This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radio-frequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow.

Internal browning in pears (Pyrus communis L. cv. Blanquilla) has been studied by NMR and MRI in order to develop a non-destructive procedure for on-line disorder identification. For NMR relaxometry, disordered tissue shows higher transverse relaxation rates compared to sound tissue, especially at higher magnetic field strength and for long pulse spacing. Membrane alteration and therefore tissue disintegration, as well as water evaporation, appear to be the main causes of this response. Correlation between relaxation times and diffusion showed that the proton pools in disordered tissue are grouped into a smaller number of populations compared to sound tissue, also highlighting cell decompartmentation in disordered tissue. At a macroscopic level, fast low angle shot MR images, effective transverse relaxation-weighted (TR 11 ms and TE 3.7 ms) and proton density-weighted (TR 7.6 ms and TE 2.5 ms), were acquired for pears at a rate of 54 mm/s. Images have been discriminated for internal breakdown according to histogram characteristics. Up to 94 and 96% of pears, respectively, were correctly classified in the former and the latter type of images. In this study a minimum value of 12% of tissue affected by breakdown was always clearly identifie

Maintaining the quality of extra virgin olive oils (EVOOs) in the current globalised market is a key challenge for the olive oil manufacturers. To maintain the quality, one major factor is to resist its oxidation during processing and storage. Resisting the oxidation in EVOOs is a challenging task requiring control of different physical and chemical factors. However, rapid, real-time, and non-destructive monitoring the oxidation in EVOOs could support in maintaining its quality during storage. The present study utilises a novel right-angle fluorescence spectroscopy-based prototype for rapid monitoring of oxidation changes in three different EVOOs varieties (Arbequina, Picual, and Cornicabra) during storage. Furthermore, the effects of light exposure on oxidation of EVOOs were also studied. Two major changes in the fluorescence spectra of EVOO samples were mainly observed: a decrease in fluorescence emission intensity at 671 nm, and an increase in fluorescence intensities at 420, 440, 464, and 515 nm. These two changes were related to a decrease in chlorophyll content and parallel formation of oxidation products. The formation of oxidation products was found to have a linear relationship with the decrease in chlorophyll content. Out of the three, the Cornicabra variety showed the highest rate of oxidation changes. Furthermore, the EVOOs degraded rapidly when get exposed to light. In a fast and non-destructive manner, the fluorescence spectroscopy-based prototype successfully monitored the oxidation changes in the EVOOs.

The sugar content of Golden Delicious apples is predicted using near infrared (NIR) spectrometry. The study focuses on the metrological characteristics of the sugar content measurement and external parameters involved in the lack of robustness of the NIR-based model. The external parameters were fruit temperature, spectrometer temperature and ambient light. The first two factors influenced the prediction accuracy: (i) a fruit temperature variation altered the prediction, the relationship seems to be described by a non-linear model within the considered temperature range, (ii) a variation of the spectrometer temperature also altered the prediction, the relationship is described by a linear function for a temperature between 4 and 30°C. Ambient light did not show to have any influence on the NIR-based model. The analysis of the metrological parameters showed a satisfactory repeatibility in sugar prediction with a low error, 0.073°Brix. The model reproducibility was good regarding bias-corrected standard error of prediction ( SEPc) without significant differences between experiments, on the other hand a bias remained even if the previous parameters were maintained constant. These results will be taken into account in future measurements, in order to improve the robustness of the NIR-based model developed for apples.

Magnetic resonance imaging was used to acquire images of the internal structure of mandarins for non-destructive seed identification. Two different types of fast MRI sequences were investigated: a gradient echo and a spiral–radial, with 484 ms acquisition time for the former compared to 240 ms for the latter. The radial–spiral option allows over-sampling of the central area of the k -space maintaining the contrast within the MRI images and so the feasibility of seed segmentation. Three segmentation techniques were applied for image post-processing: region-based, one-dimension histogram variance, and two-dimension histogram variance, among which the latter procedure has been demonstrated to give the most promising results. Image features including perimeter, compactness, maximum distance to the gravity centre, and aspect ratio were employed in a linear discriminant function, by which seed identification of mandarins could be achieved with 100% accuracy using radial–spiral sequence and 98.7% accuracy with gradient echo images. Keywords Image analysis Classification Citrus Internal quality Fruit