As the U.S. food system has evolved, advances in science and technology have helped to provide a huge variety of foods that are safe, convenient, inexpensive, distributed widely, and available year round. Individuals representing many disciplines—microbiology, chemistry, engineering, processing, packaging, sensory science, and nutrition, among others—work under the umbrella of food science to support the integrity of the food supply (Floros et al., 2010). In addition, food scientists collaborate with other disciplines (e.g., agronomists, biotechnologists, material scientists, economists, and social/behavioral scientists) to address problems in the broader “food system,” with the ultimate purpose of transforming raw, frequently inedible, and, in some cases, unsafe agricultural commodities into safe, nutritious, high-quality foods that are accepted and valued by consumers (see examples in Box 4-1). Much of this is accomplished by food processing, defined as any intentional change to a food occurring between the point of origin and availability for consumption (Floros et al., 2010). Food is processed for many different purposes and, overall, processing results in improved product characteristics such as safety, shelf life, quality, sensory attributes, and nutritional value. In more recent years, consumers have demanded additional product features such as convenience and variety to their food choices, and they expect greater transparency about the origins of their food and the type of processes utilized in manufacturing a product. New trends such as online food shopping and the use of food-on-demand services
allow for even greater individualization in consumer choice, preference, and demand.
Attaining a food supply that provides safe, healthy, appealing, and affordable foods is the shared responsibility of food and allied industries, local, state, and federal governments, and researchers and educators in academic institutions, along with consumers through their food choices and practices. Most of the necessary research and development (R&D) work to launch new commercial products is naturally initiated and conducted by the private sector. However, investigating overarching concepts in the food sciences, and solving universal, crosscutting problems, is frequently tackled with basic and applied scientific research that is conducted at public and private universities and in government laboratories. Although different stakeholder groups contribute to the funding and intellectual enterprise of
the agricultural and food-related activities, historically the research efforts have been largely supported by both public and private funds. Between 1970 and 2008, the public contribution was relatively stable at about 50 percent of the total private- and public-sector R&D funding (Clancy et al., 2016). Recently, however, the source of funding has shifted. During the period 2008 to 2013, real private investment in R&D in the agricultural and food sector rose sharply (up by 64 percent), while real public investment fell by 20 percent. Private funding has dominated R&D in food manufacturing (Clancy et al., 2016). Public support for human nutrition research has increased over the past several decades. The nutrition research includes nutrition through the life cycle, health (disease, metabolism, and metabolic mechanisms), and food science (monitoring, education, and policy; and supplements). However, the portfolio of research has changed with
increased funding from the U.S. Department of Health and Human Services and decreased support from the U.S. Department of Agriculture. The shift has affected the type of problems addressed through federal support as well as mechanism (shifting from formula funds to nonformula extramural support). From 1985 to 2009, the federal share of research funding for food sciences (food processing, preservation, and other food-related technologies) decreased from 10 to 4 percent of the total funding for nutrition research (Toole and Kuchler, 2015).
This chapter identifies important challenges faced by the postharvest food sector in making progress toward meeting future demands for a safe, nutritious, sustainable, and affordable food supply for all. It also identifies emerging opportunities, largely as a consequence of scientific and technological developments, to address these challenges, along with gaps and barriers. Concrete illustrative examples of these emerging opportunities are provided. The chapter does not address the cost and social implications of these technological advances, including factors that may limit access to new products or processes (e.g., production scale, location, or consumer resources), although it is recognized that these factors are important drivers of their ultimate adoption. Chapter 9 considers some of the socioeconomic considerations related to the scientific innovations.
Factors such as population growth, more variable weather cycles, and globalization, among others, have changed and continue to dramatically change our food system. Supply networks now offer greater consumer choice over a wide variety of products through large, interconnected markets. However, many challenges to the system have emerged. The committee identified two general challenge areas that need to be addressed over the next 20 years using the newest scientific and technological breakthroughs.
