SmellNet: A Dataset for Sensor-Based Smell Recognition and Mixture Prediction
Authors: Dewei Feng
Wei Dai
Carol Li
Alistair Pernigo
Paul Pu Liang
Venue: ICLR 2026 Poster (2026)
Keywords: Smell sensing
multimodal AI
AI for smell
smell recognition
chemistry
physical sensing
Primary area: datasets and benchmarks
TL;DR: We introduce SmellNet, a large-scale portable sensor dataset for odor classification and mixture recipe prediction, and ScentFormer, a temporal Transformer that performs strongly on both benchmarks.

Abstract: The ability of AI to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g. detecting peanut contamination or allergens in food), monitoring the manufacturing process, and sensing hormones that indicate emotional states, stress levels, and diseases. Despite these broad impacts, there are few standardized datasets, and therefore little progress, for training and evaluating AI systems' ability to "smell" in the real-world. In this paper, we use small gas and chemical sensors to create SmellNet, a comparatively large dataset for sensor-based machine olfaction that digitizes a diverse range of smells in the natural world. SmellNet contains about 828,000 time-series data points across 50 substances, spanning nuts, spices, herbs, fruits, and vegetables, and 43 mixtures among them with fixed ingredient volumetric ratios, with 68 hours of data collected. Using SmellNet, we developed ScentFormer, a Transformer-based architecture combining temporal differencing and sliding-window augmentation for smell data. For the SmellNet-Base classification tasks, ScentFormer achieves 63.3% Top-1 accuracy with GC-MS supervision, and for the SmellNet-Mixture distribution prediction tasks, ScentFormer achieves 50.2% Top-1@0.1 on the test-seen split. ScentFormer's ability to generalize across conditions and capture transient chemical dynamics demonstrates the promise of temporal modeling in sensor-based olfactory AI. SmellNet and ScentFormer lay the groundwork for sensor-based olfactory applications across healthcare, food and beverage, environmental monitoring, manufacturing, and entertainment.

Published as a conference paper at ICLR 2026
SMELLNET: A DATASET FOR SENSOR-BASED SMELL RECOGNITION AND MIXTURE PREDICTION
Dewei Feng, Wei Dai, Carol Li, Alistair Pernigo, Paul Pu Liang
MIT Media Lab and MIT EECS https: //tpt//gggtttbtt/// cOm/MIIII////// PIIMMMIT-T MMI-MIITIM SmellNet
ABSTRACT
The ability of to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g. detecting peanut contami- nation allergens in food), monitoring the manufacturing process, and sensing hormones that indicate emotional states, stress levels, and diseases. Despite these broad impacts, there are few standardized datasets, and therefore little progress, for training and evaluating Al systems' ability to 'smell' in the real-world. In this paper, we small gas and chemical sensors create SMELLNEE,, compara- tively large dataset for sensor-based machine olfaction that digitizes diverse range of smells in the natural world. SMELLNET contains about $28,000 time-series data points across 50 substances, spanning nuts, spices, herbs, fruits, and vegeta- bles, and 43 mixtures among them with fixed ingredient volumetric ratios, with 68 hours of data collected. Using SMELLNET, we developed SCENTFORMER, a Transformer-basee architecture combining temporal differencing and sliding- window augmentation for smell data. For the SMELLNET- BASE classification tasks, SCENTFORMER achieves 63.3% Top-1 accuracy with GC-MS supervision, and for the SMELLNET-MIXTUS achieves 50.2% Top- 1 @0.1 on the test-seen split. SCENTFORMER's 's ability to generalize across conditions and capture transient chemical dynamics demonstrates the promise of temporal modeling in sensor-based olfactory AI. SMELLNET and SCENTFORMER lay the groundwork for sensor-based olfactory applications across healthcare, food and beverage, environmental monitoring, manufacturing, and entertainment.
distribution prediction tasks, SCENTFORMER
CO over Time
Top-5 Predicted Ingredients
100
0.72
Cosine Similanty
0.52
0.69
NO2 Time
2.4
0:39
0.35
v0
-30
200
200
465
500
0.5
Canhesa
Reanuts Pecone
Whinute
Nut
(a) Sensor setup detecting cashew.
(b) Time-series signals from CO and NO2 sensors.
(c) Top-5 model predictions using cosine similarity.
Figure 1: Overview of our smell sensing data collection and modeling pipeline. (a) Sensor hardware setup and data capture. (b) Raw sensor readings over time. (c) Al model predictions on the substance.
INTRODUCTION
Advancements in AI have revolutionized how machines perceive and interact with the world. However, most progress has been limited to the text, vision, and audio modalities (Liang et al. 2024 Gan et al. 2022). The human sense of smell is crucial for environmental perception, social interaction, and regulating well-being. Similarly, sensor- based smell recognition (machine olfaction) can support range of applications in the entertainment, e-commerce, manufacturing, and food and beverage industries (Deshmukh et al. 2015] Vilela et al. 2019). More ambitiously, smell-based diagnostics can help in early disease detection (e.g., COVID-19) (Kwiatkowski et al. 2022), and even 'smelling'

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Published as a conference paper at ICLR 2026
hormones and indicators of emotional states, stress levels, and for early prognosis of cancer (Tillotson 2017 Zamkah et al. 2020).
Nevertheless, machine olfaction with deployable sensors remains far less developed when compared to computer vision and natural language processing. key bottleneck the lack of standardized. realistic sensor-side benchmarks for learning and evaluating models on temporal smell signals. We believe that larger-scale data and real-time Al models can enable richer feature representations of smell for accurate sensing, classification, and feature fusion between smell and other human senses. This strategy differs from past research, which has emphasized feature engineering, small datasets, and simple classification models (Achebouche et al. 2022; Guerrini al. 2017 Lee et al. 2012) 2). and processing pre-recorded smell data collected via large chemistry lab equipment (Tann et al. 2019 Lee et al. 2023), which do not work in real-time.
