Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Physicochemical fingerprint of Pera Rocha do Oeste. A PDO pear native from Portugal

“Pera Rocha do Oeste” is a pear (Pyrus communis L.) variety native from Portugal with a Protected Designation of Origin (PDO). To supply the world market for almost all the year, the fruits are kept under controlled storage. This study aims to identify which classical physicochemical parameters (colour, total soluble solids (TSS), pH, acidity, ripening index, firmness, vitamin C, total phenols, protein, lipids, fibre, ash, other compounds including carbohydrates, and energy) could be fingerprint markers of PDO “Pera Rocha do Oeste”. For this purpose, a data set constituting fruits from the same size, harvested from three orchards of the most representative PDO locations and stored in refrigerated conditions for 2 or 5 months at atmospheric conditions or for 5 months under a modified atmosphere, were selected. To validate the fingerprint parameters selected with the first set, an external data set was used with pears from five PDO orchards stored under di erent refrigerated conditions. Near infrared (NIR) spectroscopy was used as a complementary tool to assess the global variability of the samples. The lightness of the pulp; the b* CIELab coordinate of the pulp and peel; and the pulp TSS, pH, firmness, and total phenols, due to their lower variability, are proposed as fingerprint markers of this pearinfo:eu-repo/semantics/publishedVersio

SPECTROSCOPY, IMAGE ANALYSIS AND HYPERSPECTRAL IMAGING FOR FOOD SAFETY AND QUALITY: A CHEMOMETRIC APPROACH

Questo progetto di dottorato studia le differenti applicazioni delle tecniche ottiche non distruttive per la valutazione della qualit\ue0 e della shelf-life di prodotti vegetali cos\uec come l\u2019identificazione precoce di sviluppi microbici su superfici industriali. La spettroscopia, l\u2019analisi dell\u2019immagine e l\u2019analisi dell\u2019immagine iperspettrale possono giocare un ruolo importante nella valutazione sia della qualit\ue0 che della sicurezza degli alimenti grazie alla rapidit\ue0 e sensibilit\ue0 della tecnica, specialmente quando si utilizzano strumenti semplificati portatili. Un approccio statistico multivariato (chemiometria) \ue8 richiesto al fine di estrarre informazioni dal segnale acquisito, riducendo la dimensionalit\ue0 dei dati e mantenendo le informazioni spettrali pi\uf9 utili. Lo scopo del primo studio presentato \u2013 Testing of a Vis-NIR system for the monitoring of long-term apple storage \u2013 \ue8 la valutazione dell\u2019applicabilit\ue0 della spettroscopia nel visibile e vicino infrarosso (Vis-NIR) per il monitoraggio e la gestione delle mele durante lo stoccaggio a basse temperature. Per sette mesi \ue8 stata seguita l\u2019evoluzione in termini di grado zuccherino e consistenza delle mele suddivise in classi di maturazione. I risultati hanno indicato che la spettroscopia \ue8 una tecnica non-distruttiva che consente una stima accurata dei parametri chimico-fisici per la classificazione delle mele in lotti omogenei. Il lavoro descritto nel secondo paragrafo - Wavelength selection with a view to a simplified handheld optical system to estimate grape ripeness \u2013 \ue8 finalizzato all\u2019identificazione delle tre lunghezze d\u2019onda pi\uf9 importanti per il riconoscimento, direttamente in campo, dell\u2019uva pronta per essere raccolta al fine della messa a punto di un sistema semplificato e a basso costo. I coefficienti di regressione standardizzati del modello PLS (Partial Least Square) sono stati utilizzati per selezionare le variabili pi\uf9 importanti, che racchiudono l\u2019informazione pi\uf9 utile lungo l\u2019intero spettro. La stessa procedura \ue8 stata condotta per determinare la freschezza delle foglie di Valerianella durante la shelf-life - Selection of optimal wavelengths for decay detection in fresh-cut Valerianella Locusta laterr (terzo paragrafo). Lo scopo del lavoro presentato nel quarto paragrafo del primo capitolo - Comparison between FT-NIR and Micro-NIR in the evaluation of Acerola fruit quality, using PLS and SVM regression algorithms \u2013 \ue8 stimare l\u2019acidit\ue0 titolabile e il contenuto di acido ascorbico all\u2019interno del frutto acerola, utilizzando uno strumento compatto e a basso costo denominato Micro-NIR, che lavora nell\u2019intervallo spettrale 950-1650 nm. I dati spettrali sono stati modellati mediante l\u2019applicazione di due algoritmi PLS e SVM (Support Vector Machine). La capacit\ue0 predittiva dello strumento semplificato \ue8 risultata interessante per applicazioni di monitoraggio