Browsing by Author "Daniels, Andries Jerrick"

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    Development of infrared spectroscopic methods to assess table grape quality
    (Stellenbosch : Stellenbosch University, 2013-03) Daniels, Andries Jerrick; Nieuwoudt, Helene; Raath, Pieter J.; Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology.
    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.
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    Non-destructive evaluation of external and internal table grape quality
    (Stellenbosch : Stellenbosch University, 2021-03) Daniels, Andries Jerrick; Opara, Umezuruike Linus; Nieuwoudt, Hélène H; Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology.
    ENGLISH ABSTRACT: Determining the correct harvest maturity parameters of table grapes is an essential step before harvesting. The chemical analysis of table grapes to determine harvest and quality parameters such as total soluble solids (TSS), titratable acidity (TA) and pH, is very time-consuming, expensive, and destructive. Developing faster and more cost-effective methods to obtain the information can benefit the table grape industry by reducing losses suffered at the postharvest stage. There are multitudes of factors that can influence table grape postharvest quality leading to huge losses. These losses are exacerbated even further by the long list of postharvest external and internal defects that can occur, including browning in all its various manifestations. The application of cutting-edge technologies such as Fourier Transform Near-Infrared (FT-NIR) spectroscopy that can accurately assess the external and internal quality of fruit is, therefore, essential. This particularly concerns the identification of defects or assessment of the risks of defects that are likely to develop during post storage. The aim of this application would thus be to evaluate these new technologies to monitor table grape quality non-destructively, before, during, and/or after harvest. This study, therefore, focussed on the development and optimisation of faster, cost- effective, and fit-for-purpose methods to monitor harvest maturity and quality of table grapes in the vineyard before harvesting and during packaging and cold storage. Harvest of three different cultivars, namely, Thompson Seedless, Regal Seedless and Prime, happened over two seasons (2016 and 2017) from six different commercial vineyards. Five of these vineyards were in the Western Cape (two in the Hex River Valley, three in Wellington) and one in the Northern Cape (Kakamas), South Africa. Harvest occurred twice at each vineyard, at optimum ripeness and two weeks later (after the optimum harvest date). The incidence and intensity of browning on each berry on a bunch were evaluated for different defects and browning phenotypes. Quantitative harvest maturity and indicative quality parameters such as TSS, TA and pH, as well as the sensory-related parameters – sugar:acid ratio (TSS:TA ratio) and BrimA, were investigated by scanning whole table grape bunches contactless with Bruker’s MATRIX-F spectrometer in the laboratory. Partial Least Squares (PLS) regression was used to build prediction models for each parameter. Two different infrared spectrometers, namely the Bruker Multipurpose Analyser Fourier Transform Near-Infrared (MPA FT-NIR) and MicroNIR Pro 1700 were also used to determine TSS on whole table grape berries. The MicroNIR Pro 1700 was utilised in the vineyard and the laboratory and the MPA only in the laboratory. The same spectral dataset used to build the quantitative models was used to build classification models for two browning phenotypes, namely chocolate browning and friction browning. Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Networks (ANN) were used for the classification tasks. Key results showed that the incidence and intensity of different defects and browning phenotypes such as sulphur dioxide (SO2) damage were prevalent on all three white seedless table grape cultivars. The incidences of fungal infection, sunburn and abrasion damage were high on Regal Seedless and Thompson Seedless in 2016. Contact browning, mottled browning and friction browning and bruising damage had higher incidences in 2017 than in 2016. Overall, the intensity of defects was very high in 2016 except on Regal Seedless from Hex River Valley. Prime from Kakamas and Wellington had the highest intensity of defects in 2017, which appeared on the grapes after 7 weeks of cold storage. Prediction models were successfully developed for TSS, TA, TSS:TA, pH, and BrimA minus acids on intact table grape bunches using FT-NIR spectroscopy in a contactless measurement mode, and applying spectral pre-processing techniques for regression analysis with PLS. The combination of Savitzky-Golay first derivative coupled with multiplicative scatter correction on the original spectra delivered the best models. Statistical indicators used to evaluate the models were the number of latent variables (LV) used to build the model, the prediction correlation coefficient (R2p) and root mean square error of prediction (RMSE). For the respective parameters TSS, TA, TSS:TA ratio, pH, and BrimA, the number of LV used when the models were build according to a random split of the calibration and validation set were 6, 4, 5, 5 and 10, the R2p = 0.81, 0.43, 0.66, 0.27, and 0.71, and the RMSEP = 1.30 °Brix, 1.09 g/L, 7.08, 0.14, and 1.80. When 2016 was used as the calibration set and 2017 as the validation set in model building the number of LV used were 9, 5, 5, 4 and the R2p = 0.44, 0.06, 0.17, 0.05, and 0.05 and the RMSEP = 3.22 °Brix, 2.41 g/L, 14.53, 0.21, and 8.03 for for the respective parameters. Determining TSS of whole table grape berries in the vineyard before and after harvesting using handheld and benchtop spectrometers on intact table grape berries showed that spectra taken in the laboratory with the MicroNIR were more homogenous than those taken in the vineyard with the same spectrometer, over the two years investigated. The results obtained with the MPA were not as good as those obtained with the MicroNIR in the laboratory were. The model constructed with the combined data of 2016 and 2017 taken in the laboratory with the MicroNIR had the best statistics in terms of R2p (0.74) and RPDp (1.97). The model constructed with the 2017 data obtained in the laboratory with the MicroNIR had the lowest prediction error (RMSEP = 1.13°Brix). Good models were obtained using PLS-DA and ANN to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact table grape bunches with the MATRIX-F spectrometer. The classification error rate (CER), specificity and sensitivity were used to evaluate the models constructed using PLS-DA and the kappa score was used for ANN. The CER for chocolate browning (25%) was better than that of friction browning (46%) after Weeks 3 and 4 in cold storage for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 of friction browning were not as good as for chocolate browning. With ANN, the testing kappa score to classify table grape bunches as clear or having chocolate browning or friction browning showed that chocolate browning could be classified with the strong agreement after Weeks 3 and 4 and Weeks 5 and 6 and that friction browning could be classified with moderate agreement after three and four weeks in cold storage. Classification of chocolate browning and friction browning phenotypes was done using PLS-DA and ANN and the result showed that both types of browning can be classified with moderate agreement. The implications of the results of this study for the table grape industry are that the industry can move beyond just assessing methods and techniques in the laboratory towards implementation in the vineyard and the packhouse. Much quicker decisions regarding grape quality and destination of export can now be made using a combination of the MicroNIR handheld and MATRIX-F instruments for onsite quality measurement and the models to predict internal (e.g. TSS) and external (browning) quality attributes.