Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
Use of information and communication technologies to reduce food
waste
Uso de las tecnologías de la información y la comunicación para
reducir el desperdicio de alimentos
María Calzada Gento1
macalz01@ucm.es
https://orcid.org/0000-0002-6534-909X
José Ignacio Alonso Esteban2
jignacio.alonso@uah.es
https://orcid.org/0000-0002-1593-2631
Marta Mesías García3
mmesias@ictan.csic.es
https://orcid.org/0000-0003-0905-1587
Recibido: 08/03/2024; Aceptado: 12/062024
ABSTRACT
The food supply chain has shown certain deficiencies regarding food waste control and
prevention, mainly due to overproduction, lack of social awareness and commitment, and
poor coordination between different levels in the food chain. This fact is leading to
multiple environmental problems related to the emission of greenhouse gases, and
economic and social losses. The development of Information and Communication
Technologies (ICT) in the current digital era (Industry 4.0) contributes to greater
traceability of food products, obtaining safer and higher quality food and sustainable and
highly productive food systems. The use of ICT, together with changes in consumption
habits, waste management, and new government laws, are key to comprehensively
addressing the prevention of food losses and waste. The objective of this work is to carry
out a bibliographical review on the use of ICT as a technological improvement applied in
1 Graduate in Pharmay, Complutense University of Madrid, Madrid, Spain
2 Profesor Ayudante Doctor, University of Alcala, Alcala de Henares, Madrid, Spain
3 Científico Titular, Institute of Food Science, Technology and Nutrition (ICTAN-CSIC), Madrid, Spain
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
the food sector to help solve the problem of waste in all stages of the food chain, "from
farm to fork”.
Keywords: Information and Communication Technologies (ICT), digital technologies,
artificial intelligence, sustainability, food waste.
RESUMEN
La cadena de suministro de alimentos ha mostrado ciertas deficiencias en cuanto al
control y prevención del desperdicio de alimentos, debido principalmente a la
sobreproducción, la falta de conciencia y compromiso social y la mala coordinación entre
los diferentes niveles de la cadena alimentaria. Este hecho está provocando múltiples
problemas ambientales relacionados con la emisión de gases de efecto invernadero y
pérdidas económicas y sociales. El desarrollo de las Tecnologías de la Información y la
Comunicación (TIC) en la actual era digital (Industria 4.0) contribuye a una mayor
trazabilidad de los productos alimentarios, a la obtención de alimentos más seguros y de
mayor calidad y de sistemas alimentarios sostenibles y altamente productivos. El uso de
las TIC, junto con los cambios en los hábitos de consumo, la gestión de residuos y las
nuevas leyes gubernamentales, son clave para abordar de manera integral la prevención
de las pérdidas y el desperdicio de alimentos. El objetivo de este trabajo es realizar una
revisión bibliográfica sobre el uso de las TIC como mejora tecnológica aplicada en el sector
alimentario para ayudar a solucionar el problema del desperdicio en todas las etapas de
la cadena alimentaria, “de la granja a la mesa”.
Palabras clave: Tecnologías de la información y la comunicación (TIC), tecnologías
digitales, inteligencia artificial, sostenibilidad, desperdicio de alimentos.
Introduction
Currently, food waste is becoming a problem worldwide. The Food and Agriculture
Organization of the United Nations (FAO) first defined food waste as any product that is
discarded, lost, or degraded at any point in the food supply chain. It was subsequently
clarified that food waste is a component of food loss, referring to the discarding or
repurposing of food that is nutritious and safe for human consumption throughout the
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
entire food supply chain, from primary production to end consumer. Approximately one-
third of the global food production is estimated to go to waste (FAO, 2019). Within the
European Union, 20% of the food produced is wasted, which represents around 88 million
tons and a loss of 143 billion euros per year (EC, 2023).
From an ecological point of view, food waste represents 8% of anthropogenic greenhouse
gas emissions (European Parliament, 2017), with the release of 3.3 billion tons of CO2
each year (FAO, 2019). These emissions are caused in the production phases and in the
destruction of food and accumulation in landfills, due to the emission of methane gas
(Liegeard and Manning, 2020), which is the third most important source of human-
induced global warming and whose greenhouse effect is 25 times higher than that of
carbon dioxide. On the other hand, it must be considered the natural resources invested
in food that will ultimately be wasted, such as water, energy, and farmland (Liegeard and
Manning, 2020), which represents a loss equivalent to 936 billion dollars a year
(Ishangulyyev et al., 2019).
Food losses and waste are a sign of inefficient and uncoordinated functioning of food
systems and a lack of social awareness. However, a notable distinction can be observed
between developing countries and industrialized countries, as 40% of losses in developing
countries occur during the early stages of food manufacturing and processing, while in
industrialized nations, over 40% of losses occur during the later stages of retail and
consumption (FAO, 2019). In industrialized countries, consumers waste between 173 and
290 kg per capita annually (Bellemare et al., 2017). To combat this social problem, the
United Nations (UN) has included food waste as one of the Sustainable Development
Goals (SDG) proposed in the 2030 Agenda, specifically within SDG 12, which corresponds
to Responsible Production and Consumption. This Goal aims to change the current model
of production and consumption to achieve efficient management of natural resources,
implementing processes to prevent food loss, and promoting an ecological use of
chemical products and a reduction in waste generation. Regarding food waste, SDG 12.3
endeavors to cut per capita food waste at the retail and consumer level in half and reduce
food losses along the production and supply chains by 2030 (FAO, 2023).
