Content uploaded by Christopher Malefors
Author content
All content in this area was uploaded by Christopher Malefors on Nov 07, 2023
Content may be subject to copyright.
Resources, Conservation & Recycling 200 (2024) 107288
0921-3449/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Full length article
Automated quantication tool to monitor plate waste in school canteens
Christopher Malefors
*
, Erik Svensson , Mattias Eriksson
Department of Energy and Technology, Swedish University of Agricultural Sciences, Box 7032, Uppsala SE-75007 Uppsala, Sweden
ARTICLE INFO
Keywords:
Canteen
Meal planning
Sustainability aspects
Food waste
Public sector
Food service
School meal
ABSTRACT
Automated tools for waste quantication hold promise in providing preciser understanding of food waste. This
study evaluated a tool to quantify plate waste in primary school canteens. It encompassed data from 421,015
instances of food wastage. The evaluation revealed high accuracy, with the tool’s plate waste detection falling
within ±10% of manual recordings. However, the tool estimated 40% fewer individual guests compared to
manual entry due to not all students wasting food. As a result, the automatically collected data indicated a 35%
higher waste-to-guest ratio. The ndings showed that a minority of students (20%) accounted for a majority
(60%) of plate waste. Halving the waste generated by this group would reduce overall plate waste by 31%,
emphasizing the importance of tailored interventions for high-prole wasters rather than applying general
measures to all students. Targeting areas with the greatest potential can contribute to a more sustainable food
system with reduced waste.
1. Introduction
Current population and consumption trajectories stress the impor-
tance of nding solutions that meet the increased demand for energy,
fuel, clothes, and food in a fair and sustainable way (Raworth, 2012).
Changing behavior is critical to sustainable development (Bergquist
et al., 2023; Gosnell and Bazilian, 2021), so the idea of behavior nudging
has emerged as a viable option in place of, or in addition to, legislative
tools inuencing behavior, such as taxes, bans, and information.
Another way to get people to change their behavior is to use digital tools
and gamication that measure consumption and provide information on
environmental footprint, as feedback on consumer actions, to guide
change (Fraternali et al., 2019; Froehlich et al., 2010; Gram-Hanssen,
2014; Guillen et al., 2021; Koivisto and Hamari, 2019).
Reduced food waste has been recognized as a key step in transition to
a more sustainable food system (IPCC, 2019; Springmann et al., 2018).
The majority of global food waste is generated by consumers (United
Nations Environment Programme, 2021), which provides an opportu-
nity to utilize nudging and gamication as a means of guiding con-
sumers towards meeting the UN Sustainable Development Goal of
halving food waste by 2030 (United Nations, 2015). In addition, it is
essential to have tools and techniques that can effectively monitor
changes and assist in collecting primary data (Xue et al., 2017).
The food service sector, which serves food to consumers in various
formats, typically has two types of food waste problems; i) food waste
arising within the kitchen and the serving process; and ii) plate waste
left by consumers (Malefors et al., 2019). Previous food service sector
studies have shown that total food waste can be around 15 g/guest or
980 g/sale (Abdelaal et al., 2019; Juvan et al., 2017). However, some
studies indicate that waste levels can show considerable variation be-
tween different canteens within the same organization, e.g., Eriksson
et al. (2017) found that food waste level in the best-performing canteens
was only 25 % of that in canteens with the most food waste.
At present, food waste quantication is mainly performed using
manual methods, such as pen and paper or software applications
(Boschini et al., 2018; Eriksson et al., 2019) and emerging technologies
that utilize computers or tablets connected to weighing scales, enabling
users to determine mass of food waste (e.g., (Leverenz et al., 2020; WWF
Germany, 2020). The latest advances in the eld employ highly auto-
mated tools (provided by companies such as Leanpath and Kitro) that
use image recognition software to automatically categorize food waste
and its associated mass, as detected by linked weighing scales. Moni-
toring tools and methods are crucial to determine whether a reduction in
food waste is being achieved, but few studies to date have examined the
potential for utilizing automated quantication methods. While quan-
tication is essential, it is equally important that the resulting infor-
mation is promptly communicated to the relevant group for action, so
that waste can be avoided (Aschemann-Witzel et al., 2023). To achieve
this, gamication and nudging techniques could be useful motivating
tools.
* Corresponding author.
E-mail address: christopher.malefors@slu.se (C. Malefors).
Contents lists available at ScienceDirect
Resources, Conservation & Recycling
journal homepage: www.elsevier.com/locate/resconrec
https://doi.org/10.1016/j.resconrec.2023.107288
Received 16 June 2023; Received in revised form 23 October 2023; Accepted 23 October 2023
Resources, Conservation & Recycling 200 (2024) 107288
2
To reduce food waste within the food service sector, it is common to
have information campaigns that target food waste, based on the
argument that if all guests and staff are well-informed, they will waste
less food. It has been shown in a university setting that students who
receive information about food waste can achieve a waste reduction of
15 % (Whitehair et al., 2013). However, only 40 % of the students
approached in that study agreed to participate and let their tray waste be
quantied. Information campaigns run together with some nudging
schemes have also been explored. For instance, Dolnicar et al. (2020)
reduced plate waste in sun-and-beach hotel restaurants with a
game-based intervention. Removing trays and reducing plate size have
also been shown to reduce plate waste (Kallbekken and Sælen, 2013;
Thiagarajah and Getty, 2013; Obersteiner et al., 2021). Some studies
suggest that the shape of a plate, specically transitioning from round to
oval, can reduce plate waste (Richardson et al., 2021). Other studies
have found that manipulating plate size has no impact on waste (Qi
et al., 2022). A study using communication tools in the proper context
saw a reduction of 14.4 % in edible plate waste generated by hotel guests
(Antonschmidt and Lund-Durlacher, 2021). A similar nding was made
by Cozzio et al. (2021), who concluded that message-based appeals
could nudge hotel guests towards more active engagement in avoiding
food waste. Nudging has also been shown to be a successful measure in
school canteens, where such strategies were found to prevent 41 % of
plate waste, resulting in 27.2 g of food waste per portion according to
Vidal-Mones et al. (2022). Other studies have used digital tools to
interact with guests, e.g., Malefors et al. (2022) used a tablet computer
connected to a kitchen scale to monitor the amount of plate waste
produced by each student. The tablet provided instant feedback to
guests regarding the amount of food they were wasting and the envi-
ronmental impact of this waste, using a combination of gamication,
nudging, and food waste quantication. This intervention yielded a
reduction in plate waste, from 19 g per portion to just 12 g per portion
(Malefors et al., 2022). However, most of the successful interventions
described in the literature are based on the assumption that customers in
a restaurant have the same lack of knowledge, or will react to the same
nudges. Additionally, deploying multiple nudges simultaneously could
lead to synergistic or compensatory interactions (Qi et al., 2022).
