Prediction of ‘Nules Clementine’ mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy

The use of diffuse reflectance visible and near infrared (Vis/NIR) spectroscopy was explored as a non-destructive technique to predict ‘Nules Clementine’ mandarin fruit susceptibility to rind breakdown (RBD) disorder by detecting rind physico-chemical properties of 80 intact fruit harvested from different canopy positions. Vis/NIR spectra were obtained using a LabSpec® spectrophotometer. Reference physico-chemical data of the fruit were obtained after 8 weeks of storage at 8 °C using conventional methods and included RBD, hue angle, colour index, mass loss, rind dry matter, as well as carbohydrates (sucrose, glucose, fructose, total carbohydrates), and total phenolic acid concentrations. Principal component analysis (PCA) was applied to analyse spectral data to identify clusters in the PCA score plots and outliers. Partial least squares (PLS) regression was applied to spectral data after PCA to develop prediction models for each quality attribute. The spectra were subjected to a test set validation by dividing the data into calibration (n = 48) and test validation (n = 32) sets. An extra set of 40 fruit harvested from a different part of the orchard was used for external validation. PLS-discriminant analysis (PLS-DA) models were developed to sort fruit based on canopy position and RBD susceptibility. Fruit position within the canopy had a significant influence on rind biochemical properties. Outside fruit had higher rind carbohydrates, phenolic acids and dry matter content and lower RBD index than inside fruit. The data distribution in the PCA and PLS-DA models displayed four clusters that could easily be identified. These clusters allowed distinction between fruit from different preharvest treatments. NIR calibration and validation results demonstrated that colour index, dry matter, total carbohydrates and mass loss were predicted with significant accuracy, with residual predictive deviation (RPD) for prediction of 3.83, 3.58, 3.15 and 2.61, respectively. The good correlation between spectral information and carbohydrate content demonstrated the potential of Vis/NIR as a non-destructive tool to predict fruit susceptibility to RBD

Real-time nondestructive citrus fruit quality monitoring system: development and laboratory testing

This study reports on the development and laboratory testing of the This study reports on the development and laboratory testing of the nondestructive citrus fruit quality monitoring system.  Prototype system consists of a light detection and ranging (LIDAR) and visible-near infrared spectroscopy sensors installed on an inclined conveyer for real-time fruit size and total soluble solids (TSS) measurement respectively.  Laboratory test results revealed that the developed system is applicable for instantaneous fruit size (R2 = 0.912) and TSS (R2 = 0.677, standard error of prediction = 0.48 °Brix) determination.  Future applications of such system would be in precision farming for in-field orange quality determination during the harvest and for row specific yield mapping and monitoring.    Keywords: LIDAR sensor, visible-near infrared spectroscopy, fruit size, sugar conten

Non-destructive determination of pre-symptomatic biochemical markers for Peteca spot and evaluation of edible coatings for reducing the incidence of the disorder on ‘Eureka’ lemons

Masters degree. University of KwaZulu-Natal, Pietermaritzburg.International markets that import citrus fruit from South Africa have imposed regulations that involve cold sterilization at low temperatures, which cause physiological disorders such as peteca spot in lemon. The aim of this study was to, non-destructively determine pre-symptomatic biochemical markers for Peteca spot and the evaluation of edible coatings for reducing the incidence of the disorder on ‘Eureka’ lemons. The first chapter is general background which introduces the key words and clearly outlines the aim and objectives of the study. The second chapter is review of literature, which motivated the three research chapters due to the gaps found. Presymptomatic biochemical markers that are related to peteca spot were evaluated in the third chapter. The Principal Component Analysis (PCA) was able to separate fruit harvested from the inside and outside canopy positions based on their susceptibility to the disorder. Fruit harvested in the inside canopy were more susceptible to peteca spot and these were correlated with physic-chemical properties, which were typically low in the inside canopy. The efficacy of carboxymethyl cellulose (CMC) and chitosan (CH) incorporated with moringa leaf extracts (M) edible coatings on reducing the incidence of peteca spot was also evaluated in the fourth chapter. Fruit harvested from inside and outside canopy positions were assigned to five coating treatments: control, M+CMC, CMC, CH and M+CH. The most effective coating treatment in reducing the susceptibility of ‘Eureka’ lemon to peteca spot was M+CMC followed by CMC and CH. The fifth chapter focused on, non-destructively predicting peteca spot using visible to near infrared spectroscopy (vis/NIRS). Presymptomatic biochemical markers that have been related to peteca spot were successfully predicted. Lastly, general discussions and conclusions were made in chapter six as well as recommendations

Towards Sweetness Classification of Orange Cultivars Using Short‑Wave NIR Spectroscopy

The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices

Non-destructive prediction of ‘marsh’ grapefruit (citrus x paradisi MacFad) postharvest quality and physiological rind disorders using visible to near infrared spectroscopy.