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
Information and Communication Technologies (ICT) refer to a collection of technologies
that facilitate the access, production, treatment, and communication of information in
various formats, such as text, images, sound, etc., which moves around computing,
microelectronics, and telecommunications, not isolated but interconnected and in inter-
operational mode. The impact of ICT on environmental sustainability has been significant,
and these technologies can play a pivotal role in mitigating food waste. The fourth
industrial revolution (or Industry 4.0) introduces the concept of the Internet of Things
(IoT) and promotes the use of digital technologies such as artificial intelligence, robots,
big data analysis, etc., as tools to transform the operations of many companies and
encourage the creation of new business models, all with the ultimate objective of
enhancing profitability (Cane and Parra, 2020). The food industry requires an efficient
management strategy to attain improved quality, better process control, and optimal
utilization of raw materials, all of which are critical for preventing waste (Despoudi et al.,
2021). Digital technologies strive to ensure the coordination of various links in the food
supply chain, to help achieve an adequate shelf life in terms of food safety thanks to
greater traceability of the products (Stevens and Johnson, 2016), as well as to study and
facilitate the supply of the quantity of food to meet consumer demand while avoiding
overproduction, which is considered one of the main strategies to avoid food waste
(Tromp et al., 2016).
The current linear production system consists of the extraction of resources, industrial
manufacturing by companies, the use of products by consumers, and disposal, becoming
in most cases contaminant waste. The concept of circular economy advocates for a novel
approach to production and consumption, wherein the significance of products,
resources, and materials lies in their retention within the economy for as long as possible.
The strategy aims to minimize waste generation and optimize the utilization of
unavoidable waste to the greatest extent feasible (Plan de Acción de Economía Circular,
2021). All links in the food chain are responsible for preventing and reducing food waste,
including food production and processing (such as farmers, food manufacturers, and
processors), product distribution for consumption (such as the retail sector), and,
ultimately, consumers (Flores Pimentel, 2022). Food waste is primarily attributed to
households and processing, accounting for 47 and 17 million tons, respectively, which is
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
72% of the total food waste in the EU. The remaining 28% of waste comes from food
service (11 million tons, 12%), primary production (9 million tons, 10%), and wholesale
and retail (5 million tons, 5%) (as illustrated in Figure 1) (Stenmarck et al., 2016).
Undoubtedly, food waste occurs in all stages of the food supply chain, “from farm to fork”.
Figure 1. Distribution of European food waste in the different sectors of the food chain.
Source: Stenmarck et al. (2016)
In this context, the main objective of this work is to expose different technological
innovations applied in the different links of the food chain whose use can contribute to
reducing food waste and the environmental impact derived from it. A summary of the
applications of ICTs to reduce food waste in the different sectors of the food chain is
depicted in Figure 2.
Methods intended for food production
Traditionally the agricultural industry has depended on human labor with a limited
application of mechanical equipment and machinery (Baur and Iles, 2023). However,
several changes in today's society suggest a greater focus on the adoption of new
technologies such as robotics and autonomous systems (RAS) in the food supply chain.
Nowadays, it is estimated that more than 1.3-1.6 billion tons of food are wasted
throughout the entire food supply chain worldwide. These amounts are equivalent to
approximately one-third of the food production for human consumption of the world
population and more than one-quarter of the global agricultural production (Papaioannou
et al., 2022). In addition, the rise in the world population will increase the demand for
Production Processing Wholesale
and retail Food Service Household
Categoría 1 Categoría 2 Categoría 3 Categoría 4
11%
19%
5%
12%
53%
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
food by 56% by 2050, which requires improving productivity (van Dijk et al., 2021). These
aspects could be avoided with better automation and monitoring systems.
Figure 2. Applications of information and communication technologies to reduce food waste in the
different sectors of the food chain
Source: own elaboration
According to the International Organization for Standardization (ISO), an industrial robot
(IR) is a multifunctional, automatically controlled, reprogrammable manipulator in three
or more axes, which can be stationary or mobile for use in industrial automation
applications (ISO, 2012). Some of the benefits of using IR in food manufacturing are
increasing production rates, reducing cost (by eliminating expenses associated with labor
employment and insurance), decreasing production time (thanks to continuous
production avoiding employee delays due to fatigue, sick leave or vacation), improving
the quality and uniformity of products (due to its high precision), take care of the safety
of employees (by removing them from tedious tasks or unfavorable conditions), and also
improving hygiene and food safety (by eliminating human contact with food and reducing
incidents of cross-contamination). However, the uptake of RAS technologies in the food
processing industry is slow compared to other industries. IRs cannot replace humans,
rather they are a means to improve work environments and ensure efficient food
Production Processing
Wholesale
and retail
Food Service Household
Industrial applications
•Intelligent packaging
Time, temperatura, freshness and
integrity indicators
Smart Apps Smart fridgesE-agriculture
•Picking robots
•Soil sampling
(GPS)
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
production. In fact, societal perception is a major impediment to further implementation
(Bader and Rahimifard, 2020).
