Berry quality control & inspection:  improved fruit quality inspection consitency.

Berry quality control inspection app.

The farmsoft quality control app for fresh produce, seeds, meat, seafood, coffee, herbs, chili quality control & quality management.

Inventory quality control

Manage incoming fresh produce  inventory quality,  capture supplier details and photos, traceability and costs, create inventory & pallet labels, record storage location of inventory.  Bar-code inventory.

Stock-take quality control

Perform stock-takes any time by category or storage location.  Know how much  inventory you have in real time, even search by storage location.  Report by product line and storage location, or product category. 


Quality control for farm tasks, farm equipment (tractors, spray rig etc), in field fresh produce QC tests. 

Sales, shipping,  orders

Print pick sheet to pick inventory, or scan inventory / pallets onto orders, or auto select inventory,  or rapidly sell without an order.  Track paid, and unpaid invoices. 

Perform optional quality control tests on fresh produce prior to shipping.

Traceability & recalls

Mock recalls up and down supply chain.   Reduces fresh produce food safety compliance costs, makes audits easy. Optional fresh produce blockchain by CHAIN-TRACE.COM

QC tests relate to specific batches, from specific suppliers from specific farming areas.

Invoices, BOL, labels for pallets & inventory

Choose from a gallery of invoices, bill of lading, freight notes, and industry standard fresh produce labels including Walmart, Tesco, Aldi, Coles, Pick 'n Save, Woolworths and more...

Quality Control tests can be recalled back to a specific invoice if a client has an issue with quality and quotes and invoice number which can be used to find the quality test.

Batch packing

Record all batch inputs such as fruit & vegetables, packaging materials, and other raw materials.  Batch costs automatically tracked.  Batch recalls automatically track suppliers & traceability.

Batch level quality control, lookup QC tests using the batch number.


View open orders & balances. Assign orders to specific staff for picking, assign to trucks / driver, transport company.  Set loading order for multiple orders on one truck.  See when orders are ready shipped and print bill of lading, export documents, and invoices. 

Send quality information with shipments.

Quality control

Perform QC tests for incoming pepper inventory, packed, pre-shipping. Configure QC tests for ANYTHING you want to test, supplier quality control tracking.  Attach unlimited photos & documents to QC tests from your cell or tablet.  

Supplier quality control

Rapidly perform quality control tests on fresh produce from suppliers.  Compare the quality  performance of multiple suppliers, and compare quality criteria performance.  Provide quality feedback to suppliers.


Profit:  Analyze profit of each product, individual customer, and batch.  Sales:  Monitor sales progress & shipments.  Quality control dashboard: Internal quality monitoring, supplier performance & more...

Quality control labels

Optionally show a QR code on customer or consumer units that will instantly show the quality control results for that batch of fresh produce.

Value adding

For food service and processors:  specify the ingredients for each product you manufacture, farmsoft will calculate required quantities to fill open orders and schedule the batch.  Quality control tests on all finished product packed.

Unlimited sites & warehouses

Create multiple sites, specify which sites each employee can view (this restricts inventory, orders, invoices etc to selected sites).  Great for businesses with multiple locations across the country or planet.

Advanced tailoring

Add new fields to screens, choose from a wide selection of interfaces (touch based, PC based, data entry, tablet), control special business processes, activate defaults, configure automatic alerts and more...

Purchase order quality control

Order raw materials, packaging materials and more from suppliers.  Analyze orders and prices using Purchases dashboard. 

Perform quality control tests on fresh produce Purchase Order deliveries.

Re-order alerts

Receive alerts when inventory needs to be reordered, analyze inventory that will need ordering in the future, and inventory that is approaching expiry...

Finance apps

Integrate with Xero finance, or export invoices (AR) and Purchase Orders (AP) to your chosen finance app like MYOB, Quickbooks, , FreshBooks, Wave, SaasAnt, SAGE and others...

Unlimited berry quality control tests

Configure unlimited quality control tests for any fresh produce, meat, seafood, seeds, coffee, chilis, hops...

Rapid berry quality control

Perform rapid quality control (QC) tests on any fresh produce directly from  your cell or tablet app.

Better berry  packing quality now

Quality control and food safety has never been easier with industry standard quality tests, food safety checklists; or configure your own tests. 

Improve berry food safety

Farmsoft manages your business wide food safety, as an integral part of the farmsoft fresh produce business management app.

Easy berry quality traceability

Perform instant mock recalls and audits at any time, from anywhere. No need to compile reports or search for documents. International food safety standards maintained.

Increase berry  inventory quality

Improve management of the quality of incoming fresh produce from the moment it arrives at your pack house.