2.1 Challenge 1: Develop High-Quality, Nutritious Foods Produced and Distributed in a Sustainable Manner to Meet the Needs and Demands of a Diverse Consumer Population
The essential role of food is maintaining human life and health. Food promotes health because it contains nutrients that are necessary to provide energy, meet physiological needs and functions, and prevent chronic diseases. As mentioned in Chapter 1, this report does not address research efforts devoted to understanding the association between human nutrition and health, although it should be noted that this continues to be an important area of future research. Indeed, the increased recognition of the complex, and often personalized, interactions between agricultural produc-
tion, food, nutrients, and human health begs for research to improve our understanding of food and nutrient metabolism and their relationship to diet and health. Findings from this type of research could lead to more healthful foods and better diets in general, and those in accordance with the needs of specific consumer subpopulations.
It is important to recognize that humans eat foods, not nutrients, and so foods must be both nutritious and appealing. Sensory attributes are among the most important drivers of food consumption preferences (Lusk and Briggeman, 2009). The holistic sensory experience is complex, and there is an implicit causal chain of events from sensation, to experiencing pleasure, to food intake. Sensory is not only impacted by the complexity of food components from macromolecules to ingredients to formulation; there is emerging science indicating that human genetic variability plays a major role in the way individuals experience foods. Understanding the interactions between the food chemical composition and the consuming human is critical to developing products that meet consumer preferences for flavor and appearance while delivering nutrition and health benefits.
In addition to consumer appeal and healthfulness, consumers’ eating preferences are driven by many social, behavioral, and psychological factors (Lusk and McCluskey, 2018). For some consumers, ethical and environmental concerns may dominate their preferences (e.g., vegetarian protein substitutes for animal products; insects used as a source of protein); for others, place of origin and local sourcing are predominant considerations; and in other cases, perceived risks weigh heavily in food choice (e.g., choice of organic options, avoidance of genetically engineered foods or other new technologies). Improved understanding of the influence of social, behavioral, and psychological factors on the development and role of these influences is necessary, particularly as consumers are faced with choices about products developed with new technologies for some of which there is conflicting evidence on risks and benefits. One relevant ethical issue is that of consumer behavior around food loss and waste, given that 30-40 percent of the food produced in the United States is wasted, largely at the retail and consumer stages (Gunders, 2012; Buzby et al., 2014; Bellemare et al., 2017). Food supply chain participators have joined forces in initiatives to reduce waste (e.g., changing product labeling policies) but important technological innovations can be added to these efforts, including development of ways to increase product quality, shelf life, and/or safety. Other challenges are best addressed through focus on a systems approach and behavioral changes (see Conrad et al., 2018, for an example of the challenge of improved diet quality being associated with increased food waste and greater amounts of water and pesticide use).
2.2 Challenge 2: Protect the Integrity and Safety of the Global Food Supply Chain
An increasingly globalized and highly networked food supply chain has made it more challenging to protect food from intentional and unintentional microbial and chemical contamination. Although regulatory and surveillance systems are arguably better than they were 25 years ago, in many ways our current food safety system still lacks sophistication and is not nimble enough to respond swiftly when a critical issue arises.
Assurance of food safety relies on preventing contamination or removing/inactivating the contaminant if it occurs along the chain. Large amounts of food safety data are currently being collected from farm to fork, but those data can be somewhat crude (e.g., visual inspection of poultry carcasses along a processing conveyor rather than instrumentation measurements) and when measurements are made, they are simple (i.e., nonquantitative) and delayed; certainly, they most often provide only a snapshot in time. New technologies are making it possible to obtain more sophisticated data, sometimes collected continuously and/or in real time. If more precise, accurate, faster, and less expensive technologies were applied to food protection, testing could occur more often to facilitate the detection of infrequent contamination events, and to more rapidly manage and respond to food safety incidents. For example, the availability of very rapid and sensitive ways to detect harmful biological agents or chemical contaminants would result in a safer food supply, especially if detection occurred before the contaminants were widely dispersed as ingredients or through products entering the retail food system. This would be particularly the case if the methods were easy to apply and inexpensive. Identifying the most relevant data and points of collection and intervention are key to effective and integrated data systems. Field deployability would allow detection technologies to touch every phase of the farm-to-fork continuum.
