ReviewSensors for product characterization and quality of specialty crops—A review
Research highlights
▶ Advances in instrumentation have made it possible to introduce a variety of sensors. ▶ Computer vision linked to robotics is a basic component of automated operations. ▶ A need for portable equipment for use in the field and packinghouse is recognized. ▶ Wireless sensor networks increase sensing capacity for crop production and quality monitoring. ▶ The basic sensing techniques are mostly available, but need further technological development.
Introduction
Fifty years ago, a new approach to characterizing fresh food materials was created, which treated food items as physical bodies to which conventional engineering concepts and methods could be applied. The aim was to maintain and enhance the quality of food products as they go through different stages of operation from harvest to postharvest handling to retailing.
Specialty crops are defined as “fruits and vegetables, tree nuts, dried fruits and horticulture and nursery crops, including floriculture” (USDA, 2004). Non-destructive (ND) testing for properties and characteristics of specialty crops is critical for monitoring and controlling product quality and safety. Sensors play the key role in identification of product properties, and thus they have been an active research area, as evidenced by thousands of engineering research publications during the past 50 years.
Quality sensing is needed or desired for most or all agricultural commodities or foods at different stages of the production/marketing chain. Specialty crops cover a wide, diverse variety of commodities, which differ greatly in morphology, composition, and physiology. Hence it is customary to classify them into different groups according to a specific criterion. Temperate fruits, including apple, peach, pear, citrus, and table grape, are harvested manually for fresh consumption, or mechanically for processing. Tropical fruits including avocado, banana, mango, and papaya are also hand harvested, while dry (shell) fruits or nuts are often machine harvested. We should also mention that olive and grape are two fruit crops of world significance; they are mainly machine harvested, where sensors are being introduced for quality monitoring. Vegetables contain even a much greater number of commodities; they are cultivated in different types of environments, including greenhouse. For example, there are fruit vegetables (e.g., tomato, bell pepper, zucchini) and green leafy vegetables (e.g., lettuce, spinach, cabbage, small greens). Finally, ornamentals refer to potted flowers, potted green plants and cut flowers.
After harvest, specialty crops may undergo all or part of these postharvest operations before being delivered to the consumer: pre-sorting, sorting, washing, refrigeration, grading (for quality classes), wrapping, and packaging (placing into cartons, small boxes, baskets, bags, nets, etc.), cold-storage (for short term, i.e., days) or controlled or modified-atmosphere storage (for long term, i.e., months). In addition, some commodities need such special treatments as ripening using gases and temperature (peaches, citrus, bananas), individual wrapping (lettuce, broccoli, cauliflower, bell peppers), cutting and small-bag wrapping (lettuce, mixed salads, fruits), or destruction (e.g., for olive oil and wine grape). Ornamental crops represent a large share of the total production for the specialty crop industry. But there is still a lack of research and progress on development of sensors for ornamental crops, except for automatic production and handling systems which have been widely adopted by the industry.
Specialty crops are living biological products that the consumer expects them to be in the best quality and safety condition. Freshness and quality, which are important to the consumer, are affected by time, handling procedure, environmental conditions, and the processes to which they undergo. At each of these steps, the freshness and quality of specialty crop products need to be monitored and controlled. Traditional manual expertise for quality inspection is no longer adequate nowadays, and only sensors can provide solutions for monitoring and controlling the quality of specialty crops.
A number of reviews on non-invasive, fast technologies for fruit and vegetables quality sensing have been published (Chen and Sun, 1991, Abbott et al., 1997, Studman, 2001, Butz et al., 2005). Many of these review papers covered a broad range of sensing techniques, with a selected few that also discussed the feasibility of these techniques for industrial applications (Abbott, 2004, Walsh, 2005). Moreover, several recent reviews are focused on selected techniques, such as mechanical methods for firmness measurement (García-Ramos et al., 2005), size characterization techniques (Moreda et al., 2009), computer vision (Brosnan and Sun, 2002, Du and Sun, 2006), near-infrared (NIR) spectroscopy (Nicolai et al., 2007), nuclear magnetic resonance (NMR) (Aristizábal, 2007), biosensing (Mello and Kubota, 2002, Patel, 2002), wireless sensing (Ruiz-Garcia et al., 2009), and plant diseases detection (Sankaran et al., 2010). The needs for this area of research are mainly driven by the specialty crop industries to meet increasing consumer demand for better quality and safer fresh products.
A large number of recent publications on non-destructive detection of food quality are related to the utilization of electromagnetic radiation in a wide range of frequencies. Electromagnetic radiation-based technologies have shown great potential; some of them have been successfully used for monitoring the quality of specialty crops. Successful application of these technologies requires the combination of effective sensors with sophisticated mathematical models and computer algorithms to establish relationships between selected physical/chemical properties and quality attributes of the product. As a result, a large number of papers published recently are focused on utilizing different non-destructive (ND) optical techniques for quality detection of agro-food products. Great advances have been made in spectroscopy and computer vision, and these techniques are being widely used for quality inspection and control of products in many industries including food. As technologies based on VIS, NIR, mid-infrared (MIR), and ultra-violet (UV) are becoming more affordable and equipped with more user-friendly data treatment and calibration capabilities, they have fostered further development of detection procedures for different quality- and composition-related properties of fruits and vegetables.
