Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine

[EN] Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450¿1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits.This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds. The authors wish to thank Fruits de Ponent (Lleida) for providing the fruit.Munera-Picazo, S.; Amigo, JM.; Aleixos Borrás, MN.; Talens Oliag, P.; Cubero-García, S.; Blasco Ivars, J. (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control. 86:1-10. https://doi.org/10.1016/j.foodcont.2017.10.037S1108

Comparison performance of visible-nir and near-infrared hyperspectral imaging for prediction of nutritional quality of goji berry (Lycium barbarum l.)

The potential of hyperspectral imaging for the prediction of the internal composition of goji berries was investigated. The prediction performances of models obtained in the Visible-Near Infrared (VIS-NIR) (400–1000 nm) and in the Near Infrared (NIR) (900–1700 nm) regions were compared. Analyzed constituents included Vitamin C, total antioxidant, phenols, anthocyanin, soluble solids content (SSC), and total acidity (TA). For vitamin C and AA, partial least square regression (PLSR) combined with different data pretreatments and wavelength selection resulted in a satisfactory prediction in the NIR region obtaining the R2pred value of 0.91. As for phenols, SSC, and TA, a better performance was obtained in the VIS-NIR region yielding the R2pred values of 0.62, 0.94, and 0.84, respectively. However, the prediction of total antioxidant and anthocyanin content did not give satisfactory results. Conclusively, hyperspectral imaging can be a useful tool for the prediction of the main constituents of the goji berry (Lycium barbarum L.)

Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest

Tesis por compendio[ES] El objetivo de esta tesis doctoral es evaluar la técnica de imagen hiperespectral en el rango visible e infrarrojo cercano, en combinación con técnicas quimiométricas para la evaluación de la calidad de la fruta en poscosecha de manera eficaz y sostenible. Con este fin, se presentan diferentes estudios en los que se evalúa la calidad de algunas frutas que por su valor económico, estratégico o social, son de especial importancia en la Comunidad Valenciana como son el caqui 'Rojo Brillante', la granada 'Mollar de Elche', el níspero 'Algerie' o diferentes cultivares de nectarina. En primer lugar se llevó a cabo la monitorización de la calidad poscosecha de nectarinas 'Big Top' y 'Magique' usando imagen hiperespectral en reflectancia y transmitancia. Al mismo tiempo se evaluó la transmitancia para la detección de huesos abiertos. Se llevó a cabo también un estudio para distinguir los cultivares 'Big Top' y "Diamond Ray", los cuales poseen un aspecto muy similar pero sabor diferente. En cuanto al caqui 'Rojo Brillante', la imagen hiperespectral fue estudiada por una parte para monitorear su madurez, y por otra parte para evaluar la astringencia de esta fruta, que debe ser completamente eliminada antes de su comercialización. Las propiedades físico-químicas de la granada 'Mollar de Elche' fueron evaluadas usando imagen de color e hiperespectral durante su madurez usando la información de la fruta intacta y de los arilos. Finalmente, esta técnica se usó para caracterizar e identificar los defectos internos y externos del níspero 'Algerie'. En la predicción de los índices de calidad IQI y RPI usando imagen en reflectancia y transmitancia se obtuvieron valores de R2 alrededor de 0,90 y en la discriminación por firmeza, una precisión entorno al 95 % usando longitudes de onda seleccionadas. En cuanto a la detección de huesos abiertos, el uso de la imagen hiperespectral en transmitancia obtuvo un 93,5 % de clasificación correcta de frutas con hueso normal y 100 % con hueso abierto usando modelos PLS-DA y 7 longitudes de onda. Los resultados obtenidos en la clasificación de los cultivares 'Big Top' y 'Diamond Ray' mostraron una fiabilidad superior al 96,0 % mediante el uso de modelos PLS-DA y 14 longitudes de onda seleccionadas, superando a la imagen de color (56,9 %) y a un panel entrenado (54,5 %). Con respecto al caqui, los resultados obtenidos indicaron que es posible distinguir entre tres estados de madurez con una precisión del 96,0 % usando modelos QDA y se predijo su firmeza obteniendo un valor de R2 de 0,80 usando PLS-R. En cuanto a la astringencia, se llevaron a cabo dos estudios similares en los que en el primero se discriminó la fruta de acuerdo al tiempo de tratamiento con altas concentraciones de CO2 con una precisión entorno al 95,0 % usando QDA. En el segundo se discriminó la fruta de acuerdo a un valor de contenido en taninos (0,04 %) y se determinó qué área de la fruta era mejor para realizar esta discriminación. Así se obtuvo una precisión del 86,9 % usando la zona media y 23 longitudes de onda. Los resultados obtenidos para la granada indicaron que la imagen de color e hiperespectral poseen una precisión similar en la predicción de las propiedades fisicoquímicas usando PLS-R y la información de la fruta intacta. Sin embargo, cuando se usó la información de los arilos, la imagen hiperespectral fue más precisa. En cuanto a la discriminación del estado de madurez usando PLS-DA, la imagen hiperespectral ofreció mayor precisión, 95,0 %, usando la información de la fruta intacta y del 100 % usando la de los arilos. Finalmente, los resultados obtenidos para el níspero indicaron que la imagen hiperespectral junto con el método de clasificación XGBOOST pudo discriminar entre muestras con y sin defectos con una precisión del 97,5 % y entre muestras sin defectos o con defectos internos o externos con una precisión del 96,7 %. Además fue posible distinguir entre los dife[CA] L'objectiu de la present tesi doctoral se centra en avaluar la capacitat de la imatge hiperespectral en el rang visible i infraroig pròxim, en combinació amb mètodes quimiomètrics, per a l'avaluació de la qualitat de la fruita en post collita de manera eficaç i sostenible. A aquest efecte, es presenten diferents estudis en els quals s'avalua la qualitat d'algunes fruites que pel seu valor econòmic, estratègic o social, són d'especial importància a la Comunitat Valenciana com són el caqui 'Rojo Brillante', la magrana 'Mollar de Elche', el nispro 'Algerie' o diferents cultivares de nectarina. En primer lloc es va dur a terme la monitorització de la qualitat post collita de nectarines 'Big Top' i 'Magique' per mitjà d'imatge hiperespectral en reflectància i trasnmitancia. Així mateix es va avaluar la transmitància per a la detecció d'ossos oberts. Es va dur a terme també un estudi per distingir els cultivares 'Big Top' i 'Diamond Ray', els quals posseeixen un aspecte molt semblant però sabor diferent. Pel que fa al caqui 'Rojo Brillante', la imatge hiperespectral va ser estudiada d'una banda per a monitoritzar la seua maduresa, i per un altre costat per avaluar l'astringència, que ha de ser completament eliminada abans de la seua comercialització. Les propietats fisicoquímiques de la magrana 'Mollar de Elche' van ser avaluades per la imatge de color i hiperespectral durant la seua maduresa usant la informació de la fruita intacta i els arils. Finalment, aquesta tècnica es va fer servir per caracteritzar i identificar els defectes interns i externs del nispro 'Algerie'. En la predicció dels índexs de qualitat IQI i RPI usant imatge en reflectància com en trasnmitancia es van obtindre valors de R2 al voltant de 0,90 i en la discriminació per fermesa una precisió entorn del 95,0 % utilitzant longituds d'ona seleccionades. Pel que fa a la detecció d'ossos oberts, l'ús de la imatge hiperespectral en transmitància va obtindre un 93,5 % classificació correcta