When a contaminated product enters the market, or an outbreak occurs, we currently rely on piecemeal systems to perform epidemiological investigations, trace back, and trace forward, meaning public health risk remains elevated for extended periods of time, until the right information has been obtained and synthesized. A thorough and integrated data communication and management system that includes all steps in the supply chain would greatly aid traceability and reduce the public health impact of food safety events, particularly in the case of larger processors, distributors, and retailers. As stated above, technological advances over the past few decades have opened the door to faster, more accurate, and more relevant data collection in food safety. When married to algorithms that assess risk and costs and benefits, it is possible to prevent contaminated products from
entering the food chain or, if they do, prevent their further distribution and consumption in a matter of minutes or hours, not days or weeks.
There is also a need to ensure that best practices to maintain food quality are being adhered to throughout the food supply and distribution channels. For instance, data from biochemical analysis can be used to ensure that product traits such as appearance, flavor, or nutritional value are maintained. An integrated system that mapped the flow of products and ingredients, and transferred information about food quality throughout food distribution, would improve efficiency and integrity by contractors all through the supply chain and increase consumer trust. Better assurance of food quality will also aid in optimizing resource efficiencies in the system and ultimately reduce food loss and waste through improved ingredient flow and increased product shelf life.
3.1 Opportunity 1: Omics Technologies
The recently coined term foodomics refers to the use of “omics” technologies and data as they relate to the discipline of food science (Capozzi and Bordoni, 2013; Andjelkovic et al., 2017). For example, integrated analytical approaches in food chemistry and analysis can be used to increase our understanding of food composition at the molecular and even atomic levels. It is now possible to produce food “fingerprints” of chemical composition, information that is relevant to safety, quality, authenticity, security, and nutritional value (Gallo and Ferranti, 2016). Beyond food fingerprinting, omics technologies provide a means to detect, quantify, and characterize individual metabolites or combinations thereof. This is opening doors to development of improved bioactive absorption and delivery systems, and better colors and flavors, to name just a few of the applications (Gallo and Ferranti, 2016). These technologies are also particularly useful in identifying relevant volatile compounds that may serve as markers of product freshness (Wojnowski et al., 2017), for improving food quality, and for ultimately reducing food loss and waste. They may also identify molecular targets (analytes) during the development of advanced detection methods for harmful microbes, chemicals, and toxins, and therefore further improve food safety. Identification of novel biorecognition molecules used to capture and detect key analytes will make it easier to perform analyses on very complex sample matrices, a long-time obstacle to the application of advanced analytical methods to foods. Production of increasingly miniaturized analytical equipment (i.e., infrared, ultraviolet, mass spectrometry, and nuclear magnetic resonance [NMR] spectroscopy), some of which can automate sampling and analysis for real-time biochemical measurement,
offers opportunities for exquisitely sophisticated chemical analysis that may become field deployable.
The combined use of omics technologies, bioinformatics, and advanced analytical methods provides innovative means by which scientists can explore interactions between systems. In nutrition, for instance, applying omics techniques to human genetics, physiological status, the gut microbiome, and food composition can lead us closer to integrated personalized nutrition (Grimaldi et al., 2017; Kaput et al., 2017) (see Box 4-2). In sensory science, where we know that the flavor experience is multimodal, omics techniques can be used to characterize genetic and metabolic differences in consumer perception of flavor, allowing for a better understanding
of what drives food choice. When this information is used along with food fingerprinting, it becomes possible to design and produce food having ideal health benefits with greater consumer appeal.
Individual omics technologies focus on one aspect or component of a much larger system. In a health care setting, genomics can be used for genetic fingerprinting, metabolomics for metabolic profiling, sequencing and bioinformatics for elucidating characteristics of the microbiome. For a particular food, various omics techniques can be used to determine its nutrient composition, sensory characteristics, and microbiological profile. Each of these individual analyses provides characterization of what is going on in a patient or a product and constitutes a subsystem. However, to understand the entire person or product, there is also the need to elucidate how these subsystems interact with one another, forming a system of systems. For instance, most chronic diseases (e.g., diabetes and cardiovascular disease) are complex, with diet being only one contributing factor. For such diseases, there are significant gaps in knowledge about interactions between genes, diet, other behaviors (e.g., exercise and stress), and social and cultural factors, among others. Having the full scientific capabilities to understand the interactions and identify the key determinants of any particular illness or trait has yet to be realized (see Box 4-3).
