The new smartphone-based food diagnostic technologies offer significant advantages over traditional methods as they can be easily applied in various steps of the agrifood supply chain including household use and also in the food recovery field for charitable purposes, aimed at helping to reduce food waste. Further advantages include the low cost, the minimal equipment, and nonspecialized personnel required. This study evaluated the performance of two instrumental measurements of the sensors: an electronic nose (PEN3; WinMuster Airsense Analytics) and a smart portable device (FOODsniffer; ARS LAB US). The preliminary study was conducted on cape hake fillets. In order to test the performance of PEN3 and FOODsniffer, total volatile basic nitrogen (TVB-N) values were considered as the reference. Principal component analysis (PCA) and Pearson’s correlation were performed in order to compare PEN3 with TVB-N, and for the FOODsniffer evaluation, a one-way ANOVA was carried out. A significant correlation was shown between PEN3, first component, and TVB-N (
Aquatic products (mainly fish, aquatic molluscs, and crustaceans) have a critical role in the food system, providing nearly 3 billion people with at least 15% of their animal protein intake [
The main commercial fish species consumed in the EU are tuna (2.58 kg/capita), cod (2.40 kg/capita), and salmon (2.09 kg/capita). The consumption of cod has increased by 9% since 2013 [
The development of new technologies applicable at the different steps of the food supply chain can thus offer significant advantages not only for food business operators [
Electronic noses, which have already been tested considerably in various fields [
The electronic nose is a directly applicable method that requires minimum sample pretreatment and no specific consumables or reagents and is able to provide rapid analysis [
Smartphone sensors, on the contrary, could be easily used by unskilled personnel such as volunteers in charity organizations and final consumers [
The emerging need to reduce food waste but also to ensure food security to people in food poverty has led to the development of new technologies in the food business, aimed not only at preventing food losses and waste in the primary production, processing, distribution, retail, and food services but also at improving the quality of products recovered by charities [
In the third sector such as food banks, where the work is done by volunteers without specific training in the food field, the objective is to provide valid support in order to guarantee a safe second life to the food recovered [
Fishery and aquaculture products, together with other animal and vegetable proteins, are also an important source of protein and thus an essential component of a healthy diet. For this reason, these products are also essential in the diet of people in food poverty [
The aim of this study was to explore whether an electronic nose [
The two portable sensing devices were evaluated on cape hake fillets taking the measured TVB-N values into consideration as the reference (gold standard). In fact, the European Commission defined the TVB-N as the reference method and in the Decision of 8 March 1995 fixed the TVB-N limit values for three categories of fishery products [
Experiments were performed at the Food Inspection Laboratory of the Department of Health Animal Science and Food Safety in Milan (Italy). One batch of cape hake (
The batch contained fish fillets that were already skinless and portioned into individual fillets with a medium weight of 90–110 g (thawed weight 85–100 g).
Before being analysed, each fillet was washed with sterile, distilled, and deionized water in order to remove the glazing, and the fillets were then left to thaw overnight under a controlled temperature (0–4°C). In order to reproduce the use at the domestic level, the defrosting procedures were carried out as provided in the product’s specifications (Table
Product specifications.
Shelf life | |
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Production date/freezing date | 7 July 2015 |
Best before end | 7 July 2017 |
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−18°C | 18 months |
−12°C | 1 month |
−6°C | 1 week |
0–4°C | 3 days |
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Allow the product to thaw at room temperature or at refrigeration temperature; once defrosted, the product must not be frozen again and must be consumed within 24 hours |
On the first day of storage (day 1), 8 fillets were tested with all three methods: FOODsniffer, PEN3, and TVB-N. Subsequently, for six consecutive days (from day 2 to day 7), eight fillets were measured with the FOODsniffer, and when the FOODsniffer detected variations, the fillets were assessed in terms of their TVB-N and were measured with PEN3 to confirm the results. In this study, the variations detected by the FOODsniffer occurred on storage days 3 and 7. Figure
Experimental design.
