In-Situ Assessment of Starchy Food Staling Using a Triboelectric Sensor Integrated with Deep Learning

Abstract

Staling causes quality deterioration of starchy food (SF) products, often leading to serious food waste due to untimely identification of staling extent. However, the staling assessment of SF products cannot be performed promptly in the supply chain stage because current rapid detection systems are not available for sensing the indices related to staling. In this work, we developed a staling assessment system based on a triboelectric sensor enabled by deep learning. This device captures texture dynamics of SF products in situ by converting the mechanical changes, characterized by increased hardness and decreased elasticity, into distinct electrical signals. To achieve the high sensitivity required for sensing these variations, the sensor features a hierarchical hollow carbon nanofiber (HCNF) electrode that integrates zero-dimensional hollow spheres within one-dimensional nanofibers. This architecture creates nanoscale cavity confinement to suppress charge decay, while providing an ultrahigh surface area (1601.92 m²/g) to enhance signal output. The collected signals were used to train a convolutional neural network (CNN), which achieves high recognition accuracies of 96.5%–98.1% in samples with different aging rates. Notably, the proposed sensor exhibited good compatibility with the packaging materials commonly utilized for SF, suggesting that this system could non-destructively detect the staling levels of SF in the supply chain to effectively mitigate food waste.

Graphical abstract

Keywords

Starch retrogradation / Non-destructive detection / Triboelectric nanogenerator / Deep learning

Highlight

	● In-situ triboelectric sensor enables non-destructive staling assessment.
	● Hollow carbon nanofibers significantly enhance texture-related sensing.
	● 1D-CNN model classifies staling levels with 96.5%–98.1% accuracy.
	● The platform operates directly through diverse commercial packaging materials.
	● Commercial bread samples confirm the system’s practical applicability.

1 Introduction

Food waste is a major global issue, with about one-third of food produced for human consumption wasted each year, amounting to 1.3 billion tons annually [1,2]. Starchy food (SF) products, such as bread, account for a significant portion of this waste [3–5], primarily due to staling caused by starch retrogradation. This process leads to textural changes, including increased hardness and reduced elasticity, which diminish food quality and result in premature disposal [6–8]. Current methods for detecting food staling, such as X-ray diffraction (XRD) and differential scanning calorimetry (DSC) [9–11], require complex sample preparation and extended analysis times, making them impractical for real-time, on-site applications (Fig. 1A). Consequently, there is a growing need for non-destructive, real-time monitoring systems for SF quality in the supply chain.

Recent advancements in non-destructive sensors are making real-time food quality monitoring possible [12–14]. However, most reported food sensors have been developed to monitor spoilage caused by microbial growth based on changes in biogenic amine concentrations and pH values using electrochemical, fluorescent, and colorimetric methods [2,12,15–17]. Nevertheless, these approaches are less suitable for evaluating starchy food staling, which is mainly associated with starch retrogradation and texture deterioration, one of the most direct quality indicators of staling, rather than volatile spoilage markers.

Triboelectric nanogenerators (TENGs) have emerged as promising tools for food quality monitoring by converting food related mechanical/chemical changes into electrical signals, including changes in moisture [18,19], gas emissions [13,14], alcohol concentration [20], and ascorbic acid levels of foods [21]. For instance, porous wood-based TENGs coupled with gas sensors achieved a sensing response of 0.85 toward 500 μmol/mol ammonia vapor, showing superior selectivity over other typical spoilage-related volatile compounds [13]. However, TENG sensors have not yet demonstrated the capability to monitor changes in food texture, such as hardness and springiness, which are key factors in starchy food staling during storage. In addition, current TENG technologies primarily focus on modified triboelectric or packaging-related sensing materials [13,18,19], however, such built-in sensing designs often require specially fabricated packages, limiting their direct use for commercially packaged foods and increasing implementation cost. A more practical strategy is to develop conductive and sensitive back electrodes compatible with existing commercial packaging materials for texture-related signal acquisition.

Carbon fibers are widely used as flexible TENG electrodes because of their conductivity, flexibility, hydrophobicity, and relatively low cost [22–24]. However, improving their electrical properties often requires costly graphene [25,26] or additional conductive material [27]. In contrast, hollow or porous nanostructures in carbon nanofibers (CNFs) can enhance conductivity and mechanical flexibility, significantly improving electrical conductivity performance without costly graphene or complex processes [28,29]. For instance, porous carbon nanofibers have been reported to enhance TENG output [30], while porous electrospun carbon nanofibers bearing TiO₂ hollow nanospheres achieved a high surface area of approximately 585 m²/g, showed an increased specific capacitance from 170 to 191 F/g at 0.5 A/g, retaining 97% of its capacitance after 10,000 cycles [31]. Nevertheless, the potential of hollow carbon nanofiber electrodes in TENG systems, particularly for balancing charge retention and charge transport in weak texture-related sensing, remains insufficiently explored.