Improved customer satisfaction from consistent quality berry 

Customer appreciate consistent fresh produce quality control.

Berry quality control on the production line

In line and end of line fresh produce quality control ensures maximum quality without quality surprises. 


Improving the stability of high-quality traits of berry in different environments and cultivation systems for the benefit of European farmers and consumers
The GoodBerry research project focuses on the improvement of strategically important berry fruit crops, namely strawberry, raspberry, and blackcurrant. The project is based on a multi-tiered strategy interlinking research areas from molecular studies to cultivation techniques.

GoodBerry aims at:

identifying berry germplasm exhibiting an advantageous balance of production vs nutritional quality throughout the EU developing innovative production systems/ modern breeding strategies to maintain high yield in a range of European-wide environments and standardized and reliable analytical tools to evaluate berry production and fruit quality ensuring the uptake of the high quality production systems by EU growers to boost the competiveness of the European berry production.

Diversity in Metabolites and Fruit Quality Traits in Blueberry Enables Ploidy and Species Differentiation and Establishes a Strategy for Future Genetic Studies.
Blueberry is well recognized as a rich source of health promoting phytochemicals such as flavonoids and phenolic acids. Multiple studies in blueberry and other crops indicated that flavonoids and phenolic acids function as bioactive compounds in the human body promoting multiple health effects. Despite their importance, information is limited about the levels of variation in bioactive compounds within and between ploidy level and species, and their association with fruit quality traits. Such information is crucial to define a strategy to study the genetic mechanisms controlling these traits and to select for these traits in blueberry breeding programs. Here we evaluated 33 health related phytochemicals belonging to four major groups of flavonoids and phenolic acids across 128 blueberry accessions over two years together with fruit quality traits, including fruit weight, titratable acidity, total soluble acids and pH. Highly significant variation between accessions, years, and accession by year interaction were identified for most of the traits. Cluster analysis grouped phytochemicals by their functional structure (e.g., anthocyanins, flavanols, flavonols, and phenolic acids). Multivariate analysis of the traits resulted in separation of diploid, tetraploid and hexaploid accessions. Broad sense heritability of the traits estimated in 100 tetraploid accessions, ranged from 20 to 90%, with most traits revealing moderate to high broad sense heritability (H2 > 40%), suggesting that strong genetic factors control these traits. Fruit size can be estimated as a proxy of fruit weight or volume and vice versa, and it was negatively correlated with content of most of phytochemicals evaluated here. However, size-independent variation for anthocyanin content and profile (e.g., acylated vs. non-acylated anthocyanin) exists in the tetraploid accessions and can be explored to identify other factors such as genes related to the biosynthetic pathway that control this trait. This result also suggests that metabolite concentrations and fruit size, to a certain degree can be improved simultaneously in breeding programs. Overall, the results of this study provide a framework to uncover the genetic basis of bioactive compounds and fruit quality traits and will be useful to advance blueberry-breeding programs focusing on integrating these traits.

Blueberry belongs to the Ericaceae family and the genus Vaccinium section Cyanococcus. Three blueberry species, that include the highbush blueberry (HB), V. corymbosum L., rabbiteye blueberry (RB), V. ashei Reade (syn. Vaccinium virgatum Ait) and native stands of lowbush blueberry (LB), V. angustifolium Ait, are commercially grown in the United States (Kalt et al., 2001; Lyrene et al., 2003; Hancock et al., 2008; Retamales and Hancock, 2018). Among these species, the HB blueberry is widely grown in the US, accounting for ∼95% of total blueberry production (Kalt et al., 2001; Wang et al., 2019). The HB cultivars are further classified into northern highbush (NHB) and southern highbush (SHB) blueberries based on chilling requirement and winter hardiness (Lyrene et al., 2003; Hancock et al., 2008; Retamales and Hancock, 2018). In addition to these species, a number of wild diploid blueberry species have been described and are used in breeding programs as sources for low chilling requirement, disease resistance (mummy berry disease), heat tolerance and adaptation to higher soil pH (Hancock et al., 2008; Lobos and Hancock, 2015; Retamales and Hancock, 2018; Wang et al., 2019). Studies have also focused on diploid wild blueberries as important sources of bioactive metabolites including flavonoids and phenolic acids (Kalt et al., 2001; Giovanelli and Buratti, 2009; Prencipe et al., 2014; Zoratti et al., 2015; Wang et al., 2019).