FOODsniffer (ARS LAB US), created by scientists and researchers of the Kaunas University of Technology, in cooperation with the company ARS LAB, is a new and fast device used to assess the freshness of food of animal origin and specifically patented for the meat matrix [
FOODsniffer rapidly estimates the quality and safety of the raw material correlating them to the levels of volatile organic compounds present in the tested matrix, through a gas sensor system including at least two metal-oxide semiconductor sensors configured to measure NH3 and CH values. The technology is based on the detection of low concentrations of volatile compounds that are associated with deterioration.
The device is composed of a metal-oxide sensor system adapted to respond to the speed of changes in the concentration of volatile compounds, a processor designed to receive and process signals incoming from the sensor system and to turn them into a sequence of electrical signals on the basis of variation in the concentration of volatile compounds, and a Bluetooth device which, according to the algorithms in synchronization with the cloud, provides the user result to mobile devices (tablets or smartphones) [
The protocol used for the sample analysis was drawn up according to the FOODsniffer user manual.
FOODsniffer is controlled through a dedicated smartphone app which can be operated by nonspecialized personnel. It provides information on the level of freshness of raw materials: satisfactory (fresh), acceptable (to be consumed after cooking), and unsatisfactory (spoiled). FOODsniffer results are qualitative outputs also associated with colours: green (fresh), orange (to be consumed after cooking), and red (spoiled). FOODsniffer was tested in order to evaluate its potential use as an easy-to-use sensory tool both at the domestic level and for charity volunteers to assess whether a food product is fit for consumption.
The Portable Electronic Nose PEN3 (WinMuster Airsense Analytics, Schwerin, Germany) was used in this study. It has 10 metal-oxide sensors, and Table
Sensors and their applications in PEN3.
Number in the array | Sensor name | General description | Reference | |
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R1 | W1C | Aromatic | Aromatic compounds | Toluene, 10 ppm |
R2 | W5S | Broad-range | Very sensitive, broad-range sensitivity, reacts on nitrogen oxides and ozone, very sensitive to the negative signal | NO2, 1 ppm |
R3 | W3C | Aromatic | Ammonia used as a sensor for aromatic compounds | Benzene, 10 ppm |
R4 | W6S | Hydrogen | Mainly hydrogen, selectively breath gases | H2, 100 ppb |
R5 | W5C | Arom-aliph | Alkanes, aromatic compounds, less polar compounds | Propane, 1 ppm |
R6 | W1S | Broad-methane | Sensitive to methane (environment) ca. 10 ppm, broad range similar to no. 8 | CH4, 100 ppm |
R7 | W1W | Sulphur-organic | Reacts on sulphur compounds (H2S 0.1 ppm), otherwise sensitive to many terpenes and sulphur organic compounds, which are important for smell (limonene and pyrazine) | H2S, 1 ppm |
R8 | W2S | Broad-alcohol | Detects alcohols, partially aromatic compounds, broad range | CO, 100 ppm |
R9 | W2W | Sulphur-chlor | Aromatic compounds, sulphur organic compounds | H2S, 1 ppm |
R10 | W3S | Methane-aliph | Reacts on high concentrations >100 ppm, sometimes very selective (methane) | CH4, 10 ppm |
The instrument (PEN3) consists of three units: (i) a sampling and washing unit, (ii) a chamber, consisting of an electrochemical gas sensor array, and (iii) a pattern-recognition system. During the analysis, eight fillets were kept at a constant temperature in a thermostatic water bath at 18 ± 2°C to prevent the effects of temperature fluctuation and in order to create the correct headspace. All the fillets were cut into pieces of equal weight (approximately 10 g), and each one was placed in a small sealed glass vial with a capacity of 100 ml. Each analysis was repeated twice. The sealed glass vials containing the fillets were connected to the PEN3 with a probe. The headspace gas in that vials was pumped from the sampler through the sensor array at 400 ml/min. Before and after each measurement, the sensors were cleaned by air using carbon filters. Sensor response data were recorded every second. The analysis protocol was defined by setting up the E-nose parameters (flow rate, duration of measurement, etc.) according to the manufacturer’s instructions. The analysis of each fillet lasted 640 seconds. The set of signals derived from the electronic nose during the analysis takes the form of a pattern. The pattern data were analysed using WinMuster (version 1.6.2., 17 May 2014, copyright Airsense Analytics GmbH).