As step towards real-world and real-time smell sensing, we present SMELLNET, comparatively large sensor-side dataset of how food, beverages, and natural objects register on low-cost gas and chemical sensors. SMELLNET is collected by applying small sensors to 50 substances (nuts, spices, herbs, fruits, and vegetables) and 43 ingredient-level mixtures with fixed volumetric ratios across 68 hours of data. Importantly, SMELLNET contains sensor time series and metadata rather than human perceptual ratings. With 828,000 time step readings across environmental conditions, SMELLNET provides benchmark for training and evaluating machine-olfaction models on substance classification from sensor readouts.
Using SMELLNET, we developed SCENTFORMER, Transformer-base based architecture combining temporal differencing and sliding-window augmentation for smell data. For the SMELL BASE classification tasks, SCENTFORMER achieves 63.3% Top-1 accuracy with GC-MS supervision, and for the SMELLNET-MIXTUS Top-1 1 on the test-seen split. SCENTFORMER'S 's ability generalize across conditions and capture transient chemical dynamics demonstrates the promise of temporal modeling in -ensor-based machine olfaction. SMELLNET, FORMER and the source code are released in our GitHub repository (linked above) and supplementary materials to ensure reproducibility and to facilitate new applications healthcare, food and beverage, environmental monitoring, manufacturing, and entertainment.
distribution prediction tasks, SCENTFORMER achieves 50.2%
2 THEORETICAL BASIS AND RELATED WORK
While large-scale machine olfaction with low-cost sensor time series remains relatively underexplored, we are inspired by human smell sensing, the chemistry and biology of smell, and using Al to process small-scale smell data.
Human smell receptors: Olfaction, the sense of smell, allows for the detection and discrimination of odors in the environment (Stevenson, 2010). The human nose can detect and discriminate between an estimated trillion different odors, even in minute quantities (Bushdid al. 2014). This makes the human olfactory system the largest, in terms of the number and diversity of receptors, allowing for the sensing of vast chemical landscape (Sharma et al. 2019). Human olfactory perception functions through combinatorial code, where each odorant molecule binds to a specific set of olfactory receptors (ORs) in the nose (Malnic et al. 1999). This binding converts chemical information into electrical signals which are perceived in the brain (Firestein) 2001).
Smell sensors for perceiving smell have been developed, including chemical compositions of gases based on the principles of molecular interaction and chemical potential equilibrium (Brattoli et al.) 2011). These sensors can employ different scientific strategies to detect and analyze gas molecules, including those based on semiconducting materials like metal oxides (Nikolic et al.) (020). electro- chemical sensors that generate a current proportional the gas concentration (Bakker and Telting Diaz 2002). optical sensors based on different gases absorbing different wavelengths (Hodgkinson and Tatam 2012), and conductive polymers that change their conductivity when exposed to gas molecules Miasik et al. 1986). We use gas sensors due to their portability, although all sensors can suffer challenges due to sensitivity, environmental interference, reproducibility, and device calibration (Sung et al. 2024 Yan et al. 2015).
AI for smell sensing: There has been some work in using technology to process pre-recorded smell data, but these systems are not portable or work in real-time. These include graph neural net- works trained to classify smell chemical molecules (especially pre-recorded GC-MS data) (Sanchez.
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Table Representative olfactory datasets by modality. Human- Human-percepea perception datasets pair odorants with human judgments, sensor datasets pair odor sources with instrument signals. SMELLNET most diverse portable metal-oxide (MOX) sensor time-series dataset in this table, covering natural foods and controlled mixtures, with ingredient-lveel GC-MS priors.
Dataset
Stimulus
What's measured
Acquisition/ / method
Scale (rep.)
Hluman-perceptle datasets (odorant/mixture + human response)
Dravnieks Atlas
Mono-
Semantic descriptor
Human evaluation
21.3k ratings
(Dravnicks 1985)
molecular
ratings
DREAM ha lllnge
Mono-
Semantic descriptor
Human evaluation
10.0k ratings
(Keller et al. 2017)
molecular
ratings
Offactory Metamers
Molecular
Perceptual similarity
Human evaluation
49.8k judgments
Ravia cf al.
2020
mixtures
judgments
Sensor machine-olfaction datasets (odor source + sensor signals)
Coffee quality e-nose (Rodriguez al. 2010
Coffee
Gas-sensor time series
E-nose (8
58 measurements
samples
sensors), Hz for s00
Beef quality e-nose (Wijaya all 2018)
Beef cuts
Gas-sensor time series (+ env in dataset)
E-nose (MQ sensors) in uncontrolled
5 sessions (2160 min
each)
ambient conditions 16 MOX sensors, drift over months
Gas Sensor Array Drift
Pure gases
MOX array responses / features
13,910 measurements
Vergara 2012)
SMELLNET (ours)
Natural foods and ingredient
Base: channels, Mixture: 4 channels
Low-cost MOX array, controlled acquisition protocol
50 substances, 43 mixtures, 68 h, 828k timesteps
mixtures
(Grove only)
Lengeling et al. 2019: Tran et al., 2019 Lee et al. 2023), but they require data collection via large chemistry lab equipment rather than portable sensors. Past research has also emphasized human domain knowledge, feature engineering, small datasets, and simple classification models rather than large-scale data-driven learning (Achebouche al. 2022) Guerrini et al. 2017 Lee al., (0122). Electronic noses have been designed to monitor pollutants and for air quality assessment (Attallah and Morsi 2022 Payette al. 2023), but they
generally applicable for any type of smell. Methods to classify biological offactory data of mice and humans have also been proposed (Fang
al. 2024 Wang et 2021). but do not enable portable real-time sensing.