During the last decade, blueberry production and consumption have increased (Rodriguez-Saona et al., 2019) and the growing body of research supporting the health benefits associated with blueberry consumption have likely contributed to this growth (Norberto et al., 2013; Sun et al., 2019). Indeed, clinical evidence suggests that sufficient intake of blueberries provides multiple health benefits including lowering blood pressure, protecting against heart attack, preventing cancer, improving mental health and managing diabetes (Martineau et al., 2006; Krikorian et al., 2010; Stull et al., 2010; Norberto et al., 2013; Sun et al., 2019; Yang H. et al., 2019).

Important health–promoting bioactive metabolites, including flavonoids (anthocyanins, flavanols, and flavonols) and non-flavonoids such as phenolic acids, are found abundantly in blueberry (Kalt et al., 2001; Rodriguez-Mateos et al., 2012; Yousef et al., 2013, 2014; Gündüz et al., 2015; Scalzo et al., 2015; Li et al., 2017; Timmers et al., 2017; Grace et al., 2019; Wang et al., 2019). In blueberry, anthocyanins are the most abundant flavonoids, and are derivative of anthocyanidins (aglycones) by the addition of sugar moieties. The most common anthocyanidins in blueberry are delphinidin (Dp), cyanidin (Cyn), petunidin (Pet), peonidin (Peo), and malvidin (Mv). The anthocyanidins can be conjugated with sugar moieties like arabinoside, glucosides, and galactoside via the C3 hydroxyl group in ring C, and form structurally different anthocyanins. The conjugation of anthocyanidins with sugar moieties can be further modified through acylation (Norberto et al., 2013; Yousef et al., 2014; Li et al., 2017; Grace et al., 2019; Wang et al., 2019). Various patterns of conjugated sugar moieties, with or without acylation, can affect the chemical properties of anthocyanins, such as stability and bioavailability. Acylation of anthocyanins, through the addition of acyl groups such as organic acids (cinnamic and aliphatic acids) via ester bonds, improves color and tinctorial strength and increases the stability of anthocyanins at higher pH or under intense light and heat stress (Giusti and Wrolstad, 2003; Matera et al., 2015; Zhao et al., 2017; Strauch et al., 2019). Acylated anthocyanins are of special interest for use as a stable natural colorants in food industries (Giusti and Wrolstad, 2003; Matera et al., 2015; Zhao et al., 2017; Strauch et al., 2019).

Caffeic, ferulic, and chlorogenic acids are commonly reported phenolic acids. Of these, chlorogenic acid is the most abundant phenolic acid in blueberry (Grace et al., 2019; Wang et al., 2019). Similarly, flavanols (epi-catechin, catechin, proanthocyanidin B1, and proanthocyanidin B2) and flavonols (kaempferol, quercetin, and myricetin) are also commonly reported flavonoids in blueberry (Grace et al., 2019; Wang et al., 2019).

Differences in bioavailability between acylated and non-acylated anthocyanins have been reported in the literature (Kurilich et al., 2005; Charron et al., 2009; Oliveira et al., 2019). Charron et al. (2009) reported that non-acylated anthocyanins are better absorbed in the upper intestine compared to acylated anthocyanins. This effect may have been due, in part, to presence of phenolic acids as acylation agents on anthocyanins that serves to limit the transport efficiency or enzymatic conversion to aglycones in the upper intestine. While acylation does serve to stabilize anthocyanins to digestive conditions (McDougall et al., 2007; Correa-Betanzo et al., 2014), this would potentially serve to enhance their retention during intestinal passage and make them more available for catabolism by human microbiota in the large intestine (Fleschhut et al., 2006). As such, understanding factors promoting differences in acylation patterns could serve to better predict efficiencies of absorption, extent of host and microbial metabolism and ultimate health protective properties of anthocyanin rich foods.

Numerous studies have been conducted to examine genotype- or species-specific metabolites and fruit quality variability in blueberry (Kalt et al., 2001; Rodriguez-Mateos et al., 2012; Yousef et al., 2013, 2014; Gündüz et al., 2015; Scalzo et al., 2015; Li et al., 2017; Timmers et al., 2017; Wang et al., 2019). However, these studies targeted a small number of genotypes/accessions and provided limited insight into species and ploidy diversity of metabolite profiles and fruit quality traits. In addition, there is limited information about the association between metabolites with quality traits including fruit weight/size, pH, total soluble solid (TSS) and titratable acidity (TA) (Kalt et al., 2001; Rodriguez-Mateos et al., 2012; Yousef et al., 2013, 2014; Gündüz et al., 2015; Li et al., 2017; Wang et al., 2019). Most importantly, no studies have yet identified potential strategy to perform genetic analysis for these bioactive metabolites.

