Classification of red grapes according to their state of ripeness using a low-cost multispectral device

[ES] El objetivo del presente trabajo fue evaluar la idoneidad de un sensor multiespectral de bajo costo para la determinación del estado de maduración de uvas tintas. El dispositivo propuesto se basa en un sensor multiespectral, con 18 bandas de detección en el rango entre los 410 y los 940 nm. La recogida de muestras se llevó a cabo en un viñedo comercial situado en Rociana del Condado, Huelva. El dispositivo propuesto se utilizó para adquirir la respuesta espectral de 80 racimos de uva en condiciones de laboratorio. Tras esto, cada una de las muestras fue analizada mediante métodos estándar de laboratorio para obtener indicadores objetivos de su estado de maduración (sólidos solubles totales y acidez). Los 18 valores de reflectancia ofrecidos por el sensor fueron usados como datos de entrada para entrenar redes neuronales artificiales para la clasificación de las muestras de uva en función de los parámetros objetivo. Los resultados obtenidos fueron prometedores, lo cual allana el camino hacia la implementación de un sistema para la monitorización del estado de maduración de uvas asequible para los vinicultores.[EN] The present work aims to evaluate a low-cost multispectral device for non-destructive grape ripening status assessment. The proposed device is based on a multispectral sensor, with a spectral response of 18 channels in a range from 410 to 940 nm. The experimental validation was carried out in a commercial vineyard in Rociana del Condado, Huelva. The proposed device was used to analyze 80 grape samples under laboratory conditions. After being processed with the proposed device the grape samples were analyzed with standard chemical methods to generate ground truth values of ripening status indicators (solid soluble content, and acidity). The 18-reflectance data corresponding to the spectral channels of the employed sensor, were used as input variables for developing artificial neural network models to classify the berries samples based on the mentioned ripeness indicators. The obtained results were promising, which paves the way for the implementation of a portable grape ripening appraisal system affordable for grape growers

Quality evaluation of grapes for mechanical harvest using vis NIR spectroscopy

Mechanical harvest of grapes is one of the operations that mostly influence the quality of the future wine. The shaking frequency of the harvesting machine is usually adjusted on the basis of the grape berry characteristics in order to limit grape juice production that is a potential cause of uncontrolled fermentations. These evaluations usually require time, personnel and laboratory analyses. The introduction of a vis NIR system to rapidly and reliably evaluate the berry properties in field before mechanical harvest could be a good alternative. The aim of this study was to evaluate the feasibility of applying vis NIR spectroscopy as a non-destructive technique on grapes cv. Syrah and Chardonnay to predict pedicel detachment force, pH and total soluble solids before mechanical harvest. The spectral acquisitions were performed using a portable vis NIR device (600-1000 nm). An Ordinary Least Square evaluation was applied to assess vis NIR prediction ability on grapes. The system gave excellent performance in predicting pH for both varieties (R 2 = 0.99), also confirmed by the indicators SECV/M and Bias/M respectively equal to 0.024 and 0.014 for cv. Syrah and 0.002 and -0.009 for Chardonnay. The vis NIR device showed satisfactory prediction ability even regarding total soluble solids (R 2 = 0.997 for Syrah and 0.9935 for Chardonnay) with SECV/M = 0.090, Bias/M = 0.071 for cv. Syrah and SECV/M = 0.00, Bias/M = -0.002 for Chardonnay. However, the results showed the low vis NIR ability to predict detachment force for Chardonnay grapes (R 2 = 0.85, SECV/M = 1.008; Bias/M = -0.834), and an acceptable one for Syrah grapes (R 2 = 0.87; SECV/M = 0.362; Bias/M = -0.109). Since detachment force has an enormous importance in grapes mechanical harvest, the possibility of applying vis NIR spectroscopy in field before harvest is very encouraging for cv. Syrah (red grapes) and needs to be improved for cv. Chardonnay (white grapes)

Optimization of NIR Spectral Data Management for Quality Control of Grape Bunches during On-Vine Ripening

NIR spectroscopy was used as a non-destructive technique for the assessment of chemical changes in the main internal quality properties of wine grapes (Vitis vinifera L.) during on-vine ripening and at harvest. A total of 363 samples from 25 white and red grape varieties were used to construct quality-prediction models based on reference data and on NIR spectral data obtained using a commercially-available diode-array spectrophotometer (380–1,700 nm). The feasibility of testing bunches of intact grapes was investigated and compared with the more traditional must-based method. Two regression approaches (MPLS and LOCAL algorithms) were tested for the quantification of changes in soluble solid content (SSC), reducing sugar content, pH-value, titratable acidity, tartaric acid, malic acid and potassium content. Cross-validation results indicated that NIRS technology provided excellent precision for sugar-related parameters (r2 = 0.94 for SSC and reducing sugar content) and good precision for acidity-related parameters (r2 ranging between 0.73 and 0.87) for the bunch-analysis mode assayed using MPLS regression. At validation level, comparison of LOCAL and MPLS algorithms showed that the non-linear strategy improved the predictive capacity of the models for all study parameters, with particularly good results for acidity-related parameters and potassium content