Other datasets: Tab. normalizes prior olfaction datasets by the number of data points. Unlike prior pairwise human judgments, SMELLNET provides large-scale sensor time series from natural stimuli. To our knowledge, is the most diverse, open sensor-based dataset for smell, spanning 50 base substances and 43 mixtures over 68 hours.
work focused mono mnono-moleculad
CREATING SMELLNET
3.1 A SMALL AND REAL-TIME SMELL SENSOR
We use compact array of low-cost metal-oxide (MOX) gas sensors capture odor-dependent volatile response patterns. Specifically, our platform uses MQ-3, MQ-5, and the Grove Multichannel Gas Sensor V2, providing six gas-sensor channels corresponding to manufacturer-labelee responses such as carbon monoxide (CO), nitrogen dioxide (NO2), alcohol, volatile organic compounds (VOCs), liquefied petroleum gas (LPG), and ethanol (C2 We emphasize that these channel names are manufactured
manufacturer calibration labels rather than direct measurements of pure analytes in food headspace. In practice, each MOX channel broadly cross-sensitive to multiple volatile families. This overlapping sensitivity is desirable in an e-nose setting, because the joint multi-channel temporal response provides a structured, discriminative signature of common odors in foods, drinks, and other everyday substances, consistent with prior low-cost e-nose systems used for food-quality monitoring and freshness assessment (Rodríguez et al.) 2010; Wijaya et al. 2018 Mahradian 2023). The circuit diagram and full hardware specifications provided in App. A.I
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(a) Data Collection Pipeline
Pure Subsiances (50 classes)
10 Minute Sensor Reading Ht for Pare Hi Mixture
Log Data (CO,, vocs, etc.)
Reset Control Seiting
Align Sensor, GC-MS, and Language Data SSS
Downstream Thikks
Nuts
Spices
Harba
Fruits
Vegetables
Substances
T
Peanuts
Cumn
Conancer
Pinespole
Caufflower
Co3
Cacsitiation Diorthuton
Cashew
Siar Ariie
Speammi
Pruits
Simulated classes)
Nikes
CLL soni
100%
10096
2056
50%
Loraage
trange
Doact
shople
PCI oxlance!
(a) Data collection pipeline. Each ingredient undergoes six 10-minute sensing sessions different days, using controlled environment to minimize external noise. During each session, 6-channel gas sensor data is recorded at Hz and labeled with the ingredient identity, collection time, and associated metadata. We further pair each ingredient with high-resolution GC-MS data to enable multimodal learning. This setup enables the creation of structured and temporally rich dataset for representation learning of smells. (b-e) PCA projections of sensor responses. While broad category separation is evident, clusters remain partially overlapping, underscoring the challenge of distinguishing ingredients and motivating more advanced models.
Figure 2:
Than
Tex
Apple
Fore
Camin
Ganana
Conander
Strawbery
71.3%
(a)
Racarann time (minuteo)
Trainin
800
5000
20
Peach
Grorpe
Past
Stansberry
Almord
Pow
(b)
Ingredient
(c)
Mango
Figure 3: Composition of SMELL MEXTUUEEE (a) Distribution of mixtures across train and test datasets. The test set contains harder samples, including binary (77.9%) and ternary (10%) mixtures. (b) Minutes of valid odor data collected for each ingredient. (c) Ingredient co-occurrence patterns: chords indicate data mixtures, and widths represent the amount of mixture data. Each chord encompasses mixtures of different ratios. Teal chords indicate where the mixture is prominently spices nuts, while pink indicates mixtures that are prominently fruits on average. diverse odor mixtures are evenly spread across the entire substance space.
3.2 SMELL SENSING DATA COLLECTION
SMELLNNE comprises two main components: base substances for classification tasks and mixture substances for distribution prediction tasks.
SMELLNET- BASE is collected on 50 substances across classes: nuts, spices, herbs, fruits, and vegetables. The full taxonomy of substances is shown in Fig. b]... For each, we used the sensor to collect data for a 10-minute session, repeated 6 times across different days. Each session was done in controlled environment to minimize environmental factors. At the end of each session, we clear out the air in the controlled environment so that the environment returns to atmospheric conditions (see App. A.2 and App. H..5 for analysis using different time frames and detailed procedures for resetting each experiment). Fig. iillssratess the overall data collection pipeline. This gives us hour of sensor readings for each substance and 50 hours of data in total. During each session, 6-channel gas sensor
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data is recorded at 1 Hz, which gives 180,000 total data points. Data is labeled with the name of the substance, its detailed description, and the time and date of collection.
In real-world scenarios, mixtures of smells are more common, so we also collect SMELLNET- MIXTURE with mixtures of 12 base odorants. The odorants are selected from various types of fragrance oils, essential oils, and flavor extracts to form our odorant palette: almond, apple, banana, clove, coriander, cumin, garlic, mango, orange, peach, pear, and strawberry. Details are included in Tab. 17 These odorants span range of functional groups and chemical families relevant to broad range of odors, including phenols (clove), aldehydes (almond, cumin), esters (banana, pear, peach, strawberry, apple), terpenes (orange, mango, coriander), and sulfur compounds (garlic). Furthermore, we define two test sets with different generalization challenges: (1) Test-seen contains mixture ratios that appear in the training set but from different recording sessions, testing the model's ability to generalize across temporal and environmental variations. (2) Test-unseen contains novel mixture combinations never encountered during training, challenging the model's compositional generalization in entirely new ingredient pairings, or novel ratios of familiar mixtures.