3.2 Opportunity 2: Sensor Technologies
According to a recent study, the most common reasons given by consumers for discarding food were concerns about its safety and the willingness to consume only the freshest product (Neff et al., 2015). Having a technology that can “sense” product safety, quality, and/or freshness, preferably in real time, will deliver critical information to processors, distributors, and consumers, potentially resulting in better decisions about safety and food waste. Such technologies ideally would have features such as high sensitivity and specificity of analyte detection, low cost, small footprint, reliability, short time to result, and be field deployable and adaptable, among others.
Sensors are devices that detect or measure physical, chemical, or biological properties and then record, indicate, or respond to those results. Biosensors in particular are analytical devices that combine a biological component with a physicochemical detector. The biologically derived component is a material or biomimetic compound that interacts, binds, or otherwise recognizes the analyte to be detected. Increasingly, these are being identified using various omics methods (see the section above). The
interaction between the biological element and the analyte results in a signal; a detector element physicochemically transforms (transduces) that signal, and frequently amplifies it into a form that is readily measurable and sometimes quantifiable. There are many types of transducers, such as electrochemical, optical/visual, and mass based (Vigneshvar et al., 2016; Alahi and Mukhopadhyay, 2017). Table 4-1 provides a summary of some common biosensor technologies.
Nanomaterials are increasingly used as components of biosensors and can serve a variety of functions, including as immobilization supports, for signal amplification, as alternatives to enzyme labels (“nanozymes”), and to aid in signal generation and quenching (Rhouati et al., 2017). In most cases, the choice to use nanomaterials is founded on the desire
TABLE 4-1 Summary of Various Biosensors with Their Advantages and Limitations
|Method of Detection||Advantages||Limitations||Cost||References|
|Optical methods||Sensitivity is high, can detect almost in real time, and detection system is label free||Cost is very high||High||Mandal et al., 2011; Zhang, 2013|
|Electrochemical methods||Requires large quantity of sample numbers, might be automatic, and detection system is label free||Specificity is low, not suitable for low sensitivity, and needs a lot of washing steps||Low||Mandal et al., 2011; Zhang, 2013|
|Mass-based methods||Cheaper than other methods, easy operation, able to detect in real time, and detection is label free||Specificity and sensitivity are low, requires long incubation time, and regenerating crystal surface is problematic||Low||Mandal et al., 2011; Zhang, 2013|
|Nanomaterialbased sensors||User-friendly measurement, and measurement can be done in real time||Concerns regarding toxicity of the nanomaterial and may not be possible to regenerate the sensor||Medium||Pérez-López and Merkoçi, 2011|
SOURCE: Alahi and Mukhopadhyay, 2017.
to produce assays with greater sensitivity and specificity. Noble metals (e.g., gold and silver) are frequently used for signal amplification because of their unique physicochemical properties; however, carbon, magnetic, metal oxide–based, and quantum dot nanoparticles have also been used (Rhouati et al., 2017). Incorporation of a nucleic acid amplification step into the biosensor design, particularly those that do not require temperature cycling (e.g., loop-mediated isothermal amplification, recombinase polymerase amplification, and rolling circle amplification) can also increase analytical sensitivity (Giuffrida and Spoto, 2017). Examples of nanosensors in developing specific food safety applications are detailed in Wang and Duncan (2017) and in Vigneshvar et al. (2016) (see some examples in Table 4-2).
TABLE 4-2 Selected Applications of Nanosensors in Food Safety
|Acetylcholinesterase inhibition–based biosensors||Electrochemistry||Understanding pesticidal impact||Pundir and Chauhan, 2012|
|Piezoelectric biosensors||Electrochemistry||Detecting organophosphate and carbamate||Marraza, 2014|
|Quartz-crystal biosensor||Electromagnetic||For developing ultra-high-sensitive detection of proteins and liquid||Ogi, 2013|
|Microbial fuel cell–based biosensors||Optical||To monitor biochemical oxygen demand and toxicity in the environment and heavy metal and pesticidal toxicity||Gutierrez et al., 2015; Sun et al., 2015|
|Based on cellulose nanocrystals||Nanomaterials||To detect norovirus||Rosilo et al., 2014|
|Based on aptamers, single-stranded oligonucleotides (DNA or RNA) that interact with analytes with antibody-like ability||Nanomaterials||To monitor mycotoxins in various foods (e.g., wine, ground corn)||Pak et al., 2014; Xiao et al., 2015|
Sensor technologies are also highly applicable to monitoring product freshness, such as detecting biochemical parameters that are correlated with product spoilage and shelf life, particularly near product life end (Xiaobo et al., 2016). These types of sensors are usually noninvasive in nature. Examples of product attributes that can be measured are color, the presence of surface defects, and chemical composition. Technological platforms include optical, acoustical, NMR, and electrical. For example, light in the visible/near-infrared spectra can penetrate readily into biological systems, and when applied to a food can provide a “fingerprint” to assess parameters such as freshness, firmness, and texture. Biomimetic devices such as electronic noses, which are already used for personalized medicine (Fitzgerald et al., 2017), are being piloted for evaluating spoilage and shelf life of meats (Wojnowski et al., 2017).