Eight fillets were prepared for the analysis of TVB-N levels according to Regulation (EC) no. 2074/2005 [
In brief, 10 g (±0.1) of the fillet was blended with 90 ml of perchloric acid 6%. Subsequently, 50 ml of the filtrate was introduced into an apparatus for steam distillation, and to check the level of alkalinisation of the extract, several drops of phenolphthalein were added. Before extraction and steam distillation, a few drops of silicone antifoaming agent and 6.5 ml of sodium hydroxide solution were added. The steam distillation was regulated so that around 100 ml of the distillate could be produced in 10 minutes. The distillation outflow tube was submerged in a receiver with 100 ml of boric acid solution, to which three to five drops of the indicator solution were added. After distillation, the volatile bases contained in the receiver solution were determined by titration with a standard hydrochloric solution. Each analysis was repeated twice as required by Regulation (EC) no. 2074/2005. The method applied is correct if the difference between the duplicates is not greater than 2 mg/100 g. For the blind test, 50 ml of perchloric acid solution was used instead of the extract.
Finally, the TVB-N concentration was calculated using the following equation:
The data obtained from TVB-N values (gold standard), PEN3 (E-nose), and FOODsniffer were subjected to statistical analyses. The aim was to determine whether the three analysis methods could be considered as being equally reliable in evaluating the freshness of the fish. For each sample (cape hake fillets), data coming from each sensor of the electronic nose (PEN3) were analysed taking 10 seconds out of 400 seconds of the total analysis, according to the stability of the sensor responses. These values were then aggregated with the average to obtain a single measure. A principal component analysis (PCA) was also performed to extract a single indicator of freshness to be compared with TVB-N. To verify the FOODsniffer evaluation, a one-way ANOVA was carried out. All statistical procedures were carried out using IBM SPSS Statistics 24 (SPSS Inc., Chicago, IL, USA).
Figure
Measure of TVB-N on the fillets.
The effect of the number of storage days on the array response was evaluated. As a first step, radar plots were obtained to observe whether pattern differences were developed between samples analysed in different storage days. Figure
Radar plot extracted from sensor array responses at days 1 (a), 3 (b), and 7 (c) (expressed in ohm).
The measures obtained by the different PEN3 (E-nose) sensors were strongly correlated; thus, from the PCA, we had a very good result extracting a single dimension, with 93% of the variance explained, which can be considered as a single indicator of freshness for the PEN3 (E-nose). Comparing PEN3 (E-nose), the first component, and the chemical analysis results (TVB-N), the Pearson correlation between these methods was evaluated, obtaining a strong and significant correlation (
PCA.
Analysing the link between the individual sensors of PEN3 (E-nose) and the TVB-N results, a strong correlation can be seen (min = 0.65 in W3S and max = 0.98 in W1W) (Table
Correlations between the PEN3 sensors and TVB-N values.
R1 (W1C) | R2 (W5S) | R3 (W3C) | R4 (W6S) | R5 (W5C) | R6 (W1S) | R7 (W1W) | R8 (W2S) | R9 (W2W) | R10 (W3S) | ||
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TVB-N | Pearson’s correlation | − |
0.911 |
− |
0.769 |
− |
0.884 |
0.983 |
0.887 |
0.964 |
0.648 |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 |
Some of the sensors (W1C, W3C, and W5C) show significantly negative correlation coefficient values, thus indicating that as the values of TVB-N increase, the sensor signals (W1C, W3C, and W5C) decrease.
FOODsniffer provides a categorical evaluation of fish freshness: green, orange, and red alerts, which correspond to satisfactory, acceptable, and unsatisfactory levels of freshness, respectively. The qualitative nature of this measure does not enable the correlation between its results and those obtained by PEN3 (E-nose) or TVB-N to be evaluated. To overcome this, for the FOODsniffer, we performed a one-way analysis of variance (ANOVA).
