreparation on microbial communities by enhancing microbial interactions (increasing the number of network edges up to 260%) of and affecting the proportions of mutual relationships between both bacteria and fungi.These findings show the potential of microbial-based biopreparation in enhancing raspberry production whilst promoting sustainable practices and maintaining environmental homeostasis and giving inshght in holistic understanding of microbial-based approaches for advancing food science monitoring.

Hyperspectral imaging coupled with deep learning model for visualization and detection of early bruises on apples

Early bruise on apples caused by external impacts during the transportation process is commonly difficult to be detected on the apple surface, limiting the application of traditional machine vision methods in determining fruit quality. In recent years, hyperspectral imaging (HSI) has emerged as a promising technology for identifying early bruise of fruits due to its efficient and nondestructive detection. In this study, HSI data in the shortwave infrared range were collected at 2-hour and 6-hour intervals after mechanical damage. The combination of the successive projections algorithm (SPA) and principal component analysis (PCA) was used to select three key feature bands, namely, 1074 nm, 1269 nm and 1441 nm. Pseudo color transformation and band ratio algorithm were then employed to improve the contrast between damaged and healthy apple tissues for image enhancement. The fast and precise YOLOv5 (FP-YOLOv5) model achieved effective identification of apple bruises, with a high recognition rate of 95 % and a fast detection speed at 130 fps. Overall, the proposed framework based on band selection and image enhancement exhibits better performance in the detection of early apple bruises, providing useful insights for HSI combined with a deep learning model in the grading evaluation of fruit quality.

Assessment of the genetic structure of two different tomato breeding populations by Principal Component Analysis

In Plant Breeding, different populations are generated, which frequently represent different gene arrangement from a common selected gene pool. The technique of Principal Component Analysis (PCA) has been widely applied to evaluate the genetic structure of different populations. The objective of this research was to assess PCA for evaluating the genetic structure of two breeding tomato populations, one representing a final step of a breeding program (RIL population) and the other, an initial step (a six basic generations’ population, composed by two homozygous parent, their heterozygous F1 and the segregating F2 and two backcrosses). Both populations were evaluated for phenotypic quantitative traits and population structure was assessed in terms of variances and covariances. PCA was adequate for evaluating differences in genetic structure for evaluated fruit quality traits in both populations.

Yield and fruit quality evaluation of some new rootstock-scion combinations of sweet orange under agro-climatic conditions of Maharashtra, India

Yield and fruit quality evaluation of some new rootstock-scion combinations of sweet orange under agro-climatic conditions of Maharashtra, India

Testing a Simulation Model for the Response of Tomato Fruit Quality Formation to Temperature and Light in Solar Greenhouses.

Temperature and light are the key factors affecting the formation of tomato fruit quality in greenhouse cultivation. However, there are few simulation models that examine the relationship between tomato fruit quality formation and temperature and light. In this study, a model was established that investigated the relationships between soluble sugar (SSC), organic acid content (OAC), and SSC/OAC and the cumulative product of thermal effectiveness and photosynthetically active radiation (TEP) during the fruit-ripening period in a solar greenhouse. The root mean square error (RMSE) values were calculated to compare the consistency between the simulated and measured values, and the RMSE values for SSC, OAC, and SSC/OAC were 0.09%, 0.14%, and 0.358, respectively. The combined weights of quality indicators were obtained using the analytic hierarchy process (AHP) and entropy weighting method, ranking as SSC > OAC > SSC/OAC > CI > lycopene > Vc > fruit firmness. The comprehensive fruit quality evaluation value was obtained using the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) and a simulation model between comprehensive tomato fruit quality and TEP was explored. This study could accurately simulate and quantify the accumulation of tomato fruit quality during fruit ripening in response to environmental conditions in a solar greenhouse.

Molecular mechanism of abscisic acid signaling response factor VcbZIP55 to promote anthocyanin biosynthesis in blueberry (Vaccinium corymbosum)

A high content of anthocyanin in blueberry (Vaccinium corymbosum) is an important indicator to evaluate fruit quality. Abscisic acid (ABA) can promote anthocyanin biosynthesis, but since the molecular mechanism is unclear, clarifying the mechanism will improve for blueberry breeding and cultivation regulation. VcbZIP55 regulating anthocyanin synthesis in blueberry were screened and mined using the published Isoform-sequencing, RNA-Seq and qRT-PCR at different fruit developmental stages. Blueberry genetic transformation and transgenic experiments confirmed that VcbZIP55 could promote anthocyanin biosynthesis in blueberry adventitious buds, tobacco leaves, blueberry leaves and blueberry fruit. VcbZIP55 responded to ABA signals and its expression was upregulated in blueberry fruit. In addition, using VcbZIP55 for Yeast one hybrid assay (Y1H) and transient expression in tobacco leaves demonstrated an interaction between VcbZIP55 and a G-Box motif on the VcMYB1 promoter to activate the expression of VcMYB1. This study will lay the theoretical foundation for the molecular mechanisms of phytohormone regulation responsible for anthocyanin synthesis and provide theoretical support for blueberry quality improvement.