Considering the large variation observed between canteens, organiza-
tions, and products in various studies (e.g., Brancoli et al., 2019;
Eriksson et al., 2023, 2014), there is a strong risk of large variation also
between different consumers, meaning that interventions in many cases
could be wasted on consumers who are already aware or who do not
waste food.
The aims of this study were to identify food waste patterns among
pupils dining in Swedish school canteens, and to evaluate the accuracy
of using an automated quantication and feedback tool for food waste
quantication purposes. Knowledge of food waste patterns and waste
amounts is important in understanding guest characteristics when
designing intervention schemes to create a food system with less food
waste. Evaluation of automated quantication tools is important to
understand the limitations and potential of using this kind of method to
move beyond time-consuming manual data collection procedures that
are current practice today (if food waste data are collected at all).
2. Material and methods
2.1. Description of data collection and study material
The material analyzed in this study comprised plate waste data
collected in 16 Swedish primary school canteens spread out geograph-
ically in ve different municipalities. Each of the ve municipalities
operated on a 5–7-week menu rotation, implying a dish would reappear
after 5–7 weeks. This study denes plate waste as “All waste from the
plates of guests. May contain inedible part such as bones and peels”
(Malefors et al., 2019; Swedish National Food Agency, 2020). All plate
waste data collected originated from a “plate waste tracker”, a tool used
by the kitchens to make guests more aware of their plate waste, with the
ambition to lower this waste fraction. The plate waste tracker used
consists of a set of weighing scales (2 g resolution) connected to a tablet
computer running dedicated software which interacts with the guests.
The scales are positioned under a bin into which the guests throw the
food remains from their plates. Each time a weight difference occurs, the
mass of this weight change is recorded, along with a time stamp. The
interface displays how much food each guest is throwing away and the
impact of this waste in terms that the guests can relate to. The idea is that
the guests are nudged to change their behavior over time to waste less if
they get feedback on how much they are wasting. Fig. 1 gives an over-
view over the concept for the plate waste tracker and how it interacts
with guests.
To nudge guests to waste less, the interface displays different mes-
sages depending on how much each individual guest is wasting. If a
guest throws away more than 70 g, the interface shows a message with a
red background asking the guest to waste less next time and stating the
amount of food discarded. If a guest throws away 20–70 g, the same
message about throwing away less food is shown, but with an orange
background. If the guest throw away less than 20 g, the feedback is that
the guest created little food waste. The interface also allows the guest to
provide feedback on why they wasted food, with some predened al-
ternatives such as “I did not like it/It was not to my taste”, “I took too
much food”, “I did not have time to nish my meal”, “I ate it all, thanks
for the food”. Guests can provide multiple answers, but it’s preferable
that they offer just one.
In addition to visually representing the individual contribution of
plate waste from each guest, the visualization also presents guests with
the total plate waste generated during the current meal. This informa-
tion is then compared to the waste generated on the previous day within
the same week, as well as to the average waste from previous week.
Information for the previous day and the average for the previous week
are based on manually recorded values entered by kitchen staff each
time the plate waste bin is emptied. The reason for this procedure is that
staff can record the actual amount of plate waste if the scales are
tampered with by the pupils. The staff can also record the number of
guests served each day, to get the relative indicator ‘plate waste/guest’.
Fig. 2 shows the different parts of the interface and how it interacts with
the guests.
Some of the kitchens included in this study had a plate waste tracker
permanently at their location, whereas others used the device during
shorter, but more focused, periods. A total of 421,015 plate waste events
were recorded by the plate waste trackers in the 16 primary school
kitchens between 8 October 2020 and 20 February 2023.
2.2. Plate waste quantication framework and evaluation
To restrict measurements to only the lunch meal and to remove plate
waste events triggered by items other than plate waste (e.g., replacing
paper/plastic bag in the bin and placing it back on the scales), a lter
was used. The lter only looked at events between 10:00 and 14:00 h,
and only considered weights greater than 3 g and less than 500 g.
Applying this lter to all collected plate waste data reduced the number
of plate waste events to 398,991 (records of weight differences). Table 1.
shows the period in which plate waste trackers were active in the
different kitchens and the number of days, along with the number of
plate waste events (after the lter was applied) for which the plate waste
tracker recorded information. Descriptive statistics for plate waste is
also provided.
To analyze the distribution of plate waste, the plate waste data were
arranged in ascending order from the smallest to the largest amount.
This list was then divided into 10 groups based on deciles and the me-
dian value was computed for each group. This procedure was conducted
for the complete dataset, and separately for each canteen.
In a further analysis, the two decile groups within the complete
dataset that demonstrated the most signicant levels of waste were
C. Malefors et al.
Resources, Conservation & Recycling 200 (2024) 107288
3
targeted, with the aim of reducing the waste in these groups by 50 % (in
line with the objective specied in SDG12.3). To evaluate the effec-
tiveness of this approach, detected plate waste across all canteens was
aggregated and compared against the total waste amount calculated in
the scenario of 50 % waste reduction.
