Federico Pallottino

A Review on Agri-food Supply Chain Traceability by Means of RFID Technology

Radio Frequency Identification (RFID) is a technology which provides appealing opportunities to improve the management of information flow within the supply chain and security in the agri-food sector. Nowadays, food safety is considered a major requirement in several countries, in particular, the traceability of food products which is mandatory by law. Thus, technological implementation leading to traceability strengthening in the agri-food sector is crucial. The first aim of this review is to analyze the current developments in RFID technology in the agri-food sector, through an operative framework which organizes the literature and facilitate a quick content analysis identifying future research direction. RFID technology seems to be able to bring great opportunities to this sector; nevertheless, several constraints are slowing its adoption. This survey may provide readers with an exhaustive overview of opportunities and constraints for the wide adoption of RFID. The second aim of this review is to provide an updated analysis on the current developments of RFID technology for different product typologies within the agri-food industry, discussing at the same time its potential in technological and logistical development regarding different sectors of the production/distribution chain. As referenced here, RFID implementations in the agri-food sector are increasing at a fast rate, and technological advancement follows the applicability opportunities. However, real applications of RFID technologies are still limited because of various technical and economical obstacles which are also discussed.

Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy

Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO3 concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO3 concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (r = 0.94). The utilization of the proposed system, increasing efficiency, allows better knowledge of nutritional status of tomato plants, with more detailed and sharp information and on wider areas. More detailed information both in space and time is an essential tool to increase and stabilize crop quality levels and to optimize the nutrient use efficiency.

An Advanced Colour Calibration Method for Fish Freshness Assessment: a Comparison Between Standard and Passive Refrigeration Modalities

Freshness represents a pivotal aspect in fish product for both security and quality. Its evaluation still represents the key factor driving the consumer’ choices. Fish appearance is affected by many different factors that demand the contribution of different disciplines to be understood: from the physical and optical properties to the slaughtering and post-slaughtering conditions. An innovative preservation system is represented by the Passive Refrigeration PRS™ developed for the preservation and transport of perishable food products. Scientific methods for product freshness evaluation may be conveniently divided into two categories: sensorial and instrumental. In this study, an instrumental method of colour calibration and discrimination is proposed at pilot scale for automatic evaluation of gilthead seabream (Sparus aurata) freshness. We propose a non-destructive method based on the colorimetric imaging of the whole external body of seabreams to evaluate through multivariate partial least squares which approach the differences in the freshness preservation under four refrigeration modalities. The matrix of the independent variables is represented by RGB values for each pixel belonging to an extracted region of interest (129,633 values). The dependent variable is composed by two dummy variable corresponding to fresh (T0) or non-fresh (T2) individuals. T1 individuals were used as external test. The results quantified significant colorimetric differences between fresh and non-fresh fish. All fish used to create the model (T0 and T2) were correctly classified as fresh or non-fresh, while external test individuals (T1) were all classified as fresh. The proposed imaging method merges different image analysis techniques: (a) colorimetric calibration, (b) morphometric superimposition and (c) partial least square discriminant analysis modelling. This innovative and non-destructive approach allows the automatic assessment of fish freshness.

Development of a rapid soil water content detection technique using active infrared thermal methods for in-field applications.

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory–based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity.

Electronic nose application for determination of Penicillium digitatum in Valencia oranges

BACKGROUND Penicillium digitatum and Penicillium italicum are responsible for one the most serious diseases occurring during storage of citrus fruits. Its early detection allows a relevant increase in shelf life, and in situ monitoring of fungal infections represents a very efficient tool to improve storage quality. In the case of metabolic alterations due to physiological or fungal pathologies, olfactometric analysis allows the detection of specific volatile biomarkers, thus providing an effective tool for postharvest quality control of fruits and vegetables. RESULTS A total of 300 Valencia oranges were analysed with an electronic nose and results were screened by a multivariate classification technique, partial least squares discriminant analysis, in order to investigate whether the electronic nose could distinguish between Penicillium-infected and non-infected samples and to evaluate the efficiency of the group classifications. High percentages of correct classification were obtained at low levels of infection (100% for 2-5% infection in an independent test). CONCLUSION The electronic nose may be successfully applied as a reliable, non-destructive and non-contact indirect technology for the identification of fungal strains in storage rooms, especially when the infection occurs in small percentages that are not easily identifiable by classic methodologies of inspection.