in campo, soprattutto modellizzando i dati in modo non lineare. Nel secondo capitolo, \ue8 presentata l\u2019applicazione di immagini RGB per la valutazione delle superfici - Image texture analysis, a non-conventional technique for early detection of biofilm. La texture dell\u2019immagine \ue8 definita come una differenza nella distribuzione spaziale, nella frequenza e nell\u2019intensit\ue0 dei livelli di grigio in ogni pixel dell\u2019immagine. Questo metodo \ue8 stato determinante per l\u2019identificazione precoce dello sviluppo microbico su superfici normalmente impiegate nell\u2019industria alimentare. L\u2019approccio chemiometrico \ue8 stato cruciale in ogni fase del progetto di dottorato ed \ue8 definito come un approccio statistico multivariato che si applica ai dati chimici per estrarre informazione utile, ridurre il rumore di fondo e l\u2019informazione ridondante. Il lavoro presentato all\u2019inizio del terzo capitolo - Hyperspectral image analysis: a tutorial - propone una procedura standard per l\u2019elaborazione di dati tridimensionali, presentando un esempio relativo alla predizione del raffermamento del pane in cassetta. Il secondo paragrafo del terzo capitolo, presenta una applicazione dell\u2019immagine iperspettrale su acerola, focalizzata sul contenuto di vitamina C - HSI for quality evaluation of vitamin C content in Acerola fruit. In questo lavoro, \ue8 stata acquisita l\u2019immagine di dieci acerola, raccolte in funzione del livello di maturazione, definito in base al colore della buccia (cinque acerola verdi e cinque rosse). Lo spettro della polvere di vitamina C pura \ue8 stato utilizzato come riferimento per l\u2019applicazione di due algoritmi di correlazione (spectral angle mapping e correlation coefficient), consentendo la costruzione di mappe qualitative di distribuzione dell\u2019acido ascorbico all\u2019interno del frutto. Lo scopo dell\u2019ultimo lavoro presentato \ue8 la valutazione della qualit\ue0 post raccolta dell\u2019acerola - Selection of NIR wavelengths from hyperspectral imaging data for the quality evaluation of Acerola fruit. Le immagini iperspettrali di venti acerola sono state acquisite per cinque giorni consecutivi. La valutazione delle modificazioni spettrali durante il tempo ha consentito la selezione delle tre lunghezze d\u2019onda caratterizzanti il processo di maturazione/degradazione del frutto. L\u2019immagine in falsi colori, derivante dalla composizioni delle immagini alle tre lunghezze d\u2019onda di interesse, consente l\u2019identificazione precoce del processo degradativo in maniera rapida e non distruttiva. Le tre tecniche non distruttive impiegate in questo progetto di dottorato hanno dimostrato efficienza e applicabilit\ue0 per la valutazione della qualit\ue0 e della sicurezza degli alimenti, rispondendo alla necessit\ue0 dell\u2019industria alimentare di tecniche accurate, veloci e obiettive per assicurare produzioni ottimali lungo l\u2019intero processo produttivo.This PhD project regards different applications of non-destructive optical techniques to evaluate quality and shelf life of agro-food product as well as the early detection of biofilm on food plants. Spectroscopy, image analysis and hyperspectral imaging could play an important role in the assessment of both quality and safety of foods due to their rapidity and sensitivity especially when using simplified portable devices. Due to the huge amount of collected data, chemometric, a multivariate statistical approach, is required, in order to extract information from the acquired signals, reducing dimensionality of the data while retaining the most useful spectral information. The thesis is organized in four chapters, one for each technique and a final chapter including the overall conclusion. Each chapter is divided in case studies according to the matrix analysed and the data acquisition and elaboration carried out. The first chapter is about spectroscopy. The aim of the first study - Testing of a Vis-NIR system for the monitoring of long-term apple storage - is to evaluate the applicability of visible and near-infrared (Vis-NIR) spectroscopy to monitor and manage apples during long-term storage in a cold room. The evolution of the apple classes, originally created, was analysed during 7 months of storage by monitoring TSS and firmness. Vis-NIR allows an accurate estimation of chemical-physical parameters of apples allowing a non-destructive classification of apples in homogeneous lots and a better storage management. The work reported in the second paragraph - Wavelength selection with a view to a simplified handheld