In this study, we profiled metabolites (anthocyanins, phenolic acids, flavanols, and flavonols) and fruit quality traits (pH, TA, TSS, fruit weight) in 128 blueberry accessions across three ploidy levels (diploid, tetraploid, and hexaploid) over two years. The main objectives of this study were to: (1) assess variability among the blueberry accessions for different metabolites and fruit quality traits; (2) investigate the association among metabolites, and between metabolites and fruit quality traits; and (3) establish a strategy to study the genetic basis controlling metabolite accumulation in highbush blueberry.

Materials and Methods
Materials Collection and Preparation
A collection of 128 blueberry accessions was obtained from the National Clonal Germplasm Repository (NCGR), Corvallis, OR, United States. Information about these Vaccinium accessions, including name, collection sites, ploidy level and species is provided in Supplementary Table S1. The collection included 100 tetraploid (4×), 22 hexaploid (6×), and 6 diploid (2×) accessions, representing diversity in geographical origin and genetic background (Supplementary Table S1). Of the 100 tetraploid (4×) accessions, 98 accessions represent V. corymbosum, NHB, SHB, and hybrids between these two types of blueberry cultivars. Berries were harvested at ripening stage for two consecutive years (2017 and 2018). For each accession, fruit were harvested from two or three clones. Since amount of fruit available for each clone was not the same and in several case not sufficient to perform all the phenotyping assays, prior to processing, the fruits were combined and then separated into three technical replicates. The technical replicates could minimize errors associated with sample processing and fruit quality and metabolite traits phenotyping. After harvesting, the berries were stored at −80°C, shipped on dry ice to the Plants for Human Health Institute (PHHI), Kannapolis, North Carolina, United States, and stored at −80°C until processing. Frozen berries (approximately 10–30 g, three replicates), were then used for fruit quality and metabolite analyses.

Fruit Quality Trait Evaluation
Establishment of Phenotyping Method for Fruit Size
Image-based phenotyping is a powerful tool to estimate fruit quality attributes including fruit shape, size and color (Diaz-Garcia et al., 2016). While this method is high-throughput, it is also relatively time-consuming (sample set up, image acquisition and processing) if we are interested in phenotyping a single trait. Preliminary data from our lab suggested that image-based fruit volume estimation using the GiNA R package (Diaz-Garcia et al., 2016) and fruit weight were highly correlated. This association provides an opportunity to phenotype fruit size using fruit weight as a proxy for estimating fruit size, a faster phenotyping method as compared to fruit volume measurement. To verify this hypothesis, we selected 54 accessions varying in fruit weight. A minimum of 10 berries for each accession were used to estimate the fruit weight (g per fruit) of each berry. The same berries were scanned with a digital camera and the images were processed using the GiNA R package (Diaz-Garcia et al., 2016), which measured fruit volume and fruit surface area of each berry. Correlation was performed between all the measured parameters, to determine the relationship. To assess the agreement between the two measurements, fruit volume (cm3) and fruit weight (g), Bland-Altman plot was created using excel (Bland and Altman, 1986). Bland-Altman plot determines the bias (mean of the differences) and limits of agreement [bias ± 2 × SD (standard deviation)].

Phenotyping of Fruit Quality Traits
Fruit weight (g per fruit) was recorded (10–30 berries, three replicates) for fruit harvested in 2017 and 2018. In addition to fruit weight, we evaluated TSS, pH and TA. The berries used to measure fruit weight were homogenized to a puree in a Waring Commercial Blender 7012G (Torrington, CT, United States). Homogenized samples were used to determine TSS, pH and TA and to quantify anthocyanins and non-anthocyanin bioactive metabolites using high-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC-MS).

Total soluble solid was estimated using a digital hand-held “pocket” refractometer PAL-1 (Atago, Tokyo, Japan) and the results were expressed as°Brix. pH and TA were measured using 1 g of homogenized sample diluted with 30 ml pre-boiled double distilled water. The pH was measured using Accumet AB15, pH-meter (Fisher Scientific, Waltham, MA, United States). Then, TA was determined with a Mettler DL15 Auto-Titrator (Columbus, OH, United States) at pH of 8.2 using 0.02 mol L–1 sodium hydroxide. TA was expressed as percentage of citric acid (wt/wt) per 1 g FW.