Picking robots
Harvesting is a fundamental agricultural activity. Most of the harvesting, whether of crops
or fruits, is still done manually. However, the use of RAS in time- and labor-intensive
agricultural tasks such as planting, spraying, pruning, and harvesting is raising with
increasing urbanization and the lack of farmers, and it is expected to be used in 20% of
future harvesting operations (Tang et al., 2020; Baur and Iles, 2023).
The development of harvesting robots is based on their artificial vision, 3D reconstruction
and positioning capabilities. Machine vision allows robots to actively and accurately
identify and locate targets from predetermined images, as well as to collect crop data,
including information about the environment surrounding the fruit, geometry, and 3D
coordinates (Daudelin et al., 2018). 3D reconstruction refers to the establishment of a
suitable mathematical model for the representation and computational processing of
spatial objects. However, picking robots still have many technical difficulties, which make
it difficult for most to achieve real commercial applications. Much of the fruit fields have
rough terrain with large obstacles, causing strong vibrations in the robots' vision system
as they traverse the terrain and inaccurate target tracking and imaging. This complication
requires the use of automatic image blurring and dynamic target tracking algorithms
(Tang et al., 2020) that are still under investigation. On the other hand, the objects can
have different sizes, shapes, colors and textures and the background and lighting of the
crops varies continuously. For that reason, harvesting robots must be able to adapt to
different types of crops and environmental changes (Zhao et al., 2016). Finally, the main
function of the robot is to harvest the desired ripe fruit without damaging the branches
or leaves of the tree.
Recently, Australian researchers have designed an autonomous apple-picking robot
capable of identifying apples from the tree, rotating them to break the stem, and dropping
them into a carrycot. The structure consists of a mechanical arm connected to a base, a
series of cameras, a four-fingered gripper, and a suction system to grab the apples. The
vision system allows it to identify 90% of the fruits of the tree in a distance of 1.2 meters
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
thanks to the set of "deep learning" algorithms (or deep learning) and also identifies
obstacles such as leaves and branches to calculate the optimal trajectory for extraction.
The robot takes 200 milliseconds to process each image and 7 seconds to pick each apple
if it goes to maximum performance, with a success rate of 85% of the achievable fruits. In
addition, only 6% of the total fruit collected suffers damage. It works regardless of
weather conditions and in all types of lighting, including strong sunlight and rain (Wang et
al., 2022). The application of all these robotic tools during food collection favors a
responsible and efficient collection of products, thus minimizing food waste.
Soil sampling
Soil sampling consists of analyzing and estimating the capacity of the soil to supply
nutrients such as sulfur, boron, zinc, etc. to meet the needs of growing crops through
technological systems. Different soil sampling kits based on Global Positioning Systems
(GPS) have been developed to indicate nutrient and contaminant content, composition,
and other characteristics such as volatile organic compounds, acidity, and pH level and to
recommend the precise use of soil fertilizers. Marking reference sample collection
locations using GPS helps reduce sampling variability in subsequent years and creates a
better picture of changes from year to year. From the data collected, a map is created
that is sent to the automated system that delivers the fertilizer to the field, so that the
amount of fertilizer necessary for efficient crop production is applied in each zone. The
technology used to carry out this procedure is known as variable rate fertilization
technology (VRFT) and includes a GPS integrated into a computer in the tractor cabin to
recognize the position of the vehicle in real-time and modify the dose of fertilizer (Sai
Mohan et al., 2021).
The cost of spreading fertilizer based on sampling and VRT is higher, but it must be taken
into account that excessive use of fertilizers can lead to increased emissions of nitrous
oxide, a potent greenhouse gas (Nolte, 2011). In addition, this method allows for what is
known as differential harvesting, which consists of harvesting selected areas of crops that
meet certain quality requirements (such as fruits and vegetables with different
characteristics of size, color, etc.) without damaging the remaining field to mature and
dry. This ultimately reduces food waste due to the collection of food that does not meet
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
the quality standards established by retailers. In fact, currently, one in three fruits and
vegetables is thrown away based solely on the collective beauty standards of consumers
(Grewal, 2019).
Methods intended for Industrial applications
In order to ensure food safety, microbiological and chemical tests are carried out during
food production at an industrial level, but the changes that occur after processing are
normally not monitored, leading to a great loss of products (Poyatos-Racionero et al.,
2017). An example of this fact is the deterioration of raw meat through the supply chain.
It is estimated that as much as 23% of production in the meat sector is lost and wasted,
the largest share being generated at the consumption level (64%), followed by
manufacturing (20%), distribution (12%), and primary production and post-harvest (~4%)
(Karwowska et al., 2021). On the other hand, consumers constantly demand less
processed, fresh, and high-quality food products, which require quality control during and
after processing (Heising et al., 2014).
