Development of infrared spectroscopic methods to assess table grape quality

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Stellenbosch : Stellenbosch University
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.
Thesis (MScAgric)--Stellenbosch University, 2013.
Table grape quality, Table grape browning, Infrared spectroscopy, Calibratin and classification models, Theses -- Wine biotechnology, Dissertations -- Wine biotechnology