At the end of the sensing phase, an electronic reader allows signal processing so that results are displayed in a user-friendly manner. Mobile diagnostics that use Internet-of-Things technologies to link sensor output to smartphones and cameras, and are even coupled with data entry on servers or the cloud, have been reported, particularly for detection of foodborne pathogens, food allergens, antibiotic residues, and shellfish toxins, in relevant sample matrices (Rateni et al., 2017). Although handheld mobile readouts are still in development with significant need for improvement (e.g., reducing signal-to-noise ratios, miniaturization, sample preparation, data interpretation, cost, and reliability), their future is bright because they provide options for portability and real-time results, important features for managing an already complex food chain.
There are a number of practical impediments to successful, routine use of biosensor technologies in foods and environmental samples. Many different biosensors for detecting pathogens such as Salmonella in foods have been reported, but they vary widely in performance, particularly analytical sensitivity (detection limit), frequently ranging from a low of one single cell to a high of millions (Cinti et al., 2017; Silva et al., 2018). While of lesser importance in clinical settings where samples come from ill individuals and pathogen concentrations are high, this is not the case for food, water, and environmental samples. These sample types may be infrequently contaminated and when the contaminant is present, concentrations are low. Hence, for sensors to be of the greatest value in food safety, analytical sensitivity (detection limits) must be high (<10 cells) and specificity needs to be high. In addition, sample size should be large and testing done frequently in order to account for low contaminant prevalence.
In short, assay sensitivity and specificity (detection limits and low pro-
pensity for false positive and false negative results) will need to improve for sensor technologies to gain more widespread use in food and agriculture. In addition, there is a pressing need to develop sample preparation methods and protocols that will efficiently concentrate and purify an analyte from the matrix prior to use in the sensing device (Brehm-Stecher et al., 2009). This includes validating sensor performance in relevant natural sample matrices (i.e., various waters, foods, and environmental samples). Other factors related to conditions and ease of use, robustness, and cost are critical for success. Solving these practical scientific problems—along with ensuring that sensors are “fit for purpose”—will require extensive, transdisciplinary effort.
3.3 Opportunity 3: Integrated Data Management Systems
The development of omics and sensor technologies will augment the capabilities to collect increasing amounts of data in the food processing, safety, and quality realms (e.g., process validation, optimization and control, environmental, and public health data). Ultimately, the value of data to the food supply chain is to provide more and better information on which to base system optimization and management decisions regarding food processing, safety, quality control (e.g., preservation of product traits), food waste reduction, and system monitoring, among others. That means there must be an infrastructure to house massive amounts of records, and a means by which those records can be integrated and effectively used for decision-making purposes. Associated with these changes is the need to identify and understand the design and behaviors in emerging food supply chains.
In the area of food safety, there are a number of large, publicly accessible online databases used by the public health sector (inventoried in Marvin et al., 2017). Examples are the National Outbreak Reporting System, the Genome Trakr Network (whole-genome sequences of pathogens), and others used by industry, such as Combase (data for quantitative microbiology and models predict growth and inactivation of microorganisms). The availability of online searchable databases (e.g., JIFSAN’s FoodRisk.org, a metadatabase for tools and models) and of social media and crowdsourcing (e.g., iwaspoisoned.com) platforms provide other capabilities. These and other databases have clear utility for different types of applications. However, that utility would be enhanced if data and databases were integrated with one another, particularly with less publicly accessible data such as industry process monitoring and product tracking systems (e.g., GPS and radio frequency identification [RFID]; Mejia et al., 2010), quality control systems, or public food safety monitoring efforts (see also Chapter 7 on data science). There are some early examples (see Box 4-4), but a much more concerted effort is needed toward data integration to
support food-safety decision making. Hill et al. (2017) provided a proof of concept for the use of genomic and epidemiological metadata integration along with sophisticated data analytics and modeling for early detection of human infection with foodborne pathogens. The model allowed this group to find associations between DNA sequence, location of the food animal across the production chain, and human illness. With technologies that collect, transmit, store, and analyze data obtained from real-time sensors, along with a centralized system of databases with sampling and processing data, their approach holds promise and would theoretically allow the tracking and tracing of individual food units.