Due to size restrictions of the collection environment, the mixture data is collected with only the Grove Multichannel V2 sensor, with four channels spanning across NO2, C2H₃ VOC, and CO at 10 Hz. The resulting dataset comprises 18.0 hours of continuous sensor recordings with 648 ,000 data points across 1,078 distinct measurement sessions, with 679 training sessions (11.32 hours), 215 test-seen mixtures (3.58 hours), and 184 test-unseen mixtures (3.07 hours). Combining the two subsets together, we have a total of 68 hours of sensor readings for 50 base substances and 43 different mixtures, totaling 828,000 data points, making it the largest censor-based multitask smell dataset to date.
3.3 PAIRING WITH GC-MS
One potential limitation of sensor data is its low resolution, which stems from the quality of the sensors used. We therefore paired this data with preexisting open-source pre-recorded GC-MS data (BooDB Contributors 2024). GC-MS devices are large, bulky, and non-portable, but can detect the exact concentration of various compounds in substance at a high resolution. As result, pairing gas sensing data with GC-MS readings provides complementary information for this task and allows for studies of multimodal fusion and cross-modal learning (Liang et al. 2024).
3.4 SMELLNET STATISTICS
We standardize the recorded data into a common format via the sensor readings of volatile gases over the time period. We plot some examples of sensor readings in Fig. [b-c), and App. B As shown in Fig. 2)),, we apply PCA to the 6-dimensional sensor readings across all time steps to visualize ingredient-level separability. The projection reveals visible clustering according to ingredient categories, particularly for spices and fruits, which occupy distinct regions in the PCA space. This suggests that sensor responses capture category-speciff variance, potentially driven by differences in volatile compound profiles. However, we see that nuts and vegetables still largely overlap, which makes them hard distinguish. Future work on better sensor designs and algorithmic advances can potentially provide stronger signals that improve models' performance in these two categories. To further investigate within-categon category separability, we performed separate PCA only on fruits. In Fig. C)c, individual fruits form well-separated clusters, e.g., peach, mandarin orange, lemon, and banana each exhibit distinct spatial groupings at a much better separation than the global categories. As a result, we expect the model to achieve better classification results on fruits categories as compared with nuts and vegetables.
Per-substance reading statistics are reported in Tab. [8] and kernel density estimation (KDE) plots for each category are shown Figs. [6 Each sensor exhibits a distinct mean and standard deviation, indicating that preprocessing (Sec, 4.2p is necessary for stable prediction. The KDE plots also reveal category-specific response patterns across sensors. For example, the C2HHOHH channel tends to produce substantially higher readings for spices, which helps distinguish spices from other substance categories.
Fig. 3 describes the data distribution of SMELLNET-MIXTURE. In particular, with a binary mixture percentage of 77.9% and ternary mixture percentage of 10%, the test subset provides a challenging environment for the model to predict the mixture ratios. As shown in Fig. (ic). the mixtures span evenly across the entire substance space, with mixtures both within and across categories.
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(a) Data Collection & Processing
(b) Supervised Learning
Predicted Substance
(d)
Raw Mult-Channel
Models
Time Series
CLASSIFICATION
Sensar
Models
Inpul Substance
MLPs
mransformers
LSTMs
First Order
Cashew
Temporal Difference
Contrastive Learning
Predicted Substance
Cashov
MLPs
Data
Representation
OOOD
Modialty Alignmeen
Senser Representzion
Figure 4: Overview of the models used in this study. (a) Raw multi-channel time series data is collected using portable smell sensor samples input substance (e.g.. cashew). The data is optionally transformed using first-order temporal differenees emphasize signal dynamics. In parallel, high-resolution GC-MS data can be retrieved from an online database to provide chemical supervision. (b) In supervised learning, sensor data (raw or preprocessed) is passed through classification models trained to predict the correct substance among 50 classes. (c) In contrastive learning, paired sensor and GC-MS representations encoders. The resulting similarity scores rank the substance for prediction. (d) Our framework supports multiple model types-MLPP LSTMs, Transformers-eat capable ingesting either raw or temporally differenced sensor imputs to perform classification or representation learning.
aligned through modality-
DEVELOPING SCENTFORMER FOR SMELL
Developing AI for smell poses challenges: sensor data is temporal, limited in quantity, and noisy. To address these, we design SCENTFORMER with a Transformer backbone, data-efficient training, and preprocessing to handle noise.
4.1 PROBLEM SETUP AND NOTATION
Each example is a multichannel time series x = (.i,...,TT) e Rxx Our encoder So produces an embedding
(x)) RR For classification on SMELL NETT-BSEE, we map x to a label E (1. (1...., 50)- When using GC-MS data, align h with GC-MS embedding during training (App- E. For mixture distribution approximation on SMELLNET-MIXTHE
we map x to a probability vector π E [0, 1] with Σ n3 The target π encodes the ground-truth mixture fractions (see App. R.2 for label construction).
4.2 SENSOR DATA PREPROCESSING
Given the temporal nature of the data, we apply the following preprocessing procedures:
Temporal difference. Unlike GC-MS data, which provide calibrated compound level concentra- tions, the MOX sensor channels provide uncalibrated real-valued responses that are best interpreted as semi cemi--uantitatiin signals of the volatile profile. They are reliable for relative comparisons (c.g.. stronger weaker responses and temporal dynamics), but not absolute concentration readouts without device specific calibration. To capture relative sensor changes, we apply temporal difference. For each sensor channel Iy, we compute the difference over a fixed period of p samples:
Ar, 2z - It-ps
Vt> p.
