Differentiation of Apple Varieties and Investigation of Organic Status Using Portable Visible Range Reflectance Spectroscopy

Food fraud, the sale of goods that have in some way been mislabelled or tampered with, is an increasing concern, with a number of high profile documented incidents in recent years. These recent incidents and their scope show that there are gaps in the food chain where food authentication methods are not applied or otherwise not sufficient and more accessible detection methods would be beneficial. This paper investigates the utility of affordable and portable visible range spectroscopy hardware with partial least squares discriminant analysis (PLS-DA) when applied to the differentiation of apple types and organic status. This method has the advantage that it is accessible throughout the supply chain, including at the consumer level. Scans were acquired of 132 apples of three types, half of which are organic and the remaining non-organic. The scans were preprocessed with zero correction, normalisation and smoothing. Two tests were used to determine accuracy, the first using 10-fold cross-validation and the second using a test set collected in different ambient conditions. Overall, the system achieved an accuracy of 94% when predicting the type of apple and 66% when predicting the organic status. Additionally, the resulting models were analysed to find the regions of the spectrum that had the most significance. Then, the accuracy when using three-channel information (RGB) is presented and shows the improvement provided by spectroscopic data

Near-infrared spectroscopy in process control and quality management of fruits and wine

Recently, rapid quality assessment of food is an increasingly important topic. The rising demand of consumers for high quality products generates a need to establish fast and suitable analytical methods. Near-infrared (NIR) spectroscopy has turned out to be a time-saving, cheap, easy-to-use and environmentally friendly technique, which can be applied for the determination of manifold quality attributes in various kinds of food matrices. This article gives overview of the basic principles of near-infrared measurements and describes the immense field of applications, with the main focus on fruits, grapes and wine and the evaluation of wine aroma

Espectroscopia de infravermelho com transformada de Fourier na monitorização da produção de vinho

ReviewThe complexity of the wine matrix makes monitoring of the winemaking process from the grapes to the final product crucial for the wine industry. In this context, analytical methodologies that can combine good accuracy, robustness, high sample throughput, “green character”, and by preference real-time analysis, are on-demand to create high-quality vitivinicultural products. In the last years, Fourier-transform Infrared Spectroscopy (FTIR) combined with chemometric analysis has been evaluated in several studies as an effective analytical tool for the wine sector. Some applications of FTIR spectroscopy have been already accepted by the wine industry, mainly for the prediction of basic oenological parameters, using portable and non-portable instruments, but still many others are waiting to be thoroughly developed. This literature review aims to provide a critical synopsis of the most important studies assessing grape and wine quality and authenticity, and to identify possible gaps for further research, meeting the needs of the modern wine industry and the expectations of most demanding consumers. The FTIR studies were grouped according to the main sampling material used - 1) leaves, stems, and berries; 2) grape must and wine applications - along with a summary of the basic limitations and future perspectives of this analytical techniqueA complexidade da matriz do vinho torna a monitorização da sua produção, desde a maturação da uva até o produto final, fundamental para a indústria do vinho. Neste contexto, metodologias analíticas com boa exactidão, robustez, elevado rendimento de amostras, menos penalizadoras para o meio ambiente, e se possível capazes de fornecer resultados em tempo real, são muito importantes para a obtenção de produtos vitivinícolas de alta qualidade. Nos últimos anos, a Espectroscopia de Infravermelho com Transformada de Fourier (FTIR) combinada com a análise quimiométrica tem sido avaliada em diversos estudos por ser uma ferramenta analítica apropriada para o setor vitivinícola. Algumas aplicações de FTIR já foram adoptadas pela indústria do vinho, principalmente para a predição de parâmetros enológicos básicos, através de instrumentos portáteis e não portáteis, mas há ainda um enorme potencial de desenvolvimento a explorar. A presente revisão da literatura tem como objetivo fornecer uma sinopse crítica dos estudos mais importantes realizados para avaliação da qualidade e autenticidade do vinho e identificar possíveis lacunas para investigação futura, indo ao encontro das necessidades da indústria vinícola moderna e das expectativas dos consumidores mais exigentes. Os estudos sobre FTIR foram agrupados de acordo com o principal material de amostragem - 1) folhas, engaços e bagos; 2) mostos e vinhos - juntamente com informação sobre as limitações básicas e perspectivas futuras desta técnica analíticainfo:eu-repo/semantics/publishedVersio