Extraction and Quantification of Anthocyanins and Non-anthocyanin Metabolites
Extraction and Sample Preparation for HPLC and LC-MS Analysis
An aliquot (3 g) of the homogenized blueberry puree was weighed in a 30-mL centrifuge tube. After the addition of 8 ml of 80% methanol in water (containing 5% formic acid), this mix was homogenized using a PRO0250 (PRO Scientific Inc., Oxford, CT, United States) for 2 min to extract polyphenols. The homogenate was centrifuged (Sorvall RC-6 plus, Asheville, NC, United States) for 2 min at 4,000 rpm. The supernatant was collected in a 25-ml volumetric flask. The residue was then extracted two more times, once with 8 ml of the same solvent, and then with 100% methanol. Supernatants were collected and brought to a final volume of 25 ml. About 1 ml of each sample was diluted with equal volume of methanol-water-formic acid, 65:35:5 and filtered (0.22 μm PTFE membrane) prior to HPLC-PDA analysis for anthocyanins and chlorogenic acid.

For LC-MS analysis of other phenolic compounds of low concentrations (flavonols, flavanols, and phenolic acids), a solid phase extraction (SPE) procedure was performed to remove sugars that may cause decrease in sensitivity of the MS system. An aliquot of 1–1.5 mL from each sample/replicate was centrifuged at 15,000 rpm for 5 min. In a 96-deep well plate (loading plate), 1.2 mL water (1% formic acid, FA), 10 μL fisetin (200 μg/mL, final concentration, 5 μg/mL), and 200 μL extract were mixed. The SPE plate (Phenomenex; StrataTM 96 Well Plate 10 mg/Well) was preconditioned with methanol (1% FA), then with water (1% FA). The samples were transferred from the loading plate to the SPE plate and drained by gravity. The 96-SPE well plate was washed 5 times with 600 μL water (1% FA) and dried about 20 min using positive pressure manifold. Finally, the samples from the SPE plate were eluted with 300 μL methanol (0.1% FA) to a 96-well collection plate. Then, 90 μL water (0.1% FA) and 10 μL internal standard (phlorizin 200 μg/mL, final concentration 5 μg/mL) was added to each well. The collection plate was covered with a sealing mat and immediately transferred to the auto-sampler of the MS system for analysis.

HPLC Analysis
HPLC analysis was conducted to quantify anthocyanins and chlorogenic acid. Standards, cyanidin-3-galactoside, cyanidin-3-glucoside, and malvidin-3-galactoside, were obtained from Chromadex (Irvine, CA, United States). Delphinidin-3-glucoside was purchased from Cayman Chemicals (Ann Arbor, MI, United States). Delphinidin-3-galactoside, malvidin-3-glucoside, petunidin-3-glucoside, myricetin-3-glucoside, kaempferol-3-glucoside, and syringetin-3-glucoside were obtained from Extrasynthese (Genay Cedex, France). Cyanidin-3-arabinoside and peonidin-3-glucoside were obtained from Polyphenols (Sandnes, Norway).

Each of the nine anthocyanin reference compounds and chlorogenic acid standard were individually dissolved in methanol-water-formic acid, 65:35:5, at a concentration of 5 mg/mL. Equal volumes from each standard solution were mixed together and diluted with the solvent mix to prepare a standard stock mix solution (200 μg/mL). Eight standard working solutions, used for the calibration curve, were prepared by appropriate dilution of the stock mix solution (2–175 μg/mL). The reference standard mix dilutions were injected to generate an eight-point calibration curve for each compound, separately. Standard curves were linear with R2 > 0.9997 ± 0.0007.

The chromatography was conducted on an Agilent 1260 HPLC with diode array detector (DAD) (Agilent Technologies, Santa Clara, CA, United States). Separation of anthocyanins was performed on a Supelco C-18 column (25 cm × 4.6 mm × 5 μm), and the temperature of the column oven was maintained at 30°C. The eluents were water (formic acid 5%, v/v) (A) and methanol (B), with a gradient of 10–20% B (0–5 min), 20–25% B (5–20 min), 25–30% B (20–25 min), 30–35% B (25–30 min), 35–90% (30–43 min), and isocratic at 90% B (43–46 min). The column was then re-equilibrated for 4 min at 5% B, at the flow rate of 1 ml/min. Absorption was recorded at 520 nm for anthocyanins, and 280 nm for chlorogenic acid. Not all anthocyanins present in blueberry are commercially available; therefore, anthocyanins with no standard reference were quantified as their corresponding glucoside or galactoside equivalent. The lowest limit of detection (LLD) for all anthocyanins was in the range of 1.24–1.91 ppm, 0.96 ppm for chlorogenic acid (Supplementary Table S2).