In addition to sensor sensitivity, recognition accuracy in TENG-based food quality detection is affected by sample representativeness, environmental conditions, and applied force [32,33]. Machine learning provides an effective approach for extracting multidimensional features from complex TENG signals and enabling real-time classification [17,34,35]. However, algorithms specifically targeting the staling levels of starchy foods have rarely been developed.

To address these challenges, we developed a packaging-compatible S-TENG sensor that integrates a hierarchical hollow carbon nanofiber (HCNF) back electrode with machine-learning-assisted signal recognition (Fig. 1B). Without opening the package, the sensor converts staling-induced texture changes into electrical signals from commercial packaging surfaces. The hollow-sphere-embedded HCNF electrode improves weak texture-related signal acquisition, while signals collected under different staling conditions were used to train the CNN, which decodes staling-specific waveform patterns and achieves 96.5%–98.1% classification accuracy for starch-based foods. This non-destructive platform integrates texture sensing with deep learning for in-situ staling assessment, enabling rapid quality classification and helping reduce unnecessary starchy food waste.

2 Materials and Methods

2.1 Synthesis of hollow carbon spheres

Typically, a round-bottom flask was filled with 280 mL of ethanol and 40 mL of ultrapure water and uniformly stirred with a multi-point magnetic stirrer. The mixture was then transferred to a heating platform at 30 °C for 60 min. Next, 15 mL of ammonia water, 10 mL of tetraethyl orthosilicate, and 10 mL of tetrapropoxysilane were sequentially added into the above mixture under continuous stirring for 90 min to form a homogeneous solution. Afterward, 1.56 g of resorcinol and 2.25 mL of formaldehyde solution were added into the above homogeneous solution and kept stirring at 30 °C for 24 h to obtain a yellow solution. The yellow solid powder was collected after centrifugation and washing with ultrapure water, and drying at 80 °C for 24 h to obtain SiO₂@SiO₂/RF nanospheres as precursors. The precursors were immediately transferred to a tube furnace, calcining at 1000 °C with a heating rate of 5 °C/min under an argon atmosphere for 3 h to obtain a black solid powder, named with solid carbon spheres (SCS). After etching with 10% hydrofluoric acid solution for 24 h, the SCS was finally converted into hollow carbon spheres (HCS).

2.2 Synthesis of HCS fibers

HCS fibers were synthesized using a facile electrospinning method. In a typical procedure, 1.2 g of the as-synthesized HCS powders were initially dispersed in N,N-dimethylformamide solvent via sonication for 60 min. Then, 0.3 g of polyacrylonitrile [molecular weight (MW) = 15,000] was added into the above suspension, which was stirred at 80 °C for 24 h to form a homogeneously dispersed solution. Subsequently, the precursor solution was transferred into a syringe and connected to a direct current (DC) high-voltage power supplied with a stainless-steel nozzle. The high voltage, feeding rate, and rotational speed of the metal drum and distance between the needle tip and the collector were fixed at 10 kV, 13 μL/min, 300 r/min, and 10 cm, respectively. After drying overnight in a vacuum, the HCS fibers were collected.

2.3 Fabrication of hollow carbon nanofiber films

The above synthesized HCS fibers were first treated at 220 °C for 2 h under an air atmosphere, followed by a further thermal annealing process at 600 °C for 3 h with a ramping rate of 3 °C/min under the protection of argon gas. After cooling down to room temperature, the self-supported hollow carbon nanofiber films (HCNFs) were successfully fabricated.

2.4 Characterization of hollow carbon spheres, HCS fibers and HCNFs

TEM images were obtained on a Talos F200X microscope (Thermo Fisher Scientific, USA) operated at an acceleration voltage of 200 kV. Before TEM characterization, sample powders were dispersed in absolute ethanol and ultrasonically dispersed. The morphology of electrode material was observed by a Hitachi Regulus 8100 SEM (Hitachi, Japan) with an accelerating voltage ranging from 0.5 kV to 30 kV. The electrical conductivity of the HCNFs was measured by Keithley 2400 and a four-point measurement method was used to eliminate contact resistance.

2.5 Fabrication and characterization of the S-TENG device

S-TENG is fabricated via a straightforward assembly procedure. Cut two pieces of 15 cm × 20 cm size packaging film as friction layers. Subsequently, the two films are aligned, with the bread sandwiched in between, and the thermoplastic sealing equipment is employed to seal the packaging film in all four directions. Following this, two pieces of HCNFs (10 cm × 10 cm) are affixed onto the upper and lower layers of the packaging bag using conductive tape. The resulting assembly forms a TENG capable of functioning in a contact separation mode.