Until a few years ago, food packaging only played a passive barrier role, protecting food
from those external factors capable of degrading it. However, there are currently
innovative intelligent packaging systems on the market capable of permanently
monitoring the quality status of food products and sharing information with the
consumer. This permanent monitoring not only minimizes unnecessary food waste by
improving food safety (Poyatos-Racionero et al., 2017) and reducing the risk of consumer
misinterpretation (Liegeard and Manning, 2020) but also improves traceability
throughout the supply chain, reduces time and material costs derived from the methods
of analysis of packaged foods, decreases the environmental impact, increases the
attractiveness of the packaging and maximizes the efficiency of the food industry
(Poyatos-Racionero et al., 2017).
Intelligent packaging
Smart packaging can be defined as systems that monitor the conditions of food during its
life cycle to provide information related to the quality of packaged products (Heising et
al., 2014). The quality of food products changes during their life cycle since foods are
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
perishable by nature, and the changes depend on the type of product, the type of
packaging and the conditions in the supply chain. However, it must be taken into account
that smart packaging increases the cost and therefore it will only be profitable if the
income from the increase in sales and/or the reduction of waste is greater than the
increase in price. This makes the cost and shelf life of the food important criteria for its
application. In products with relatively stable intrinsic quality attributes and long shelf life
(such as soft drinks, canned goods, and sweets) it is not appropriate to incorporate smart
packaging. However, expensive and highly perishable products (such as meat or fish) are
the main application areas since their intrinsic quality properties change rapidly after
processing, are difficult to estimate, and cause significant economic losses from spoilage
of these foods. In addition, the compatibility of the systems with the food must be
monitored (Heising et al., 2014).
In the European Union, the development and application of smart packaging is still
limited, although it is a very dynamic field with continuous advances (Realini and Marcos,
2014). Currently, packaging is generally authorized under Regulation (EC) No. 1935/2004
on materials and objects intended to come into contact with foods, which defines the
special requirements that must be met. The main barriers to use that have prevented a
more significant implementation in the market are the technical limitations associated
with the use of these technologies, the high cost of packaging (which can amount to 50-
100% of the total value of the final product) and the lack of consumer confidence in the
security of these systems (Realini and Marcos, 2014). Another aspect that still needs to
be clarified is the recycling of packaging since the additional waste generated by the
installation and production of smart packaging is actually contradictory to the objective
of reducing the amount of food wasted.
The intelligent function can be obtained through indicators or sensors (Realini and
Marcos, 2014), which determine the presence, absence, or concentration of a substance
in the food or the intensity of a certain reaction, with the following differentiation: the
sensors measure a parameter or identify analytes in the food but must be connected to
an external device to convert the sensor signal into an observable response, while
indicators integrate the measurement system and provide information through directly
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
visible change (for example, different color intensities). There are three types of indicators
or sensors used in smart packaging (Heising et al., 2014):
- Environmental conditions: these indicators monitor the conditions that may give rise
to changes in the quality characteristics of food, for example, time and temperature
indicators, gas leak indicators, and relative humidity sensors. Depending on the
monitoring factor, the systems are placed inside or outside the package.
- Quality attributes: indicators directly monitor quality attributes of the product itself,
for example, biosensors and freshness indicators. They are generally located inside the
container.
- Data carriers: the function of these indicators is to guarantee traceability, automation,
protection against theft or protection against counterfeiting of food products, with the
ultimate goal of making the flow of information within the supply chain more efficient
(Müller and Schmid, 2019). They include barcode labels and radio frequency identification
(RFID) tags and are often placed on tertiary packaging. Data carriers will not be developed
in this work.
Time and Temperature Indicators (TTI)
Temperature is one of the most important environmental factors in determining food
preservation. Deviations in it can cause the growth of microorganisms and compromise
the safety and shelf life of food products. In addition, incorrect freezing can denature the
proteins in meat or other products (Zhang et al., 2023). TTIs detect mechanical, physical,
chemical, electrochemical, enzymatic, or microbiological changes that depend on the time
elapsed since packaging and that accelerate with increasing temperature (Poyatos-
Racionero et al., 2017). The measured values are expressed as visual changes, such as
color changes or mechanical deformations, which must be irreversible and correlate well
with the rate of deterioration of the quality of the food. They allow continuous monitoring
of storage conditions and, consequently, they can report a break in the cold chain and be
used as indirect indicators of shelf life.
Three types of TTI can be found: critical temperature indicators that reveal whether
products have been heated above or cooled below a permissible temperature; partial
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
history indicators that indicate if a product has been subjected to a temperature that
causes a change in its quality; and full history indicators that record the entire
temperature profile throughout the food supply chain (Müller and Schmid, 2019). An
example of a full history TTI is Fresh-Check, based on a polymerization reaction. A clear
center indicates that the TTI is new; if the color matches the outer ring, the product must
be consumed soon, and if the center is dark, the freshness of the food is no longer
guaranteed (Müller and Schmid, 2019).
Freshness indicators
Freshness indicators monitor the quality of fresh food products during storage and
transportation by reporting changes that occur because of microbial growth or
metabolism. Reasons for loss of freshness may be unfavorable conditions or exceeded
durability. Changes in the concentration of metabolites such as glucose, organic acids (e.g.