Elucidating the impact of ploidy level on biochemical content accumulation in haskap (Lonicera caerulea L. subsp. edulis (Turcz. ex Herder) Hultén) fruits: A comprehensive approach for fruit assessment

Artificially induced polyploidies in haskap (Lonicera caerulea L. subsp. edulis (Turcz. ex Herder) Hultén) were used in a systematic investigation to uncover the complex interplay between ploidy variations and the metabolic processes that govern the accumulation of biochemical compounds in fruits. This study used two distinct measurement units to support a rich analytical output to enhance the robustness of fruit quality evaluation substantially. Strong positive linear relationships were identified between ploidy levels and specific substances, including quinic acid and sorbitol. Conversely, several other substances had little or weak linear correlation with ploidy, underscoring that ploidy level can influence the accumulation of biochemical compounds in a substance-specific manner. This study reveals that tetraploid fruits, particularly individual accessions, demonstrate superior quality compared to other ploidy levels. This superiority is more notably pronounced when evaluated regarding content per fruit (CPF). Our findings provide key insights into the effects of ploidy on fruit quality with potential implications for future haskap breeding and cultivation practices. They also underscore the need for an in-depth understanding of the genetic and physiological factors that drive these variations. Such knowledge is crucial for strategically harnessing these variations and optimizing the quality and yield of haskap fruit production.

Investigating the tolerance of different strawberry cultivars to Botrytis cinerea infection and its relation with fruit quality

BACKGROUND: Strawberries are a delicate, high nutritional value fruit with an extremely short shelf life and high susceptibility to tissue infection, mainly by Botrytis cinerea. Control of the disease requires an extensive amount of fungicide that is applied in varying complexes because the pathogen easily develops resistance against the active compounds. Planting tolerant cultivars seems to be a promising alternative for fruit growers, but there are currently no cultivars available combining tolerance to B. cinerea with attractive horticultural traits. OBJECTIVE: In this study, four well-defined strawberry cultivars (‘Romina’, ‘Cristina’, ‘Silvia’, and ‘Sibilla’) were selected and tested under treatment with Botrytis cinerea to determine the tolerance of each cultivar and its association with fruit quality. METHODS: Fruits were harvested and then stored for four days after inoculation (treatment) or not (control) with spores of B. cinerea; then, every day the level of decay was detected, and fruits were frozen for successive evaluation of fruit quality. RESULTS: The ‘Silvia’ cultivar is the one that demonstrated a higher level of tolerance to Botrytis infection during the treatment, and the ‘Romina’ cultivar was the cultivar most susceptible to the treatment. The results of the study also highlighted qualitative changes in all strawberry cultivars infected with Botrytis cinerea, by a decrease in the soluble solids content and an increase of acids. Generally, in all cultivars, phenolic acids, and vitamin C decreased in both control and infected but there was a strong decrease in infected fruit compared to control. Anthocyanin content increased in control fruits but strongly decreased in infected. CONCLUSION: As a preliminary outcome, a positive correlation was obtained between fruit nutritional quality and tolerance to decay, as a high amount of nutritional compound content provides lower susceptibility of fruits to Botrytis cinerea. To confirm this result more studies need to be done.

A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries' Pomological Traits.

Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400-1000 nm) and short-wave infrared (SWIR) (935-1720 nm) for predicting four strawberry quality attributes (firmness-FF, total soluble solid content-TSS, titratable acidity-TA, and dry matter-DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product's marketability.

Evaluation of the effect of nanocellulose edible coating on strawberries inoculated with Aspergillus flavus through image analysis

The impact of a coating solution containing chitosan, lignocellulosic nanofibers (LCNF), and raspberry leaf extract on strawberry quality was assessed. Strawberries were coated, inoculated with Aspergillus flavus (a major aflatoxin producer), and stored at 5 ± 1 °C for 16 days. An automated fruit quality evaluation system was developed, and daily images were taken for analysis. ImageJ software was used to evaluate colour, size loss, and fungal damage. Coating solution significantly reduced fruit size loss, with coated strawberries showing 18.4 ± 12.8% size loss after 16 days, compared to 30.2 ± 8.4% in the control group and 25 ± 10.7% in uncoated strawberries. Regarding colour, lower colour changes were observed in coated fruit compared to the control during storage (p ≤ 0.05). Fungal damage did not significantly differ between treatments during storage (p > 0.05). Furthermore, the study demonstrated that image analysis and visual estimation methods for assessing fruit fungal damage yielded similar results, with a strong correlation (R2 = 0.87). This suggests that the proposed image analysis methodology is effective for the assessment of fungal damage in strawberries compared to visual estimation. Chitosan-LCNF-raspberry extract coating solution shows promise in enhancing strawberry quality by reducing size loss during storage and managing fungal development.