optical system to estimate grape ripeness - is aimed to identify the three most significant wavelengths able to discriminate grapes ready to be harvested directly in the field. Wavelengths selection was carried out with a view to construct a simplified handheld and low-cost optical device. Standardized regression coefficients of the PLS model were used to select the relevant variables, representing the most useful information of the full spectral region. The same approach was followed to discriminate freshness levels during shelf-life of fresh-cut Valerianella leaves - Selection of optimal wavelengths for decay detection in fresh-cut Valerianella Locusta Laterr. (third paragraph). The aim of the work presented in the fourth paragraph of the first chapter - Comparison between FT-NIR and Micro-NIR in the evaluation of Acerola fruit quality, using PLS and SVM regression algorithms - is to estimate titratable acidity and ascorbic acid content in acerola fruit, using a MicroNIR, an ultra-compact and low-cost device working between 950 \u2013 1650 nm. The spectral data were modelled using two different regression algorithms, PLS (partial least square) and SVM (support vector machine). The prediction ability of Micro-NIR appears to be suitable for on field monitoring using non-linear regression modelling (i.e. SVM). In the second chapter, image analysis was performed. The traditional RGB imaging for the evaluation of image texture, a specific surface characteristic, is presented. The texture of an image is given by differences in the spatial distribution, in the frequency and in the intensity of the values of the grey levels of each pixel of the image. This technique was applied for the early detection of biofilm in its early stages of development, when it is still difficult to observe it by the naked eye, was evaluated (Image texture analysis, a non-conventional technique for early detection of biofilm). In the third paragraph, image and spectroscopy were combined in hyperspectral imaging applications. Data analysis by chemometric was crucial in any stage of my PhD project. Chemometric is a multivariate statistical approach that is applied on chemical data to extract the useful information avoiding noise and redundant data. At the beginning of the third chapter - Hyperspectral image analysis: a tutorial - proposes an original approach, developed as a flow sheet for three-dimensional data elaboration. The method was applied, as an example, to the prediction of bread staling during storage. The first application about hyperspectral on acerola is focused on the vitamin C content - HIS for quality evaluation of vitamin C content in Acerola fruit. Ten different acerola fruits picked up according to two different stages of maturity, based on the colour of the peel (5 green and 5 red acerola), were analysed. The spectra of pure vitamin C powder was used as references for computing models with two different correlation techniques: spectral angle mapping and correlation coefficient allowing the construction of a qualitative distribution map of ascorbic acid inside the fruit. The aim of the last one work presented is to evaluate acerola post-harvest quality - Selection of NIR wavelengths from hyperspectral imaging data for the quality evaluation of Acerola fruit. Hyperspectral images of 20 acerolas were acquired for five consecutive days and an investigation of time trends was carried out to highlight the most important three wavelengths that characterized the ripeness/degradation process of the Acerola fruit. The false-colour RGB images, derived from the composition of the three interesting wavelengths selected, data enable early detection of the senescence process in a rapid and non-destructive manner. In conclusion, the three non-destructive optical techniques applied in this PhD project have proved to be one of the most efficient and advanced tools for safety and quality evaluation in food industry answering the need for accurate, fast and objective food inspection methods to ensure safe production throughout the entire production process

APPLICATION OF VIS/NIR SPECTROSCOPYFOR RIPENESS EVALUATIONAND POSTHARVEST QUALITY ANALYSISOF AGRO-FOOD PRODUCTS