The median plate waste values for the highest 1 % and the lowest 1 %
quantiles were also examined for all the plate waste observations.
Furthermore, the median plate waste was calculated across all canteens
in the entire dataset, allowing the ndings to be compared with those in
previous studies. The median value was chosen as a measure of central
tendency due to its robustness in handling outliers, as described in
Quinn and Keough (2002).
To connect each amount of plate waste to the reason given by the
guest as to why they threw away food, the weight difference in plate
waste was associated with the next occurring feedback event on the
tablet computer within a 15-s time frame. Fig. 3 gives an overview of
Fig. 1. Overview of how the guests interact with the plate waste tracker. The guests throw their plate waste into a bin which sits on weighing scales. The banana peel
icon symbolizes all types of plate waste, regardless of their edibility. The scales are connected to a tablet computer that displays the weight of the items thrown away,
among other information. All the information collected is also sent to a central database.
Fig. 2. Overview of the interface of the plate waste tracker software and the different elements. The top part of the interface gives information about the impact of
the plate waste generated (approximately) in the canteen in terms that the students can relate to, for instance the number of cinnamon buns that 12 kg of plate waste
represents. The middle part of the interface displays how today’s accumulated levels of plate waste relates to the previous day’s and the previous week’s average. This
information is also relative to a goal that the canteen has set, in this case 12 kg. Individual feedback is displayed to the right of this information and changes
depending on how much food is wasted. The bottom part of the interface lets the guests give feedback on why they are wasting food.
C. Malefors et al.
Resources, Conservation & Recycling 200 (2024) 107288
4
how a weight difference was recorded in the database and its relation-
ship to guest feedback on the tablet computer: Waste was rst thrown in
the bin and when the new weight was stable (after roughly 0.9 s), it took
0.5 s before the difference was recorded in the database. This weight was
then displayed to the guest via the interface of the tablet computer. If the
guest indicated a reason for their food waste or their perception of the
food on the tablet computer, this feedback event was associated with the
weight difference. Reasons for wasting food were then evaluated by
looking at lowest 80 % of plate waste compared with the highest 20 % of
plate waste where feedback events could be tied to the plate waste
events. 95 % condence intervals for each feedback category highlight
potential signicant differences between the feedback categories within
the lowest 80 % of plate waste versus the highest 20 % of plate waste
events (Wasserstein et al., 2019).
In the best-case scenario, a weight change was directly followed by
feedback provided by the guest (Scale ->Feedback). These two events
could then be connected and analyzed as reasons for wasting food.
However, it was also possible for other combinations of events to occur,
such as two (or more) feedback events without a detected scale change
in between (Feedback ->Feedback), due to a guest not throwing away
any food but providing feedback to the canteen anyway, or at least two
scale changes without any feedback given on the tablet computer (Scale
->Scale), if a guest chose not to provide feedback or waited more than
15 s before doing so. A descriptive summary of all the combinations of
these feedback events was made, as aggregated values for all partici-
pating canteens.
To evaluate the accuracy of the automated plate waste detection
procedure, which measures waste by recording weight differences,
compared with the manual data entered by the kitchen staff at the end of
each day or bin emptying, the median waste in kilograms per day was
compared for each method. Similarly, to determine the number of guests
discarding food, the median number of weight differences (plate waste
events) was compared against the median number of guests recorded
manually by the kitchen staff per day. The number of guests recorded
manually by the kitchen staff was based on the number of plates, as
described by Malefors et al. (2021). To ensure reliable results, only
canteens with over 20 observations were considered in evaluating the
plate waste tracker’s ability to detect waste and guests accurately in
comparison with manual recording by kitchen staff. Waste-to-guest ratio
was calculated based on the average waste per day for each procedure
and the average number of guests served per day, and used to assess the
efciency of both the automated and manual procedures.
Table 1
Summary of where and when the plate waste trackers were active in the 16 participating primary school kitchens, and number of plate waste events captured by each
tracker and associated descriptive statistics.
School Municipality No. of plate
waste
events
Start date End date No. of quantication
days
Median plate waste
(g)
Average plate waste
(g)
Standard deviation plate
waste (g)
1 1 15,639 2020–12–15 2022–04–20 149 16 33 44
2 1 96,980 2020–12–15 2023–02–20 427 14 31 43
3 1 44,191 2020–12–11 2023–01–27 185 12 37 59
4 1 64,058 2020–12–14 2023–02–20 453 24 39 45
5 1 22,824 2020–12–14 2022–02–17 220 8 19 31
6 1 39,573 2021–09–07 2023–02–01 287 28 48 54
7 2 61,657 2022–04–28 2023–02–20 149 56 81 81
8 3 7356 2020–10–08 2020–11–27 35 28 48 60
9 3 12,028 2020–10–08 2021–06–14 58 24 42 51
10 3 17,835 2020–10–08 2021–09–01 73 12 30 44
11 4 7912 2020–10–12 2020–11–24 32 14 32 44
12 5 7546 2022–11–17 2023–02–17 46 26 44 52
13 5 160 2023–02–20 2023–02–20 1 24 34 36
14 5 383 2022–10–17 2022–10–24 5 20 30 30
15 5 599 2022–05–03 2022–05–06 4 24 37 41
16 5 250 2023–01–23 2023–01–27 5 20 37 46
Fig. 3. Flowchart describing how plate waste weight differences were recorded and how feedback events were linked to these weight differences.
C. Malefors et al.
Resources, Conservation & Recycling 200 (2024) 107288
5
3. Results
It was found that 20 % of plate waste events across all participating
kitchens accounted for 60 % of all plate waste. All kitchens reported
similar ndings, with canteen number 3 having the highest proportion
of waste (69 %) coming from 20 % of all the plate waste events and
kitchen 14 have the lowest proportion (52 %). Half of all plate waste
events in all canteens accounted for 11.2 % of all plate waste. Fig. 4
shows the waste rate per decile for all 16 school canteens, and the
pattern for the individual canteens.