The electrical measurement of S-TENG was conducted using a commercially available Tektronix oscilloscope (TBS1102B, USA) in a laboratory setting, with an ambient temperature of 25 °C. The operating frequency was determined using a stopwatch.

2.6 Preparation of starchy food samples

To make the samples more comparable, samples were based on toasts and the aging rate was controlled by adjusting their composition. It is widely recognized that oil and sugar have a highly significant effect on the aging rate of bread, so we use oil and sugar free toast, oily and sugary toast, and toast with oil, sugar and anti-aging agents as the representative of fast, medium and slow staling samples respectively. At the same time, to ensure that the aging properties of the samples resemble those in commercially available products, the samples were treated with calcium propionate and sodium dehydroacetate to inhibit bacterial growth, and stored at room temperature (25 °C).

To preliminarily evaluate the applicability of the trained CNN model to real retail products, commercial bread samples were purchased from local supermarkets and tested in their original packaging. The storage time of each commercial sample was calculated from the production date labeled on the package to the test date. Product information, including sugar/oil content and anti-staling additives, was recorded according to the ingredient labels.

The aging rates of three representative samples were analyzed by XRD, SAXS, and TA. Analysis of the bread network structure diagram, XRD diffraction pattern, and SAXS pattern reveals a clear increase in the density of the starch gel network in all toast samples as the storage duration progresses. However, the rate of increase differs among the three bread types, with the density reaching its peak at approximately 7 days, 15 days, and 28 days, respectively (Figs. S6, S7 and Table S1). Concurrently, the elasticity of all three bread types reaches its minimum value around these time intervals (Figs. 4 and S12). These results suggest that it is reasonable to represent starch-based foods as fast-aging, medium-aging, and slow-aging by utilizing oil and sugar free toast, oily and sugary toast, and toast with oil, sugar and anti-aging agents, respectively.

2.7 Determination of starch retrogradation extent

Recrystallization degree: The bread samples were sealed and stored at a temperature of 4 °C, followed by freeze-drying, grinding, and sieving of the samples according to their respective storage durations. XRD patterns were obtained through step scanning with an interval of 0.02° in the 2θ range of 4°–40°.

The periodic structure of starch gel: SAXS was used to characterize the fractal structures and mesh size following our earlier reported method [36].

Texture qualities: Using the Texture Analyzer (TA. XT Plus Texture Analyzer, UK) to determine the texture quality of bread at different storage periods: using a P35 probe, the compression speed was 1 mm/s, compression to 70% of the original height, compression hold time is 2 s, and the recovery time is 10 s.

Microstructure of aged food: The microstructure of aged food was examined using a μCT scanner (RX Solutions, EasyTom150, Chavanod, France). Each scan involved collecting 1600 radiation projections with a resolution of 20 µm within a 360° range, with a source voltage of 150 kV and a current of 200 µA. The reconstructed sample imaging volume was generated using the filtered back projection algorithm implemented in Octopus Reconstruction software. Subsequent image processing was conducted in Avizo 9.7 (VSG).

2.8 Kappa consistency test

To assess the agreement between conventional methods and the S-TENG system, the kappa statistic is employed, which accounts for non-random agreements. The equation representing the kappa statistic is as follows [37]:

(1)\kappa =({\mathit{a}}_{\mathrm{o}}-{\mathit{a}}_{\mathrm{e}})/(1-{\mathit{a}}_{\mathrm{e}})

where a_o is the observed agreement and a_e is the expected agreement.

2.9 Signal processing and software interface

Data acquisition: The recognition system circuit employs surface mount device (SMD) components to fabricate a printed circuit board (PCB) with dimensions of 8.5 cm × 10 cm. A power supply with a voltage of 3.3 V is utilized to provide power to the circuit. The signal is read using the analog-to-digital converter (ADC) of the low-power STM32H750 microcontroller. Wireless data transmission is achieved through the implementation of the BLE module HC-05, with the corresponding codes being generated using the Keil uVision5 software.

Signal processing and analysis: Signal processing steps including data normalization and the deep learning classification algorithm were implemented with Jupyter notebooks (6.5.4), using the Keras in Python (3.9.16) with a TensorFlow (version 1.14.0) backend. In the proposed 1D CNN architecture, three convolutional layers are featured with 16, 32, and 64 filters and a kernel size of 5, each followed by a max-pooling layer.