Agro-food composition at harvest time is one of the most important factors determining the future quality of final products (e.g. wine from grapes and oil from olives). Quality parameters change in function of different product matrix. Measurement of fruits characteristics that impacts on product quality is a requirement for production improvement. Inspection of fruits during ripening is a critical point in all agri-food production chains. This control is usually performed only on small samples that are not always representative of the whole lot. The importance of this monitoring operation is easy to understand, as it determines the economic value of the entire stock. Traditionally, fruits quality evaluation is achieved by a visual and taste assessment of them and evaluation of the traditional quality parameters such as total soluble solids, acidity and texture. The conventional methods to determine fruits quality parameters are time consuming, require preparation of samples, are often expensive, and generally highlight only one or a few aspects of fruits quality. Therefore, there is a strong need in the modern food industry for a simple, rapid, and easy\u2010to\u2010use method for objectively evaluating the quality of fruits. This kind of tool would enable real\u2010time analyses at the receiving station and would allow the preliminary decision\u2010making about grapes during consignment thank to the rapid analysis of various parameters simultaneously. Since food quality is not an individual attribute but it contains a number of inherent characteristics of the food itself, to measure the optical properties of food products has been one of the most studied non-destructive techniques for the simultaneous detection of different quality parameters. In fact, the light reflected from food contains information about constituents in the inner layers of sample and at foodstuff surface also. To achieve objectives of this work, e.g. ripeness evaluation and postharvest quality characteristics of agro-food products, visible near\u2010infrared (vis/NIR) spectroscopy was chosen. In particular, vis/NIR spectroscopy is a rapid and non-destructive technique requiring minimal sample processing before analysis; coupled with chemometric methods, appears to be one of the most powerful analytical tools for studying food products. Chemometrics is an essential part of vis/NIR spectroscopy in food sector. To extract useful information present in the spectra multivariate analysis was carried out. Principal component analysis (PCA) was used for a qualitative analysis of the data and PLS regression analysis as a technique to obtain quantitative prediction of the parameters of interest. The general aim of this work is to study the application of vis/NIR spectroscopy for ripeness evaluation and postharvest quality analysis of agro-food products. In particular this technology was tested to analyse ripening parameters of olives and grapes before to be processed, or to monitor freshness decay of fresh-cut lettuce and apples during long cold storage in controlled atmosphere. Moreover, the feasibility of a simplified handheld and low-cost optical device, based on measurement and processing of diffuse spectral reflectance at a few appropriately selected wavelengths was proposed. This study was focused on identifying the most significant wavelengths able to discriminate in a quick and simple way (i) directly in the field, the blueberries, the grapes, the olives ready to be harvested, (ii) on-line, for the real time monitoring of trend of craft beer fermentation and to estimate qualitative and quantitative parameters or (iii) during shelf life, freshness levels of fresh-cut Lamb\u2019s lettuce (Valerianella locusta Laterr.). The final aim of this work is to realize a simplified modular optical device (with few selected wavelengths) for single sample, non-destructive, and quick prediction of fruit ripeness degree and quality parameters evaluation. The first prototype of simplified optical device was realized for red grapes study and is characterized by the presence of four LEDs emitting at the wavelengths of interest. LED technology was chosen as illumination source of the sample, and allows obvious advantages in term of simplification and cost reduction. The design of the prototype of the simplified optical device was realised with particular attention to versatility and modularity. The possibility to adjust the light source with a specific choice of wavelengths for LEDs, makes it possible to use the same simplified optical device for many different application. This modular design allows an easy adjustment for different objective and for different kind of food sample matrix

The non-invasive assessment of avocado maturity and quality