LC-MS Analysis
LC-MS analysis was conducted to quantify non-anthocyanin compounds including flavonols, flavanols and phenolic acids. Standards, procyanidins B1 and B2, catechin, epicatechin, caffeic acid, 2,4-dihydroxybenzoic acid, quercetin glucoside and galactoside and quercetin arabinoside were purchased from Sigma-Aldrich (St. Louis, MO, United States). Phlorizin was used as an internal standard; Fesitin was used to measure the efficiency of SPE, and both were purchased from Sigma-Aldrich. The analysis was performed on a hybrid IT-TOF mass spectrometer (Shimadzu LC-MS-IT-TOF, Kyoto, Japan) equipped with two LC-20AD pumps, a SIL-20AC autosampler, a CTO-20A column oven, an SPD-M20A PDA detector, a CBM-20A system controller coupled to an IT-TOF-MS through an ESI interface. All data were processed by Shimadzu LCMS lab Solution Version 1.2. The mass spectrometer was programmed to carry out a full scan over m/z 70–100–700 (MS1) and m/z 70–500 (MS2) in the negative ionization mode. The heat block and curved desolvation line (CDL) temperature were maintained at 200°C; nitrogen was used as the nebulizing gas at a flow rate of 1.5 L/min, and as the drying gas at 75 kPa; the interface voltage was (+), 4.5 kV; (-), -3.5 kV; and the detector voltage was 1.80 kV.

The chromatography was performed on a Shim-pack XR-ODS column (50 mm × 3.0 mm × 2.2 μm) (Shimadzu, Japan), and the temperature of the column oven was maintained at 50°C. The eluents were water (FA 0.5%, v/v) (A) and methanol (B), with a gradient of 5–50% B (0–17 min), 50–80% B (17–18 min), and 80–5% B (18–19 min). The column was then re-equilibrated for 1 min at 5% B, at the flow rate of 0.6 mL/min. Compounds were quantified as their extracted-ion chromatograms (EIC) in the negative ion mode using phlorizin as an internal standard. The lowest limits of detection for all non-anthocyanin compounds ranged from 0.41 to 2.66 ppm as presented in Supplementary Table S2.

Data Processing and Statistical Analyses
Analysis of Variance, Trait Heritability, and Correlation Analyses
To assess the magnitude of variation within and between ploidy groups, we computed a minimum, maximum and range of variation for all metabolites and fruit quality traits. Fold-change values were calculated independently for each metabolite and fruit quality trait, dividing the maximum value by the minimum value of each trait within ploidy group. To normalize metabolite data, a log2 transformation was applied on quantified values. Analysis of variance (ANOVA) was performed to partition individual metabolites and fruit quality related trait according to ploidy, accession number, year, and accession by year interaction. Best linear unbiased estimate (BLUE) data obtained from the linear model were used as the phenotypic values for all subsequent analyses. Broad-sense heritability (H2) was estimated using variance components calculated from the restricted maximum likelihood (REML), calculated as follows

where, δ2g, δ2gy, and δ2e are variance components of accessions, [genotype x environment] interaction, and residual variations, respectively; y is the number of environments (number of years in this study, 2) and r is the number of replications (3).

Pearson Coefficient of Correlation was performed to find the relationship among traits and for the two-year data, independently. The correlation was visualized using the R package corrplot (Wei et al., 2017).

Multivariate Analysis of Metabolites and Fruit Quality Traits
BLUE data obtained from linear effects model were used as an input file for hierarchical clustering (HC), principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). HC combines similar individuals or variables into clusters and arranges these clusters into a hierarchy while PCA is a technique used to reduce dimensionality of the data by finding linear combinations (dimensions; in this case, the number of metabolite and fruit quality traits) of the data. HC was performed with the Spearman and Ward’s methods, and were visualized as a heatmap with a dendrogram using the heatmap.2 R package (Warnes et al., 2016). PCA was performed using the R package FactoMiner (Lê et al., 2008) as a non-supervised method to identify key traits with the largest effect on the overall variability and to evaluate the effect of genetic background on fruit quality and metabolite profiles among different accessions. PLS-DA (supervised version of PCA) was performed using metaboanalyst (Chong et al., 2018) to examine the classification of accessions based on ploidy groups/species and also identify key features using variable importance for the projection (VIP) > 1. The model fitness was evaluated using model accuracy, Q2 and R2 values of the model as described (Szymańska et al., 2012).