L-lactic acid), ethanol, carbon dioxide, biogenic amines, volatile nitrogen, ATP degradation
products, or sulfuric compounds during storage indicate microbial growth, and then they
can be used as indicators of freshness (Arvanitoyannis and Stratakos, 2012). On the
market, freshness indicators used in smart packaging can be based on the indirect
detection of metabolites through color indicators (for example pH) or direct detection of
target metabolites using biosensors (Realini and Marcos, 2014).
An example of an anthocyanin-based pH detection freshness indicator is SensorQ™,
capable of reporting the formation of biogenic amines of microbiological origin in
packaged meat and poultry, although it has not achieved successful commercialization.
The label detects the gaseous byproducts of foodborne bacteria growing inside the
package and indicates that a critical level of bacterial growth has been reached by a color
change (from orange to brown) (Panjagari et al., 2021).
A biosensor is a device that analyzes the concentration of a specific target component
with a biological sensing element. Thus, it converts a biologically induced recognition
event (for example, based on an antibody, an enzyme, or a microorganism) into a
detectable signal through a transducer that converts the biochemical signals into a
quantifiable electrical response (Naresh and Lee, 2021). An example is the Food Sentinel
System, developed to detect foodborne pathogens with a specific antibody attached to a
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
membrane that forms part of a barcode. The presence of contaminating bacteria causes
the formation of a localized dark bar, making the barcode unreadable when scanned.
Integrity indicators
The simplest integrity indicators are time indicators, which report how long a product has
been open. The tag is activated at the time of consumption so that when the seal is
broken, a timer is triggered and the indicator changes color over time (Realini and Marcos,
2014).
Gas indicators report on the quality status of a food product based on the indoor
atmosphere. Its usefulness is associated with the use of modified atmospheres in
packaging (MAP), which usually replace oxygen with other gases (such as carbon dioxide
or nitrogen) to increase the shelf life of food products. For this reason, most indicators
monitor oxygen and carbon dioxide concentrations, although others also monitor water
vapor, ethanol, hydrogen sulfide, and other gases whose concentrations closely correlate
with the progress of food spoilage (Meng et al., 2014). As an example, MAP packaging for
meat products typically consists of high CO2 levels (20–80%) and a residual O2
concentration (<0.1–1%, depending on the efficiency of the packaging equipment)
(Realini and Marcos, 2014), therefore oxygen indicators are often used to detect leaks.
The most common are colorimetric indicators, which consist of a redox dye (such as
methylene blue) and a strong reducing agent (such as glucose in an alkaline medium). In
the absence of oxygen (concentrations 0.1%), most of the dye is in the reduced and
colorless state, but in the presence of levels greater than or equal to 0.5%, the dye is
oxidized and is observed a color change.
Methods intended for wholesale and retail and for food services
In Europe, restaurants are the second source of food waste generated at consumption
following households. Similarly, in the US, families and restaurants waste approximately
39 million tons of food per year (Principato et al., 2018). The development of ICT has
radically changed the life cycle of food, with the appearance of numerous websites and
applications to share and redistribute foods. These types of apps only help address
symptoms, but raising awareness and taking action is the first step towards more
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
sustainable behavior to reduce food waste. The motivations for their use must go beyond
reducing food waste to make them useful and desirable for long-term use. For example,
the Too Good To Go app allows users to save money while saving food, and thus reducing
food waste, which is a secondary positive (Nguyen, 2020).
Too Good To Go
Too Good To Go (TGTG) was born in Denmark in 2016. The app uses the user's geographic
location to offer them a "surprise pack" of nearby local stores at a reduced Price. The pack
contains food that has not been sold and would otherwise have been wasted and that the
user must pick up at set times. “Surprise packs” are so called because the consumer does
not know in advance what products are included, which depends on the food that is going
to be wasted that day and that the establishment cannot predict (Vo-Thanh et al., 2021).
TGTG is the leading application in the fight against food waste. Social, functional, and
emotional values are the success factors for TGTG to fulfill its social mission of reducing
food waste, which consequently helps reduce carbon dioxide emissions. On the other
hand, it makes it possible to obtain an economic gain since its objective is to create a link
between clients and producers, which can sell products that are going to be thrown away
and recover expenses, while consumers save money (Nguyen, 2020). In addition, this app
contributes to addressing social needs by allowing all people to have access to quality
food at an affordable price that they may not be able to afford. Likewise, it helps users to
acquire a sense of citizenship and social responsibility. Finally, it allows food companies
to increase exposure to new customers, be more respectful of the environment, and
strengthen their brand image (van der Haar et al., 2019).
Survey results from an experimental study indicated three main motivations for using the
TGTG app by consumers: the desire to reduce food waste, save money, and have a
surprise experience. On the other hand, despite the fact that customers pay a reduced
price for a "surprise pack", 48% valued the purchased foods as a normal purchase, 46%
valued it positively for avoiding food waste and only 7% rated the food as inferior to other
regular products (Vo-Thanh et al., 2021). Results of other investigations also indicate that
58% of TGTG users have visited new establishments thanks to this application, of which
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
76% return as regular customer,s and therefore the application is a very useful
commercial tool for business (van der Haar, 2019).
Methods intended for household
As mentioned before, 53% of food waste occurs at the household level (Stenmarck et al.,
2016), of which up to 45.8% is avoidable and is related to the awareness and behavior of
users (Cappelletti et al., 2022). For that reason, the development of strategies to prevent
food waste is considered the first step to achieving ambitious goals in terms of
sustainability, rather than focusing on the waste itself and how it is dealt with (Martin-
Rios et al., 2018).