Horticultural products in today's modern market must have high quality standards. Consumer demand for consistent quality agricultural produce remains strong and continues to increase, this will lead to the development and subsequent increased availability of sophisticated techniques, sensors, and user-friendly non-invasive systems for measuring product quality indices. The inability to consistently guarantee internal fruit quality is a major factor not only for the Australian avocado industry but also the entire horticulture sector. Poor fruit quality is seen as a key factor affecting consumer confidence and impacts on supply chain efficiency and profitability. Removing fruit quality inconsistencies while providing the consumer with a consistent quality product is a vital commercial consideration of the Australian avocado industry for both domestic and export markets. Many fruit quality attributes affecting consumer acceptance are assessed using traditional methods that are generally subjective, labour intensive and costly. Commercially, avocado maturity is measured destructively by the determination of dry matter (DM) content, moisture content (MC) or oil content, all of which are highly correlated. Maturity is an important component in avocado fruit quality and a prime factor in palatability. A rapid, non-destructive measurement system that can accurately and simultaneously monitor external and internal attributes of every avocado fruit either in the field or in an in-line setting, is highly desirable for ensuring consistent product quality over an extended season, increasing industry marketability and profitability. The utility of near infrared (NIR) spectroscopy was investigated as a non-invasive assessment tool for estimating avocado maturity and thereby eating quality based on dry matter content of whole intact fruit primarily for the avocado variety 'Hass'. The technique was also assessed for detecting bruises and for predicting rot susceptibility as an indication of shelf-life for possible implementation in a commercial in-line application. The project also investigated the importance of the calibration model development process to incorporate seasonal and geographical variability to ensure model robustness. NIR spectroscopy has an obvious place in agriculture and environmental applications with its core strength in the analysis of biological materials, plus low cost of analysis, simplicity in sample preparation, no chemical reagent requirements, simultaneous analysis of multiple constituents, good repeatability and high throughput capability. The commercially available NIR spectroscopy systems assessed in this project highlighted the potential of NIR spectroscopy and its suitability for application in a commercial in-line setting for predicting avocado maturity and palatability of whole intact avocados, based on DM content. With horticultural products, the major challenge of implementing NIR spectroscopy is to ensure that the calibration model is robust, that is, that the calibration model holds across growing seasons and potentially across growing districts. The present project represents the first study to investigate the effect of seasonal variation on model robustness to be applied to avocado fruit. It found that seasonal variability has a significant effect on model predictive performance for DM in avocados. The robustness of the calibration model, which in general limits the commercial application for the technique, was found to increase across seasons when more seasonal variability was included in the calibration set. Across the seasons it achieved predictive performances in this case in the range of: validation coefficient of determination (Rᵥ²) of 0.76 – 0.89, root mean square error of prediction (RMSEP) of 1.43 - 1.97%, and standard deviation ratio's (SDR) of 2.0 to 3.1. Similarly, there are spectral differences between geographical regions and that specific regional models may have significantly reduced predictive performance when applied to samples containing biological variability from a different growing region. As with seasonal variability, this can be addressed by incorporating multiple geographical growing regions into the calibration model to account for the biological variability to improve model robustness as demonstrated in this study (i.e., Rᵥ² of 0.89, RMSEP of 1.51%, and SDR of 3.6). Furthermore, when models are constructed to include both season and geographical variability, model performance can be more robust when dealing with a broader range of future sample variability. This was demonstrated with calibration models constructed to incorporate 3 years of seasonal variability and encompassing 3 geographical regions, obtaining predictive performances ranging from Rᵥ ² 0.87 - 0.89; RMSEP of 1.42 - 1.64% and SDR of 2.7 - 3.1 across the various geographical regions. NIR