Feature extraction of the time sequence: By setting up four position-specific convolution kernels in a rising and falling cycle of the electrical signal, which are located in the rising phase, peak, falling phase, and trough, respectively, to fully extract key features of the time sequence, the key features mainly include peak value, peak-to-peak value, RMS value, average value, curve coverage area, autocorrelation, and entropy.

2.10 Statistical analysis

All experiments and measurements were performed in at least triplicate, and continuous data were expressed as mean ± standard deviation. For evaluation of the deep learning model, model performance was quantified using overall accuracy, macro-averaged and weighted-average precision, recall, and F1 scores. The 95% Wilson confidence interval (CI) was calculated for the overall accuracy based on the hold-out validation dataset, which was not used for model training. The Kappa consistency test was used to evaluate the agreement between the CNN-predicted classifications and the staling levels assigned from conventional measurements.

3 Results and Discussion

3.1 Device structure and working mechanism

Fig. 2A illustrates the starch-based food (SF) staling recognition system, which primarily consists of commercial packaging bags, the SF product itself, and the hollow carbon nanofiber films (HCNFs) serving as flexible electrodes attached to both outer surfaces of the packaging bag, integrated with a customized printed circuit board (PCB). As illustrated in Fig. 2B, the electrification pairs were composed of the SF item itself and the packaging material (PM) layer, which converted the mechanical energy generated when the PM was gently pressed onto the SF surface, generating electrical energy through the coupling of the triboelectric effect and electrostatic induction [38,39]. The HCNF electrodes are responsible for transferring the charge generated by friction into the PCB (The integrated circuits are shown in Fig. S4). The charge is then processed through signal conditioning and transmitted wirelessly to a computer, where it undergoes algorithmic analysis to identify the staling level of the SF products. This non-destructive approach eliminates the need for opening the package, overcoming the limitations of conventional destructive testing methods such as XRD and DSC.

Specifically, the staling triboelectric nanogenerator (S-TENG) operated in vertical contact separation mode, as illustrated in Fig. S1A (i–v). The original configuration [Fig. S1A (i)] was disrupted by mechanical contact between the PM and SF during pressing, which generated charges with opposite polarities on the PM and SF surfaces [Fig. S1A (ii)]. Gradual separation between the PM and SF caused charges to flow from the bottom electrode to the top electrode through an external circuit, due to the presence of a potential difference [Fig. S1A (iii)], until equilibrium was reached [Fig. S1A (iv)]. The subsequent approach of PM to the SF surface caused the accumulated positive charge on the top electrode to flow back to the bottom electrode through the external circuit [Fig. S1A (v)], until equilibrium was reached. As a result, the contact-separation between the SF and PM in the S-TENG generated an alternating potential and current through the external circuit [40]. For example, the open-circuit voltage of the S-TENG was around 20 V when the contact electrification pairs consisted of fresh bread (10 cm × 10 cm) and a low-density polyethylene packaging bag (Fig. S1B).

3.2 Structural and functional characterization of HCNF electrodes

The S-TENG consists of three functional layers, including a polyimide encapsulation layer, a nanofiber conductive layer, and the electrification layers, as illustrated in Fig. 2B. The nanofiber conductive layer consisted of hollow carbon nanofiber films (HCNFs), which were synthesized by electrospinning a suspension of hollow carbon spheres (HCS) and polyacrylonitrile (PAN). The HCS, derived from calcined and etched SiO₂@SiO₂/RF nanospheres, were uniformly distributed within the PAN matrix during electrospinning to form composite nanofibers. For comparison, non-etched solid carbon nanofibers (SCNFs) were prepared under identical conditions but without the etching step. After annealing, a self-supported flexible HCNF film was obtained, as depicted in Fig. 2C.

The HCNFs were characterized for starch-based food staling detection. As shown in Fig. 3A, the HCNFs exhibit thin, lightweight, and flexible properties, with stable conductivity even under various bending angles (Fig. 3B), making them suitable for integration with irregularly shaped food surfaces without performance degradation (Fig. 3C–3F). In contrast, SCNFs showed a 72.09%–85.41% reduction in conductivity under similar bending stress (Fig. 3B, red column), attributed to their rigid solid-sphere structure. This conductivity stability can be attributed to their unique nanofiber structure, composed of HCSs. As revealed by the FESEM image (Fig. 3G and 3H), the HCSs are aligned along the longitudinal axis of electrospun PAN nanofibers, resulting in a coarse, tightly packed nanofiber morphology with diameters of ~300 nm. Furthermore, high-magnification SEM images (Fig. 3H) show a frog-egg-like structure of densely packed HCSs. This structure gives the nanofiber membrane porosity. Transmission electron microscopy (TEM) images showed that the etched samples exhibited well-defined hollow carbon nanospheres (Fig. 3I), whereas the non-etched counterparts retained a solid spherical morphology (Fig. 3J). Nitrogen adsorption-desorption isotherms (Fig. 3K and 3L) quantitatively demonstrate the significant enhancement in porosity, where the etched HCNFs exhibit a specific surface area of 1601.92 m²/g, representing an eighteen-fold increase compared to the non-etched counterparts (86.82 m²/g) and substantially exceeding that of other carbon nanofiber modifications, such as TiO₂-coated carbon nanofibers (~585 m²/g) [31]. Similarly, according to the cumulative pore volume curve (Fig. 3L), the cumulative pore volume surged from 0.09 cm³/g (non-etched CNF) to 2.88 cm³/g (etched CNF), demonstrating superior porosity compared to other carbon-based triboelectric materials.