When p = 0, we skip differencing and use the raw signal Fz.
Sliding windows. Due to the limited number available recordings, we partition each file into smaller windows with size with stride of w/2 increase the effective dataset size. This strategy is popular in time-series domains to improve model generalization (Norwawi 202T).
Standardization.
All training data aggregated to compute statistics for standardization. which are then applied to both training and evaluation sets. This ensures comparability across samples and reduces the influence of sensor noise.

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4.3 SCENTFORMER ARCHITECTURE
We employ pre-norm Transformer encoder over windowed sensor sequences, projected to latent dimension D and augmented with optional positional encodings and learnable [CLS] token. Sequences are processed by stacked Transformer layers, with variable lengths supported via key-padding masks. The encoder output is pooled (mean or [CLS] ) into a fixed-size vector. We attach classification head for 50-way odor recognition, and auxiliary heads for mixture presence and proportion prediction (see App. F.1 for implementation details).
4.4 TRAINING OBJECTIVES
We study three objectives: supervised classification from gas sensors, cross-modal alignment with GC-MS, and mixture ratio prediction.
Supervised classification. We train SCENTFoRMeR directly SMELL.NET-BASH windows for 50-way classification, using a softmax output and minimizing cross-entropy loss.
Cross-modal alignment. To leverage GC-MS data (FooDB Contributors, 2024 Liang et al. 2024
we adopt symmetric contrastive learning objective (Radford et al. [202]] Socher et al. 2013
that aligns sensor and GC-MS embeddings (see App. F2 for the full formula). We use GC-MS data as an ingredient-level chemistry prior rather than a per-sample sensor input. Concretely, for each ingredient we precompute a fixed GC-MS embedding from FooDB derived volatile-compound information. In the main paper we use a binned mass-spectral embedding (Xspec), while App. C additionally compares a coarse seemental-compont During cross-modal training, sensor-window embeddings are aligned to these ingredient-level GC- MS embeddings via a contrastive objective. At inference time, no new GC-MS measurement required. Full details on GC-MS source coverage, embedding construction, and encoding ablations are provided in App.
embedding (X as an alternative encoding.
Mixture prediction. For NE-LLNET-MIXTTRE windows, SCENTFORMER predicts normalized 12-D ratios. We optimize composite loss combining KL divergence, hinge- penalty, and focal BCE (see App. F.3 for details).
EXPERIMENTS
We evaluate SCENTFORMER on SMELLNET for both single-substance classification and mixture distribution prediction. Specifically, we seek to answer the following research questions:
RQ1:
To what extent can we classify single substance odors from SMELLNET- BASE alone, and which preprocessing choices contribute most accuracy?
RQ2: Does modal training with paired GC-MS data improve downstream sensor-only classifi- cation, and by how much?
RQ3: Can we predict the composition of mixtures from sensor time-series, and which preprocessing methods work best?
5.1 EVALUATION METRICS
report Top-1 accuracy, Top-5 accuracy, macro-F1, and per-category accuracy, Top-1 and Top-5 measure whether the ground-truth ingredient appears the top I or top predicted classes, respectively. We report macro-F1 to provide class-balanced summary that is less sensitive to fresuunyy differences than accuracy alone.
For EMELLNET-BASER
For SMELLNET-MIXTURE we evaluate predicted mixture proportions using mean absolute error (MAE), Top- 0.01 accuracy, and dynamic Top-K K hit rate. MAE measures the average absolute deviation between the predicted and ground-trute mixture proportions. For Top- @0.1, prediction counted as correct the predicted proportion is within #0.1 of the ground-truth value on non-zero target components. We use the 0.1 threshold because mixture ratios in our dataset are defined on a 0. grid. Thus, 1 corresponds to one step of the recipe resolution. Smaller thresholds would be below the precision of the mixing protocol, while substantially larger thresholds would be insufficiently discriminative for nearby ratios (e.g., 30:70 vs. 50:50). We additionally report a dynamic Top-K hit rate, where K equals the number of non-zero components in the target mixture (see App. 1.2 for details). As supplementary mixture metrics, we also report KL divergence and cosine similarity between the predicted and ground-truth 12-D mixture distributions in the App. H.2
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Table 2: Single-ingredient odor classification SMELLNET-BASE (RQ1) and improvements from GC-MS integration (RQ2). We vary preprocessing choices by window size (w (50, 100)) and period differencing e (0,25)) Temporal differencing (p 25) yields large gains over raw signals, longer windows (w 100) improve stability, and temporal models consistently outperform non-temporal baselines. Adding GC-MS supervision via contrastive learning further boosts weaker models but has mixed effects on temporal architectures. A Acc@1 reports the change in accuracy relative the sensor-only baseline.
Sensor-only (RQ1)
Cross-modal (RQ2)
A Acc@1 (X-S)
Model
Window
Acc@11
Acc@51
F11
Acc@11 1
Acc@5t
F17
MLP
50
0
21.9
55.8
17.2
24.1
58.3
20.7
+2.2
MLP
25
18.2
49.0
17.8
24.7
58.7
24.5
+6.5
MLP
21.0
54.4
17.4
57.2
20.4
+3.3
MLP
100
25
26.8
59.7
24.7
30.8
63.7
28.6
+4.0
CNN
0
25.5
61.2
23.3
27.7
69.9
23.8
+2.2
CNN
50
25
46.9
81.7
46.1
45.7
82,5
44.8
-1.2
CNN
100
0
29.5
66.6
24.9
26.7
71.3
24.0
-2.8
CNN
100
25
52.7
85.6
50.5
58.9
88.4
57.0
+6.2
LSTM
50
0
29.3
72.2
25.9
31.9
62.8
28.8
+2.6
LSTM
50.6
84.7
48.8
50.6
79.5
49.6
+0.0
LSTM
100
0
28.8
58.0
27.2
35.2
64.9
32.7
+6,4
LSTM
100
25
57.9
87.0
56.0
56.9
85.2
55.2
-1.0
SCENTFORMER (ours)
Transf.