spectroscopy shows great promise for the application in a commercial, in-line setting for the non-destructive evaluation of impact damage (bruising) and rot susceptibility of whole avocado fruit, although optimisation of the technology is required to address speed of throughput and environmental issues. The adoption of a rapid, non-invasive method to identify fruit that are less prone to rots and internal disorders would allow selection of fruit that could be sent to more distant markets with greater confidence that it will arrive in acceptable quality, thus ensuring maximum yield and higher returns for the producer and marketer. The ability of the NIR classification models to accurately predict rot development of hard green avocado fruit (stage 0 ripeness) into two classes, ≤10% and >10% of flesh affected, ranged from 65-84% over the three growing seasons. When the rot classes were defined as ≤30% and >30% the accuracy ranged from 69%-77%. In relation to impact damage (bruising), trials conducted over three growing seasons using an NIR spot assessment technique found hard green fruit at stage 2 ripeness, that were deliberately bruised could be correctly detected with 70-79% accuracy after 2-5 hours of impacting and with 83-89% accuracy after 24 hours. For eating ripe (stage 4) fruit, the accuracy was 60-100% after 2-5 hours of impacting and 66-100% after 24 hours across the three growing seasons. This indicates that in a commercial situation it would be an advantage to hold the fruit for 24 hours before undertaking NIR scanning

Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)

La tesi di dottorato è incentrata sull'analisi di tecnologie non distruttive per il controllo della qualità dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi riguarda l'applicazione del sistema di visione artificiale per valutare la qualità delle foglie di rucola fresh-cut. La tesi è strutturata in tre parti (introduzione, applicazioni sperimentali e conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio della qualità dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i) la variabilità dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli più semplici rispetto al machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di dottorato è stato svolto dall'Università di Foggia, dall'Istituto di Scienze delle Produzioni Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). L’attività di ricerca è stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualità della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione non distruttiva della qualità di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the control quality of agri-food products, along the whole supply chain. In particular, the thesis concerns the application of computer vision system to evaluate the quality of fresh rocket leaves. The thesis is structured in three parts (introduction, experimental applications and conclusions) and in 5 chapters, the first and second focused on non-destructive technologies and in particular on computer vision systems for monitoring the quality of agri-food products, respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the estimation of quality aspects, considering different aspects: (i) the variability due to the different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii) development and exploitation of the advantages of new models simpler than the machine learning used in the previous experiments. The research work of this doctoral thesis was carried out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of National Research Council (CNR). It was conducted within the Project SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR- PRIN 2017, and aimed at sustaining quality of production and of the environment using low input agricultural practices and non-destructive quality evaluation

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot‐air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non‐invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non‐invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non‐invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.Bundesanstalt für Landwirtschaft und Ernährung http://dx.doi.org/10.13039/501100010771German Research Foundation (DFG‐Deutsche Forschungsgemeinschaft) http://dx.doi.org/10.13039/501100001659Institut Penyelidikan dan Kemajuan Pertanian Malaysia http://dx.doi.org/10.13039/501100007702Federal Ministry of Food and Agriculture http://dx.doi.org/10.13039/501100005908Coordination of European Transnational Research in Organic Food and Farming Systems http://dx.doi.org/10.13039/501100011598the Universität KasselPeer Reviewe