Critically, this porosity-conductivity synergy may offer dual functional advantages for signal stability and electrical output. First, the hollow cavities within the carbon spheres may serve as charge-retention sites. This is supported by the significantly slower surface charge decay rate compared with the non-etched CNFs (Fig. 3M), and is consistent with charge-retention behaviors reported in other hollow porous systems [41]. Second, the interconnected fibrous network provides continuous conductive pathways through a three-dimensional architecture comprising nitrogen-enriched carbon matrices and radially aligned hollow nanofibers. This configuration may facilitate charge transfer through the fibrous network, which is consistent with the stable conductivity observed under different bending angles (Fig. 3B). Similar conductive carbon architectures have also been reported to improve charge transport in related systems [42]. Furthermore, the high specific surface area of the etched HCNFs (1601.92 m²/g) increases the available interface area for contact electrification, which may contribute to the enhanced triboelectric output. These combined structural features—hollow cavities for charge retention, continuous fibrous pathways, and high-surface-area interfaces—are consistent with the fivefold enhancement in triboelectric output. Under a 10 N force, the HCNFs generated an open-circuit voltage of 20 V for starch-based foods, compared with only 4 V from the non-etched films (Fig. 3N). Similar structural effects have been reported in 1D/0D hybrid triboelectric system [43], and porous structures have also been shown to improve electrical output and mechanical stability in TENG system [30]. By integrating these structural advantages, the HCNF-based electrodes enable sensitive staling detection through aging-dependent electrical output variations.

In addition, compared with conventional non-destructive food quality monitoring methods, such as optical/spectral imaging, gas sensing, acoustic analysis, and impedance-based sensing, the HCNF-based S-TENG provides a flexible and self-powered route for capturing texture-related contact-separation electrical responses through existing packaging materials, thereby improving compatibility with commercially packaged starchy foods without requiring package redesign.

From the perspective of practical application, the HCNF-based S-TENG offers a low-material-cost, reusable, and stable external sensing strategy based on a porous-carbon electrode, avoiding built-in disposable labels, expensive conductive additives, and easily consumable sensing reagents. The carbonized porous framework reduces stability concerns associated with volatile or degradable sensing components, while the bending tolerance shown in Fig. 3B further supports its potential for practical starchy food monitoring.

3.3 Sensing staling performance of S-TENG

During the storage of starchy foods, the staling behavior, primarily driven by the aging of starch molecules, resulted in changes to the bread structure and texture properties [44,45]. According to the bread images obtained by X-ray micro-computed tomography (µCT), as shown in Figs. 4A and S5, gradual densification of the bread structure was observed with increased storage time. This was due to the crystalline structures that formed through interaction between the starch molecules [46], which was confirmed by the X-ray diffractometer (XRD) results. As shown in the XRD diffraction patterns of the bread (Fig. S6), the intensity of the diffraction peak near 17°, which was representative of starch B-type crystallization, gradually increased with prolonged storage time [47,48]. This led to an increase in the ordering of the bread structure and a decrease in the mesh size of the gluten network. The latter was confirmed by the small-angle X-ray scattering (SAXS) patterns and corresponding fitting results (Fig. S7 and Table S1) [36,49–51]. This progressive densification with aging hampered electron transfer in SF, resulting in a gradual decrease in the intensity of the S-TENG output, as shown in Figs. 4B and S8–S10.

Critically, non-etched carbon nanofiber sensors (SCNFs) exhibited rapid signal degradation, with measurable signals only detectable up to Day 5, followed by unreliable outputs beyond Day 5 due to excessive noise (Fig. S11). In contrast, the etched HCNF-based S-TENG maintained robust signal resolution over 15 days, showing a controlled attenuation of 73.91% (Fig. 4B), thereby enabling precise tracking of structural densification throughout the aging process.

