Fruit Quantity & Quality Analysis for HT Study

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A work by Tony Rho

hyungmin.rho@tamu.edu

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This document is written to describe the analysis procedure of the fruit quantity records and the fruit quality assay results. For the quantity analysis, a number of metrics at harvest such as total plant and fruit numbers, total fresh weight of fruit, marketable fruit number and fresh weight, etc. were recorded by the author and the others.¹ For the quality analysis, multiple fruit character analytic asssays, including carotenoid, sugar, and amino acid content determination were performed by two groups of external scientists.² The fruits were harvested in mid-September, 2018 from the plants grown in high-tunnels (HT) and open field (OF). These plants were grown for the High-Tunnel study performed by the author and the others³ in 2018. There is source code containing global parameters and functions to execute some of the codes below. These resources will be released as a seperate R-script file in the future when the package development for this research project is completed. The script file contains many useful calculators, constants, unit converters, and user created functions.

Introduction

It is a concensus that HT production system benefit both quantity and quality (a.k.a. physical and chemical attributes) of crops grown within although the effects vary upon regional climates, species of crops, planting dates, and cultivars of crops (Zhao and Carey 2009; Wallace et al. 2012; Jayalath et al. 2017). Growing interests in introduction of HT production system on the High Plains initiated the HT research and the team chose peppers and tomatoes as trial materials considering their signicance in local fresh produce markets.

Many papers highlighted the benefits of HT production system on increases in quantity, but quality aspect often seemed to be ignored in HT research. A recent paper (Lee et al. 2018) demonstrated fruit quality components of vegetables was highly impacted by production system. According to this report, volatile profiles of four tomato varieties – comprised by +40 compounds – grown in HT were remarkedly different from those grown in OF, which might have affected the nutritional and organoleptic properties of tomatoes. Although the paper has established a firm ground of the benefit of using HT in growing tomotoes, information about the quality parameters such as contents of organic and amino acids, carbohydrates, carotenoids is still missing.

Conventional quantity analysis was carried out with statistical tests. Quality components are also important; we analyzed various quality related biochemical compounds in fruits of peppers and tomatoes using HPLC-MS.

Procedures

1. Import packages & source code
2. Import & tidy data sets – harvest data (harv) & fruit data (frt)
   • harvest dataframe (harv)
   • fruit dataframe (frt) broken down into…
     • carb dataframe (carb),
     • quality dataframe (qual),
     • ascorbic acid dataframe (asc),
     • carotenoid dataframe (caro),
     • flavonoid dataframe (flav),
     • capsaicinoid dataframe (caps), &
     • amino acid dataframe (amin).
3. Analyze the data
   • peppers
     • harv,
     • carb,
     • asc,
     • caro,
     • flav, &
     • caps.
   • tomatoes
     • harv
     • carb,
     • qual,
     • asc,
     • caro, &
     • amin.

Step 1: Import packages & source code

library(tidyverse)  # includes many useful data manipulation & exploration pkgs
library(lubridate)  # deals with timeseries data
library(DT)  # creates interactive data tables
library(scales)  # enables graphical modifications on ggplot2 objects
library(directlabels)  # enables graphical modifications on ggplot2 objects
library(nlme)  # builds & analyzes linear mixed effect model 
library(lsmeans)  # conducts a post hoc comparison 
library(knitr)  # compiles html objects & generates a report file
library(openxlsx)  # exports tabulated dataframes or R-objects to xlsx files
library(psych)  # computes descriptive statistics
library(ggradar)  # builds radar charts with ggplot2
library(stringr)  # deals with strings in dfs

source("0_ht_src.R")  # provides useful unit conversion & calculation tools

The source code also contains package loading functions that are outlined above.

Step 2: Import & tidy data sets

Step 2.1: Fruit quantity at harvest

Fresh fruit peppers and tomatoes were harested on 135 days after transplanting. A 10’-wide section in the middle of each crop stand was chosen and the number of the crops in the selected section was recorded. From these select plants the fruits were hand-harvested and fresh weight of the samples was measured on a scale on site. The samples were transferred to a lab and the total fruit count was made. Marketable quality fruits were graded by observation on the appearance of the fruits – inspection was made on mechanical and biological defects on the fruit surfaces. The marketable portion was counted and weighed separately from the entire portion.

harv is a dataset containing conventional quantity metrics for fresh market production of peppers and tomatoes. Consult Table 1 for details. Reference yield data are brought from the USDA National Agricultural Statistics Service database.

