Thesis (MSc Food Sc)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: Post-storage quality of the stone fruit, apricots and peaches, is the major factor determining
their suitability for canning after cold storage in South Africa. Short harvesting periods and
the limited capacity of the factory to process the large quantities of fruit within two days after
delivery, necessitates cold storage until canning. Apricots develop internal breakdown,
whereas peaches develop internal breakdown accompanied by loosening of the skin and
adhesion of the flesh to the stone. The deterioration takes place within the fruit during a cold
storage period of one to two weeks. The tendency of the fruit to develop internal defects can,
to date, not be identified prior to storage and are only discovered after destoning during
canning. Near infrared spectroscopy (NIRS) combined with chemometrics were investigated
as a non-destructive method to predict post-storage quality in Bulida apricots and clingstone
peach cultivars. Near infrared (NIR) spectra (645-1201 nm), measured on the intact fruit just
after harvesting, were correlated with subjective quality evaluations performed on the cut and
destoned fruit after cold storage. The cold storage periods for apricots were four weeks (2002
season) and three and two weeks for peach cultivars for the 2002 and 2003 seasons,
respectively. Soft independent modelling by class analogy (SIMCA) and multivariate adaptive
regression splines (MARS) were applied to the spectral and reference data to develop models
for good and poor post-storage quality. The ability of these models to predict post-storage
quality was evaluated in terms of recognition (sensitivity) and rejection (specificity) of the
samples in independent validation sets. Total correct classification rates of 50.00% and
69.00% were obtained with Bulida apricots, using SIMCA and MARS, respectively.
Classification results with apricots showed that MARS performed better than SIMCA and is
thus recommended for this application. Total correct classification rates of 53.00% to 60.00%
(SIMCA) and 57.65% to 65.12% (MARS) were obtained for data sets of combined peach
cultivars within seasons and over both seasons. Additional aspects of fruit quality were
investigated to identify possible indices of post-storage quality. Classification trees were used
to find correlations between the post-storage quality and the fruit mass, diameter, firmness
and soluble solids content (SSC). Among these, fruit diameter and firmness were the major
indices of post-storage quality. Accurate predictions of firmness could not be achieved by
near infrared spectroscopy (NlRS), making the combination of NIRS and classification trees
not yet suitable for predicting post-storage quality. NIRS was further used to predict poststorage
SSC within seasons in Bulida apricots and intact peach cultivars. This confirmed
sufficient NIR light penetration into the intact fruit and also provided a further application of
NIRS for ripeness evaluation in the canning industry. Validations on peach samples obtained correlation coefficients (r) of 0.77-0.85 and SEP-values of 1.35-1.60 °Brix using partial least
squares (PLS) regression. MARS obtained r = 0.77-0.82 and SEP = 1.42-1.55 °Brix.
Predictions of sse in apricots were less accurate, with r = 0.39-0.88, SEP = 1.24-2.21 °Brix
(PLS) and r = 0.51-0.82, SEP = 1.54-2.19 °Brix (MARS). It is suggested that the accuracy of
sse measurements, and the subsequent predictions, were affected by the cold storage
periods as well as internal variation within the fruit. This study showed that a combination of
NIRS and chemometrics can be used to predict post-storage quality in intact peaches and
apricots. A small scale feasibility study showed that 4% (R117 720) (apricot industry) and 3%
(R610 740) (peach industry) of production losses can be saved if this method is implemented
in the South African canning industry. Although it was difficult to assign specific chemical
components or quality attributes to the formulation of the storage potential models, important
hidden information in the spectra could be revealed by chemometric classification methods.
NIRS promises to be a useful and unique quality evaluation tool for the South African fruit
canning industry. Several recommendations are made for the canning practices to reduce
losses and for future research to improve the current prediction models. / AFRIKAANSE OPSOMMING: Die kwaliteit van die steenvrugte, appelkose en perskes, is die hoof bepalende faktor vir hul
geskiktheid vir inmaakdoeleindes na koelopberging in Suid-Afrika. Die vrugte moet opgeberg
word by lae temperature vir een tot twee weke, aangesien die oestydperk kort is en die
kapasiteit van die fabriek te beperk is om die groot hoeveeheid vrugte dadelik in te maak.