de fruites amb os normal i 100 % amb os obert usant models PLS-DA i 7 longituds d'ona. Els resultats obtinguts en la classificació dels cultivares 'Big Top' i 'Diamond Ray' van mostrar una fiabilitat superior al 96,0 % per mitjà de l'ús de models PLS-DA i 14 longituds d'ona, superant a la imatge de color (56,9 %) i a un panell sensorial entrenat (54,5 %). Quant al caqui, els resultats obtinguts van indicar que és possible distingir entre tres estats de maduresa amb una precisió del 96,0 % usant models QDA i es va predir la seua fermesa obtenint un valor de R2 de 0,80 usant PLS-R. Pel que fa a l'astringència, es van dur a terme dos estudis similars en què el primer es va discriminar la fruita d'acord al temps de tractament amb altes concentracions de CO2 amb una precisió al voltant del 95,0 % usant QDA. En el segon, es va discriminar la fruita d'acord a un valor de contingut en tanins (0,04 %) i es va determinar quina part de la fruita era millor per a realitzar aquesta discriminació. Així es va obtindre una precisió del 86,9 % usant la zona mitjana i 23 longituds d'ona. Els resultats obtinguts per la magrana van indicar que la imatge de color i hiperespectral posseïxen una precisió semblant a la predicció de les propietats fisicoquímiques usant PLS-R i la informació de la fruita intacta. No obstant això, quan es va usar la informació dels arils, la imatge hiperespectral va ser més precisa. Quant a la discriminació de l'estat de maduresa usant PLS-DA, la imatge hiperespectral va oferir major precisió (95,0 %) usant la informació de la fruita intacta i del 100 % usant la dels arils. Finalment, els resultats obtinguts pel nispro indiquen que la imatge hiperespectral juntament amb el mètode de classificació XGBOOST va poder discriminar entre mostres amb i sense defectes amb una precisió del 97,5 % i entre mostres sense defectes o amb defectes interns o externs amb una precisió del 96,7 %. A més, va ser possible distingir entre[EN] The objective of this doctoral thesis is to evaluate the potential of the hyperspectral imaging in the visible and near infrared range in combination with chemometrics for the assessment of the postharvest quality of fruit in a non-destructive, efficient and sustainable manner. To this end, different studies are presented in which the quality of some fruits is evaluated. Due to their economic, strategic or social value, the selected fruits are of special importance in the Valencian Community, such as Persimmon 'Rojo Brillante', the pomegranate 'Mollar de Elche', the loquat 'Algerie' or different nectarine cultivars. First, the quality monitoring of 'Big Top' and 'Magique' nectarines was carried out using reflectance and transmittance images. At the same time, transmittance was evaluated for the detection of split pit. In addition, a classification was performed to distinguish the 'Big Top' and 'Diamond Ray' cultivars, which look very similar but have different flavour. Whereas that for the 'Rojo Brillante' persimmon, the hyperspectral imaging was studied on the one hand to monitor its maturity, and on the other hand to evaluate the astringency of this fruit, which must be completely eliminated before its commercialization. The physicochemical properties of the 'Mollar de Elche' pomegranate were evaluated by means of hyperspectral and colour imaging during its maturity using the information from the intact fruit and arils. Finally, this technique was used to characterise and identify the internal and external defects of the 'Algerie' loquat. In the prediction of the IQI and RPI quality indexes using reflectance and transmittance images, R2 values around 0.90 were obtained and in the discrimination according to firmness, accuracy around 95.0 % using selected wavelengths was obtained. Regarding the split pit detection, the use of the hyperspectral image in transmittance mode obtained a 93.5 % of fruits with normal bone correctly classified and 100% with split pit using PLS-DA models and 7 wavelengths. The results obtained in the classification of 'Big Top' and 'Diamond Ray' fruits show accuracy higher than 96.0 % by using PLS-DA models and 14 selected wavelengths, higher than the obtained with colour image (56.9 %) and a trained panel (54.5 %). According to persimmon, the results obtained indicated that it is possible to distinguish between three states of maturity with an accuracy of 96.0 % using QDA models and its firmness was predicted obtaining a R2 value of 0.80 using PLS-R. Regarding astringency, two similar studies were carried out. In the first study, the fruit was classified according to the time of treatment with high concentrations of CO2 with a precision of around 95.0 % using QDA. In the second, the fruit was discriminated according to a threshold value of soluble tannins (0.04 %) and was determined what fruit area was better to perform this discrimination. Thus, an accuracy of 86.9 % was obtained using the middle area and 23 wavelengths. The results obtained for the pomegranate indicated that the use of colour and hyperspectral images have a similar precision in the prediction of physicochemical properties using PLS-R and the intact fruit information. However, when the information from the arils was used, the hyperspectral image was more accurate. Regarding the discrimination by the state of maturity using PLS-DA, the hyperspectral image offered greater precision, of 95.0 % using the information from the intact fruit and 100 % using that from the arils. Finally, the results obtained for the 'Algerie' loquat indicated that the hyperspectral image with the XGBOOST classification method could discriminate between sound samples and samples with defects with accuracy of 97.5 % and between sound samples or samples with internal or external defects with an accuracy of 96.7 %. It was also possible to distinguish between the different defects with an accuracy of 95.9 %.Munera Picazo, SM. (2019). Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/125954TESISCompendi

