Thesis (MScAgric)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: The two white seedless table grape cultivars, Regal Seedless and Thompson Seedless fulfil a
very important role in securing foreign income not only for the South African table grape
industry, but the South African economy as a whole. These two cultivars, however, are like so
many other white table grape cultivars, also prone to browning, especially netlike browning on
Regal Seedless and internal browning on Thompson Seedless grapes. This leads to huge
financial losses every year, since there is no established way to assess at harvest, during
storage or during packaging, whether the grapes will eventually turn brown. In other words,
there is no well-known protocol of assessing the browning risk of a particular batch of grapes
prior to export. Numerous studies have been undertaken to determine the exact cause of
browning and how it should be managed, but to date, no chemical or physical parameter has
been firmly associated with the phenomenon.
The overall aim of this study was thus to find an alternative way to deal with the problem by
investigating the potential of near infrared (NIR) spectroscopy as a fast, non-destructive
measurement technique to determine the browning potential of whole white seedless table
grapes. A secondary aim was the determination of optimal ripeness of table grapes. In this way
harvest maturity and quality indicative parameters namely total soluble solids (TSS), titratable
acidity (TA), pH, glucose and fructose, also associated with the browning phenomenon, was
quantified using models based on infrared spectra.
Three different techniques (a) Fourier transform Near Infrared (FT-NIR), (b) Fourier
transform – Mid Infrared (FT-MIR) and (c) Fourier transform – Mid Infrared Attenuated Total
Reflectance (FT-MIR ATR) spectroscopy were investigated to determine these parameters. This
was done so that a platform of different technologies would be available to the table grape
industry.
The grapes used in this study were harvested over two years (2008 and 2009) and were
sourced from two different commercial vineyards in the Hex River valley, Western Cape, South
Africa. Different crop loads (the total amount of bunches on the vines per hectare) were left for
Regal Seedless (75 000, 50 000 and 35 000) and for Thompson Seedless (75 000 and 50 000).
Three rows were used for Regal Seedless and two rows for Thompson Seedless. Each row had
six sections which each represented a repetition for each crop load. In 2008 these cultivars
were harvested early at 16°Brix, at optimum ripeness (18°Brix) and late at 20°Brix. In 2009 they
were harvested twice at the optimum ripeness level.
Berries from harvested bunches were crushed and the juice was used to determine the
reference values for the different parameters in the laboratory according to their specific
methods. The obtained juice was also scanned on the three different instruments. Different
software (OPUS 6.5 for the FT-NIR and FT-MIR ATR instruments and Unscrambler version 9.2
for the FT-MIR instrument) as well as different spectral pre-processing techniques were also
evaluated before construction of the models for all the instruments.
Partial least squares (PLS) regression was used for the construction of the different
calibration models. Different regression statistics, that included the root mean square error for
prediction (RMSEP); the coefficient of determination (R2); the residual prediction deviation
(RPD) and the bias were used to evaluate the performance of the developed calibration models.
Calibration models which are fit for screening purposes were obtained on the FT-NIR and FTMIR
ATR instruments for TSS (11.40 - 21.80°Brix) (R2 = 85.92%, RMSEP = 0.71 °Brix RPD =
2.67 and bias = 0.03°Brix), pH (2.94 - 3.9) (R2 = 85.00%, RMSEP = 0.08 RPD = 2.59 and bias =
-0.01) and TA (4.3 - 13.1 g/L), (R2 = 90.77%, RMSEP = 0.48 g/L RPD = 3.30 and bias = -0.03
g/L). Models for fructose (46.70 – 176.82 g/L) (R2 = 74.66%, RMSEP = 9.28 g/L RPD = 2.00 and
bias = 1.10 g/L) and glucose (20.36 – 386.67 g/L) (R2 = 70.71%, RMSEP = 11.10 g/L RPD = 1.87 and bias = 1.64 g/L) were obtained with the FT-NIR and FT-MIR ATR instruments that
were in some instances fit for screening purposes and in some instances unsuitable for
quantification purposes. The FT-MIR instrument gave models for all the parameters that were
not yet suitable for quantification purposes.
Combined spectral ranges used for calibration were often similar for some parameters,
namely 12 493 - 5 446.2 for TSS and pH, 6 101.9 - 5 446.2 for TSS, TA and fructose and
4 601.5 - 4 246.7 for pH and fructose on the FT-NIR instrument, 2 993.2 - 2 322.3 for pH, TA
and glucose and 1 654.3 - 649.4 for pH and glucose on the FT-MIR ATR instrument and
sometimes they were adjacent (3 996.6 - 3 661.2, 3 663.5 - 3 327.7 and 3 327.2 - 2 322.3 for
TSS and glucose, 1 988.3 - 1 652.8 and 1 654.3 - 649.4 for TSS, pH and TA. Other times they
were overlapping (1 654.3 - 649.4 and 1 318.8 - 649.4) for pH, TA and fructose on the FT-MIR
ATR instrument. This is a very good sign for transfer of this technology to a handheld device,
where adjacent and/ or overlapping wavenumbers are crucial. Instruments which have to
determine different parameters over large spectral ranges are not only impractical, because the
instrument has to be big, but because it is also very expensive.