Tydens hierdie opbergingstydperk vind agteruitgang in die vrugte plaas. Dit word in
appelkose gekenmerk deur interne verval en in perskes gekenmerk aan interne verval,
tesame met enlos skil en die vaskleef van die vrugvlees aan die pit. Tot dusver, bestaan daar
geen metode om hierdie tipe agteruitgang in vrugte voor opberging te identifiseer nie. Dit
word eers na opberging opgemerk wanneer die vrugte ontpit word. Naby-infrarooi
spektroskopie (NIRS), gekombineerd met chemometriese metodes is gebruik om
opbergingspotensiaal in Bulida appelkose en taaipitperske kultivars te bepaal. enKorrelasie is
gemaak tussen naby-infrarooi (NIR) spektra, gemeet op die heel vrugte voor opberging en
subjektiewe evaluering van kwaliteit, geïdentifiseer op die gesnyde vrugte na opberging. Die
opbergingstydperke vir perskes was vir drie en twee weke vir die 2002 en die 2003 seisoene,
onderskeldeflk, terwyl die appelkose vir vier weke opgeberg is. Twee chemometriese
metodes, "soft independent modelling by class analogy" (SIMCA) en "multivariate adaptive
regression splines" (MARS) is gebruik om die spektra en ooreenstemmende subjektiewe data
te kombineer en modelle is ontwikkel vir goeie en swak opbergingspotensiaal. Die vermoë
van die modelle om die vrugkwaliteit na die opbergingstydperk te voorspel, is geêvalueer in
terme van herkenning en verwerping van vrugtemonsters in onafhanklike toetsstelle. Totale
korrekte klassifikasies van 50.00% and 69.00% is verkry vir Bulida appelkose, met SIMCA en
MARS, onderskeidelik. Die klassifikasie resultate het gewys dat MARS beter gevaar het as
SIMCA en word dus sterk aanbeveel vir hierdie toepassing. Totale korrekte klassifikasies van
53.00% tot 60.00% (SIMCA) and 57.65% tot 65.12% (MARS) is verkry vir gekombineerde
perskekultivars tussen seisoene en oor seisoene. Verdere aspekte van vrugkwaliteit is
geêvalueer om enmoontlike indeks van opbergingspotensiaal te verkry. Klassifikasiebome is
gebruik om en korrelasie te vind tussen kwaliteit na opberging en vrugmassa, deursnee,
fermheid en totale oplosbare vastestowwe (TOV). Diameter en fermheid het die meeste
gekorreleer met die kwaliteit na opberging. Voorspellings van fermheid deur die gebruik van
naby infrarooi spektroskopie (NIRS) was ~gter nie akkuraat nie. Dus word die kombinasie
van klassifikasiebome en NIRS om opbergingspotensiaal te voorspel nie tans aanbeveel nie.
NIRS is verder gebruik om TOV te voorspel binne seisoene in heel Bulida appelkose en perskekultivars. Dit is uitgevoer om voldoende NIR ligpenitrasie in die vrugte te bevestig en
ook om 'n verdere toepassing van kwaliteitsbepaling (as indeks van soetheid en rypheid) vir
die inmaakindustrie te verskaf. Validasies is op perskemonsters uitgevoer en
korrelasiekoêffisiente (r) van 0.77-0.85 en voorspellingsfoute van 1.35-1.60 °Brix is verkry met
"partial least squares" (PLS) regressie. MARS het r = 0.77-0.82 and voorspellingsfoute =
1.42-1.55 °Brix verkry. Die akkuraatheid van die TOV meetings en gevolglike voorspellings is
waarskynlik beïnvloed deur interne variasie binne die vrugte sowel as die opbergings
tydperke wat verloop het tussen metings. Hierdie studie wys dat NIRS en chemometriese
metodes wel gebruik kan word om opbergingspotensiaal in heel perskes in appelkose te
voorspel. 'n Kosteberekening het gewys dat besparings van 4% (R117 720) (appelkoos
industrie) en 3% (R610 740) (perske industrie) moontlik is indien NIRS en MARS
geïmplementeer word. Alhoewel dit moeilik was om spesifieke chemiese komponente en
.sekere kwaliteitsaspekte aan die ontwikkeling van die modelle te koppel, is belangrike
verborge informasie in die spectra uitgebring deur chemornetriese metodes. NIRS beloof om
'n bruikbare en unieke kwaliteitskontrole maatstaf te wees vir die Suid-Afrikaanse
inmaakindustrie. Verskeie aanbevelings is gemaak vir die inmaakpraktyke om verliese te
voorkom en ook vir toekomstige navorsing om die huidige klassifikasiemodelle te verbeter.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/53557 |
| Date | 12 1900 |
| Creators | Myburgh, Lindie |
| Contributors | Manley, Marena, Joubert, Elizabeth, Stellenbosch University. Faculty of AgriScience. Dept. of Food Science. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
| Format | 148 pages : illustrations |
| Rights | Stellenbosch University |
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