The waveform characteristics of the S-TENG output further validated its ability to resolve aging-induced texture variations. As shown in Figs. 4C and S12, more intermolecular interactions after starch aging resulted in increased firmness and decreased resilience of the bread samples, which fundamentally changed the waveforms of the S-TENG sensor output. As shown in Fig. 4D and E, the peak-to-peak voltage formed during pressing due to the good elasticity of fresh bread gradually disappeared as the elasticity of the bread decreased. Notably, on Day 0, the signal-to-noise ratio (SNR) of unetched SCNFs (4.78 dB, Fig. S11A) was substantially lower than that of etched HCNFs (20.1 dB, Fig. 4E), underscoring the inherent advantage of the hollow architecture in capturing interfacial charge dynamics. On Day 5, the SNR of SCNFs further degraded to 2.83 dB (Fig. S11B), whereas HCNFs maintained a stable ratio of 5.04 dB under identical conditions. By Day 10, SCNFs were unable to detect elasticity-related signals (Fig. S11C), while HCNFs continued to exhibit detectable spikes, even after 15 days of storage (Fig. 4E). This difference indicates that the hollow-sphere-embedded nanofiber structure helps preserve weak texture-related signals during late-stage staling, likely by providing abundant interfacial sites and continuous conductive pathways. In contrast, the solid architecture of SCNFs lacks sufficient porosity and structural flexibility, resulting in weaker charge response and higher noise when elasticity-related deformation becomes subtle. Accordingly, the retained waveform fidelity of HCNFs enabled more reliable tracking of elasticity degradation, a critical texture parameter, whereas SCNFs’ rigid structure introduced signal distortion and noise in late-stage aging (Fig. S11C), rendering them unsuitable for long-term monitoring.

The above results demonstrated that S-TENG could reveal variations in the densification of internal structure and the texture quality of the SF products. Structure and texture quality generally serve as intuitive indicators to reflect the age of SF, along with the index that generally affects the consumer acceptability of SF products. This means that SF staling can be quantified by the magnitude and waveform shapes of the S-TENG output signal.

3.4 Applied to SFs packaging with different packaging materials

There is a wide range of packaging materials for starchy foods on the market, making it necessary to develop recognition systems that can adapt to different materials for in-situ non-destructive monitoring. Therefore, to assess the adaptability of the sensor, we conducted evaluations on the six most commonly used packaging materials for SF products. In the market, SF packaging materials typically fall into two main categories, namely, polyolefins and paper, with polypropylene (PP) and polyethylene (PE) as the predominant types of polyolefins [52]. Specifically, low-density polyethylene (LD-PE) and polyethylene-polyamide (PE-PA) represented the PE category, while PP and biaxially oriented polypropylene (BOPP) represented the PP category. Additionally, paper and paper-polyethylene terephthalate (PET) composite served as representatives of paper packaging bags (Fig. 5A). The voltage outputs of these packaging materials are shown in Figs. 5B and 5C.

As shown in Figs. 5B and 5C, the strength of electrical signal output from the plastic materials surpassed that of paper, regardless of wh ether the SF was fresh or stale. The output performance followed the order of LD-PE > BOPP > PP > PE-PA > paper-PET > paper, which was associated with the electronegativity difference between the PM and SF, with a higher electronegativity difference between PM and SF corresponding to a greater output value. In the triboelectric sequence, paper and starch both had a tendency to acquire electrons, whereas PP and PE were more prone to losing electrons [53,54]. As a result, the electronegativity similarity between the paper and starch-based foods diminished charge transfer between them. However, it should be noted that even packaging materials with limited electrification properties, such as paper, have a signal-to-noise ratio (SNR, i.e., the ratio of the signal to the background noise) of about 20 dB (Fig. 4E), which is sufficient to allow the recognition of the staling extent over the noise.

Crucially, although the absolute voltages varied among the different types of bags, their waveform variations with aging remained highly consistent (Figs. 5B and 5C). Compared with fresh bread, stale bread generated weaker and less sharp voltage waveforms across all packaging materials, exhibiting reduced peak intensities and less pronounced rebound-related signal features. This waveform evolution was fundamentally attributed to starch retrogradation-induced structural densification and resilience loss, which physically weakened the contact-separation deformation and rebound between the packaging material and the bread surface during pressing. This consistent aging-dependent observation implied that the proposed system is robust and suitable for a wide range of PMs utilized in commercial SF packaging.