The main cause of waste by consumers is the acquisition of more food than necessary due
to a lack of planning when buying and a lack of knowledge about the amounts of food
consumed at home (Aschemannn-Witzel et al., 2015). Another of the main factors (15-
30%) is the misunderstanding of the labeling (or lack of clarity of the product information)
and the confusion between the expiration date and the best-before date (European
Commission, 2016). At this point, it should also be mentioned the careless attitude of
consumers regarding the proper storage and preservation of food, the anxiety about food
safety, the limited awareness of the population about the scope of the problem, and the
related environmental impact and the strategies of market, which encourage consumers
to buy more products than necessary (Cappelletti et al., 2022). It is important to teach
consumers how to buy, store, prepare and dispose of food in a more sustainable way.
With this purpose, smart applications can be incorporated into the home, which have
been proven to be an effective tool to reduce food waste.
Smart refrigerators
Smart refrigerators are able to connect to the internet and interact with consumers even
from outside the home. Brands such as LG, Samsung, Siemens or Bosch have been
incorporating them into their catalog of refrigerators, in order to answer the following
questions: what products are there at home?, in what quantity?, where are they stored?,
when were they purchased?, when did they expire?, were foods consumed?, and if so,
totally or partially? (Cappelletti et al., 2022). It is important that the system be as intrusive
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
as possible for users and that they are the center of the development process so that each
new feature is incorporated according to their needs and requirements to be understood
and accepted by consumers. These systems add additional functions to conventional
refrigerators that can help reduce waste by monitoring data related to food products and
preventing them from being thrown away. The most prominent are:
- Tracking of the food stored in the fridge, the quantity available, and the date of
purchase, opening, and/or expiration. The smart expiration system can send push
notifications to the mobile phone so that if something is expired, a warning message
prompts the user to discard the item. However, in the case of the best-before date, the
system recommends smelling or tasting the product before wasting it. Regarding products
that are about to expire, the application can notify the consumer with visual alerts (for
example, the expiration date turns red) and if a new product of the same type is
purchased, remember to consume the oldest one in advance. At this point, it is important
to note that the system should not overload users with warning messages (Cappelletti et
al., 2022).
- Self-regulation of internal environmental conditions and proper placement of food in
the refrigerator, both in order to optimize storage. This helps to increase shelf-life of
foods, which will consequently lead to less waste (Liegeard and Manning, 2020).
- Management of the shopping list, remembering the products available at home, with
relative quantities and expiration dates. Access to refrigerator information (for example,
through a webcam) is possible from remote locations, which allows knowing the amount
of food need to buy, if the shopping list has not been made. In addition, periodic products
(considered those that are purchased at least once a week) can be added to the list of
suggested products based on the remaining quantity (Cappelletti et al., 2022).
- Intelligent suggestion of recipes, ordering them according to the maximum number of
ingredients available at home and those closest to their expiration date (Cappelletti et al.,
2022).
Although they provide multiple advantages, there are limitations to their entry into the
market, mainly including price, privacy concerns, data ownership and lack of control by
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
consumers, and the time and effort required to learn how to use them, as well as the
problematic in homes with low Internet connection (Chan et al., 2009). Despite its high
cost, some research has suggested that the incorporation of these systems could save
families up to 2.23% of their weekly purchase (Figure 3) (Chan et al., 2009).
Figure 3. Weekly cost related to foods in a four-member family and save by the technological solution
implemented in the fridge.
Source: Cappelletti et al. (2022)
Conclusions
Food overproduction and waste is a global problem in which all links in the food chain are
involved, with great environmental, economic, and social repercussions. There is no single
treatment to address the problem, and yet a transition to a more sustainable model is
necessary to avoid the current environmental crisis. The food sector is constantly evolving
due to growing demand, consumer needs, and the development of new technologies. The
digital revolution of recent years (Industry 4.0) and the development of Information and
Communication Technologies (ICT) are essential tools to fight against waste and
contribute to optimizing processes, improving traceability, favoring coordination
Categoría 1 Categoría 2 Categoría 3
192.91 €
7.74 €
33.06 € 27.84 €
233.71 €
228.49 €
Save 2.23% peer week
Food production Use End of life
Food production. Cost related to the purchase of food
Use. Cost related to the use of the household appliances to store and prepare meals
End of life. Cost related to the food purchased, but not consumed
without with technological solution implemented in the fridge
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
throughout the supply chain, as well as developing solutions for future challenges.
Technical innovations such as the application of robotics in agriculture, the development
of smart packaging and refrigerators, and the use of new mobile applications are some of
the examples proposed to achieve greater efficiency and productivity and establish new
forms of interaction, involving the consumer as an end-user, since a change in habits and
responsible consumption would significantly help to reduce waste as a joint task of
society. Future research should be aimed at improving the feasibility of ICT in the food
sector and giving visibility to existing ones.