Rapid Prediction of Nutrient Concentration in Citrus Leaves Using Vis-NIR Spectroscopy

[EN] The nutritional diagnosis of crops is carried out through costly foliar ionomic analysis in laboratories. However, spectroscopy is a sensing technique that could replace these destructive analyses for monitoring nutritional status. This work aimed to develop a calibration model to predict the foliar concentrations of macro and micronutrients in citrus plantations based on rapid non-destructive spectral measurements. To this end, 592 'Clementina de Nules' citrus leaves were collected during several months of growth. In these foliar samples, the spectral absorbance (430-1040 nm) was measured using a portable spectrometer, and the foliar ionomics was determined by emission spectrometry (ICP-OES) for macro and micronutrients, and the Kjeldahl method to quantify N. Models based on partial least squares regression (PLS-R) were calibrated to predict the content of macro and micronutrients in the leaves. The determination coefficients obtained in the model test were between 0.31 and 0.69, the highest values being found for P, K, and B (0.60, 0.63, and 0.69, respectively). Furthermore, the important P, K, and B wavelengths were evaluated using the weighted regression coefficients (BW) obtained from the PLS-R model. The results showed that the selected wavelengths were all in the visible region (430-750 nm) related to foliage pigments. The results indicate that this technique is promising for rapid and non-destructive foliar macro and micronutrient prediction.This work is co-financed by the PNDR and GVA-IVIA (projects 52203, 52204 and by the EU through the ERDF of GVA 2021-2027). Maylin Acosta thanks IFARHU-SENACYT for the Professional Excellence Scholarships, contract No. 270-2021-020. Sandra Munera thanks the Juan de la Cierva-Formación contract (FJC2021-047786-I) co-funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR.Acosta, M.; Quiñones, A.; Munera, S.; De Paz, JM.; Blasco, J. (2023). Rapid Prediction of Nutrient Concentration in Citrus Leaves Using Vis-NIR Spectroscopy. Sensors. 23(14):1-11. https://doi.org/10.3390/s23146530111231

Rapid Prediction of Nutrient Concentration in Citrus Leaves Using Vis-NIR Spectroscopy

The nutritional diagnosis of crops is carried out through costly foliar ionomic analysis in laboratories. However, spectroscopy is a sensing technique that could replace these destructive analyses for monitoring nutritional status. This work aimed to develop a calibration model to predict the foliar concentrations of macro and micronutrients in citrus plantations based on rapid non-destructive spectral measurements. To this end, 592 ‘Clementina de Nules’ citrus leaves were collected during several months of growth. In these foliar samples, the spectral absorbance (430–1040 nm) was measured using a portable spectrometer, and the foliar ionomics was determined by emission spectrometry (ICP-OES) for macro and micronutrients, and the Kjeldahl method to quantify N. Models based on partial least squares regression (PLS-R) were calibrated to predict the content of macro and micronutrients in the leaves. The determination coefficients obtained in the model test were between 0.31 and 0.69, the highest values being found for P, K, and B (0.60, 0.63, and 0.69, respectively). Furthermore, the important P, K, and B wavelengths were evaluated using the weighted regression coefficients (BW) obtained from the PLS-R model. The results showed that the selected wavelengths were all in the visible region (430–750 nm) related to foliage pigments. The results indicate that this technique is promising for rapid and non-destructive foliar macro and micronutrient prediction

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

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables

Detection and quantification of paprika powder adulteration by near infrared (NIR) spectroscopy