Another advantage of implementing especially FT-NIR spectroscopy as a fast, accurate and
inexpensive technique for determining harvest maturity and quality parameters is because no
sample preparation is necessary and very little waste (few single berries tested) is produced.
This is a pre-requisite which is highly recommended in the green era that we are currently living
in and will do so for aeons to come. A platform of technologies has now been made available
through this study for the determination of the respective parameters in future table grape
samples by just taking their spectra on one of the instruments. Indeed something that has not
been possible or available for the South African table grape industry before. Berries for the browning experiments were scanned on a FT-NIR instrument immediately
after harvest (before cold storage) and again after cold storage. Before cold storage they were
scanned on each side of the berry and after cold storage they were scanned twice on a brown
spot if browning was present and twice on a clear spot, irrespective of whether browning was
present or not. Inspection of the berries for the incidence of browning after cold storage
revealed that Regal Seedless had a higher incidence of browning (68% in 2008 and 66% in
2009) than Thompson Seedless (21% in 2008 and 25% in 2009). Regal Seedless was also
more prone to external browning, specifically netlike browning, whereas Thompson Seedless
was more prone to internal browning, despite the different phenotypes of browning that were
present on both.
Principal component analysis (PCA) done on the spectra obtained before and after cold
storage revealed that NIR can capture the changes related to cold storage with the first principal
components explaining almost 100% of the variation in the spectra. Classification models also
build using PCA was based on spectra of berries that remained clear before and after cold
storage and those that turned brown after cold storage. Classification models of berries based
on spectra obtained after cold storage (browning present) had a better total accuracy (94% for
training- and 87% for test datasets), than the classification models based on spectra obtained
before cold storage (79% for training- and 64% for test datasets). The implication of this is that
the current models will be able to classify berries in terms of those which have turned brown
already and those that remained clear better after cold storage than before cold storage, which
is the critical stage where we want to actually know whether the berries will turn brown or not.
The potential, however, to use NIR spectroscopy to detect browning before harvest already on
white seedless grapes is still present, since all these models were built using the whole NIR
spectrum. No variable selection was thus done and all the different browning phenotypes were
also used together. Further analysis of the data will thus be based on using variable selection techniques like particle swarm optimization (PSO) to select certain wavelengths strongly
associated with the browning phenomenon and only on the main types of browning (netlike on
Regal Seedless and internal browning on Thompson Seedless). This study has major
implications for the table grape industry, since it is the first time that the possibility to predict
browning with other methods than visual inspection, especially before cold storage, is shown. / AFRIKAANSE OPSOMMING: Die twee wit pitlose tafeldruif kultivars, Regal Seedless en Thompson Seedless onderskeidelik,
speel 'n baie belangrike rol in die verkryging van buitelandse inkomste, nie net vir die Suid-
Afrikaanse tafeldruif industrie nie, maar ook vir die Suid-Afrikaanse ekonomie as 'n geheel.
Hierdie twee kultivars is egter, soos baie ander wit kultivars, ook geneig tot verbruining. Dit is
veral netagtige verbruining op Regal Seedless en interne verbruining op Thompson Seedless
wat pertinent is. Hierdie belangrike kwaliteitsprobleme lei jaarliks tot groot finansiële verliese,
aangesien daar huidiglik geen gevestigde prosedure is om voor oes, tydens opberging of
tydens verpakking te bepaal of die druiwe uiteindelik gaan verbruin nie. Met ander woorde, daar
is geen gevestigde protokol vir die beoordeling van die verbruinings risiko van 'n bepaalde
groep druiwe voor dit uitgevoer word nie. Talle studies is alreeds onderneem om vas te stel wat
die presiese oorsaak van hierdie verskynsel is en hoe dit bestuur moet word, maar geen enkele
aspek wat bestudeer is kon tot op hede, herhaaldelik ge-assosieer word met die presiese
oorsaak van verbruining nie.