Analysis of the top 1 % of all plate waste events across all school
canteens showed that these events accounted for 8 % of all plate waste,
whereas the bottom 1 % accounted for 0.09 % of all plate waste. The
reported median plate waste for all canteens was 20 g. Fig. 5 illustrates a
scenario where the top 20 % of plate wasters successfully reduced their
waste by 50 %, leading to an overall 31 % decrease in plate waste across
the studied canteens. The combined plate waste recorded in all canteens
during the period amounted to approximately 17 tonnes. If the top 20 %
of plate wasters were to halve their plate waste, the projected mass of
plate waste would be around 11.7 tonnes.
Of the 398,991 recorded weight differences, it was possible to link
55,505 feedback events to guests giving feedback on why they wasted
food. Among reasons given by the bottom 80 % (plate waste range 4–66
g/plate) for why they wasted food, a majority (56 %) responded that
they were happy with the food and ate it up, while the remaining 44 %
responded that the food was either not to their taste, or they took too
much or did not have enough time to nish their plates. Of the guests
that represented the top 20 % of all plate waste (range 66–500 g/plate),
56.8 % gave the reason that the food was not to their taste or that they
took too much or did not have enough time to nish it. Fig. 6 summa-
rizes the answers from the guests that could be matched to a plate waste
event and also divides the answers into the bottom 80 % and top 20 % of
plate waste events. The difference in plate waste events between the
bottom 80 % and the top 20 % is signicant for each category. There are
no overlapping condence intervals, except for the “not enough time to
eat” category.
A total of 164,890 feedback events were recorded by the guests
across all the canteens. Examining the order revealed that most were
Scale ->Scale events (350,022), where there was no feedback event in
between. There were also 93,900 Feedback ->Feedback events, where
there was no scale recording in between.
To understand how well the plate waste tracker detected waste
amount and number of guests, canteens that could provide more than 20
days of observations of each type was evaluated. Table 2 displays the
waste and guest differences for the individual school canteens, along
with information about how many days on which both guests and plate
waste events were recorded. In the school canteens that could provide
such data, the number of tracker-detected guests (plate waste events)
was lower than the number of manually recorded portions. The average
number of detected guests/day was around 40 % lower than the average
number of recorded guests/day. Across all kitchens that fullled the
ltering criteria, the difference between manually recorded plate waste
and the amount of tracker-detected plate waste per day was around 7 %.
The largest difference in the plate waste tracker’s ability to detect waste
was observed in canteen 11, where the amount of detected plate waste
was 31 % lower than the amount of manually recorded plate waste. The
remaining canteens had plate waste that was within ±10 % of the
manually recorded value.
As derived from Table 2, the manual recordings showed a value of
31 g waste/guest,
1
while the automatic procedure resulted in 48 g/
guest
2
(13 kg/268 guests). The manual recording procedure therefore
resulted in a waste-to-guest-ratio that was 35 %
3
lower than the auto-
matically detected value for the eight canteens that could provide more
than 20 days of observations.
4. Discussion
In this study of primary school canteens, it was found that most plate
waste (60 %) came from a relatively small proportion of guests (20 %),
while the majority of pupils wasted only a small amount or did not waste
any food at all. This conrms ndings in other areas that a small pro-
portion of events account for a majority of the impact, e.g., it has been
shown that all humans contribute to climate change, but not equally
(Chancel, 2022). In the present study, the results were based on auto-
mated tracker-recorded plate waste events, which means that additional
guests present who did not throw away any plate waste were not re-
ected in the results. For instance, the detected number of guests per day
Fig. 4. Plate waste rate per decile in (a) all 16 participating school canteens and (b) in the individual school canteens. The segments of each ring represents one decile
and the number printed in each segment in (a) indicates the proportion of plate waste occurring in that segment.
1
Calculated as 14 kg/ 449 guest *1000)
2
Calculated as 13 kg/ 268 guests * 1000)
3
Calculated as (1-31/48)
C. Malefors et al.
Resources, Conservation & Recycling 200 (2024) 107288
6
(or of plate waste events) was 10–51 % lower than the number manually
recorded by the kitchen staff at the different canteens. A similar pattern
was observed on examining the order of the events, where there was a
substantial number of feedback events in a row with no recorded weight
change in between that could be associated with a feedback event. This
probably means that guests gave feedback to the tablet computer, but
did not deposit any plate waste in the bin. If this were the case, then the
approach used underestimated the degree of food waste inequality be-
tween the guests.
It is clear that not every canteen or every guest had the same
Fig. 5. Scenario in which the top 20 % of plate wasters manage to reduce their waste by half, resulting in a 31 % reduction in overall plate waste in the can-
teens studied.
Fig. 6. Reasons given for wasting food by the bottom and top fractions of plate wasters: Not enough time to eat; Took too much; It was not my taste; I ate up
my food. The numbers in parentheses represent the 95 % condence interval range (rounded), and the vote count for each category is also provided.
C. Malefors et al.
Resources, Conservation & Recycling 200 (2024) 107288
7
problem, and that it was not evenly distributed. For instance, if the high-
prole wasters (in the top 20 %) could manage to reduce their food
waste by half, this would lead to an overall reduction in plate waste of
31 % in the canteens studied. If the highest-prole wasters (top 1 %)
could manage to halve their waste, this would lead to a decrease in
overall food waste of 4 %. Targeting these groups of wasters could
potentially have a huge impact and, since half of their current food
waste is still a large amount in comparison with many other pupils, this
is probably a moderate estimate of their reduction potential.