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

The potential use of non destructive optical-based techniques for early detection of chilling injury and freshness in horticultural commodities

The increasing concern and awareness of the modern consumer regarding food including fruits and vegetables, has been oriented the research in the food industry to develop rapid, reliable and cost effective methods for the evaluation of food products including the traceability of the product history in terms of storage conditions. Since the conventional destructive analysis methods are time consuming, expensive, targeted and labor intensive, non-destructive methods are gaining significant popularity. These methods are being utilized by the food industry for the early detection of fruits defects, for the classification of fruits and vegetables on the basis of variety, maturity stage, storage history and origin and for the prediction of main internal constituents. Since chilling injury (CI) occurrence is a major problem for chilling sensitive products, as tropical and sub-tropical fruit and vegetables, prompt detection of CI is still a challenge to be addressed. The incorrect management of the temperature during storage and distribution causes significant losses and wastes in the horticultural food chain, which can be prevented if the product is promptly reported to the correct temperature, before that damages become irreversible. For this reason, rapid and fast methods for early detection of CI are needed. In the first work of this thesis, non-destructive optical techniques were applied for the early detection of chilling injury in eggplants. Eggplant fruit is a chilling sensitive vegetable that should be stored at temperatures above 12°C. For the estimation of CI, fruit were stored at 2°C (chilling temperature) and at 12°C (safe storage temperature) for a time span of 10 days. CIE L*a*b* measurements, reflectance data in the wavelength range 360–740 nm, Fourier Transformed (FT)-NIR spectra (800–2777 nm) and hyperspectral images in the visible (400–1000 nm) and near infrared (900–1700 nm) spectral range were acquired for each fruit. Partial least square discriminant analysis (PLSDA), supervised vector machine (SVM) and k-nearest neighbor (kNN) were applied to classify fruit according to the storage temperature. According to the results, although CI symptoms started being evident only after the 4th day of storage at 2°C, it was possible to discriminate fruit earlier using FT-NIR spectral data with the SVM classifier (100 and 92% non-error-rate (NER) in calibration and cross validation, respectively, in the whole data set. Color data and PLSDA classification possessed relatively lower accuracy as compared to SVM. These results depicted a good potential of for the non-destructive techniques for the early detection of CI in eggplants. Similarly, in the second experimental part of the thesis, hyperspectral imaging in Vis-NIR and SWIR regions combined with chemometric techniques were used for the early estimation of chilling injury in bell peppers. PLSDA models accompanied by wavelength selection algorithms were used for this purpose, with accuracies ranging from 81% and 87% non-error-rate (NER) based on the wavelength ranges used and variables selected. PLSR models were developed for the prediction of days of cold storage resulting in R²CV = 0.92 for full range and R²CV = 0.79 using selected variables. Based on the results, it was concluded, that Vis-NIR hyperspectral imaging is a reliable option for on-line classification of fresh versus refrigerated fruit and for identifying early incidence of CI. Inspired by the results obtained from previous studies a third study regarded the use of nondestructive techniques for the estimation of freshness of eggplants using color, spectral and hyperspectral measurements. To this aim, fruit were stored at 12°C for 10 days. Fruit were left at room temperature (20°C) for 1 day after sampling which was done with a 2-day interval, simulating one-day of shelf life in the market. PLSR models were developed using the spectral and hyperspectral data and the storage days, allowing safe assessment of the freshness of the fruits along with the utilization of SPA for variable reduction. The results depicted strong correlation between storage days, FT-NIR spectra and the hyperspectral data in the Vis-NIR range with accuracies as high as RC> 0.98, RCV> 0.94, RMSEC < 0.4 and RMSECV< 0.8, followed by lower accuracies using color data. The results of this study may set the basis to develop a protocol allowing a rapid screening and sorting of eggplants according to their postharvest freshness either upon handling in a distribution center or even upon the reception in the retail market. In the last work, as a deeper investigation, the effect of temperature and storage time on the FTNIR spectra was statistically investigated using ANOVA-simultaneous component analysis (ASCA) on eggplant fruit as a crop model. Also in this case, fruit were stored at 2 and 12 °C, for 10 days. Sensorial analysis, electrolyte leakage (EL), weight loss and firmness were used, as the reference measurements for CI. ASCA model proved that both temperature, duration of storage, and their interaction had a significant effect on the spectral changes over time of eggplant fruit. Followed by ASCA, PLSDA was conducted on the data to discriminate fruit based on the storage temperature. In this case, only the WL significant in the ASCA approach for temperature were considered, allowing to reach 87.4±2.7% as estimated by a repeated double-cross-validation procedure. The outcomes of all these studied manifested a promising, non-invasive, and fast tool for the control of CI and the prevention of food losses due to the incorrect management of the temperature in the horticultural food chain