Die oorkoepelende doel van hierdie studie was dus om 'n alternatiewe manier te kry om
hierdie probleem aan te spreek. ‘n Ondersoek na die potensiaal van naby infrarooi (NIR)
spektroskopie as 'n vinnige en nie-vernietigende metings tegniek om die verbruinings potensiaal
van ‘n wit pitlose tafeldruifkorrel wat nog heel is te bepaal, is onderneem. 'n Sekondêre doel
was om die bepaling van optimale rypheid van tafeldruiwe te onderosek. Op hierdie manier is
oesrypheid, en die kwaliteitsfaktore, naamlik totale oplosbare vastestowwe (TOVS), titreerbare
suur (TS), pH, glukose en fruktose, wat ook gekoppel word aan die voorkoms van verbruining,
deur middel van infrarooi (IR) spektroskopie modelle gekwantifiseer. Drie verskillende infrarooi
metodes naamlik (a) die Fourier transform naby infrarooi (FT-NIR), (b) Fourier transform - Mid
Infrarooi (FT-MIR) en (c) Fourier transform - Mid Infrarooi Verswakte Totale Refleksie (FT-MIR
VTR) spektroskopie is gebruik om die aspekte te bepaal. Dis gedoen sodat 'n platform van
tegnologie beskikbaar sou wees vir die tafeldruif industrie. Die druiwe wat in hierdie studie gebruik is, is oor twee jaar (2008 en 2009) en van twee
verskillende kommersiële wingerde in die Hexriviervallei, Wes-Kaap, Suid-Afrika ge-oes.
Verskillende oesladings (die totale aantal trosse op die wingerdstokke per hektaar) is vir Regal
Seedless (75 000, 50 000 en 35 000) en Thompson Seedless (75 000 en 50 000) gelaat. Daar
is drie rye gebruik Regal Seedless en twee vir Thompson Seedless. Elke ry het ses vakkies
gehad wat dan verteenwoordigend was van ‘n herhaling vir elke oeslading. In 2008 is hierdie
kultivars by vroeë rypwording (16°Brix), by optimale rypheid (18°Brix) en by laat rypheid
(20°Brix) geoes. In 2009 is dit twee keer by die optimale rypheidsgraad geoes. Vir die bepaling
van oesrypheid, en die kwaliteitsapekte is verskillende sagteware (OPUS 6.5 op die FT-NIR en
FT-MIR VTR instrumente en Unscrambler weergawe 9.2 vir die FT-MIR instrument) sowel as
verskillende spektrale voor-verwerking tegnieke ëvalueer voor die konstruksie van die kalibrasie
modelle op die verskillende instrumente.
Parsiële kleinste kwadraat (PKK) regressie is gebruik vir die opstel van kalibrasiemodelle
vir die bepaling van laasgenoemde aspekte. Verskillende statistieke gegewens is gebruik om
die kalibrasie modelle te evalueer, naamlik die bepalingskoëffisiënt (R2), die vierkantswortelgemiddelde-
kwadraat fout vir voorspelling (VGKV), relatiewe voorspellingsafwyking (RVA) en
sydigheid. Kalibrasie modelle wat geskik is vir keuring is verkry op die FT-NIR en FT-MIR VTR
instrumente vir TOVS (11.40 – 21.80°Brix) (R2 = 85.92%, VGKV = 0.71°Brix, RVA = 2.67 en
sydigheid = 0.03°Brix), pH (2.94 – 3.9) (R2 = 85.00%, VGKV = 0.08 g/L, RVA = 2.59 en
sydigheid = -0.01 g/L), en TS (4.3 – 13.1 g/L), (R2 = 90.77%, VGKV = 0.48 g/L RVA = 3.30 en
sydigheid = -0.03 g/L). Modelle vir fruktose (46.70-176.82 g/L) (R2 = 74.66%, VGKV = 9.28 g/L
RVA = 2.00 en sydigheid = 1.10 g/L) en glukose (20.36 – 386.67 g/L) (R2 = 70.71%, VGKV = 11.10 g/L RVA = 1.87 en sydigheid = 1.64 g/L) is verkry met die FT-NIR en FT-MIR VTR
instrumente wat in sommige gevalle gepas was vir keuringsdoeleindes en in sommige gevalle
nie geskik was vir kwantifiserings doeleindes nie. Die FT-MIR-instrument het modelle vir al die
aspekte gegee wat nog nie vir kwantifiserings doeleindes of vir keuringsdoeleindes geskik was
nie.
Gekombineerde spektrale reekse is gebruik vir die kalibrasies wat dikwels soortgelyk was
vir sommige aspekte naamlik 12 493 - 5 446.2 vir TOVS en pH, 6 101.9 - 5 446,2 vir TOVS, TS
en fruktose en 4 601.5 - 4 246.7 vir pH en fruktose op die FT-NIR instrument, 2 993.2 - 2 322.3
vir pH, TA en glukose en 1 654.3 – 649.4 vir pH en glukose op die FT-MIR VTR instrument.
Andersyds, was dit aangrensend (3 996.6 - 3 661.2, 3 663.5 - 3 327.7 en 3 327.2 - 2 322.3) vir
TOVS en glukose, 1 988.3 - 1 652.8, 1 654.3 – 649.4 vir TOVS, pH en TS en ander tye was dit
weer oorvleuelend 1 654.3 – 649.4 en 1 318.8 – 649.4 vir pH, TS en fruktose op die FT-MIR
VTR instrument. Dit is 'n baie goeie teken vir die oordrag van hierdie tegnologie na ‘n
handgedraagde instrument, waar aanliggende en/of oorvleuelende golfnommers noodsaaklik is.