Fruit Weight and Image-Based Volume Phenotyping Methods
Correlation analysis indicated that fruit weight and image-based volume measurements are highly (P < 0.001, r = 0.99) correlated (Figure 1A). Correlation provides information about the strength of the relationship between two measurements, but not agreement between the two measurements. Therefore, we assessed the agreement between the measurements, fruit volume (cm3) and fruit weight (g per fruit) with the Bland–Altman plot (Figure 1B). Here, the mean difference (bias) is 0.08, where the limits of agreement are −0.0734 and 0.25, indicating that 95% of the differences between the two measurements are within this range. Only one observation lies outside the 95% confidence interval, suggesting that the two measurements have an acceptable level of agreement, meaning that the absolute value of the numerical scale used to express the two measurements agree. Therefore, fruit weight can be used as a proxy to estimate fruit volume. It is also important to note that fruit surface area and fruit volume are also highly (r > 0.99) correlated traits (Figure 1C), suggesting that both fruit volume and fruit surface area can be highly predictable based on fruit weight. Furthermore, we also estimated fruit volume from 12 accessions with other fruit volume estimation methods including water displacement and texture analyzer and found that both methods are strongly (r > 0.99) correlated with fruit weight (Data not shown).

Quality of fruit has to be considered a central trait to optimize fruit chain management since consumers want agricultural products that both taste good and are nutritious. In order to satisfy consumer’s demands more effort and attention has to be devoted, from a scientific and practical perspective, to improve and optimize quality upon delivery to the consumers without compromising the agronomic traits that growers demand.

Quality of soft fruit berries, such as blueberry, strawberry, and raspberry, is a complex trait, which includes visual attractiveness (color, size, and shape), overall flavor (taste and texture), and the extraordinarily nutritional properties. Among these traits, texture, flavor, and appearance directly impact postharvest performance and consumer appreciation and therefore fruit marketability. Although the importance of these factors can hardly be underestimated, breeding efforts have historically been mainly oriented to improve fruit appearance and storability. However, selection for improved shelf-life and appearance properties may have unintended negative consequences on other fruit quality traits, for instance, aroma and nutritional value. Loss of aroma has already been identified throughout the breeding efforts of other fruit species, such as strawberry, peach, apple, and tomato. This quality decline can be heightened by the fact that breeding selection for flavor occurs nearly by chance (not assisted), since flavor is currently not considered as a discriminating trait in the early selection phase. This limitation is also strengthened by the complex and time-consuming phenotyping protocols ordinarily used, which makes the analytical screening of large populations plant material unfeasible.

Defining fruit quality trait priorities is challenging since trait relevance depends largely on which supply chain actor is involved in the decision-making process. In particular, understanding the stability of each quality trait during different storage and/or agronomical conditions may allow a better definition of future breeding strategies aimed, for example, at the selection of accessions suitable to improve distinct market sector performance. For this purpose, comprehensive investigations and a tight synergy of analytical approaches from different branches of knowledge are needed. The expanded use of inexpensive, high-quality, and high throughput omics techniques is expected to soon provide elucidation of the genetic and physiological regulation of fruit quality.

This Research Topic aims to collect the most recent advances on recent scientific progress concerning the quality of soft fruit berries (i.e. strawberry, raspberry, blueberry, blackberry, acai, black currant, goji, and chokeberries). These studies should focus on innovative omics technologies (i.e. genomics, transcriptomics, proteomics, metabolomics, or metagenomics) and multidisciplinary approaches of quality management throughout the entire production chain, from breeding selection to consumer consumption. Since the composition and quality of fruit is modulated at various biological levels, we especially encourage the submission of studies that integrate two or more ‘omics’ platforms, and those applying systems biology approaches. We welcome research articles, reviews, short notes, and opinion articles focused on:

- New insights into biological and physiological phenomena that affect quality and nutritional aspects of soft fruit berries (i.e. the effect of different light spectra on fruit phytochemical content; the effect of controlled stress condition on fruit quality traits, such as the VOC production; the role of fruit microbiota on ripening and storability)
- New insights on the genetic regulation of quality traits of soft fruit berries
- Development of genetic and molecular markers to assist breeding activity

Please note that descriptive studies that report responses of growth, yield or quality to agronomical treatments are not considered if they do not progress physiological understanding of these responses. Additionally, omics studies should provide at least one experimental support (e.g. in vivo or in vitro) in order to show that one, or more, candidates might indeed be involved in the biological process under analysis.

Keywords: Quality, Berries, Phenomics, Metabolomics, Genomics, Crop Management, Processing

Novel stable QTLs identification for berry quality traits based on high-density genetic linkage map construction in table grape
Aroma, berry firmness and berry shape are three main quality traits in table grape production, and also the important target traits in grapevine breeding. However, the information about their genetic mechanisms is limited, which results in low accuracy and efficiency of quality breeding in grapevine. Mapping and isolation of quantitative trait locus (QTLs) based on the construction of genetic linkage map is a powerful approach to decipher the genetic determinants of complex quantitative traits.