Dynamic simulation driven design and management of production facilities in agricultural/food industry

An industrial plant in the agro-food sector can be considered a complex system as it is composed of numerous types of machines and it is characterized by a strong variation (seasonality) in the agricultural production. Whenever the dynamic behavior of the plants during operation is considered, system and design complexities increase. Reliable operation of food processing farms is primarily dependent on perfect balance between variable supply and product storage at each given time. To date, the classical modus operandi of food processing management systems is carried out under stationary and average conditions. Moreover, most of the systems installed for agricultural and food industries are sized using average production data. This often results in a mismatch between the actual operation and the expected operation. Consequently, the system is not optimized for the needs of a specific company. Also, the system is not flexible to the evolution that the production process could possibly have in the future. Promising techniques useful to solve the above-described problems could possibly be borrowed from demand side management (DSM) in smart grid systems. Such techniques allow customers to make dynamically informed decisions regarding their energy demand and help the energy providers in reducing the peak load demand and reshape the load profile. DSM is successfully used to improve the energy management system and we conjecture that DSM could be suitably adapted to food processing management. In this paper we describe how DSM could be exploited in the intelligent management of production facilities serving agricultural and food industry. The main objective is, indeed, to present how methods for modelling and implementing the dynamic simulation used for the optimization of the energy management in smart grid systems can be applied to a fruit and vegetables processing plant through a suitable adaptation

Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt

In this study, ultra-violet (UV) and short-wave infra-red (SWIR) hyperspectral imaging (HSI) was used to measure the concentration of phenolic flavour compounds on malted barley that are responsible for smoky aroma of Scotch whisky. UV-HSI is a relatively unexplored technique that has the potential to detect specific absorptions of phenols. SWIR-HSI has proven to detect phenols in previous applications. Support Vector Machine Classification and Regression was applied to classify malts with ten different concentration levels of the compounds of interest, and to estimate the concentration respectively. Results reveal that UV-HSI is at its current development stage unsuitable for this task whereas SWIR-HSI is able to produce robust results with a classification accuracy of 99.8% and a squared correlation coefficient of 0.98 with a Root Mean Squared Error of 0.32 ppm for regression. The results indicate that with further testing and development, HSI may potentially be exploited in an industrial production environment