Table 1 A summary of fruit quantity metrics used in the analysis.

Data	Type	Metric	Variable Name	Calculation
harv	count	total plant #	tot_plt_num	-
-	-	total fruit #	tot_frt_num	-
-	-	tot frt # per plt	frt_num_plt	tot_frt_num/tot_plt_num
-	-	marketable frt #	mrk_frt_num	-
-	-	mrk frt # per plt	mrk_frt_num_plt	mrk_frt_num/tot_plt_num
-	weight	tot frt FW	tot_frt_fw	-
-	-	frt FW per plt	frt_fw_plt	tot_frt_fw/tot_plt_num
-	-	frt FW per frt	avg_fw	tot_frt_fw/tot_frt_num
-	-	mrk frt FW	mrk_frt_fw	-
-	-	mrk frt FW per plt	mrk_frt_fw_plt	mrk_frt_fw/tot_plt_num
-	-	mrk frt FW per frt	mrk_avg_fw	mrk_frt_fw/mrk_frt_num
-	yield	yield	yield	tot_frt_fw/given area
-	-	mrk yield	mrk_yield	mrk_frt_fw/given area

Step 2.2: Fruit quality after harvesting

To relate any changes in fruit quality to leaf and crop characteristics modified by HT, leaf carbohydrate content was also quantified separately by HPLC-RID. Leaf and fruit samples were collected on 139 and 135 days after transplanting for HT and OF each. Ten leaves from each plant were collected from different heights in the canopy and put into a 50 mL centrifuge tube. Three fruits from each plant were collected from different heights in the canopy and put into a plastic bag. The leaf samples were transferred to a -80^oC freezer and stored at the freezer until a further analysis. The fruit samples were diced and homogenized into 50 mL centrifuge tubes and stored in a -80^oC freezer until a further analysis.

For carbohydrate extraction, approximately 100 mg of the fresh leaf samples for both peppers and tomatoes was sliced under sterile conditions and transferred to a 1.5 mL microcentrifuge tube containing a sterilized metal bead inside, and pulverized to fine powder by fast spinning the tube on a homogenizer for 2 mins. 1 mL of methanol was added to the tube, mixed well, and stored at a 4^oC fridge. The samples were shipped to an analytic laboratory ( USDA-ARS-SJVASC) where soluble non structural carbohydrate contents were assayed. Soluble non structural carbohydrate – glucose (Glc), fructose (Frc), and sucrose (Suc) – contents of the leaves were quantified by using an HPLC system with an RI detector. A microparticulate, macroporous, polystyrene resin analytic column was used to ion-separate the carbohydrates ( Supelcogel C-610H , MilliporeSigma Co., St. Louis, MO), running 0.1 mL of a dilute phosphoric acid solution.

The fruit samples were shipped to another analytic lab ( Texas A&M Univ VFIC) to perform a battery of fruit quality and phytonutrient assays. For peppers, flavonoid, ascorbic and dehydroascorbic acid, carotenoid, carbohydrate, and capsaicinoid contents of fruits were analyzed by HPLC. For tomatoes, basic quality parameters (titrable acidity, Brix, and pH) were measured along with ascorbic and dehydroascorbic acid, carotenoid, carbohydrate, and amino acid contents of fruits by HPLC.

frt is a complete data book for sub data sets covering the profile of these phytonutrients and compounds from the HT study. Reference nutrient data are retrieved from the USDA FoodData Central (FDC) webpage. USDA-FDC provides a comprehensive database for searching nutrient profiles of food compiled from varying sources. The nutrient components in the FDC database include proximates, minerals, vitamins, amino acids, fatty acids, and key secondary metabolites in certain cases. The referece values were accessed and used to compare the analyzed quality parameters of the peppers and tomatoes.

frt data set is comprised of the following sub sets of the data:

• carb: Non-structural carbohydrate content of leaf and fruit tissue including Glucose (Glc), Fructose (Frc), and Sucrose (Suc)
• qual: Quality parameters of fruit including Soluble Sugar Content (SSC, Brix) and Acidity in pH (Ph), Titrable Acidity (Acid)
• asc: Ascorbic acid content of fruit including Ascorbic Acid (Asc), Dehydro Ascorbic Acid (De_asc), and Total Ascorbic Acid (Tot_asc). Ascorbic acids are one of the essential micronutrients known as Vitamin C
• caro: Carotenoid content of fruit including \(\beta\)-Carotene (B_car), Trans-Lycopene (T_lyc), and four forms of Cis-Lycopenes – 5-cis-lycopene (C5_lyc), 7-cis-lycopene (C7_lyc), 13-cis-lycopene (C13_lyc), and 15-cis-lycopene (C15_lyc). Carotenoids form another essential micronutrient Vitamin A and lycopenes are key precursors in the Vitamin A biosynthesis
• amin: Amino acid content of fruit including Asparagine (Asn), Glutamine (Glu), Serine (Ser), Aspartic Acid (Asp), Hydroxy Proline (H_pro), Threonine (Thr), \(\beta\)-Alanine (B_ala), GABA (Gaba), Proline (Pro), Methionine (Met), Valine (Val), Tryptophan (Trp), Phenylalanine (Phe), Isoleucine (Ile), and Leucine (Leu). Amino acids are used in the protein biosynthesis.

The following table summarizes the sub-data sets and variables within.

Table 2 A summary of chemical assay data sets.

Data	Type	Compound	Abbreviation
carb	carbohydrate	glucose	Glc
-	-	fructose	Frc
-	-	sucrose	Suc
qual	quality	sugar	Brix
-	-	acidity	Ph
-	-	acidity	Acid
asc	ascorbic acid	ascorbic acid	Asc
-	-	dehydro asc	De_asc
-	-	total asc	Tot_asc
caro	carotenoid	\(\beta\)-carotene	B_car
-	-	trans-lycopene	T_lyc
-	-	5-cis lyc	C5_lyc
-	-	7-cis lyc	C7_lyc
-	-	13-cis lyc	C13_lyc
-	-	15-cis lyc	C15_lyc
amin	amino acid	asparagine	Asn
-	-	glutamine	Glu
-	-	serine	Ser
-	-	aspartic acid	Asp
-	-	proline	Pro
-	-	hydroxy pro	H_pro
-	-	threonine	Thr
-	-	\(\beta\)-alanine	B_ala
-	-	GABA	Gaba
-	-	methionine	Met
-	-	valine	Val
-	-	tryptophan	Trp
-	-	phenylalanine	Phe
-	-	isoleucine	Ile
-	-	leucine	Leu

The reference range data set is retrieved from the FoodData Central database published by USDA-ARS. The unit of the reported values in the data set is per 100 g fresh weight.

Step 3: Analyze the data

Step 3.1: Quantity (physical characteristics)

Step 3.1.1: Counts

The total number of fruit harvested from the 10’-distance was greater under HT compared with OF (P < 0.001). The number of marketable quality fruit for fresh market consumption was also greater under HT (P = 0.012). The percentage of marketable quality fruit was around 29.1 and 27.9% for the HT and the OF tomatoes, respectively, which was not significantly different. The increases in the total fruit count was derived from more fruits were set per plant under HT. A 78.2% increase by HT was found in the total number of fruit per plant (P = 0.026) while the total number of plant in the sampling area did not change (P = 0.428).

Step 3.1.2: Weights

The total weights of fruit and marketble fruit were both larger for the HT tomatoes than the OF tomatoes (P = 0.008 and 0.003, respectively). Contrary, the average fruit weights per fruit were not significantly different between the two. This suggests that the increase in fruit FW per plant by HT (P = 0.013) stemed from more tomato fruits set under HT.

Step 3.1.3: Yields

As a consequence, the tomato yield under HT was remarkedly higher compared to the one under OF – an increase by 134.8% (P = 0.008). The marketable yields were lower than the total yields, but again a highly significant increase (134%, P = 0.008) was found in the HT tomatoes. The tomato yield from HT was greater than the Texas average over the last ten years (23.7 Mg FW/ha), but lower than the US average (31.9 Mg FW/ha).

Step 3.2: Quality (chemical characteristics)

Step 3.2.1: Carbohydrates

For FW basis measures, no differences are found between HT and OF tomatoes. The total amount of the carbohydrates quantified fall close in the reference range excpet the Glc level. The OF tomatoes had slightly higher carbohydrate content in leaves, marginally different from the HT tomatoes (P = 0.064).