Over the past 10 years, a number of new technologies based on electromagnetic properties have emerged, whereas great progress has also been made on other existing technologies. They include X-ray, nuclear magnetic resonance (NMR) or magnetic resonant imaging (MRI), fluorescence, and with less success until now, electrical impedance and permittivity (mainly microwave), thermal sensing and selective gas/volatile sensing. These developments have opened new areas of research as well as new applications for sensing quality of specialty crops.
This review covers different sensing techniques, with emphasis on those emerging technologies like NMR, MRI, wireless sensor networks (WSN) and radio-frequency identification (RFID), for fruits and vegetables and their potential for industrial applications.
Section snippets
Computer vision for internal quality
Numerous review articles have been published on computer vision technology for quality inspection of food and agricultural products (Chen et al., 2002, Brosnan and Sun, 2004, Aguilera and Briones, 2005) and horticultural products in particular (Abbott, 2004, Butz et al., 2005, Nicolai et al., 2007). This section provides a brief review of selected vision technologies, especially those emerging technologies that are showing great promise for assessing internal quality of horticultural products,
Nuclear magnetic resonance (NMR) spectroscopy and imaging
Since the discovery of the magnetic resonance phenomenon in 1946 and subsequent achievements, nuclear magnetic resonance (NMR) has become one of the most significant non-invasive techniques for internal inspection of biological objects (see Table 1). Derived from NMR are NMR spectroscopy, NMR relaxometry and magnetic resonance imaging (MRI). For NMR spectroscopy resonance frequency encodes the chemically equivalent nuclei populations at different electronic and chemical environments so that the
Computer vision for external quality and defects
Kader (2001) classified the quality attributes of fresh horticultural produce in four groups: appearance, texture, flavor, and nutritional factors. Appearance traits include size or dimension, shape, surface texture, surface color, and external or surface defects. Appearance factors define external quality and directly influence consumers in purchasing a product, and they can be evaluated by means of computer vision techniques. For some authors (Brosnan and Sun, 2004) the terms computer vision
NIR and IR spectroscopy
Different NIR and IR spectroscopic techniques currently are being used for specialty crops. Table 3 summarizes some relevant applications published in the past two decades.
Mechanical methods for firmness measurement
Mechanical techniques have been developed to non-destructively measure some quality parameters of fruit and vegetables, mainly for firmness estimation, providing an alternative to the destructive Magness–Taylor penetrometry (García-Ramos et al., 2005, Nicolaï et al., 2006). Major mechanical techniques include the measurement of variables extracted from quasi-static force-deformation curves, the analysis of impact forces, and the measurement of acoustic responses to vibrations and impacts.
Acoustic response for firmness and structural defects
Non-destructive techniques of using acoustic and vibrational characteristics for determining internal properties of fruits and vegetables, mainly flesh texture, have been the subject of numerous investigations over the past several decades. In order to obtain an objective and non-destructive measurement of firmness, several techniques (Chen and Sun, 1991, Abbott, 1999) and theoretical models (Huarng et al., 1993) about the dynamic behavior of these biological materials were developed many years
Chemical sensors
There is a need for quick testing for both individual chemical compounds and composite mixtures of different nature (Snopok and Kruglenko, 2002); also a non-destructive, non-invasive approach is desirable, able to correlate information available on the product with the stage of freshness and quality. Low-cost and continuous monitoring of chemical and microbiological quality (including microbiological examination of food: aerobic colony count, presence and/or number of pathogens), with fast
Biosensors
The biological recognition element of a biosensor can be classified into two main classes: biocatalysts (enzymes, microorganisms, tissue materials) and bioligands (antibodies, nucleic acids, lectins). The traditional transducers are electrochemical, optical and thermal. The latest generation of biosensors (affinity biosensors) combine the classical measurement principles with piezoelectric and magnetic transducers (Castillo et al., 2004).
More than 10 years ago Lowe (1999) already expressed that
Wireless sensing in specialty crops
The use of wireless sensor technologies (WST) in specialty crops offers new features both in terms of sensing and communications that never have been available before. Recent advances in wireless sensor networking (WSN) technology have led to the development of low-cost, low power, multifunctional sensor nodes. Sensor nodes enable environment sensing together with data processing. They are able to network with other sensors systems and exchange data with external users. The application of this
Summary and conclusions
This review has attempted to provide an overview of existing and promising sensing technologies with an emphasis on their current and future application potential for the specialty crop industry. Several technologies were reviewed, mainly: (1) electromagnetic sensors, spectroscopic and computer vision; (2) mechanical contact and acoustic sensors; (3) biosensors; and (4) wireless sensors networks.
Advances in laboratory instrumentation have made it possible to introduce a variety of sensors for
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