On the Use of Imaging Spectroscopy from Unmanned Aerial Systems (UAS) to Model Yield and Assess Growth Stages of a Broadacre Crop

Snap bean production was valued at $363 million in 2018. Moreover, the increasing need in food production, caused by the exponential increase in population, makes this crop vitally important to study. Traditionally, harvest time determination and yield prediction are performed by collecting limited number of samples. While this approach could work, it is inaccurate, labor-intensive, and based on a small sample size. The ambiguous nature of this approach furthermore leaves the grower with under-ripe and over-mature plants, decreasing the final net profit and the overall quality of the product. A more cost-effective method would be a site-specific approach that would save time and labor for farmers and growers, while providing them with exact detail to when and where to harvest and how much is to be harvested (while forecasting yield). In this study we used hyperspectral (i.e., point-based and image-based), as well as biophysical data, to identify spectral signatures and biophysical attributes that could schedule harvest and forecast yield prior to harvest. Over the past two decades, there have been immense advances in the field of yield and harvest modeling using remote sensing data. Nevertheless, there still exists a wide gap in the literature covering yield and harvest assessment as a function of time using both ground-based and unmanned aerial systems. There is a need for a study focusing on crop-specific yield and harvest assessment using a rapid, affordable system. We hypothesize that a down-sampled multispectral system, tuned with spectral features identified from hyperspectral data, could address the mentioned gaps. Moreover, we hypothesize that the airborne data will contain noise that could negatively impact the performance and the reliability of the utilized models. Thus, We address these knowledge gaps with three objectives as below: 1. Assess yield prediction of snap bean crop using spectral and biophysical data and identify discriminating spectral features via statistical and machine learning approaches. 2. Evaluate snap bean harvest maturity at both the plant growth stage and pod maturity level, by means of spectral and biophysical indicators, and identify the corresponding discriminating spectral features. 3. Assess the feasibility of using a deep learning architecture for reducing noise in the hyperspectral data. In the light of the mentioned objectives, we carried out a greenhouse study in the winter and spring of 2019, where we studied temporal change in spectra and physical attributes of snap-bean crop, from Huntington cultivar, using a handheld spectrometer in the visible- to shortwave-infrared domain (400-2500 nm). Chapter 3 of this dissertation focuses on yield assessment of the greenhouse study. Findings from this best-case scenario yield study showed that the best time to study yield is approximately 20-25 days prior to harvest that would give out the most accurate yield predictions. The proposed approach was able to explain variability as high as R2 = 0.72, with spectral features residing in absorption regions for chlorophyll, protein, lignin, and nitrogen, among others. The captured data from this study contained minimal noise, even in the detector fall-off regions. Moving the focus to harvest maturity assessment, Chapter 4 presents findings from this objective in the greenhouse environment. Our findings showed that four stages of maturity, namely vegetative growth, budding, flowering, and pod formation, are distinguishable with 79% and 78% accuracy, respectively, via the two introduced vegetation indices, as snap-bean growth index (SGI) and normalized difference snap-bean growth index (NDSI), respectively. Moreover, pod-level maturity classification showed that ready-to-harvest and not-ready-to-harvest pods can be separated with 78% accuracy with identified wavelengths residing in green, red edge, and shortwave-infrared regions. Moreover, Chapters 5 and 6 focus on transitioning the learned concepts from the mentioned greenhouse scenario to UAS domain. We transitioned from a handheld spectrometer in the visible to short-wave infrared domain (400-2500 nm) to a UAS-mounted hyperspectral imager in the visible-to-near-infrared region (400-1000 nm). Two years worth of data, at two different geographical locations, were collected in upstate New York and examined for yield modeling and harvest scheduling objectives. For analysis of the collected data, we introduced a feature selection library in Python, named “Jostar”, to identify the most discriminating wavelengths. The findings from the yield modeling UAS study show that pod weight and seed length, as