Orientador: Douglas Fernandes BarbinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: A páprica é uma das especiarias mais consumidas no mundo, e devido aos seus atributos sensoriais desejáveis, ela apresenta um alto valor de mercado. Embora especiarias como o pó de páprica sejam usadas e consumidas apenas em pequenas quantidades, elas estão presentes em muitos alimentos processados. Em razão disso, ela se torna susceptível a adulteração por motivação econômica. Por esse motivo, muitos esforços têm sido feitos no desenvolvimento de técnicas analíticas para detecção dessas práticas fraudulentas. No entanto, muitas dessas técnicas tradicionais são destrutivas, utilizam reagentes químicos e seu uso é dispendioso e demorado. Por outro lado, técnicas de espectroscopia vibracional, aliadas a quimiometria, surgem como uma alternativa promissora na detecção de adulteração na indústria de ervas e especiarias. O uso dessas técnicas traz como vantagens a rapidez e a natureza não-destrutiva das análises. Dessa forma, a espectroscopia de infravermelho próximo (NIR) tem sido utilizada com êxito, na verificação da autenticidade e no controle de qualidade desses produtos. Diante disso, o presente trabalho teve como objetivo investigar as potencialidades da espectroscopia NIR, em conjunto com a análise multivariada, na detecção e quantificação de substâncias estranhas (fécula de batata, goma arábica e urucum), em páprica em pó. Na determinação dos níveis de adulteração, foi utilizada a regressão por mínimos quadrados parciais (PLSR). Melhores resultados da calibração PLSR foram obtidos com um número reduzido de variáveis, aplicando o método de seleção de variáveis a partir do gráfico dos coeficientes de regressão. Como resultado, para os modelos PLSR reduzidos construídos a partir dos dados espectrais de NIR, os coeficientes de determinação de predição (R2p) foram 0,960, 0,968 e 0,874 para fécula de batata, goma arábica e urucum, respectivamente e os erros quadráticos médios de predição (RMSEP) foram 1,86, 1,68 e 1,74, respectivamente. Finalmente, a análise discriminante de mínimos quadrados parciais (PLS-DA) foi o método utilizado para estabelecer um modelo de classificação para discriminar amostras de páprica adulteradas e não adulteradas e também identificar o tipo de adulteração. Assim, este método de classificação mostrou-se bastante eficiente, com especificidade maior que 90% e taxa de erro menor que 2%, para todos os modelos construídos. Os resultados obtidos neste estudo mostraram que a espectroscopia NIR, combinada com a quimiometria podem ser uteis para a rápida detecção e/ou quantificação da adulteração em páprica em póAbstract: Paprika is one of the most consumed spices in the world, and because of its desirable sensory attributes, it has a high market value. Although spices such as paprika powder are used and consumed only in small amounts, they are present in many processed foods. Because of this, it becomes susceptible to adulteration by economic motivation. For this reason, much effort has been expended in developing analytical techniques to detect such fraudulent practices. However, many of these traditional techniques are destructive, use chemical reagents and their use is expensive and time consuming. On the other hand, techniques of vibrational spectroscopy, combined with chemometrics, appear as a promising alternative in the detection of adulteration in the herb and spice industry. The use of these techniques brings as advantages the speed and the non-destructive nature of the analyses. Thus, near infrared spectroscopy (NIR) has been successfully used to verify the authenticity and quality control of these products. The objective of this study was to investigate the potential of NIR spectroscopy, in conjunction with the multivariate analysis, in the detection and quantification of foreign substances (potato starch, acacia gum and annatto) in powdered paprika. In the determination of adulteration levels, partial least squares regression (PLSR) was used. The best results of the PLSR calibration were obtained with a reduced number of variables, applying the method of selection of variables from the graph of the regression coefficients. As a result, for the reduced PLSR models built with NIR spectral data, the prediction determination coefficients (R2p) were 0.960, 0.968 and 0.874 for potato starch, acacia gum and annatto, respectively, and the mean squared errors of prediction (RMSEP) were 1.86, 1.68 and 1.74, respectively. Finally, the discriminant analysis of partial least squares (PLS-DA) was the method used to establish a classification model to discriminate adulterated and unadulterated paprika samples and also to identify the type of adulteration. Hence, this method of classification proved to be efficient, with specificity greater than 90% and error rate lower than 2%, for all models constructed. The results obtained in this study showed that NIR spectroscopy, combined with chemometrics may be useful for the rapid detection and / or quantification of paprika powder adulterationMestradoEngenharia de AlimentosMestre em Engenharia de AlimentosCAPE