A common strategy for canteens that have recognized their food
waste issue is to deploy various information or awareness campaigns
aiming to lower food waste. Previous studies have shown that this can in
some cases be quite successful (Manomaivibool et al., 2016; Pinto et al.,
2018), whereas other studies have not observed the same potential
(Whitehair et al., 2013). This indicates that information or awareness
campaigns are quite blunt and can only lower food waste to a certain
extent, if at all, which is likely to relate to how large the problem was to
start with. For instance, Eriksson et al. (2019) found that hotels and
restaurants reduced their food waste by 61 % on quantifying food waste
and displaying the result, although the variation between establish-
ments was large and an important factor in achieving a large reduction
was to start with a high level of food waste (the larger the initial prob-
lem, the greater the potential for improvement). Food waste reduction is
likely subject to the law of diminishing returns (Meier et al., 2021), so
further actions are needed beyond information and awareness cam-
paigns to target the guests that account for most plate waste.
Nudging guests to throw away less food can be an option and some
studies have found it to be successful in lowering plate waste. For
instance, Roe et al. (2022) found that individually tailored food waste
reduction interventions in a household setting reduced plate waste by
79 %, although the sample of participants in their study was small. The
plate waste trackers used in this study have previously been demon-
strated to lower the amount of plate waste by 37 % (not statistically
signicant) and serving waste by 62 %, but the amount of plate waste in
that study was already low, with initial plate waste of 19 g/guest
(Malefors et al., 2022). In the present study the median level of plate
waste was 20 g/guest, quantied in 16 canteens using the plate waste
trackers, compared with two canteens in Malefors et al. (2022). The
median level in this study is similar to that reported in other studies
covering the Swedish public catering sector in general and primary
schools in particular (Malefors, 2022; Swedish National Food Agency,
2021).
To date, policy makers and practitioners in the public catering sector
seeking to achieve food waste reductions have targeted all pupils with
the same information, campaigns, and nudges. The results in the present
study suggest that this is a waste of effort, as the majority of the pupils
targeted do not have any practical possibility to reduce their food waste.
Instead, greater potential to achieve food waste reductions lies in tar-
geting a much smaller group of high-prole wasters that have real
potential to reduce their waste. Efforts should therefore be made to
devise interventions that reach this minority of pupils and change their
behavior in a desired direction. It is possible that information and
nudging can still be useful methods, but the messages provided need to
be adapted for the minority rather than the majority and ideally should
be tailored to different consumer groups.
A secondary aim in this study was to assess the accuracy of an
automatic quantication procedure compared with manually recording
the amount of waste and the number of guests. The average amount of
plate waste at most participating canteens was within ±10 %, but one
canteen exceeded this range of variation and had 31 % lower amount of
tracker-detected plate waste compared with manually recorded plate
waste. The tracker-detected number of guests also deviated from the
manually entered number. An important question thus arises regarding
the comparability of waste and guest data captured by different methods
when converted into a relative indicator (waste-to-guest ratio). Use of an
automated system may result in a lower number of detected guests, as it
will only record guests who actually waste food, leading to a higher
waste-to-guest ratio compared with the manual recording procedure.
This is exemplied by the 35 % lower waste-to-guest ratio observed for
the manual recording procedure compared with the automated pro-
cedure in this study. This difference should be considered when
reporting food waste values to management or to national statistics.
However, it is unlikely that automated systems will replace manual re-
cordings any time soon and both methods are likely to co-exist for some
time. A potential compromise worth considering is to employ both
manual and system-detected methods for data entry. In this study,
canteen staff did not have access to information on the automatically
calculated waste-to-guest ratio and instead the statistics displayed on the
interface were based on the manually entered information, which
allowed staff to maintain a sense of control and make the key gure
comparable to the manual records.
Another factor to consider is that it is difcult to automatically
determine the reasons for food waste. In the present study, we were able
to link 55,505 feedback events to a corresponding plate waste event, out
of a total of 164,890 feedback events. However, it should be noted that
in most cases only scale events were recorded, without any corre-
sponding feedback events, partly due to the pandemic situation where
some canteens made the tablet computer inaccessible to guests due to
hygiene concerns. Despite this limitation, using an automated system to
capture guest feedback is still benecial, as it is a cost-effective way to
gather opinions compared with conducting surveys. Although it may be
challenging to link the feedback to a waste event automatically, can-
teens can still act based on the feedback they receive. The idea is for
canteens to understand how guests perceive certain menus through their
feedback. By identifying which dishes might lead to more food waste,
canteens can introduce customized interventions, possibly decreasing
serving waste. This concept has been previously demonstrated in a study
where plate waste trackers were found to lower serving waste by 38 g/
Table 2
Waste and guest differences for the individual school canteens, and number of observations for the different cases. Only canteens that could provide more than 20
observations of each type were considered in the evaluation. Plate waste (kg) is rounded with two digits precision.
School Plate waste Portions
Observations Automatically detected
waste/day (kg)
Manually recorded
waste/day (kg)
Δ% Observations Automatically detected
guests/day
Manually recorded
guests/day
Δ%
2 235 8.1 7.5 8 182 217 344 ¡37
3 107 10 11 ¡9 229 343 554 ¡38
6 225 9 8.6 5 232 168 319 ¡47
7 141 35 37 ¡5 140 439 800 ¡45
8 28 12 12 0 29 225 362 ¡38
9 47 10 11 ¡9 47 233 475 ¡51
10 66 8 8.8 ¡9 68 260 288 ¡10
11 28 9.7 14 ¡31 26 258 450 ¡43
Average: 110 13 14 ¡7 119 268 449 ¡40
C. Malefors et al.
Resources, Conservation & Recycling 200 (2024) 107288
8
guest, although other circumstances may also have had an effect (Mal-
efors et al., 2022).