3.5 Effectiveness characterization of signal real-time acquisition

Data transmission in real time was achieved by using a customized PCB board. The PCB consisted of three parts, as shown in Figs. S4A and S4B: the signal conditioning circuit, the microcontroller (MCU) with an analog-to-digital converter (ADC), and a Bluetooth data transceiver. Given the fact that starch-based food products with a high degree of aging and packaging materials with poor electrification performance tend to exhibit lower output voltage, the PCB circuit employs a high impedance and a filter to mitigate signal interference and effectively suppress undesired high-frequency electrostatic noise. As shown in Fig. S4C, the customized PCB board avoids noise effectively, and the signal-to-noise ratio is improved by 174.39% over the pre-filtering value. After the analog-to-digital conversion, the converted digital waveforms fell within ± 3.3 V, as Figs. S13A and S13B shows, and the ADC signal waveform of the aged sample presented weak peak-to-peak voltage.

3.6 Deep learning-enabled classification and recognition

The S-TENG signals collected from SF samples with different staling degrees showed complex waveform variations, making simple linear or visual assessment potentially unreliable [55,56]. Deep learning has been employed as an effective strategy for extracting features from complex TENG-generated signals [57–59]. In this work, a deep learning neural network was used to extract features from S-TENG waveforms, focusing on frequency and amplitude characteristics (Fig. 6). To find an appropriate deep learning model, different types of deep learning models were evaluated based on the S-TENG signal of the SF with varying degrees of staling. Comparative accuracy analysis of the trained models demonstrated that the 1D-CNN model outperformed its counterparts (Table S2).

The standard application of deep learning involves a training process and validation process [60] (Fig. 6). To ensure the practicality of the identifying system, it is crucial to establish a comprehensive dataset during the training process. To this end, the electrical signals were collected from the S-TENG sensor of numerous SF samples with different known staling levels under controlled storage to generate a waveform dataset for training and testing the 1D-CNN model. The model input records were collected from SF products with three staling rates (validation approach of staling speed described in the Methods section), which were grouped according to the conventional equipment results, including XRD, SAXS, and texture analyzer, as commonly employed in SF assessment processes [61–64] (Supplementary Note 1, Tables S4 and S5 provide the details of the classification basis). The samples were manually labeled as fresh (level 1), relatively fresh (level 2), lightly stale (level 3), and highly stale (level 4) (Figs. 6A and S13C). Moreover, during the construction of the S-TENG signal dataset, samples with fast, medium, and slow aging rates were individually packaged with six types of common packaging bags, and stored for 7, 15, and 28 days, while the voltage signals were separately collected at intervals of 1, 3, and 7 days. Furthermore, considering the influence of pressing strength on signal intensity, each sample was measured under three predefined force levels: light, medium, and heavy (Fig. 6A). This design enabled the CNN to recognize staling-related features under different pressing intensities, reducing dependence on a single applied force. In addition to dataset expansion, key time-series features, including peak value, peak-to-peak value, average value, RMS value, and autocorrelation, were extracted to improve CNN classification performance (Fig. 6B, Methods). The first four features are the key embodiment of the electrification ability of starch-based food, whereas the last term is related to the regularity and uncertainty of the signal sequence. Together, the optimization of the dataset and the algorithm of feature extraction resulted in classification accuracies in the range of 96.5%–98.1% for the SF samples with different staling rates and storage times (Fig. S14).

To provide a more rigorous statistical evaluation of the CNN model, a total of 936 S-TENG waveform records were collected for CNN model construction, with 748 records used for training and the remaining 188 records reserved for validation under a hold-out validation strategy. The validation results were further analyzed using a row-normalized confusion matrix and class-wise performance metrics. The CNN correctly classified 182 of 188 validation records, yielding 96.81% accuracy (95% Wilson CI: 93.21%–98.53%). Detailed class-wise precision, recall, and F1 scores are listed in Table S6. Macro-averaged precision, recall, and F1 score were 96.37%, 96.66%, and 96.50%; weighted averages were 96.84%, 96.81%, and 96.81%. The row-normalized confusion matrix (Fig. S17) showed that misclassifications occurred only between adjacent staling levels, consistent with the gradual nature of starch retrogradation, as starch chain recrystallization is a continuous structural transition rather than an abrupt step-change. Overall, these results demonstrate that the CNN model can reliably extract multidimensional features from S-TENG waveforms for accurate staling classification.

The trained CNN was used to predict the aging degree of the samples with different storage days and staling rates, and the CNN-predicted classifications were compared with the staling levels assigned from conventional measurements using the Kappa consistency test [37], with the results shown in Figs. S13D–S13F. As presented in Fig. S13G and Table S5, Kappa values exceeded 0.7 for all comparisons and were greater than 0.8 for resilience (RS) and relative crystallinity (RC), indicating strong agreement between CNN predictions and conventional measurements [37].