Step 3.2.2: Quality parameters

No significant changes between HT and OF tomatoes are found in the quality parameters assayed – SSC and acidity. There is no available reference to these parameters.

Step 3.2.3: Ascorbic acids

No differences in ascorbic acid content between HT and OF tomatoes. Total ascorbic acid contents of both HT and OF tomatoes were higher than the reference range.

Step 3.2.4: Carotenoids

For FW basis measures, all the quantified carotenoids except \(\beta\)-carotene were lower in the HT tomatoes – 5, 7, 13, 15-cis-lycopene, and trans-lycopene (P < 0.01). The trans-lycopene levels of the HT and the OF tomatoes were both higher than the reference level.

Step 3.2.5: Amino acids

For FW basis measures, a few amino acids were found higher in the OF tomatoes. Glu (P = 0.019), Phe (P < 0.001), and Thr (P = 0.016) in the OF tomates were present more in the OF tomatoes. However, these values from the HT and the OF tomatoes were lower than the reference ranges except Asp.

Step 3.3: Collective (under construction)

This section is intended to be left empty, but will be restructured when plans are made. Tentative plan is creating a quant & qual matrix using a heat map plot, a trait chart using a radar (a.k.a. spider) plot, or a correlation matrix using a tile plot. PCA could also be implemented on the data and would provide useful information.

Conclusions

In conclusion, HT production system increased quantity of fruit both in peppers and tomatoes. Especially tomatoes benefited more from HT production system compared with peppers. For tomatoes, more fruits were set per plant under HT. This led to an increase in fruit FW per plant although FW of an individual fruit did not differ. As a result, tomatoes under HT yielded more marketable fresh fruits and accordingly higher yield and marketable yield. Despite of the clear differences in quantity parameters, quality parameters did not differ except a few compounds. Lycopene and a few amino acids were found lower in the HT tomatoes compared with OF tomatoes. Comparisons of the fruit quality parameters to the reference ranges revealed that contents of total ascorbic acid and trans-lycopene of HT and OF tomatoes were greater while amino acid contents were lower than the reference ranges except Asp.

References

Jayalath, Theekshana C., George E. Boyhan, Elizabeth L. Little, Robert I. Tate, and Suzanne O’Connell. 2017. “High Tunnel and Field System Comparison for Spring Organic Lettuce Production in Georgia.” HortScience 52 (11): 1518–24. doi:10.21273/HORTSCI12284-17.

Lee, Jisun H.J., G. K. Jayaprakasha, Charlie M. Rush, Kevin M. Crosby, and Bhimanagouda S. Patil. 2018. “Production system influences volatile biomarkers in tomato.” Metabolomics 14 (7). doi:10.1007/s11306-018-1385-1.

Wallace, Russell W., Annette L. Wszelaki, Carol A. Miles, Jeremy S. Cowan, Jeffrey Martin, Jonathan Roozen, Babette Gundersen, and Debra A. Inglis. 2012. “Lettuce yield and quality when grown in high tunnel and open-field production systems under three diverse climates.” HortTechnology 22 (5): 659–68. doi:10.1111/j.1529-8817.2010.00908.x.

Zhao, Xin, and Edward Carey. 2009. “Summer production of lettuce, and microclimate in high tunnel and open field plots in kansas.” HortTechnology 19 (1): 113–19.

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1. Dr. Paul Colaizzi and Melanie Baxter from USDA-ARS-CPRL at Bushland TX, Drs. Charles Rush, Qingwu Xue from Texas A&M AgriLife Research at Amarillo TX, James Gray, Jewel Arthur, Jared Bull, student workers from Texas A&M AgriLife Research at Bushland TX.↩

2. Dr. Christopher Wallis from USDA-ARS-SJVASC at Parlier CA and Dr. Bhimu Patil from Texas A&M University at College Station TX.↩

3. Dr. Paul Colaizzi and Melanie Baxter from USDA-ARS-CPRL at Bushland TX, Drs. Charles Rush, Qingwu Xue from Texas A&M AgriLife Research at Amarillo TX, James Gray, Jewel Arthur, Jared Bull, student workers from Texas A&M AgriLife Research at Bushland TX.↩