Although the results of this study are promising, there are some
limitations that should be considered. First, the sample size of partici-
pating canteens was relatively small, with only 16 canteens contributing
data. While these canteens were geographically dispersed and partici-
pated voluntarily, there may be some selection bias inherent in this
approach. It is not uncommon for technological solutions to be volun-
tary, but this may limit the generalizability of the ndings. Future
studies assessing the reliability of automated food waste quantication
tools would benet from a larger sample size and inclusion of canteens
from other parts of the food service sector. It is worth noting that while
we refer to a group of ‘high-prole wasters’, we were unable to deter-
mine whether the same individuals exhibited this behavior consistently
over time or whether the group consisted of different individuals over
time. To gain a better understanding, surveys or on-site observations
would need to be conducted in conjunction with the plate waste
trackers. Additionally, the data collected by the plate waste trackers
could be cross-referenced with student schedules to identify specic
classes or groups of students with high levels of waste, allowing for
targeted interventions. Food waste levels are recognized to vary over
time, inuenced by various factors, including the composition and
quality of the menu. In this study, the system solely records plate waste
by weight. To gain a more comprehensive understanding of what enters
the waste bin, the system could potentially be enhanced to include the
content of the bin through technologies like cameras and innovative
image recognition. This expansion could shed light on the correlation
between specic menus and varying levels of plate waste. Furthermore,
this could enable feedback to guests on the plate waste on particular
menu generated compared to its previous serving, offering a more direct
comparison than the current practice of comparing daily waste levels
without considering the menu variations. Additionally, modifying the
interface to display feedback as averages instead of total accumulated
plate waste would ensure that each individual’s contribution affects the
average. This change would prevent the last few students each day from
being the sole reason the plate waste exceeds the target.
Another potential improvement to the plate waste trackers used in
this study would be to expand the quantication of food waste to include
its true cost, as proposed by Martin-Rios et al. (2023). Such an approach
may be particularly relevant for establishments outside the public
catering sector where customers pay for their meals directly. By incor-
porating this additional information, plate waste trackers could provide
an even more comprehensive assessment of the economic and environ-
mental impact of food waste, and potentially motivate customers to
reduce food waste by highlighting its nancial cost.
By further personalizing the messages targeted at the high-wasting
minority of pupils, it is possible to achieve a greater impact and signif-
icantly reduce food waste, thereby contributing to a more sustainable
food system with less waste.
5. Conclusions
Automated tools for quantifying food waste are an emerging tech-
nology with some promise. This study demonstrated the effectiveness of
one such tool, the plate waste tracker, in accurately detecting plate
waste with a high level of precision (within ±10 % of values manually
recorded by staff). By detecting waste directly from plates, this tool also
provided insights into the number of guests discarding food, which was
approximately 40 % lower on average than the number obtained when
staff manually counted plates and entered the information. Conse-
quently, the automatically collected data indicated a 35 % higher waste-
per guest ratio than that derived from manual information.
The tool provided guests with the opportunity to provide feedback
on the reasons behind their food waste. However, automatically deter-
mining the specic reasons for wasting food and linking them to the
actual waste proved to be a challenging task, so the ability to identify
certain behaviors associated with food waste based on this feedback
remains elusive. As automated tools for quantifying food waste become
more prevalent, it is crucial to understand the results produced by these
methods in comparison with manual approaches. The automated tools
have an advantage over manual recordings in that they can track food
waste with greater granularity. Because the waste was not measured as
an aggregate value in the present study, the automated approach was
able to reveal that a minority of students (20 %) were responsible for a
signicant proportion (60 %) of all plate waste. If this waste alone could
be halved, this would reduce overall food waste by 31 %. Therefore,
identifying measures that target high-prole wasters would have a
substantial impact in reducing plate waste overall. To date, policy
makers and practitioners in the public catering sector seeking to achieve
food waste reduction have targeted all pupils with the same information,
campaigns, and nudges. The results in the present study suggest that this
is a waste of effort, as most of the pupils do not have any practical
possibility to reduce their food waste. Greater potential lies in targeting
a much smaller group of high food wasters that have real potential to
reduce their waste. Efforts should therefore be made to devise in-
terventions that reach this minority of pupils and change their behaviors
in a desired direction. This, together with other actions, is necessary to
achieve a more sustainable food system.
CRediT authorship contribution statement
Christopher Malefors: Conceptualization, Methodology, Visualiza-
tion, Data curation, Formal analysis, Writing – original draft, Writing –
review & editing. Erik Svensson: Software, Data curation, Formal
analysis, Writing – review & editing. Mattias Eriksson: Conceptuali-
zation, Methodology, Funding acquisition, Writing – review & editing.
Declaration of Competing Interest
The authors declare the following nancial interest/personal re-
lationships which may be considered as potential competing interests:
The authors Christopher Malefors, Erik Svensson, and Mattias Eriksson
developed the plate waste tracker used. Christopher Malefors and Mat-
tias Eriksson are shareholders in the company Matomatic AB, which
owns the rights to the plate waste tracker.
Data availability
The authors do not have permission to share data.
Acknowledgements
This work was supported by the H2020 project LOWINFOOD (Multi-
actor design of low-waste food value chains through the demonstration
of innovative solutions to reduce food loss and waste). LOWINFOOD is
funded by the European Union’s Horizon 2020 research and innovation
program under Grant Agreement no. 101000439. The views reected in
this article represent the professional views of the authors and do not
necessarily reect the views of the European Commission or other
LOWINFOOD project partners.
References
Abdelaal, A.H., McKay, G., Mackey, H.R., 2019. Food waste from a university campus in
the Middle East: drivers, composition, and resource recovery potential. Waste
Manag. 98, 14–20. https://doi.org/10.1016/j.wasman.2019.08.007.
Antonschmidt, H., Lund-Durlacher, D., 2021. Stimulating food waste reduction
behaviour among hotel guests through context manipulation. J. Clean. Prod. 329,
129709 https://doi.org/10.1016/j.jclepro.2021.129709.
Aschemann-Witzel, J., Asioli, D., Banovic, M., Perito, M.A., Peschel, A.O., Stancu, V.,
2023. Dening upcycled food: the dual role of upcycling in reducing food loss and
waste. Trends Food Sci. Technol. 132, 132–137. https://doi.org/10.1016/j.
tifs.2023.01.001.