Bibliographical references
Arvanitoyannis, I. S., & Stratakos, A. C. (2012). Application of modified atmosphere
packaging and active/smart technologies to red meat and poultry: A review. Food
Bioprocess Technology, 5, 1423–1446. https://doi.org/10.1007/s11947-012-0803-z
Aschemannn-Witzel, J., de Hooge, I., Amani, P., et al. (2015). Consumer-Related Food
Waste: Causes and Potential for Action. Sustainability, 7, 6457–6477.
https://doi.org/10.3390/su7066457
Bader, F., & Rahimifard, S. (2020). A methodology for the selection of industrial robots in
food handling. Innovative Food Science and Emerging Technologies, 64, 102379.
https://doi.org/10.1016/j.ifset.2020.102379
Baur, P., & Iles, A. (2023). Replacing humans with machines: a historical look at technology
politics in California agriculture. Agriculture and Human Values, 40, 113–140.
https://doi.org/10.1007/s10460-022-10341-2
Bellemare, M. F., Çakir, M., Peterson, H. H., et al. (2017). On the measurement of food
waste. Am J Agric Econ.:99:1148–1158.
Cane, M., & Parra, C. (2020). Digital platforms: Mapping the territory of new technologies
to fight food waste. British Food Journal, 122, 1647–1669.
https://doi.org/10.1108/BFJ-06-2019-0391
Cappelletti, F., Papetti, A., Rossi, M., et al. (2022). Smart strategies for household food
waste management. Procedia Computer Science, 200, 887–895.
https://doi.org/10.1016/j.procs.2022.01.286
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
Chan, M., Campo, E., Estève, D., et al. (2009). Smart homes-current features and future
perspectives. Maturitas, 64, 90–97.
https://doi.org/10.1016/j.maturitas.2009.07.014
Daudelin, J., Jing, G., Tosun, T., et al. (2018). An integrated system for perception-driven
autonomy with modular robots. Science Robotics, 3, 1–6.
https://doi.org/10.1126/scirobotics.aat49
Despoudi, E., Bucatariu, C., Otles, S., et al. (2021). Food waste management, valorization,
and sustainability in the food industry. In Galanakis, C. M. (Ed.) Food Waste Recovery.
Processing Technologies, Industrial Techniques, and Applications (pp. 3-19).
Academic Press.
EC (European Commission). (2023). EU Food Loss and Waste Prevention Hub.
https://ec.europa.eu/food/safety/food_waste/eu-food-loss-waste-prevention-
hub/about
European Commission Directorate-General for Health and Food Safety (2016). Market
study on date marketing and other information provided on food labels and food
waste prevention.
European Parliament (2017). European Parliament resolution of 16 May 2017 on initiative
on resource efficiency: reducing food waste, improving food safety. Official Journal
of the European Union, 307, 25–43.
FAO (Food and Agriculture Organization of the United Nations) (2023). Sustainable
development goals, https://www.fao.org/sustainable-development-goals/en/
FAO (Food and Agriculture Organization of the United Nations) (2019). The state of food
and agriculture: Moving forward on food lost and waste.
https://www.fao.org/3/ca6030en/ca6030en.pdf
Flores Pimentel, B., Misopoulos, F., & Davies, J. (2022). A review of factors reducing waste
in the food supply chain: The retailer perspective. Cleaner Waste Systems, 3, 100028.
https://doi.org/10.1016/j.clwas.2022.100028
Grewal, L. (2019). The Self-Perception Connection: Why Consumers Devalue Unattractive
Produce. Journal of Marketing, 83, 89–107.
https://doi.org/10.1177/0022242918816319
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
Heising, J. K., Dekker, M., Bartels, P. V., et al. (2014). Monitoring the quality of pershable
foods: Opportunities for intelligent packaging. Critical Review in Food Science and
Nutrition, 54, 645–654. https://doi.org/10.1080/10408398.2011.600477
Ishangulyyev, R., Kim, S., & Lee, H. (2019). Understanding Food Loss and Waste. Why Are
We Losing and Wasting Food? Foods, 8(8), 297.
https://doi.org/10.3390/foods8080297
ISO 8373:2012 (2012). Robots and robotic devices – Vocabulary.
https://www.iso.org/standard/55890.html
Karwowska, M., Łaba, S., & Szczepanski, K. (2021). Food Loss and Waste in Meat Sector.
Why the Consumption Stage Generates the Most Losses? Sustainability, 13, 6227.
https://doi.org/10.3390/su13116227
Liegeard, J., & Manning, J. (2020). Use of intelligent applications to reduce household food
waste. Critical Review in Food Science and Nutrition, 60, 1048–1061.
https://doi.org/10.1080/10408398.2018.1556580
Martin-Rios, C., Demen-Meier, C., & Gössling, S. (2018). Food waste management
innovations in the foodservice industry. Waste Management, 79, 196–206.
https://doi.org/10.1016/j.wasman.2018.07.033
Meng, X., Kim, S., Pulingundla, P., et al. (2014). Carbon dioxide and oxygen gas sensors-
possible application for monitoring quality, freshness and safety of agricultural and
food products with emphasis on importance of analytical signals and their
transformation. Journal of the Korean Society for Applied Biological Chemistry, 57,
723–733. https://doi.org/10.1007/s13765-014-4180-3
Müller, P., & Schmid, M. (2019). Intelligent Packaging in the Food Sector: A Brief Overview.