Characterisation of grapevine berry samples with infrared spectroscopy methods and multivariate data analyses tools

Thesis (MSc)--Stellenbosch University, 2015.ENGLISH ABSTRACT: Grape quality is linked to the organoleptic properties of grapes, raisins and wine. Many advances have been made in understanding the grape components that are important in the quality of wines and other grape products. A better understanding of the compositional content of grapes entails knowing when and how the various components accumulate in the berry. Therefore, an appreciation of grape berry development is vitally important towards the understanding of how vineyard practices can be used to improve the quality of grapes and eventually, wines. The more established methods for grape berry quality assessment are based on gravimetric methods such as colorimetry, fluorescence and chromatography. These conventional methods are accurate at targeting particular components, but are typically multi-step, destructive, expensive, polluting procedures that might be technically challenging. Very often grape berries are evaluated for quality (only) at harvest. This remains a necessary exercise as it helps viticulturists and oenologists to estimate some targeted metabolite profiles that are known to greatly influence chemical and sensory profiles of wines. However, a more objective measurement of predicting grape berry quality would involve evaluation of the grapes throughout the entire development and maturation cycle right from the early fruit to the ripe fruit. To achieve this objective, the modern grape and wine industry needs rapid, reliable, simpler and cost effective methods to profile berry development. By the turn of the last millennium, developments in infrared instrumentation such as Fourier-transform infrared (FT NIR) and attenuated total reflectance Fourier-transform infrared spectroscopy (ATR FT-IR) in combination with chemometrics resulted in the development of rapid methods for evaluating the internal and external characteristics of fresh fruit, including grapes. The advancement and application of these rapid techniques to fingerprint grape compositional traits would be useful in monitoring grape berry quality. In this project an evaluation of grape berry development was investigated in a South African vineyard setting. To achieve this goal, Sauvignon blanc grape berry samples were collected and characterised at five defined stages of development: green, pre-véraison, véraison, post-véraison and ripe. Metabolically inactivated (frozen in liquid nitrogen and stored at -80oC) and fresh berries were analysed with FT-IR spectroscopy in the near infrared (NIR) and mid-infrared (MIR) ranges to provide spectral data. The spectral data were used to provide qualitative (developmental stage) and quantitative (metabolite concentration of key primary metabolites) information of the berries. High performance liquid chromatography (HPLC) was used to separate and quantify glucose, fructose, tartaric acid, malic acid and succinic acid which provided the reference data needed for quantitative analysis of the spectra. Unsupervised and supervised multivariate analyses were sequentially performed on various data blocks obtained by spectroscopy to construct qualitative and quantitative models that were used to characterise the berries. Successful treatment of data by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) gave statistically significant chemometric models that discriminated the berries according to their stages of development. The loadings from MIR models highlighted the important discriminant variables responsible for the observed developmental stage classification. The best calibration models to predict metabolite concentrations were obtained from MIR spectra for glucose, fructose, tartaric acid and malic acid. The results showed that both NIR and MIR spectra in combination with multivariate analysis could be reliably used to evaluate Sauvignon blanc grape berry quality throughout the fruit’s development cycle. Moreover, the methods used were fast and required minimal sample processing and no metabolite extractions with organic solvent. In addition, the individual major sugar and organic acids were accurately predicted at the five stages under investigation. This study provides further proof that IR technologies are robust and suitable to explore high-throughput and in-field application of grape compound profiling.AFRIKAANSE OPSOMMING: Druifkwaliteit word gekoppel aan die organoleptiese eienskappe van druiwe, rosyntjies en wyn. Baie