Integrated data and data management systems can also be applied with the goal of improving resource efficiencies in the food system. Implementation of such a data management system in food networks supports efforts to optimize food processes and recycle and reduce waste during and after manufacturing as an operational concept in order to achieve a “circular economy” that makes best use of the range of waste streams in the agricultural and food system (see, for instance, the North American Initiative on Food Waste Reduction and Recovery [CEC, 2018] and the European Union’s AgroCycle [UCD, 2018]). A more integrated and holistic data management systems approach to thinking about minimizing product and food waste may focus on identifying food waste conversion methods for edible and nonedible purposes. In such a distribution system, a seamless supply
chain of foods and ingredients with a leaner, simpler, and transparent data management system would be vital. Considerations of system design and science are discussed in Chapter 8.
An example of an innovative exchange system developed to facilitate the diversion of surplus retail food products to distribution sites is the one recently developed with food banks. Efficiencies are gained through the development of networks and exchanges for distribution of surplus foods from retailers to food banks and distribution centers. Integration of real-time data on available foods and existing needs provides a mechanism for redirecting food to help feed the hungry and reduce food waste (Prendergast, 2017). Within the industry itself, goals for reducing food waste can be accomplished by setting standards for ordering, receiving, preparing, processing, packaging, serving, and tracking food production.
One technology that has enormous potential to revolutionize the management and storage of data and to facilitate the integration of food distribution systems, among other applications, is blockchain (see Chapter 7 on data science). Blockchain (also called open, distributed ledgers) is a system in which a continuously growing list of decentralized and encrypted records (blocks) are linked so that it can be securely distributed across peer-to-peer networks. Blockchain allows for highly transparent and instantaneous transfer of product data associated with many attributes, including the safety and quality of food, as well as environmental stewardship, all arising from activities such as routine monitoring, inspection and audit, accreditation, and laboratory analyses. The improvements from implementation of blockchain technology will also benefit consumers as they demand more detailed information about product sourcing, origin, processes, and production methods. For example, offering verified product sourcing, non-GM (genetically modified) or “organic” products to the consumer requires systems to preserve and track segregation through the supply chain. Consumers and other buyers are able to access information on the product via smartphone applications and other data platforms. Although in its infancy, blockchain is an important emerging technology (although it has its detractors) that may allow integration of detailed information across different platforms and ownership structures and provide verifiable information that consumers seek from manufacturers’ claims. Although some applications that link purchases to specific retail outlets or consumers have benefits at the consumer level (e.g., in the case of food safety or other product recalls), little is known about consumer response to data systems extended to the retail consumer level. See Box 4-5 for more concrete examples of the application of blockchain technology to the management of data on the food supply chain.
As companies are increasingly exploring the uses of blockchain technology in the agriculture and food arena, both challenges and solutions are arising. A recent report aimed at better understanding the implications and needs of the blockchain technology to stakeholders (e.g., producers, manufacturers, traders, and product standard organizations) identified the following key challenges: access and implementation of the technology, the need for a workforce that can adapt and learn new competencies, privacy concerns, considerations related to regulatory frameworks, interoperability, and compatibility with existing systems (Ge et al., 2017). Specific stakeholder groups have identified cost and knowledge of the technology as main challenges (IFIC Foundation, 2018).
3.4 Opportunity 4: Materials Sciences and Engineering
Food scientists apply engineering principles to design novel processing and packaging technologies that result in profound improvements to the quality, safety, acceptability, and shelf life of foods. Depending on the technology (e.g., thermal, aseptic, microwave, pulsed light, ohmic heating, high pressure, freezing, and refrigeration), these processes offer advantages such as improved product quality (organoleptic characteristics that resemble the fresh product), reduced energy usage, smaller footprint (better portability), and lower environmental impact (Neetoo and Chen, 2014). Some of these technologies may be particularly well suited for certain foods or venues, especially those in which large capital outlay for food processing is not possible or economically feasible.