50
0
35.1
70.9
33.1
34.7
69.0
31.4
00.4
Transf.
50
25
50.6
85.0
49.5
52.6
82.5
51.7
+2.0
Transf.
100
39.9
74.7
35.7
39.5
70.4
35.2
-0.4
Transf.
100
25
56.1
87.4
55.5
63.3
86.1
61.7
+7.2
5.2 EXPERIMENTAL SETUP
We evaluate SCENTFORMER on base-substance recognition and distribution prediction.
For each ingredient, we use the last acquisition day (Day 6) as the held-out test day and train the preceding five days. We compare SCENTFORMER against non-temporal baseline (MLP) and temporal baselines (CNN and LSTM). To assess robustness to day-to-day acquisition shifts beyond this fixed last-day split, additionally report leave-one-day-our evaluation SMELLNET BASE App. H.4 H.4
SMELLNET-BASE:
To study temporal dynamics, we evaluate temporal differencing (p (0, 25)) and segment sensor streams into windows of size w e (50, 100). All models are trained for 90 epochs with batch size 32, using learning rates (3 10-4 10-3,3 10-3), and we report the final checkpoint for each configuration. We further investigate cross-modal training variants that incorporate paired GC-MS supervision via contrastive learning.
SMELLNET-MIXTURE Mixture experiments use 12 odorants with both seen (novel sessions, known ratios) and unseen (zero-shot transfer) test splits. Models are trained with window sizes é (50, 100). batch size 64, 60 epochs, and the same learning rate grid as SMELLNET: -
Each SMELLNET MIXTURE sample defined by a volumetric recipe over 12 base odorants (c.g., binary and ternary mixtures), which we normal- ize to 12-D target vector. The model predicts this continuous mixture dis- tribution from the sensor time series, The goal is to recover the intended recipe proportions rather than exact gas-phase concentrations, which also depend on volatility and headspace dynamics.
NET-MIXTURMM
Table 3: Distribution prediction on seen combinations (RQ3). SCENTFoRMER achieves the best overall performance, with the highest Top-1 1 and competitive Top-K accuracy, highlighting the importance of temporal modeling for resolving overlapping odor signals.
Model
Window MAE J Top- @0.11
Top-K (%)
MLP
50
0.0428
41.0
85.0
100
0.0586
33.7
78.9
CNN
50
0.0404
48.1
86.7
100
0.0476
36.2
87.0
LSTM
50
0.0399
46.4
89.3
100
0.0430
46.5
86.3
SCENTFORMER
0.0395
50.2
87.9
100
0.0417
47.9
89.0

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Fig. [ shows the hardware setup. Fig. 4 illustrates the evaluation pipeline, and full implementation de- tails, additional hyperparameters, and reasons behind each choice are provided in App. G.2
5.3
RQI: PREPROCESSING CHOICES EVALUATION
Based on the results in Tab. we highlight three key findings:
Finding 1.1: Temporal differencing substantially improves accuracy. Adding temporal differenc- ing (p 25) mostly outperforms raw signals (p = 0), with an average gain of 16. 1% across models and window sizes. This demonstrates that temporal changes in sensor values carry critical discriminative information.
We do not find the gains from explicit temporal differencing surprising. Although temporal models can in principle learn lag-like transforms from raw sequences, time-series pipelines often use simple domain-motivated transforms to expose informative dynamics before learning (e.g., delta features in speech (Furui 1986 Young et al., 2006), differencing and log-returns in finance (Tsay 2010 Campbell et al. 1997)). For MOX sensors, absolute responses are affected by drift, device-specific offsets, and slow baseline shifts, whereas temporal changes more directly capture odor response dynamics. Consistent with this, temporal models already outperform MILP on raw signals 0), and differencing further improves performance.
Finding 1.2: Larger windows provide more stable patterns. Window size w = 100 generally yields higher accuracy than w 50, as longer temporal context captures more stable dynamics, though at the cost of fewer training and test samples. See App. I.1 for window calculation.
Finding 1.3: Temporal models outperform non-temporal baselines. Across preprocessing settings, CNN, LSTM, and SCENTFORMER consistently achieve higher accuracy than MLPs. This indicates that temporal structure in the sensor response carries discriminative information beyond static channel magnitudes. The result is consistent with Findings 1.1-1.2, which show that preprocessing choices that better expose temporal changes further improve performance.
5.4 RQ2: GC-MS INTEGRATION
Tab. 2 shows the effect of adding GC-MS supervision via contrastive learning.
Finding 2.1: GC-MS supervision yields modest gains for weaker models. Raw-signal inputs (p 0) and non-temporal architectures more of the consistent gains, showing that GC-MS embeddings provide complementary structure that compensates for limited model capacity.
Finding 2.2: GC-MS supervision has mixed effects for stronger temporal models. With GC-MS supervision, we see improvements can large or slightly negative. Temporal architectures already capture much of the discriminative signal, but GC-MS further refines embeddings by grounding them molecular structure, suggesting the two signals complement rather than replace each other.
Finding 2.3: Effects depend on architecture and preprocessing. In some cases (e.g., CNN at w 50, p= 25), alignment brings little or even negative improvement, indicating that GC-MS can conflict with strong short-range features. This underscores that value depends on how well the base model and preprocessing prepare features for cross-modal alignment.