vooruitgang is reeds gemaak in die begrip van druifkomponente wat belangrik is vir die kwaliteit van wyn en ander druifprodukte. ’n Beter begrip van die samestellende inhoud van druiwe behels om te weet wanneer en hoe die verskeie komponente in die korrel opgaar. ’n Evaluasie van druiwekorrel-ontwikkeling is dus uiters belangrik vir ’n begrip van hoe wingerdpraktyke gebruik kan word om die kwaliteit van druiwe, en uiteindelik van wyne, te verbeter. Die meer gevestigde maniere vir die assessering van druiwekorrelkwaliteit is gebaseer op gravimetriese metodes soos kolorimetrie, fluoressensie en chromatografie. Hierdie konvensionele metodes is akkuraat om spesifieke komponente te teiken, maar behels tipies veelvuldige stappe en is prosesse wat destruktief en duur is, besoedeling veroorsaak, asook moontlik tegnies uitdagend is. In baie gevalle word druiwekorrels (eers) tydens oes vir kwaliteit geëvalueer. Hierdie is steeds ’n noodsaaklike oefening omdat dit wingerdkundiges en wynkundiges help om die metabolietprofiele wat daarvoor bekend is om ’n groot invloed op die chemiese en sensoriese profiele van wyn te hê en dus geteiken word, te skat. ’n Meer objektiewe meting om druiwekorrelkwaliteit te voorspel, sou die evaluering van die druiwe dwarsdeur hulle ontwikkeling- en rypwordingsiklus behels, vanaf die vroeë vrugte tot die ryp vrugte. Om hierdie doelwit te behaal, benodig die moderne druiwe- en wynbedryf vinnige, betroubare, eenvoudiger en kostedoeltreffende metodes om ’n profiel saam te stel van korrelontwikkeling. Aan die einde van die vorige millennium het ontwikkelings in infrarooi instrumentering soos Fourier-transform infrarooi (FT NIR) en attenuated total reflectance Fourier-transform infrarooi spektroskopie (ATR FT-IR) in kombinasie met chemometrika gelei tot die ontwikkeling van vinnige metodes om die interne en eksterne kenmerke van vars vrugte, insluitend druiwe, te meet. Die vooruitgang en toepassing van hierdie vinnige tegnieke om ‘vingerafdrukke’ te bekom van die samestellende kenmerke sal nuttig wees vir die verbetering van druiwekorrelkwaliteit. In hierdie projek is ’n evaluering van druiwekorrelontwikkeling in ’n Suid-Afrikaanse wingerdligging ondersoek. Ten einde hierdie doel te bereik, is Sauvignon blanc druiwekorrelmonsters op vyf gedefinieerde stadiums van ontwikkeling versamel en gekarakteriseer: groen, voor deurslaan, deurslaan, ná deurslaan en ryp. Metabolies geïnaktiveerde (bevrore in vloeibare stikstof en gestoor teen -80oC) en vars korrels is met FT-IR spektroskopie in die naby infrarooi (NIR) and mid-infrarooi (MIR) grense geanaliseer om spektrale data te verskaf. Die spektrale data is gebruik om kwalitatiewe (ontwikkelingstadium) en kwantitatiewe (metabolietkonsentrasie van belangrikste primêre metaboliete) inligting van die korrels te verskaf. High performance liquid chromatography (HPLC) is gebruik om glukose, fruktose, wynsteensuur, appelsuur en suksiensuur te skei en te kwantifiseer, wat die verwysingsdata verskaf het wat vir die kwantitatiewe analise van die spektra benodig word. Ongekontroleerde en gekontroleerde meervariantanalises is opeenvolgend op verskeie datablokke uitgevoer wat met spektroskopie verkry is om kwalitatiewe en kwantitatiewe modelle te verkry wat gebruik is om die korrels te karakteriseer. Suksesvolle behandeling van die data deur hoofkomponent analise (principal component analysis (PCA)) en ortogonale parsiële kleinstekwadraat diskriminant analise (partial least squares discriminant analysis (OPLS-DA)) het statisties betekenisvolle chemometriese modelle verskaf wat die korrels op grond van hulle ontwikkelingstadia onderskei het. Die ladings vanaf die MIR-modelle het die belangrike diskriminantveranderlikes beklemtoon wat vir die klassifikasie van die waargenome ontwikkelingstadium verantwoordelik is. Die beste kalibrasiemodelle om metabolietkonsentrasies te verkry, is vanuit die MIR-spektra vir glukose, fruktose, wynsteensuur en appelsuur bekom. Die resultate toon dat beide die NIR- en MIR-spektra, in kombinasie met meervariantanalise, betroubaar gebruik kan word om Sauvignon blanc druiwekorrelkwaliteit dwarsdeur die vrug se ontwikkelingsiklus te evalueer. Verder is die metodes wat gebruik word, vinnig en het hulle minimale monsterprosessering en geen metabolietekstraksies met organiese oplosmiddel benodig nie. Daarbenewens is die vernaamste suiker en organiese sure individueel akkuraat voorspel op die vyf stadia wat ondersoek is. Hierdie studie verskaf verdere bewys dat IR-tegnologieë robuus en geskik is om hoë-deurset en in-veld toepassings van profielsamestelling van druiweverbindings te ondersoek