Advances in materials science and nanotechnology, as applied to production of packaging materials, holds great promise for advancing quality and safety of food products. Active packaging, of which modified atmosphere packaging is an early example, is a system in which the food, the material, and the environment interact dynamically by incorporating oxygen scavengers, antimicrobials, and/or moisture adsorbents into the food packaging materials (Mlalila et al., 2016). These active compounds may or may not be released into the food, or prevent unwanted substances from entering the package, and, in so doing, they can improve product quality, ensure safety, and/or extend shelf life. For example, nanocomposite materials (i.e., polymers in combination with nanoparticles) provide both barrier and chemical protection to foods (Pradhan et al., 2015). “Smart” food packaging refers to a system that undergoes automatic changes in micro- or nanostructures as a consequence of dynamic changes to the environment (Mlalila et al., 2016). The materials that have smart properties are those able to control their interfacial properties. These largely consist of self-cleaning, self-cooling, and self-heating technologies, already designed for the health care sector, that are now being applied to food systems. Intelligent packaging systems are able to monitor the conditions, quality, and/or safety of a food, particularly during distribution and storage, and provide the consumer some evidence of product status (Mlalila et al., 2016). In some ways, this technology relates back to the biosensors discussed above in Opportunity 2. The output of intelligent packaging can be expressed in the form of data (e.g., at the level of specific product or lot using barcodes, RFID, or digital watermark) or as light (e.g., light-emitting diodes or holograms). All of these provide information that can then be included as a basis for decision-making and management systems. While monitoring food quality and freshness with indicators is routine in the food industry sector, intelligent packaging technologies are extremely well suited for detecting metabolites occurring as a consequence of food spoilage, and thus may
have relevance for reducing food loss and waste at both the industry and consumer levels (see example in Box 4-6). From the consumer perspective, communicative packaging has emerged as a potential tool to address concerns about product quality, safety, and the consumer demand for specific product information as they make purchasing decisions.
Although alternative food processing and packaging technologies have the potential to deliver better quality, nutrition, safety, and acceptability to food products, some questions related to the need to decrease the energetic footprint (e.g., energy and water savings, reliability) and environmental impacts (e.g., emissions or environmental degradation due to the use of plastic packaging materials) are unresolved. Likewise, acceptability of these
relatively new technologies by the consumer still poses questions. Relative to nanomaterials, consideration of potential unintended consequences of their use is critical. Safety concerns focus on the potential interactions between nanomaterials and the food matrix, particularly potential toxicity to consumers and environmental impacts. Because these materials are very recent in their introduction to the market, there are relatively few data available to systematically assess health or environmental risks, and legislators err on the side of caution when it comes to regulatory decision making. Similarly, consumer acceptance of new technologies may be an issue and depend ultimately on the degree of trust consumers place on the products themselves (Roosen et al., 2015).
4.1 Barrier 1: Consumer Acceptance
One important barrier to the implementation of technological advances in the food science and technology area is the need to better understand and anticipate consumers’ food-related behaviors and choices, including the role of social and environmental factors, and underlying receptiveness to and understanding of information about products and processes. Identifying factors that determine consumer acceptance and choices over product attributes and qualities is essential information to determining the success in producing foods that will be purchased and consumed (e.g., Lusk et al., 2014).
Traditionally, consumers respond to market prices and other monetary signals in their product selection. However, there is increased evidence that financial incentives (such as taxes and subsidies applied to products), social factors, and context of food choices, as well as other behavioral motivators or nudges can encourage or discourage food-related behavior. Ignoring the need to better understand and anticipate consumer food behaviors, drivers, and trade-offs may limit consumer acceptance of new products, technologies, and market innovations. The need to better account for consumers’ perceptions of risk around new technologies also underpins the need for education and strategies to best communicate the nature of food production, processing technologies, and the science involved so that consumers can make thoughtful and informed decisions in food selection, handling, and preparation. This applies to the need for effective food labeling approaches as well as basic communication about scientific and technological advances. A 2017 National Academies of Sciences, Engineering, and Medicine report (NASEM, 2017) highlights the need to understand the optimal communication approaches for use under different circumstances,
and to recognize that many people do not make food selection and choice decisions based solely on scientific evidence.