C. Malefors et al.
Resources, Conservation & Recycling 200 (2024) 107288
9
Bergquist, M., Thiel, M., Goldberg, M.H., van der Linden, S., 2023. Field interventions for
climate change mitigation behaviors: a second-order meta-analysis. Proc. Natl. Acad.
Sci. 120, e2214851120 https://doi.org/10.1073/pnas.2214851120.
Boschini, M., Falasconi, L., Giordano, C., Alboni, F., 2018. Food waste in school canteens:
a reference methodology for large-scale studies. J. Clean. Prod. 182, 1024–1032.
https://doi.org/10.1016/j.jclepro.2018.02.040.
Brancoli, P., Lundin, M., Bolton, K., Eriksson, M., 2019. Bread loss rates at the supplier-
retailer interface – Analysis of risk factors to support waste prevention measures.
Resour. Conserv. Recycl. 147, 128–136. https://doi.org/10.1016/j.
resconrec.2019.04.027.
Chancel, L., 2022. Global carbon inequality over 1990–2019. Nat. Sustain. 5, 931–938.
https://doi.org/10.1038/s41893-022-00955-z.
Cozzio, C., Tokarchuk, O., Maurer, O., 2021. Minimising plate waste at hotel breakfast
buffets: an experimental approach through persuasive messages. Br. Food J. 123,
3208–3227. https://doi.org/10.1108/BFJ-02-2021-0114.
Dolnicar, S., Juvan, E., Grün, B., 2020. Reducing the plate waste of families at hotel
buffets – A quasi-experimental eld study. Tour. Manag. 80, 104103 https://doi.org/
10.1016/j.tourman.2020.104103.
Eriksson, M., Christensen, J., Malefors, C., 2023. Making food waste illegal in Sweden –
Potential gains from enforcing best practice in the public catering sector. Sustain.
Prod. Consum. 35, 229–237. https://doi.org/10.1016/j.spc.2022.11.003.
Eriksson, M., Malefors, C., Callewaert, P., Hartikainen, H., Pietil¨
ainen, O., Strid, I., 2019.
What gets measured gets managed – Or does it? Connection between food waste
quantication and food waste reduction in the hospitality sector. Resour. Conserv.
Recycl. X 4, 100021. https://doi.org/10.1016/j.rcrx.2019.100021.
Eriksson, M., Osowski, C.P., Malefors, C., Bj¨
orkman, J., Eriksson, E., 2017. Quantication
of food waste in public catering services – A case study from a Swedish municipality.
Waste Manag. 61, 415–422. https://doi.org/10.1016/j.wasman.2017.01.035.
Eriksson, M., Strid, I., Hansson, P.-A., 2014. Waste of organic and conventional meat and
dairy products—A case study from Swedish retail. Resour. Conserv. Recycl. 83,
44–52. https://doi.org/10.1016/j.resconrec.2013.11.011.
Fraternali, P., Cellina, F., Herrera Gonzales, S.L., Melenhorst, M., Novak, J., Pasini, C.,
Rottondi, C., Rizzoli, A.E., 2019. Visualizing and gamifying consumption data for
resource saving: challenges, lessons learnt and a research agenda for the future.
Energy Inform. 2, 22. https://doi.org/10.1186/s42162-019-0093-z.
Froehlich, J., Findlater, L., Landay, J., 2010. The design of eco-feedback technology. In:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
Presented at the CHI ’10: CHI Conference on Human Factors in Computing Systems.
ACM, Atlanta Georgia USA, pp. 1999–2008. https://doi.org/10.1145/
1753326.1753629.
Gosnell, G.K., Bazilian, M.D., 2021. Changing behaviour is the key to solving the climate
challenge. Nat. Hum. Behav. 5, 294. https://doi.org/10.1038/s41562-020-01047-8.
–294.
Gram-Hanssen, K., 2014. New needs for better understanding of household’s energy
consumption – behaviour, lifestyle or practices? Archit. Eng. Des. Manag. 10,
91–107. https://doi.org/10.1080/17452007.2013.837251.
Guillen, G., Hamari, J., Quist, J., 2021. Gamication of Sustainable Consumption: a
systematic literature review. In: Presented at the Hawaii International Conference on
System Sciences. https://doi.org/10.24251/HICSS.2021.163.
IPCC, 2019. Climate Change and Land: an IPCC special report on climate change,
desertication, land degradation, sustainable land management, food security, and
greenhouse gas uxes in terrestrial ecosystems.
Juvan, E., Grün, B., Dolnicar, S., 2017. Biting off more than they can chew: food waste at
hotel breakfast buffets. J. Travel Res. 57, 232–242. https://doi.org/10.1177/
0047287516688321.
Kallbekken, S., Sælen, H., 2013. `Nudging’ hotel guests to reduce food waste as a
win–win environmental measure. Econ. Lett. 119, 325–327. https://doi.org/
10.1016/j.econlet.2013.03.019.
Koivisto, J., Hamari, J., 2019. The rise of motivational information systems: a review of
gamication research. Int. J. Inf. Manag. 45, 191–210. https://doi.org/10.1016/j.
ijinfomgt.2018.10.013.
Leverenz, D., Moussawel, S., Hafner, G., Kranert, M., 2020. What influences buffet
leftovers at event caterings? A German case study. Waste Manag. 116, 100–111.
https://doi.org/10.1016/j.wasman.2020.07.029.
Malefors, C., 2022. Food Waste Reduction in the Public Catering Sector. Swedish
University of Agricultural Sciences, Uppsala, Sweden.
Malefors, C., Callewaert, P., Hansson, P.-A., Hartikainen, H., Pietil¨
ainen, O., Strid, I.,
Strotmann, C., Eriksson, M., 2019. Towards a baseline for food-waste quantication
in the hospitality sector—quantities and data processing criteria. Sustainability 11,
3541. https://doi.org/10.3390/su11133541.
Malefors, C., Strid, I., Hansson, P.-A., Eriksson, M., 2021. Potential for using guest
attendance forecasting in Swedish public catering to reduce overcatering. Sustain.