The ANOVA results confirmed a good agreement between FOODsniffer, TVB-N (
As reported in Table
Descriptive statistics.
FOODsniffer | Mean | Std. error | 95% confidence interval | ||
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Lower bound | Upper bound | ||||
TVB-N | Green | 1.188 | 0.782 | −0.662 | 3.037 |
Orange | 20.648 | 0.689 | 19.018 | 22.277 | |
Red | 68.813 | 2.430 | 63.066 | 74.559 | |
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R1 (W1C) | Green | 0.878 | 0.013 | 0.846 | 0.910 |
Orange | 0.673 | 0.035 | 0.590 | 0.756 | |
Red | 0.242 | 0.017 | 0.202 | 0.282 | |
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R2 (W5S) | Green | 2.157 | 0.139 | 1.828 | 2.487 |
Orange | 20.394 | 0.833 | 18.425 | 22.362 | |
Red | 147.926 | 11.079 | 121.729 | 174.123 | |
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R3 (W3C) | Green | 0.928 | 0.007 | 0.911 | 0.946 |
Orange | 0.832 | 0.015 | 0.796 | 0.868 | |
Red | 0.478 | 0.023 | 0.424 | 0.532 | |
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R4 (W6S) | Green | 1.029 | 0.056 | 0.897 | 1.160 |
Orange | 1.131 | 0.011 | 1.106 | 1.155 | |
Red | 1.326 | 0.011 | 1.299 | 1.353 | |
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R5 (W5C) | Green | 0.970 | 0.012 | 0.942 | 0.997 |
Orange | 0.916 | 0.006 | 0.903 | 0.930 | |
Red | 0.661 | 0.023 | 0.608 | 0.715 | |
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R6 (W1S) | Green | 1.896 | 0.175 | 1.482 | 2.309 |
Orange | 3.544 | 0.152 | 3.185 | 3.902 | |
Red | 10.141 | 0.787 | 8.279 | 12.003 | |
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R7 (W1W) | Green | 3.428 | 0.178 | 3.007 | 3.849 |
Orange | 21.763 | 0.269 | 21.128 | 22.398 | |
Red | 66.042 | 0.810 | 64.127 | 67.958 | |
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R8 (W2S) | Green | 1.746 | 0.124 | 1.452 | 2.039 |
Orange | 4.078 | 0.270 | 3.439 | 4.718 | |
Red | 13.328 | 1.092 | 10.746 | 15.911 | |
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R9 (W2W) | Green | 3.095 | 0.140 | 2.763 | 3.427 |
Orange | 13.021 | 0.242 | 12.448 | 13.594 | |
Red | 47.899 | 1.526 | 44.290 | 51.508 | |
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R10 (W3S) | Green | 1.274 | 0.053 | 1.149 | 1.399 |
Orange | 1.312 | 0.012 | 1.283 | 1.340 | |
Red | 1.546 | 0.047 | 1.435 | 1.658 |
In this preliminary study, FOODsniffer and PEN3 were evaluated on the basis of their predictive performance in the food diagnostics field.
These results confirmed that a smart portable device associated with good prevention practices could potentially be useful to reduce food waste in the agrifood supply chain and especially for household use and also in the food recovery field for charitable purposes. FOODsniffer proved to be a valid and easy tool to use for nonspecialized personnel such as charity volunteers. However, further laboratory tests associated with studies to test the practical feasibility of using food sniffers by such volunteers are necessary.
As already highlighted in other studies, PEN3 confirmed its excellent performance in supporting traditional laboratory methods and proved to be a useful fast screening method for food business operators.
The development of new technologies is crucial in order for Europe to take effective action against food waste and reduce food poverty for the benefit of social, economic, and environmental sustainability.
The data used to support the findings of this study are included within the article.
The authors declare that they have no conflicts of interest.