Foods, 8(1), 16. https://doi.org/10.3390/foods8010016
Naresh, V., & Lee, N. (2021). A Review on Biosensors and Recent Development of
Nanostructured Materials-Enabled Biosensors. Sensors (Basel), 21(4), 1109.
https://doi: 10.3390/s21041109
Nguyen, T. H. (2020). Employing gamification to support sustainable food consumption.
Analysis and Redesign of the Too Good To Go mobile app. [Master thesis, Umea
University].
Revista Científica Ciencia y Tecnología Vol 24 No 43
http://cienciaytecnologia.uteg.edu.ec
Nolte, K. D. (2011). The integration of variable rate technologies for a soil applied
herbicide in leafy green production. Journal of Soil Science and Environmental
Management, 2,159–166.
Panjagari, N. R., Raman, R. K., Uma, K., et al. (2021). Freshness indicators for real-time
quality evaluation of packaged animal origin foods: A mini-review. Indian Journal of
Animal Health, 60(2), 153–166. https://doi.org/10.36062/ijah.2021.spl.02421
Papaioannou, H., Mazzei, R., Bazzarelli, F., et al. (2022). Agri-Food Industry Waste as
Resource of Chemicals: The Role of Membrane Technology in Their Sustainable
Recycling. Sustainability, 14, 1483. https://doi.org/10.3390/su14031483
Plan de Acción de Economía Circular 2021-2023 (2021).
https://www.miteco.gob.es/es/calidad-y-evaluacion-ambiental/temas/economia-
circular/plan_accion_eco_circular_def_nipo_tcm30-529618.pdf
Poyatos-Racionero, E., Ros-Lis, J. V., & Vivancos, J. L. (2017). Recent advances on
intelligent packaging as tools to reduce food waste. Journal of Cleaner Production,
172, 3398–3409. https://doi.org/10.1016/j.jclepro.2017.11.075
Principato, L., Pratesi, C. A., & Secondi, L. (2018). Towards Zero Waste: an Exploratory
Study on Restaurant managers. International Journal of Hospitality Management, 74,
130–137. https://doi.org/10.1016/j.ijhm.2018.02.022
Realini, C. E., & Marcos, B. (2014). Active and intelligent packaging systems for a modern
society. Meat Science, 98, 404–419. https://doi.org/10.1016/j.meatsci.2014.06.031
Sai Mohan, S., Ajay, A., & Jayan, P. R. (2021). GPS and Sensor Based Technologies in
Variable Rate Fertilizer Applications. International Journal of Agriculture Environment
and Biotechnology, 14, 21–27. https://doi.org/10.30954/0974-1712.01.2021.4
Stenmarck, G., Jensen, A., Quested, C., et al. (2016). Moates T. Estimates of European
Food Waste Levels. IVL Swedish Environmental Research Institute: Stockholm,
Sweden.
Stevens, G. C., & Johnson, M. (2016). Intergrating the supply chain.25 years on.
International Journal of Physical Distribution & Logistics Management, 46, 19–42.
https://doi.org/10.1108/IJPDLM-07-2015-0175
Calzada, Alonso, Mesías.
Use of information and communication technologies to reduce food waste
Tang, Y., Chen, M., Wang, C., et al. (2020). Recognition and Localization Methods for
Vision-Based Fruit Picking Robots: A Review. Frontiers in Plant Science, 11, 510.
https://doi.org/10.3389/fpls.2020.00510
Tromp, S. O., Haijema, R., Rijgersber, H., et al. (2016). A systematic approach to preventing
chilled-food waste at the retail outlet. International Journal of Production Economics,
182, 508–518. https://doi.org/10.1016/j.ijpe.2016.10.003
van der Haar, S., & Zeinstra, G. (2019). The impact of Too Good To Go on food waste
reduction at the consumer household level: An explorative study. Food & Biobased
Research. Wageningen University.
van Dijk, M., Morley, T., Rau, M. L., et al. (2021). A meta-analysis of projected global food
demand and population at risk of hunger for the period 2010–2050. Nature, 2, 494–
501.
Vo-Thanh, T., Zaman, M., Hasan, R., et al. (2021). How a mobile app can become a catalyst
for sustainable social business: The case of Too Good To Go. Technological
Forecasting and Social Change, 171, 1–12.
https://doi.org/10.1016/j.techfore.2021.120962
Wang, X., Kang, H., Zhou, H., et al. (2022). Geometry-aware fruit grasping estimation for
robotic harvesting in apple orchards. Computers and Electronics in Agriculture, 193,
106716. https://doi.org/10.1016/j.compag.2022.106716
Zhang, G., Zhu, C., Walayat, N., et al. (2023). Recent development in evaluation methods,
influencing factors and control measures for freeze denaturation of food protein.
Critical Reviews in Food Science and Nutrition, 63, 5874–5889
https://doi.org/10.1080/10408398.2022.2025534
Zhao, Y., Gong, L., Huang, Y., et al. (2016). Robust tomato recognition for robotic
harvesting using feature images fusion. Sensors, 16, 1–12.
https://doi.org/10.3390/s16020173