Quantitative Method for Shape Description of Almond Cultivars (Prunus amygdalus Batsch)

The aim of the present work was to propose a rapid, non-invasive, and quantitative image analysis method based on elliptic Fourier analysis (EFA) and on carpological measurements to discriminate between 18 cultivars and shape groups of almond kernels and in-shell fruit. The shape groups were identified using two clustering techniques: a non-hierarchic method (k-means) and a hierarchical one (Ward’s method). Both methods found the same numbers of groups for in-shell fruit and kernels. The obtained results indicate that such differences can be used to discriminate among shape groups. This method was not efficient in discriminating single cultivars. In order to classify fruit into shape groups, a partial least squares discriminant analysis was applied. This analysis applied on the 18 cultivar groups showed low percentages of correct classification for both in-shell (38.58%) and kernels (31.36%). The same analysis computed on shape groups shows percentages of correct classification higher than 89%. Merging EFA, clustering methods, and modeling techniques set the basis for the implementation of an automated online fruit sorting. A Matlab script was developed to determine the right number of clusters in k-means clustering.

Hyperspectral Visible–Near Infrared Determination of Arsenic Concentration in Soil

The development of rapid techniques, such as hyperspectral spectrophotometry, for investigating arsenic (As) soil contamination could be of great value with respect to conventional methods. This study was conducted to detect As concentrations in artificially polluted soils (from 25 to 1045 mg kg−1) through hyperspectral visible–near infrared spectrophotometry and to compare two multivariate statistical regression analyses: partial least squares and support vector machines. The correlation coefficient r is greater in the partial least squares in both model (0.93%) and test (0.87%) with respect to support vector machines (0.88% for the model and 0.82% for the test). The most important model variables extracted from the variable importance in projection scores resulted the absorption peaks at 580, 660, 715, and 780 nm. Bands in the visible spectra are not directly associated to As, but the metalloid can interact with the main spectrally active components of soil permitting to multivariate statistical models to screen As concentrations.

Assessment of quality-assured Tarocco orange fruit sorting rules by combined physicochemical and sensory testing

BACKGROUND The aim of this study was to extract a sorting rule for Tarocco orange fruit from several physicochemical and sensory tests performed on a marketable lot of 399 Tarocco orange fruits. RESULTS The elastic tension at 5% strain (T₅ ) was found to be linearly correlated (r = 0.65) with the Magness-Taylor (MT) index. Thus T₅ was regarded as a non-destructive parameter quantifying fruit firmness and used to categorise the aforementioned lot in three different firmness classes, high (HF), medium (MF) and low (LF). Only the MT index, fruit rind thickness near the fruit peduncle, lightness coefficient and yellow/blue hue component of the orange flesh, as well as total soluble solid content, confirmed the validity of this discrimination at the significance level of 5%. Sensory professionals recognised the greater compactness (7 ± 2) but lower ease of peeling (4 ± 2) and segment separation (4 ± 2) of the HF oranges with respect to the corresponding sensory attributes of orange fruits grouped in the MF and LF classes. CONCLUSION To limit the costly rejection of Tarocco orange fruit considered too soft, especially after long-term shipping, it would be reasonable to select only fruits characterised by a compressive force or tension at 5% strain in the range 23-41 N or 300-540 N m⁻¹ respectively.

Thermographic medium-far ground-based proximal sensing for in-field wheat Stagonospora nodorum blotch detection

Thermal imaging is a potential remote sensing tool for estimating fungal wheat diseases. This study for the first time investigated the suitability of infrared thermography as rapid non-destructive technique to detect Stagonospora nodorum blotch wheat infection observing differences in temperatures due to loss of water content in infected wheat. Analyses were conducted in-field using medium-far ground-based proximal sensing technique. The study demonstrated statistically significant differences between images relative to different plots planted with two cultivars treated with three different conditions: artificially inoculated with Stagonospora nodorum (IN), treated with fungicide (TT) and not inoculated nor treated (NT). This study is oriented on medium-far ground-based proximal sensing in order to frame an area of medium extension (~10–20 m2), placing the acquisition system at the bottom of each plot with a height of 4 m. Fifty-three thermal images of durum wheat plants have been acquired at growth stage 83, with a FLIR (S40) thermo-camera. A randomized split-plot design with three replicates has been utilized. Regions of interest were extracted from each plot image and thus, mean temperature and relative · standard deviation were calculated. The two-tailed (Wilcoxon) Mann-Whitney U test has been used to evidence whether the medians of couples of diverse treatments were different. Considering the whole samples significant differences (p < 0.05) have been observed between IN and TT.

Non-destructive Proximal Sensing for Early Detection of Citrus Nutrient and Water Stress

This chapter reports the application of non-destructive optical-based technologies for the rapid and efficient assessment of the nutritional status and water stress detection improving their use efficiency. In the proximal sensing section, it was presented the use of spectral and hyperspectral imaging to evaluate the plant nutritional status. Proximal sensing offers the opportunity to rapidly collect a huge amount of crop canopy information. In the infrared thermography and thermometry section, results about their use to assess plant water stress analysing canopy and soil temperature variation were reported. Finally, the use of spectrophotometry and of the chlorophyll meter for the citrus nutrient detection is presented. The analyses of data were carried out by linear regressions and by multivariate statistics.