Principle and applications of near‐infrared imaging for fruit quality assessment—An overview

SummaryThe quality (i.e. the degree of excellence and acceptance to the consumer) evaluation and safety of fruit and other agricultural products is by far the main challenging task in the food industries. Hence, the development of efficient non‐invasive, real‐time and rapid‐quality assessment tools and techniques is the need of the hour. This review paper aims to provide the basic or fundamental knowledge regarding the principle, potential and application of near‐infrared imaging‐based computer vision systems. Non‐invasive technologies, based on optical properties, are the most applicable and important for real‐time quality assessment. Absorption, reflection and scattering before the transmission of incident light are phenomenon properties of biological materials. The principle and application of NIR imaging (i.e. medical imaging) in agriculture for quality detection were studied from various published research papers. Thus, collected pieces of information are summarised, concluded and discussed in a unique way in this report. The NIR spectrum of electromagnetic waves has some basic benefits of light penetration into the upper layer and makes visualisation of the targeted area of objects. Several studies have proved that the non‐destructive system based on visible and non‐visible imaging in combination has a significant role in quality defect detection in fruits. In NIR imaging, the artefacts caused by light illumination can be reduced and it will help in the visualisation of biological tissues and hidden defects just below the peel.

Comprehensive evaluation of fruit quality for premium Wangmo Castanea mollissima plants

In this study, the fruit phenotype and quality of 32 superior Wangmo Castanea mollissima plants (designated GM1 to GM32) were examined to identify the trait characteristics of different cluster groups and germplasms with excellent comprehensive performance. The goal was to provide a theoretical basis and research foundation for collecting high-quality germplasm resources and breeding superior cultivars of Wangmo C. mollissima. Ten fruit phenotypic traits and 13 quality traits were measured and analyzed in these 32 superior Wangmo C. mollissima plants. Cluster analysis and principal component analysis (PCA) were used to perform a comprehensive evaluation. Extremely significant positive correlations (P<0.01) were observed for 15 pairs of fruit phenotypic and quality traits, and significant positive correlations (P<0.05) were observed for 16 pairs of traits. Highly significant negative correlations (P<0.01) were observed for 4 pairs of fruit phenotypic and quality traits, and significant negative correlations (P<0.05) were observed for 15 pairs. The plants were divided into three groups by cluster analysis: the first group had large fruits and good fruit quality, the second group had small fruits and poor fruit quality, and the third group had medium-sized fruits with a high starch content. Four principal components were extracted from the 23 traits by PCA, contributing 76.23% of the variance. The ten plants with the highest comprehensive quality were GM32, GM31, GM29, GM1, GM8, GM17, GM10, GM30, GM3 and GM28. The results of this study provide a reference for the development and utilization of Wangmo C. mollissima germplasm resources.

Fruit Quality Evaluation of Red Raspberry Cultivars Grown in Western Serbia

Fruit Quality Evaluation of Red Raspberry Cultivars Grown in Western Serbia

Digital image processing applications in agriculture with a machine learning approach

Abstract. Digital image processing involves the manipulation of images by digital means, and its use has been increasing exponentially in recent decades. It is applied in a diverse range of fields including medicine, remote sensing, robotic vision, and audiovisual processing. Image processing technologies are widely used as a proficient tool in the agriculture sector. The combination of digital image processing and machine learning not only provides new insights into crop health and environmental circumstances, but also helps farmers reach timely and precise decisions. Using machine learning methods, this study examines digital image processing applications in agriculture, focusing on plant disease identification, fruit quality evaluation, weed detection, yield mapping, and robotic harvesting, to other issues. In essence, this study integrates existing knowledge at the interface of digital image processing, machine learning, and agriculture, providing insights into a promising and growing topic with significant implications for sustainable and resilient food production.

Evaluation of Fruit Yield and Quality of Netted Melon, Water and Nutrient Use Efficiency in a Closed Hydroponic System

Evaluation of Fruit Yield and Quality of Netted Melon, Water and Nutrient Use Efficiency in a Closed Hydroponic System