Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time consuming for such high dimensional data sets and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, nondestructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

Artificial vision systems are powerful tools for the automatic inspection of fruits and vegetables. Typical target applications of such systems include grading, quality estimation from external parameters or internal features, monitoring of fruit processes during storage or evaluation of experimental treatments. The capabilities of an artificial vision system go beyond the limited human capacity to evaluate long-term processes objectively or to appreciate events that take place outside the visible electromagnetic spectrum. Use of the ultraviolet or near-infrared spectra makes it possible to explore defects or features that the human eye is unable to see. Hyperspectral systems provide information about individual components or damage that can be perceived only at particular wavelengths and can be used as a tool to develop new computer vision systems adapted to particular objectives. In-line grading systems allow huge amounts of fruit or vegetables to be inspected individually and provide statistics about the batch. In general, artificial systems not only substitute human inspection but also improve on its capabilities. This work presents the latest developments in the application of this technology to the inspection of the internal and external quality of fruits and vegetables.

Background The increasing demand for quality assurance in agro-food production requires sophisticated analytical methods for in-line quality control. One of these techniques is visible and near-infrared (VIS-NIR) spectroscopy, which has low running costs, does not need sample preparation, and is non-destructive, environmentally friendly, and fast. Despite these advantages, only a limited amount of research has been conducted on VIS-NIR in-line applications to measure, control, and predict quality in fruits and vegetables. Scope and Approach The applicability of VIS-NIR spectroscopy for the off-line and in-line monitoring of quality in postharvest products has been addressed in this review. The document focuses on the comparison between the two processes for the same agro-food product, highlighting the main advantages and disadvantages, problems, solutions, and differences. Key Findings and Conclusions VIS-NIR techniques, combined with chemometric methods, have shown great potential due to their fast detection speed, and the possibility of simultaneously predicting multiple quality parameters or distinguishing between products according to the objectives. Being able to automate processes is a great advantage compared to routine off-line analyses, mainly due to the savings achieved in time, material, and personnel. However, in numerous cases, in-line implementation has not been accomplished in the corresponding studies, hence the scarcity of real in-line applications. Recent demands, together with the advances being made in the technology and a reduction in the price of equipment, makes VIS-NIR technology an analytical alternative for continuous realtime food quality controls, which will become predominant in the next few years.

The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods.

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