Instrumente wat verskillende aspekte oor groot spektrale reekse moet bepaal is nie net
onprakties, omdat die instrument groot moet wees nie, maar dit is ook baie duur.
Nog 'n voordeel van die implementering van veral FT-NIR spektroskopie as 'n vinnige,
akkurate en goedkoop tegniek vir die bepaling van oesrypheid, en die kwaliteit aspekte van
druiwe is omdat daar geen monster voorbereiding nodig is nie en baie min afval (paar enkele
korrels word gemonster) geproduseer word. 'n Voorvereiste wat sterk aanbeveel kom in die
groen era waarin ons tans leef en nog vir eeue van nou af gaan doen. ‘n Platform van
tegnologie is nou beskikbaar gestel deur middel van hierdie studie vir die bepaling van die
onderskeie aspekte in toekomstige tafeldruif monsters deur net op een van die instrumente
hulle spektra te neem. Inderdaad iets wat nie voorheen moontlik of beskikbaar was vir die Suid-
Afrikaanse tafeldruif industrie nie. Korrels vir die verbruiningseksperimente is geskandeer direk na oes (voor koelopberging)
en weer na koelopberging. Dit was voor koelopberging op elke kant van die korrel skandeer en
na koelopberging was dit twee maal skandeer op 'n bruin vlek indien verbruining teenwoordig
was en twee keer op 'n helder plek, ongeag of verbruining teenwoordig was of nie. Inspeksie
van die korrels vir die voorkoms van verbruining na koelopberging het aan die lig gebring dat
Regal Seedless 'n hoër voorkoms van verbruining (68% in 2008 en 66% in 2009) as Thompson
Seedless (21% in 2008 en 25% in 2009) gehad het. Regal Seedless was ook meer geneig om
eksterne verbruining, spesifiek netagtige verbruining te vertoon, terwyl Thompson Seedless
meer geneig was om interne verbruining te vertoon, ten spyte van die verskillende fenotipes van
verbruining wat teenwoordig was op beide kultivars.
Hoofkomponente analise (HKA) is op die spektra gedoen voor en na koelopberging en
naby infrarooi spektroskopie het aan die lig gebring dat die veranderinge wat verband hou met
koelopberging met die eerste hoofkomponent (HK) verduidelik kan word met byna 100% van
die variasie in die spektra wat daarin vasgevang is. Klassifikasiemodelle is ook deur die gebruik
van HKA gebou en was gebaseer op die spektra van korrels wat vekry is voor en na
koelopberging asook die wat verkry is nadat korrels verbruin het na koelopberging.
Klassifikasiemodelle van korrels wat gebaseer was op spektra na koelopberging (verbruining
teenwoordig) het 'n beter algehele akkuraatheid (94% vir opleidingsdata en 87% vir toetsdata),
getoon as die klassifikasiemodelle wat gebaseer was op spektra van korrels voor koelopberging
(79% vir opleidings data en 64% vir toetsdata). Die implikasie hiervan is dat die huidige modelle
in staat sal wees om korrels beter te klassifiseer in terme van diegene wat alreeds verbruin het
en die wat nie verbruin het na koelopberging as daardie voor koelopberging, wat juis die kritieke
stadium is waar ons wil weet of die korrels wel gaan verbruin of nie. Daar is wel potensiaal wat verder ontgin kan word, aangesien al hierdie modelle gebou is deur gebruik te maak van die
hele NIR spektrum. Geen veranderlike seleksie is dus gedoen nie en al die verskillende
verbruiningsfenotipes is ook saam gebruik in die opstel van die modelle. Verdere analise van
die data sal dus gebaseer word op die gebruik van veranderlike seleksie tegnieke soos deeltjie
swerm optimisasie (DSO) wat sekere golflengtes kies wat sterk verband hou met die verbruining
verskynsel en slegs die belangrikste tipes van verbruining (netagtig op Regal Seedless en
interne verbruining op Thompson Seedless) sal gebruik word. Hierdie studie het 'n baie
belangrike implikasie vir die tafeldruifbedryf, want dit is die eerste keer dat die moontlikheid om
verbruining te voorspel met ander metodes as visuele inspeksie, veral voor koelopberging,
getoon word. / The Postharvest and Innovation Programme, for financing this study
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/80369 |
| Date | 03 1900 |
| Creators | Daniels, Andries Jerrick |
| Contributors | Nieuwoudt, Helene H., Raath, Pieter J., Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
| Format | 78 p. : col. ill. |
| Rights | Stellenbosch University |
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