To preliminarily evaluate the applicability of the trained CNN model to commercial bread products, commercial breads with different sugar/oil contents and anti-staling additive conditions were tested, and the results are shown in Table S3. For these commercial samples, the storage time was calculated from the production date labeled on the package to the test date. Their true labels were assigned according to the staling-level classification criteria described in Supplementary Note 1 and Table S4. The CNN-predicted labels were consistent with the true labels for all samples, providing preliminary evidence that the model can be applied to commercial bread products with different formulations. Nevertheless, larger-scale testing using products from multiple manufacturers, batches, formulations, and storage conditions is still required for comprehensive real-life validation. Finally, to facilitate practical use of the recognition system, a user-friendly mobile application was developed to display the real-time staling identification results (Fig. S15).

3.7 Validation of food with a nonlinear relationship between aging and quality

The relationship between the aging degree of starch and the quality of starchy food will not always be linear. For example, rice noodles require appropriate aging before obtaining optimum product quality, but over-aging will reduce their eating quality [6,36]. Thus, the quality of rice noodles rises at first and then declines with the increase of starch aging. Therefore, we used newly extruded rice noodles as samples to evaluate the system’s assessment capability on SF with a nonlinear relationship between the aging extent and product quality.

The process of monitoring the quality of SF items using the S-TENG sensor system during the production process is illustrated in Figs. 7A and 7B for rice noodles packaged within the packaging material of PE-PA. As shown in Fig. 7C, the hardness of the rice noodles increased monotonically with increased storage time, while their resilience initially increased up to a storage time of 6 h, and then subsequently decreased with prolonged storage. Rice noodles with good palatability usually have good elasticity and toughness, but excessive hardness increases their brittleness (that is, they are easy to break), so high hardness is detrimental to the textural quality of rice noodles. According to the relationship between hardness, elasticity, and palatability, the texture quality of the rice noodles initially increased and then decreased with increased aging degree. The S-TENG signal presented a similar fluctuation trend in the aging process. As indicated in Fig. 7D, the amplitude of the S-TENG output signal first increased and then declined during rice noodle aging, with a maximum signal amplitude obtained after 6 h of storage. Consequently, the optimal aging time of the rice noodle product could be gauged according to the time at which the S-TENG sensor registered a maximum amplitude output.

The process of employing the trained 1D-CNN model for predicting the aging time of the sample is shown in Figs. 7E and 7F. The testing results showed a classification and recognition accuracy exceeding 90% (Fig. S16), and the samples stored for 6 h were successfully labeled as moderately staling samples, while the samples stored for 8 h were classified as highly aging samples (Fig. 7F). It is interesting to note that although the signal intensity of the 8 h sample was not significantly different from that of the 0 h sample, the trained-CNN still identified the samples that had been aged for 8 h as excessive aging. This is because the recognition of the CNN depended not only on the intensity, but also on the signal feature. As shown in the output voltage diagram of 0 h and 8 h samples (Fig. 7D), the intensity of the noise-like signal of the 8 h sample was obviously stronger than the 0 h sample. This noise-like signal was caused by the firm texture of the rice noodles due to excessive staling. Therefore, for the 0 h sample with lower firmness, the noise-like signal between the rigid noodle surface and the PM friction is smaller.

Collectively, these findings provided evidence that the proposed system was effective in monitoring aging progression and controlling the quality of starch gel food in real-time, either by observing the variation in signal intensity of the S-TENG output or through recognition by the trained CNN.

4 Conclusion and Perspectives

In summary, a non-destructive, packaging-compatible triboelectric sensing system was developed for rapid and in-situ staling assessment of starchy foods (SFs). The system uses a hollow carbon nanofiber (HCNF) film as the back electrode, which provides a porous and conductive structure for sensitive and stable texture-related signal acquisition. By converting staling-induced changes in hardness and elasticity into electrical waveforms and integrating them with CNN-based recognition, the system achieved 96.5%–98.1% classification accuracy for SF samples with different staling rates. The system therefore provides a practical route for non-destructive shelf-life monitoring and timely quality classification in supply-chain scenarios. As a scenario-based estimate, even a 0.1%–1.0% reduction in premature disposal through accurate staling assessment would correspond to 1.3–13 million tons less starchy food waste annually, based on a global food waste value of 1.3 billion tons per year. Overall, this sensing approach offers a promising smart-packaging strategy for rapid staling assessment, supply-chain quality management, and reduction of unnecessary starchy food loss.

Despite these promising results, further work is still needed before practical use. The long-term durability and reusability of the sensor should be tested under repeated operation, and the effects of humidity and temperature fluctuations should be evaluated in more realistic storage conditions. In addition, future studies should include more starchy food products, packaging types, production batches, and supply-chain scenarios to further assess the robustness, scalability, and economic feasibility of this sensing strategy.

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The Author(s) 2026. This article is published by Higher Education Press.