Prod. Consum. 25, 162–172. https://doi.org/10.1016/j.spc.2020.08.008.
Malefors, C., Sundin, N., Tromp, M., Eriksson, M., 2022. Testing interventions to reduce
food waste in school catering. Resour. Conserv. Recycl. 177, 105997 https://doi.org/
10.1016/j.resconrec.2021.105997.
Manomaivibool, P., Chart-asa, C., Unroj, P., 2016. Measuring the impacts of a save food
campaign to reduce food waste on campus in Thailand. Appl. Environ. Res. 13–22.
https://doi.org/10.35762/AER.2016.38.2.2.
Martin-Rios, C., Rogenhofer, J., Sandoval Alvarado, M., 2023. The true cost of food
waste: tackling the managerial challenges of the food supply chain. Trends Food Sci.
Technol. 131, 190–195. https://doi.org/10.1016/j.tifs.2022.12.005.
Meier, T., Von Borstel, T., Welte, B., Hogan, B., Finn, S.M., Bonaventura, M.,
Friedrich, S., Weber, K., Dr¨
ager De Teran, T., 2021. Food waste in healthcare,
business and hospitality catering: composition, environmental impacts and
reduction potential on company and national levels. Sustainability 13, 3288. https://
doi.org/10.3390/su13063288.
Obersteiner, G., Gollnow, S., Eriksson, M., 2021. Carbon footprint reduction potential of
waste management strategies in tourism. Environ. Dev. 39, 100617 https://doi.org/
10.1016/j.envdev.2021.100617.
Pinto, R.S., Pinto, R.M., dos, S., Melo, F.F.S., Campos, S.S., Cordovil, C.M.-S., 2018.
A simple awareness campaign to promote food waste reduction in a University
canteen. Waste Manag. 76, 28–38. https://doi.org/10.1016/j.wasman.2018.02.044.
Qi, D., Li, R., Penn, J., Houghtaling, B., Prinyawiwatkul, W., Roe, B.E., 2022. Nudging
greater vegetable intake and less food waste: a eld experiment. Food Policy 112,
102369. https://doi.org/10.1016/j.foodpol.2022.102369.
Quinn, G.P., Keough, M.J., 2002. Experimental Design and Data Analysis For Biologists.
Cambridge University Press, Cambridge, UK ; New York.
Raworth, K., 2012. A safe and just space for humanity: can we live within the doughnut?
Richardson, R., Prescott, M.P., Ellison, B., 2021. Impact of plate shape and size on
individual food waste in a university dining hall. Resour. Conserv. Recycl. 168,
105293 https://doi.org/10.1016/j.resconrec.2020.105293.
Roe, B.E., Qi, D., Beyl, R.A., Neubig, K.E., Apolzan, J.W., Martin, C.K., 2022.
A Randomized Controlled Trial to Address Consumer Food Waste with a Technology-
aided Tailored Sustainability Intervention. Resour. Conserv. Recycl. 179, 106121
https://doi.org/10.1016/j.resconrec.2021.106121.
Springmann, M., Clark, M., Mason-D’Croz, D., Wiebe, K., Bodirsky, B.L., Lassaletta, L.,
Vries, W.de, Vermeulen, S.J., Herrero, M., Carlson, K.M., Jonell, M., Troell, M.,
DeClerck, F., Gordon, L.J., Zurayk, R., Scarborough, P., Rayner, M., Loken, B.,
Fanzo, J., Godfray, H.C.J., Tilman, D., Rockstr¨
om, J., Willett, W., 2018. Options for
keeping the food system within environmental limits. Nature 562, 519–525. https://
doi.org/10.1038/s41586-018-0594-0.
Swedish National Food Agency, 2021. Matsvinn i Kommunala f¨
orskolor, Skolor Och
¨
aldreboenden - Kartl¨
aggning Av Matsvinnet 2020 (No. L 2921 Nr 22).
Livsmedelsverket, Uppsala.
Swedish National Food Agency, 2020. Handbok F¨
or Minskat Matsvinn - f¨
or verksamheter
Inom Vård. skola och omsorg.
Thiagarajah, K., Getty, V.M., 2013. Impact on plate waste of switching from a tray to a
trayless delivery system in a university dining hall and employee response to the
switch. J. Acad. Nutr. Diet. 113, 141–145. https://doi.org/10.1016/j.
jand.2012.07.004.
United Nations, 2015. Transforming Our World: The 2030 Agenda for Sustainable
Development. New York.
United Nations Environment Programme, 2021. Food Waste Index Report 2021. Nairobi.
Vidal-Mones, B., Diaz-Ruiz, R., Gil, M., J, 2022. From evaluation to action: testing
nudging strategies to prevent food waste in school canteens. Waste Manag. 140,
90–99. https://doi.org/10.1016/j.wasman.2022.01.006.
Wasserstein, R.L., Schirm, A.L., Lazar, N.A., 2019. Moving to a World Beyond “ p <0.05.
Am. Stat. 73, 1–19. https://doi.org/10.1080/00031305.2019.1583913.
Whitehair, K.J., Shanklin, C.W., Brannon, L.A., 2013. Written Messages Improve Edible
Food Waste Behaviors in a University Dining Facility. J. Acad. Nutr. Diet. 113,
63–69. https://doi.org/10.1016/j.jand.2012.09.015.
W.W.F. Germany, 2020. Collection and recording of food waste from out-of-home-
catering - Overview of policy frameworks and existing recording tools.
Xue, L., Liu, G., Partt, J., Liu, X., Van Herpen, E., Stenmarck, Å., O’Connor, C.,
¨
Ostergren, K., Cheng, S., 2017. Missing food, missing data? A critical review of
global food losses and food waste data. Environ. Sci. Technol. 51, 6618–6633.
https://doi.org/10.1021/acs.est.7b00401.
C. Malefors et al.