In the present work, a final integrated map consisting of 3411 SLAF markers on 19 linkage groups (LGs) with an average distance of 0.98 cM between adjacent markers was generated using the specific length amplified fragment sequencing (SLAF-seq) technique. A total of 9 significant stable QTLs for Muscat flavor, berry firmness and berry shape were identified on two linkage groups among the hybrids analyzed over three consecutive years from 2016 to 2018. Notably, new stable QTLs for berry firmness and berry shape were found on LG 8 respectively for the first time. Based on biological function and expression profiles of candidate genes in the major QTL regions, 3 genes (VIT_08s0007g00440, VIT_08s0040g02740 and VIT_08s0040g02350) related to berry firmness and 3 genes (VIT_08s0032g01110, VIT_08s0032g01150 and VIT_08s0105g00200) linked to berry shape were highlighted. Overexpression of VIT_08s0032g01110 in transgenic Arabidopsis plants caused the change of pod shape.

A new high-density genetic map with total 3411 markers was constructed with SLAF-seq technique, and thus enabled the detection of narrow interval QTLs for relevant traits in grapevine. VIT_08s0007g00440, VIT_08s0040g02740 and VIT_08s0040g02350 were found to be related to berry firmness, while VIT_08s0032g01110, VIT_08s0032g01150 and VIT_08s0105g00200 were linked to berry shape.

Marker assisted selection (MAS) technology has been widely used to improve traditional breeding accuracy and efficiency in perennial crops [1]. In recent years, one of the major objectives of grape breeding is to develop molecular markers related to traits of interest for genetic selection of target phenotypes [2]. But it needs to clarify the genetic determinisms for each given trait firstly. Quantitative trait loci (QTLs) mapping is one of the key and efficient approaches for dissecting complex traits in grapevine.

Numerous QTLs for grapevine relevant traits, including berry weight and size [3,4,5,6], sweetness and acids [7, 8], seedlessness [4, 5, 9, 10], disease-resistance traits [11,12,13,14,15], and so on, have been mapped. In this decade, consumers pay more and more attention on grape quality, not only the taste (flavor, texture and so on) but also the appearance traits (color, berry size and shape). Promoting berry quality traits has also been the endless pursuit of grapevine breeders. Muscat flavor, berry firmness and berry shape are three important quality traits in the breeding of new table grape varieties. Their genetic controlling mechanisms have attracted extensive attentions.

For Muscat flavor, a major QTL on linkage group (LG) 5 has been identified in three different F1 segregating progenies [16,17,18]. And 1-deoxy-d-xylulose-5-phosphate synthase (VvDXS) has been suggested as the possible candidate gene [19]. Guo et al. [20] have investigated that berry flavor was associated with chromosome 5 too, while the significant single nucleotide polymorphisms (SNP) associated with berry flavor was identified on VIT_205s0020g03860 (homocysteine S-methyltransferase 2). Some other QTLs with smaller effects have been found on LG1, 7, 10 and 12 [16,17,18, 21].

Like Muscat flavor, grape berry firmness also follows complex quantitative inheritance. That QTLs for berry firmness distributed on the different LGs has been investigated in different mapping populations. Carreño et al. [22] have firstly mapped the QTLs for berry firmness on LGs 1, 4, 5, 9, 10, 13, and 18 in ‘Muscat Hamburg’ × ‘Sugraone’ and ‘Ruby Seedless’ × ‘Moscatuel’. In the progeny of ‘Ruby Seedless’ × ‘Sultanina’, the determinants of this trait are located on LG 8 and 18 [23]. While Ban et al. [24] have found two QTLs for firmness located on LGs 3 and 10 in a ‘V. labruscana’ × ‘V. vinifera’ cross. The most recent study has reported three QTLs all detected on LG 18 in the progeny of ‘Muscat Hamburg’ and ‘Crimson Seedless’ [25]. However, most of the analyses have been performed using genetic linkage maps constructed with SSR markers, which resulted in the relatively large QTL intervals, and thus hinders the subsequent candidate genes identification.

For berry shape, to our knowledge, few studies have dealt with it in table grapes, although the diversity in berry shape is great for different grape cultivars. The wine grapes are generally round or nearly round. While, today’s cultivated table grapes have diverse shapes, which can be divided into round, nearly round, broad ellipsoid, narrow ellipsoid, ovoid, obovoid, heart-shape, cylindric and so on. However, the reason for this diversity is still unknown.

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