Table 4: Distribution prediction unseen combina- tions with w 50 (RQ3), SCENTFORMER achieves the best overall performance, but accuracy drops sub- stantially compared to the seen setting, highlighting the challenge of generalizing to novel odor mixtures.
Model
Top- @0.1f
Top-K (%)t
MLP
11.7
34.0
CNN
12.4
36.4
5.5 RQ3: MIXTURE PREDICTION
LSTM
11.8
34.2
SCENTFORMER
16.0
38.9
Finding 3.1: SCENTFORMER outperforms other architectures on mixture prediction. As shown in Tab. 3 SCENTFORMER achieves the best results across both Top-1@0. 1 and Top- accu- racy. This indicates that strong temporal modeling, which benefits single-substang recognition, is equally important for resolving overlapping signals in mixtures.
Finding 3.2: Accuracy drops sharply for unseen mixtures. Tab, Ash.ws that performance degrades when evaluating on mixtures not seen during training. This suggests limited generalization: the model transfers poorly to novel ratios, even when all individual components were seen. Accuracy

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100
MLP
1.00
CNN
100
1STM
100
ScentFormer (ours)
Accurscy (accg], N4)
Accuracy (xcc83, Ni
Regular Contractive
80
60
40
4b
Accurncy
40
Poot
Herda
Huts
sprcad
vegetabled
Protts
Herbo
Huta
Spreed
Vegutabled
Prito
Herbo
Hets
Splced
Vegutabled
Prutta
Herda
Neto
Sprewl
Vepetabled
Figure 5: Per-category accuracy (acc@) 1) for four models at lag p 25. Bars are paired category (Regular vs. Contrastive). Non-temporal models underperform vegetables, while temporal models more robust across categories.
degradation indicates sensitivity to composition shift rather than merely class imbalance or session effects.
Finding 3.3: Top-K performance remains well above chance in unseen setting, Although unseen mixtures are harder, SCENTFORMER still achieves substantially higher Top-K accuracy than random guessing (around 16.7%, see App. I.2). This suggests that the learned representations encode meaningful compositional structure, enabling the model to narrow predictions plausible subset of substances even without explicit training on those mixtures.
These results show that SCENTFORMER is effective at mixture prediction, but generalization to unseen mixtures remains challenging. While temporal modeling provides clear benefits, scaling to the combinatorial complexity of real-world odors may require compositional training strategies, data augmentation, or domain adaptation.
5.6 DISCUSSION FUTURE WORK
To better understand the limitations of our models, we examine the per-category classification accuracy reported in Fig. 5 Several systematic patterns emerge. First, the non-temporal baseline shows pronounced weaknesses on certain categories, particularly vegetables. Vegetables exhibit substantial overlap with other categories in the PCA of sensor profiles (Fig. 22. making them harder to separate. MLP reaches relatively high accuracy on spices, whose sensor signatures are more distinct. This pattern is consistent with high within-class variance and overlapping volatile compound distributions.
In contrast, temporal architectures demonstrate stronger robustness across categories. By modeling temporal dynamics, these models capture transient variations in sensor readings that help differentiate harder classes. For example, SCENTFORMER maintains relatively balanced performance across all five categories, whereas MI. shows large category-speciff disparities.
We also observe that contrastive training with GC-MS supervision provides differential benefits. For weaker models, alignment with molecular structure consistently improves recognition across categories, suggesting that the external chemical signal compensates for limited representational capacity. For temporal models, however, the effects are mixed, indicating that temporal modeling already captures much of the discriminative signal. This motivates future work on whether higher- resolution sensing or richer chemical supervision can provide additional gains.
key limitation is that data collected in controlled settings without explicit background and environment subtraction across diverse ambient conditions, so performance may degrade under unseen environments, airflow patterns, or sensor placements. Future data collection should include explicit background subtraction protocols and broader cross-environe coss-envvronmmnt sampling to better evaluate out-of-domain robustness.
Additional ablations and channel- -hannel-activity analyses are provided in App.
CONCLUSION
In this work, we introduced SMELLNET, a comparatively large benchmark for sensor- sensor-based machine olfaction using low-cost portable hardware. Built using portable and low-cost gas sensors, it captures over 828,000 data points across 50 base substances and 43 mixtures, paired with high-resolution GC-MS chemical data. SMELLLNET establishes benchmark for studying olfactory AT at scale,
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enabling both single-substance recognition and mixture prediction tasks. SMELLNET also inspires the design of SCENTFORMER, a Transformer- based architecture combining temporal differencing and widdiogwwnndow augmentation for smell data. We believe SMELLNET will serve as a foundation for future research in Al for smell and various real-world applications.
7 ETHICS STATEMENT
This work presents SMELLNET, a large-scale dataset for smell recognition using portable gas sensors. The dataset consists entirely of time-series sensor readings from chemical compounds in food substances and natural objects. No human subjects were involved in data collection, and the dataset contains no personally identifiable information. All data were collected using commercially available sensors measuring volatile organic compounds from common food items purchased from public retailers.
Our collection system and classification models have minimal environmental impacts. Our sensor system is energy efficient, and can be powered by a USB cable with 5W input. The models lightweight. On a single NVIDIA L40S (driver 550.54.14, CUDA 12.4) at batch size 32, SCENT- FORMER achieves mean per-window latency of 0.0191-0.0479 ms, shown in App. H.6
While SMELLNET is designed to advance research olfactory AI with beneficial applications in food safety, healthcare, and environmental monitoring, we recognize that smell sensing technology could potentially be misused. We encourage responsible use of this dataset and the resulting models, particularly regarding privacy considerations in real-world deployments.