4.2 Barrier 2: Regulatory Context
Scientific advancements in technologies related to food processing and product design, packaging, and handling may be limited by existing regulations, such as food law and product identity standards. A few examples are provided here. Many of the emerging food processing technologies (i.e., ohmic heating, ultrasound, or pulsed light) have not been validated for their ability to meet the mandated microbial inactivation standards for protection of public health. It may not be prudent from food safety and liability standpoints to use these processes commercially until such validations are conducted and reviewed. The inclusion of nanotechnology-based products (e.g., in packaging materials and for microencapsulation) may be met with regulatory scrutiny because these are not composed of materials that are generally recognized as safe. There is also the possibility that sensor devices or novel packaging materials may be prohibited based on current food adulteration regulations. The replacement of pathogen culture methods with whole-genome sequencing is being questioned because historically, proof of product adulteration in recall or outbreak situations relies on having a pure culture of the implicated organism, not simply evidence of the presence of its DNA. The practical use of technologies intended to collect data at a faster rate may be hindered if they have negative effects on other aspects of the process that fall under regulatory scrutiny, such as adhering to maximum line speeds in meat processing plants. Integrated and blockchain data systems offer the opportunity to digitize record keeping, some of which may be relevant for regulatory purposes (e.g., data from hazard analysis and critical control points or other preventive controls plans). However, relevant agencies may not yet be able to accommodate transfer of information using their current data management systems.
4.3 Barrier 3: Economics and Other Considerations
A relatively large share of investment in innovation and technologies for foods is done through the private sector where private returns to investment dictate technology choice with less emphasis placed on the public benefit. However, there remains a critical need for basic sciences and applications in which the payoffs advance science more broadly to benefit the public’s and the private sector’s interests. Furthermore, some basic research requires significant investment in underlying infrastructure. As an example, system-wide innovation and data networks often require large, upfront expenditures to develop and support data infrastructures.
However, interoperability of systems and data networks between the various participants in the supply chain is required to effectively monitor and maintain the safety and integrity of the food system, and to support efforts to integrate sustainability opportunities. With funding predominantly from private sources, the allocation of resources to research and research infrastructure may not address the highest-priority public needs.
Several of the scientific advances discussed above will provide more improved instrumentation and allow for collection of more sophisticated data. Training will be necessary to ensure that the existing and emerging workforce has the scientific skills to use these instruments, analyze the data, and make appropriate decisions that capitalize on the value of these new technologies.
Ultimately, consumer practices and food choice will determine the ability of product and process development to successfully improve product safety, quality, and design. Advances in behavioral sciences and effective communication about science, technology, risk, and decision-making communication are required to underpin successful adoption in the market.
Emerging technologies (e.g., omics, biosensors, and nanotechnology) have the potential to advance or transform the production of high-quality, safe, nutritious, and sustainable food products that meet the needs and demands of a diverse consumer population. Solving the fundamental and applied scientific problems necessary to use these technologies more widely will require multidisciplinary collaboration and funding mechanisms. Research efforts need to be transdisciplinary, involving not only food scientists but also those in other disciplines ranging from data and computer science, engineering, synthetic biology, and the social sciences, and many more. The committee identified the following high-priority research areas:
- Profile and/or alter food traits for desirability (such as chemical composition, nutritional value, intentional and unintentional contamination, and quality and sensory attributes) via improvements in processing and packaging technologies, the design and functionality of sensors, and the application of “foodomic” technologies.
- Provide enhanced product quality, nutrient retention, safety, and consumer appeal in a cost-effective and efficient manner that also reduces environmental impact and food waste by developing, optimizing, and validating advanced food processing and packaging technologies.
- Support improved decision making to maximize food integrity, quality, safety, and traceability, as well as to reduce food loss and
waste by capitalizing on data analytics, integration, and the development of advanced decision support tools.
- Enhance consumer understanding and acceptance of innovations in food production, processing, and safe handling through expanded knowledge about consumer behavior and risk-related decisions and practices.
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