Monitoring grape ripeness using a voltammetric electronic tongue

The use of a voltammetric electronic tongue as a tool to monitor grape ripeness is proposed herein. The electronic tongue consists of eight metallic electrodes housed inside a stainless steel cylinder. The study was carried out over a period of ca. 1 month (August 2012) on different grape varieties (Macabeo, Chardonnay, Pinot Noir, Cabernet Sauvignon, Shyrah, Merlot and Bobal) from various vineyards near Requena and Utiel (Valencia, Spain). Apart from the electrochemical studies, the physico-chemical parameters, such as, Total Acidity, pH and °Brix, were also determined in grapes. The PCA models, obtained using the physico-chemical or electrochemical data, showed variation of ripenesswith time.Moreover the studywas completed by using partial least squares (PLS) regression in an attempt to establish a correlation between the data collected from the electronic tongue and Total Acidity, pH and °Brix values. A good predictive modelwas obtained for the prediction of Total Acidity and °Brix. These results suggest the possibility of employing electronic tongues to monitor grape ripeness and of, therefore, evaluating the right time for harvesting.The financial support from the Spanish Government (project MAT2012-38429-C04-01) and the Generalitat Valenciana (Valencian Regional Government; project PROMETEO/2009/016) is gratefully acknowledged.Campos Sánchez, I.; Bataller Prats, R.; Armero, R.; Gandía Romero, JM.; Soto Camino, J.; Martínez Mañez, R.; Gil Sánchez, L. (2013). Monitoring grape ripeness using a voltammetric electronic tongue. Food Research International. 54(2):1369-1375. https://doi.org/10.1016/j.foodres.2013.10.011S1369137554

Estimation of total soluble solids in grape berries using a hand-held NIR spectrometer under field conditions

BACKGROUND Recent studies have reported the potential of near infrared (NIR) spectral analysers for monitoring the ripeness of grape berries as an alternative to wet chemistry methods. This study covers various aspects regarding the calibration and implementation of predictive models of total soluble solids (TSS) in grape berries using laboratory and in-field collected NIR spectra. RESULTS The performance of the calibration models obtained under laboratory conditions indicated that at least 700 berry samples are required to assure enough prediction accuracy. A statistically significant error reduction (ΔRMSECV = 0.1°Brix) with P Peer reviewe

On-The-Go VIS plus SW - NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard

Visible-Short Wave Near Infrared (VIS + SW - NIR) spectroscopy is a real alternative to break down the next barrier in precision viticulture allowing a reliable monitoring of grape composition within the vineyard to facilitate the decision-making process dealing with grape quality sorting and harvest scheduling, for example. On-the-go spectral measurements of grape clusters were acquired in the field using a VIS + SW - NIR spectrometer, operating in the 570-990 nm spectral range, from a motorized platform moving at 5 km/h. Spectral measurements were acquired along four dates during grape ripening in 2017 on the east side of the canopy, which had been partially defoliated at cluster closure. Over the whole measuring season, a total of 144 experimental blocks were monitored, sampled and their fruit analyzed for total soluble solids (TSS), anthocyanin and total polyphenols concentrations using standard, wet chemistry reference methods. Partial Least Squares (PLS) regression was used as the algorithm for training the grape composition parameters' prediction models. The best cross-validation and external validation (prediction) models yielded determination coefficients of cross-validation (R-cv(2)) and prediction (R-P(2)) of 0.92 and 0.95 for TSS, R-cv(2) = 0.75, and R-p(2) = 0.79 for anthocyanins, and R-cv(2) = 0.42 and R-p(2) = 0.43 for total polyphenols. The vineyard variability maps generated for the different dates using this technology illustrate the capability to monitor the spatiotemporal dynamics and distribution of total soluble solids, anthocyanins and total polyphenols along grape ripening in a commercial vineyard

Classification of smoke contaminated Cabernet Sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithms

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers

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

Comparative Study of Multivariate Methods to Identify Paper Finishes Using Infrared Spectroscopy

Recycled paper is extensively used worldwide. In the last decades its market has expanded considerably. The increasing use of recycled paper in papermaking has led to the production of paper containing several types of impurities. Consequently, wastepaper mills are forced to implement quality control schemes for evaluating the incoming wastepaper stock, thus guarantying the specifications of the final product. The main objective of this work is to present a fast and reliable system for identifying different paper types. Therefore, undesirable paper types can be refused, improving the performance of the paper machine and the final quality of the paper manufactured. For this purpose two fast techniques, i.e., Fourier transform mid-infrared (FTIR) and reflectance near-infrared (*IR) were applied to acquire the infrared spectra of the paper samples. *ext, four processing multivariate methods, i.e., principal component analysis (PCA), canonical variate analysis (CVA), extended canonical variate analysis (ECVA) and support vector machines (SVM) were employed in the feature extraction –or dimension reduction– stage. Afterwards, the k nearest neighbors algorithm (k**) was used in the classification phase. Experimental results show the usefulness of the proposed methodology and the potential of both FTIR and *IR spectroscopic methods. Using the FTIR spectrum in association with SVM and k** the system achieved maximum classification accuracy of 100%, whereas using the *IR spectrum in association with ECVA or SVM and k** the system achieved maximum classification accuracy of 96.4%Postprint (published version

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