two different yield indicators, can be explained with R2 as high as 0.93 and 0.98, respectively. Identified wavelengths resided in blue, green, red, and red edge regions, and 44-55 days after planting (DAP) showed to be the optimal time for yield assessment. Chapter 6, on the other hand, evaluates maturity assessment, in terms of pod classification, from the UAS perspective. Results from this study showed that the identified features resided in blue, green, red, and red-edge regions, contributing to F1 score as high as 0.91 for differentiating between ready-to-harvest vs. not ready-to-harvest. The identified features from this study is in line with those detected from the UAS yield assessment study. In order to have a parallel comparison of the greenhouse study against the UAS study, we adopted the methodology employed for UAS studies and applied it to the greenhouse studies, in Chapter 7. Since the greenhouse data were captured in the visible-to-shortwave-infrared (400-2500 nm) domain, and the UAS study data were captured in the VNIR (400-1000 nm) domain, we truncated the spectral range of the collected data from the greenhouse study to the VNIR domain. The comparison experiment between the greenhouse study and the UAS studies for yield assessment, at two harvest stages early and late, showed that spectral features in 450-470, 500-520, 650, 700-730 nm regions were repeated on days with highest coefficient of determination. Moreover, 46-48 DAP with high coefficient of determination for yield prediction were repeated in five out of six data sets (two early stages, each three data sets). On the other hand, the harvest maturity comparison between the greenhouse study and the UAS data sets showed that similar identified wavelengths reside in ∼450, ∼530, ∼715, and ∼760 nm regions, with performance metric (F1 score) of 0.78, 0.84, and 0.9 for greenhouse, 2019 UAS, and 2020 UAS data, respectively. However, the incorporated noise in the captured data from the UAS study, along with the high computational cost of the classical mathematical approach employed for denoising hyperspectral data, have inspired us to leverage the computational performance of hyperspectral denoising by assessing the feasibility of transferring the learned concepts to deep learning models. In Chapter 8, we approached hyperspectral denoising in spectral domain (1D fashion) for two types of noise, integrated noise and non-independent and non-identically distributed (non-i.i.d.) noise. We utilized Memory Networks due to their power in image denoising for hyperspectral denoising, introduced a new loss and benchmarked it against several data sets and models. The proposed model, HypeMemNet, ranked first - up to 40% in terms of signal-to-noise ratio (SNR) for resolving integrated noise, and first or second, by a small margin for resolving non-i.i.d. noise. Our findings showed that a proper receptive field and a suitable number of filters are crucial for denoising integrated noise, while parameter size was shown to be of the highest importance for non-i.i.d. noise. Results from the conducted studies provide a comprehensive understanding encompassing yield modeling, harvest scheduling, and hyperspectral denoising. Our findings bode well for transitioning from an expensive hyperspectral imager to a multispectral imager, tuned with the identified bands, as well as employing a rapid deep learning model for hyperspectral denoising

Application of Analytical Chemistry to Foods and Food Technology

The application of analytical chemistry to the food sector allows the determination of the chemical composition of foods and the properties of their constituents, contributing to the definition of their nutritional and commodity value. Furthermore, it is possible to study the chemical modifications that food constituents undergo as a result of the treatments they undergo (food technology). Food analysis, therefore, allows us not only to determine the quality of a product or its nutritional value, but also to reveal adulterations and identify the presence of xenobiotic substances potentially harmful to human health. Furthermore, some foods, especially those of plant origin, contain numerous substances with beneficial effects on health. While these functional compounds can be obtained from a correct diet, they can also be extracted from food matrices for the formulation of nutraceutical products or added to foods by technological or biotechnological means for the production of functional foods. On the other hand, the enormous growth of the food industry over the last 50 years has broadened the field of application of analytical chemistry to encompass not only food but also food technology, which is fundamental for increasing the production of all types of food