Innovative and non-destructive technologies to evaluate quality of rocket leaves for ready to eat salads

‘Rocket’ is a collective name to indicate many species of green leaves belonging to Brassicacea family and are significantly consumed in the Mediterranean countries either as stand-alone salads or mixed with other vegetables. They are well known for their pungent smell, bitter flavor and high nutritional value. Rocket leaves are commercially grown as perennial and annual species, the former known as perennial wall rocket also known as wild rocket (Diplotaxis tenufolia(L.) DC.) and the latter named annual garden rocket (Eruca satvia Mill.). The Diplotaxis tenufolia plant can achieve a height of 80 centimeters (cm) and is characterized by a tap root and lengthy leaves. A typical leaf of Diplotaxis tenufolia is fleshy, oblong and deeply lobed, possessing sharp apexes. On the other hand, Eruca satvia Mill. grows to achieve a height of 40 cm possessing lyrate-pinnatifid leaves, having an enlarged terminal lobe and smaller lateral lobes with a rosette shaped arrangement of the leaves. Eruca satvia Mill. species as compared to the Diplotaxis tenufolia possesses a thin tap root and is characterized by a rigid unbranched stem. Both the species manifest similar morphological and nutritive aspects possessing a characteristic bitter taste based on the account of glucosinolates present. In the modern era, the consumer awareness regarding food safety, origins of the produce, nutritional value and demand for minimally processed fresh produce has led the food industry to explore rapid, reliable and cost effective methods for the evaluation of food products and their shelf-life since the conventional destructive analysis methods are time consuming, expensive, targeted and labor intensive. In this regard, non-destructive methods are gaining significant popularity which are assisting the food industry for the early fruits defect detection, fruits and vegetable classification on the basis of variety, maturity stage and origin and for the prediction of main internal constituents, mainly soluble solids and acids, and physical properties like firmness. On the industrial scale a significant weightage is given towards achieving fresh produce with superior quality in terms of vitamins, antioxidant activity, phenols and secondary metabolites. Rising concerns regarding the nutritional composition led many research works to evaluate the feasibility of the spectral profiles in the visible near infrared range (Vis-NIR), near infrared range (NIR) and hyperspectral images (HSI) for prediction and mapping of desired compounds in fresh produce. It is important to mention that non-destructive techniques cannot completely replace the conventional methods but can serve to assist these techniques saving time, expenses and labor. On the other hand, the non-destructive methods need no sample preparation once the model is developed making the prediction process quick. In this research work non-destructive techniques have been illuminated with respect to their potentiality in rocket leaves with special emphasis on hyperspectral imaging for the quality assessment of the fresh-cut rocket leaves accompanied by a basic introduction of the non-destructive image analysis techniques. In the first research work the feasibility of using spectral profiles for the estimation of the shelf life of the rocket leaves was evaluated using a multivariate accelerated shelf life testing (MASLT) approach. Spectral changes over time were modeled by using principal component analysis (PCA) and as variation to the conventional method, partial least squares (PLS) method. Kinetic charts were built fitting the first principle component (PC1) and the first latent variable (LV1) scores versus time. In both cases, the kinetics were described by a first order reaction, and the model performance was evaluated by the R2 values which ranged between 0.73 to 0.95 for samples stored at three different temperatures, one of them being the market temperature while the rest were categorized as accelerated temperatures which are usually higher than the market temperature. The cut-off value was calculated by judging the unacceptable spectra of samples at the accelerated temperature, as a result of which the shelf life of rocket leaves was estimated using the MASLT approach. The shelf life estimation was done using PCA based MASLT conventionally used as well as using a newly introduced methodology i.e. PLS based MASLT yielding encouraging results in both cases particularly in case of the PLS based MASLT. On the other hand, since the literature regarding quality evaluation of rocket leaves or any other leafy vegetables over time shows that the potential of hyperspectral imaging in the visible and near infrared regions has not been investigated pursing the aim of prediction and mapping of internal constituents. Hence hyperspectral imaging data was evaluated employing Partial Least Squares regression (PLSR) for the prediction of Vitamin C, ascorbic acid (AA), dehydroascorbic acid (DHAA), antioxidant activity and phenols in wild rocket (Diplotaxis tenuifolia) over a storage span of 12 days at 5oC. Hyperspectral images of the wild rocket leaves were acquired in the Vis-NIR (400-1000nm) and the NIR (900-1700nm) ranges using different data pretreatments and wavelength selection techniques. The model reliability was checked by the root mean square error (RMSE) and R2 values. Among the predicted parameters Vitamin C, AA, antioxidant activity and phenols were predicted satisfactorily in the NIR range. The prediction maps for the parameters were calculated to follow the changes over the storage period yielding more reliable results in the NIR range. All the results indicated that hyperspectral imaging combined with multivariate data possess the capability to provide reliable information regarding the shelf life estimation of the rocket leaves as well as for the prediction and mapping of the internal constituents

Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops

Nowadays, consumer awareness of the impact of site of origin and method of production on the quality and safety of foods, and particularly of fresh produce, is driving the research towards developing various techniques to assist present certifications, traceability, and audit procedures. With regard to horticultural produce, consumer preferences have shifted to fruit and vegetables, which are healthy and ecologically produced, and toward processed foods having sustainable or social certifications and with sites of origin clearly reported on the label. Some recent studies demonstrate the potentiality of near infrared (NIR) technology (including hyperspectral imaging) for discriminating fresh and processed horticultural products based on their composition, quality attributes, and origin. These studies principally mention that each biological tissue possesses a fingerprint NIR spectrum, which consists of a unique and characteristic pattern of radiation, distinguishing a particular biological tissue from physically and/or chemically different samples. Particularly, recent studies discriminated apples, wine, wheat kernels, and derived flours based on their geographical origins. Spectral information allowed discrimination among growing methods (organic and conventional) for asparagus and strawberry fruits, and among harvest dates for fennels, table grapes, and artichokes. Moreover, information about freshness and storage days after minimal processing can be obtained. Recent literature and original results will be discussed. From our perspective, present results suggest that these techniques may have a potentiality to increase information about product history, but if and only if the variability captured by the classification models is vast in terms of diverse samples belonging to various cultivars, varieties, harvest times, cultural practices, geographical origins, storage conditions, and maturity stages, while being used as a complementary method to the conventional ones—either to make an initial screening of critical features, or to add to the amount of available information. Lacking the inclusion of these parameters could result in good classification results, but the reliability of the classification in this case would be dubious in terms of assessment of the factor contributing towards correct classification

Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops

Nowadays, consumer awareness of the impact of site of origin and method of production on the quality and safety of foods, and particularly of fresh produce, is driving the research towards developing various techniques to assist present certifications, traceability, and audit procedures. With regard to horticultural produce, consumer preferences have shifted to fruit and vegetables, which are healthy and ecologically produced, and toward processed foods having sustainable or social certifications and with sites of origin clearly reported on the label. Some recent studies demonstrate the potentiality of near infrared (NIR) technology (including hyperspectral imaging) for discriminating fresh and processed horticultural products based on their composition, quality attributes, and origin. These studies principally mention that each biological tissue possesses a fingerprint NIR spectrum, which consists of a unique and characteristic pattern of radiation, distinguishing a particular biological tissue from physically and/or chemically different samples. Particularly, recent studies discriminated apples, wine, wheat kernels, and derived flours based on their geographical origins. Spectral information allowed discrimination among growing methods (organic and conventional) for asparagus and strawberry fruits, and among harvest dates for fennels, table grapes, and artichokes. Moreover, information about freshness and storage days after minimal processing can be obtained. Recent literature and original results will be discussed. From our perspective, present results suggest that these techniques may have a potentiality to increase information about product history, but if and only if the variability captured by the classification models is vast in terms of diverse samples belonging to various cultivars, varieties, harvest times, cultural practices, geographical origins, storage conditions, and maturity stages, while being used as a complementary method to the conventional ones―either to make an initial screening of critical features, or to add to the amount of available information. Lacking the inclusion of these parameters could result in good classification results, but the reliability of the classification in this case would be dubious in terms of assessment of the factor contributing towards correct classification

Early discrimination of mature-and immature-green tomatoes (Solanum lycopersicum L.) using fluorescence imaging method

Detecting mature-green and immature-green tomatoes using non-destructive approaches is a challenge for the fresh produce industry. Hyperspectral fluorescence imaging technique with excitation wavelength at 365 nm and UV–vis CCD camera was used for early non-destructive detection of mature-green and immature-green fruit from 200 randomly harvested green tomatoes. Conventional destructive analysis regarding locule gel development and seed texture were assessed to assign the maturity stage of the fruit. In addition soluble solid content (SSC), pH, total acidity (TA), and color were measured, on the training set and on the prediction set, in this case also after 10 d of storage. Fluorescence intensity at the surface of immature-green fruit was higher in the red region (690 nm) than that of mature-green fruit, suggesting that hyperspectral fluorescence imaging can be an effective classification tool. A univariate classification method was used to distinguish mature-green and immature-green tomatoes based on the grey scale values extracted from fluorescence imaging, with a non-error rate of 96 % in calibration and 100 % in external prediction. Hence, a non-destructive method for the early distinction of mature-green from immature-green tomatoes is available

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.)