Master of Science in Horticulture. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file

Carbohydrate Analysis by NIRS-Chemometrics

Near-infrared spectroscopy (NIRS) is a high-throughput, low-cost, solvent-free, and nondestructive analytical tool. Chemometrics is the science that employs statistical and mathematical methods to explain near-infrared spectra; it has been proven that when they are coupled, their effectiveness highly improved in-depth carbohydrate characterization. This chapter focuses on the fundamentals of near-infrared spectroscopy in the study of carbohydrates, as well as the application of partial least squares regression (PLSR) and principal component analysis (PCA), as the most useful chemometric techniques involved in carbohydrate analysis. The theoretical aspects and practical applications starting from simple to complex carbohydrates mixtures are covered. Indeed, the contributions from different fields extend the implementation of near-infrared spectroscopy from industrial quality control to scientific research

Non-destructive Quality Monitoring of Fresh Fruits and Vegetables

Quality determines the shelf life as well as selling price of fresh fruit or vegetable and therefore, quality monitoring and testing of fresh commodities have paramount importance in their postharvest handling and supply chain management. Most of the methods used to assess fruits and vegetables quality are destructive in nature. Now-a-days, various mechanical, optical, electromagnetic, and dynamic non-destructive methods are gaining importance due to ease in operations, faster turn over and reliability. Some of the non-destructive techniques (NDT) are currently being used in laboratories, research institutions and food packaging and processing industries, whereas, some methods are still at developmental stage. Various NDT with respect to their principle and applications such as impact test, electronic nose, time-resolved reflectance spectrometry (TSR), near infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), X-Ray, ultra sonic, acoustic impulse response method, electrical conductivity methods etc., are discussed in this review

Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons

Sugar content is one of the most important properties of potato tubers as it directly affects their processing and the final product quality, especially for fried products. In this study, data obtained from spectroscopic (interactance and reflectance) and hyperspectral imaging systems were used individually or fused to develop non-cultivar nor growing season-specific regression and classification models for potato tubers based on glucose and sucrose concentration. Data was acquired over three growing seasons for two potato cultivars. The most influential wavelengths were selected from the imaging systems using interval partial least squares for regression and sequential forward selection for classification. Hyperspectral imaging showed the highest regression performance for glucose with a correlation coefficient (ratio of performance to deviation) or r(RPD) of 91.8(2.41) which increased to 94%(2.91) when the data was fused with the interactance data. The sucrose regression results had the highest accuracy using data obtained from the interactance system with r(RPD) values of 74.5%(1.40) that increased to 84.4%(1.82) when the data was fused with the reflectance data. Classification was performed to identify tubers with either high or low sugar content. Classification performance showed accuracy values as high as 95% for glucose and 80.1% for sucrose using hyperspectral imaging, with no noticeable improvement when data was fused from the other spectroscopic systems. When testing the robustness of the developed models over different seasons, it was found that the regression models had r(RPD) values of 55(1.19)–90.3%(2.34) for glucose and 35.8(1.07)–82.2%(1.29) for sucrose. Results obtained in this study demonstrate the feasibility of developing a rapid monitoring system using multispectral imaging and data fusion methods for online evaluation of potato sugar content

Effect of canopy position and non-detructive determination of rind biochemical properties of citrus fruit during postharvest non-chilling cold storage.

Doctor of Philosophy in Horticultural Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.No abstract provided.This thesis is a compilation of manuscripts where each individual chapter is an independent article/manuscript introduced disjointedly

Application of NIR spectroscopy and chemometrics in quality control of wild berry fruit extracts during storage

Extracts of wild berry fruits (Bilberry, Cranberry, Raspberry and Strawberry) were analysed using the near infrared spectroscopy (NIRS). NIRS can be used quantitatively and qualitatively to detect, identify, and qualify raw materials like berry fruits and to control their quality. The spectra of fruit products were measured by diffuse trans-reflectance and raw spectra were used for chemometric analysis. The range of NIR used in this research was 904–1699 nm with the aim to use the potential of near-infrared spectroscopy to detect differences between berry-fruits themselves, differences between the sample based on the preparation procedure as well if it is possible to detect the staleness of the samples (fresh and one month old). NIR is a fast and non-destructive analytical method that in association with chemometric modelling becomes a powerful tool for application in food industry. The chemometric methods applied to the recorded NIR data were classification analysis based on principal component (PC) scores and partial last squared regression (PLSR) model. Analyses of the spectrums reveals changes in specific wavelengths of 904-1181 nm that are related to the 3rd CH overtone and 2nd overtone of the OH stretch of H2O as well as in the range of 1434- 1635 nm which correspond to C–H combinations and the 1stovertone of the bonds. The simple PLS regression showed that the influence of the storage on the NIR spectra of samples is high (R2=0.79). This study showed that near infrared spectroscopy has potential to distinguish extracts based on unprocessed freshwild berry fruit extracts, sample preparation procedure and their quality decrease affected by the storage. All findings indicate the advisability of use of NIR for on-line monitoring of product quality and selection