REPRODUCIBILITY STATEMENT
To support reproducibility, provide comprehensive materials and documentation. The complete SMELLNET dataset (828,00) 000 timesteps across 50 base substances and 43 mixtures) is available in the GitHub repository linked at the beginning of the paper, along with detailed instructions data collection protocols and sensor specifications. Our sensor hardware configuration fully documented in App. A including circuit diagrams and component specifications. All preprocessing steps for the sensor time-series data are described in Sec. 4.2 with implementation details provided in our released codebase. Model hyperparameters are listed in Sec. 5.2 and App. F. App. G For the GC-MS integration experiments, we provide the GC-MS preprocessing and descriptor- consttrcionttt methods used the paper, including the spectrum- based pipeline used in the main paper and an alternative pipeline in App. [ The repository also includes links to the public database used. The mixture label construction methodology detailed App. E.2 and the complete list of odorants and their sources is provided in Tab. [7 The dataset follows the hierarchical structure shown in Fig. [5 with CSV files organized by ingredient and recording session.
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A SENSORS AND DATA COLLECTION ENVIRONMENT
A.1
SENSOR HARDWARE SPECIFICATIONS
MQ5
Analog
MQ3
Tump
Hugeon
Min
Chupes
Anpa
Postr
Garle:
Presch
Coniantter
Manggg
Anneelici
Banaraa
O
0.0.0.
Almand
Herbs
Simeebrry
Cashew
Orange
Chestruus
Fruis
Lemon
Pistachoss
Pmnapplee
Adafruit Feather
Hazelrut
Kml
Wainuss
Nuts
Sensor,
Asparagus
Pecans
Drazil Not
vegetables
Caulifooee
Catbage
12C
Più Not
Potato
Peanus
Star Anise
Tomato
Cirnaman
Sineet Potato
Arocadoo
Radiah
Musterd
Brasse Spratts
Chansi
Broccali
Mutichancee!
USB
cumen
Satton
Alrppce
(a) Circuit diagram of sensor hardware setup.
(b) Sunburst taxonomy of ingredient categories.
Figure 6: Overview of the SMELLNET dataset and sensing setup. (a) Our constructed portable smell sensor detects readings of various gases and atmospherie factors through 3 gas sensors. (b) SMELLNET includes smell sensor readings of 50 substances spanning nuts, spices, herbs, fruits, and vegetables.
MQ3
MQ5
Multichannel Gas Sensor V2
Figure 7: Sensor hardware architecture diagram our prototype platform. The final camera-ready experiments use six gas-sensor channels from MQ-3, MQ-5, and the Grove Multichannel Gas Sensor V2 (manufacturer- labeled responses including CO. NO,, OHI VOC, Alcohol, and LPG). Additional components shown in the schematic legacy prototype sensors and
used in the final reported experiments.
Our sensing device was constructed from a suite of commercially available gas sensors selected to provide broad coverage of volatile organic compounds (VOCs). Importantly, C2H,OH and Alcohol refer to labels from two different physical sensor elements (with overlapping but non- non-identical cross- sensitivity). not two distinct target chemicals in modeling. Fig. [7 shows the assembled sensor platform with all components labeled, and Tab, lists the components used to build the device. We include these details enable readers reproduce the sensor used for data collection. Together,
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Table 5: Overview of smell sensors used in SMELLNET. Datasheets: MQ-3 Hanwei Electronics Co., Ltd. n.d.ap. MQ-S|Hanwer Electronics Co., Ltd. (n..bb), Grove Multichannel V2 Seeed Studio St..j
Sensor
Manufacturer
Description
MQ-3
Hanwei Electronics
Metal-oxide (MOX) sensor optimized for detecting alcohol vapors. Detects LPG, natural gas, and coal gas (combustible gas detec- tion).
MQ-5
Hanwei Electronics
Grove Multichannel V2
Scced Studio
Modular 4-channel MOX sensor measuring NO,, CO, CH, and VOC.
Table 6: Top feature contributions two principal components (PC1 and PC2) of the sensor data. Features are sorted by contribution magnitude.
Feature
PC1
PC2
Magnitude
LPG
0.1514
0.7410
0.7563
Alcohol
0.1731
0.5548
0.5812
NO2
0.5044
-0,2123
0,5473
O2HşOH
0.5119
-0.1683
0.5389
VOC
0.5035
-0.1859
0.5367
CO
0.4206
0.1869
0.4603
these sensors provide complementary sensitivities and were integrated maximize coverage of odor-related volatile signals in our dataset.
A.2 CONTROLLED ENVIRONMENT
To ensure consistency and minimize external interference during data collection, all sensing sessions were conducted in controlled environment. During each 10-minute recording interval, we placed both the food sample and the sensor array inside a transparent container. This enclosure prevented environmental factors such as airflow, human movement, or ambient contaminants from affecting the sensor readings. The container allowed gas emitted from the food to accumulate and diffuse evenly, while shielding the sensors from external disturbances such as changes in ambient composition caused by people walking nearby. Between sessions, we ventilated the enclosure restore ambient conditions and eliminate residual smells from previous trials. After each session, we carefully monitor how the values change overall. Once all the sensor values stable for 10 minutes, consider the environment to have returned to ambient conditions, and we proceed to the next ingredient for the session.
Despite these precautions, certain ambient factors, such as background NO₃ levels, varied across different days and could not be entirely eliminated.
B SENSOR DATA
B.
MORE PCA
Fig. shows PCA projections for nuts spices, herbs, and vegetables. Across categories, we observe distinct ingredient-level clustering, indicating that raw sensor signals inherently encode discriminative patterns. Notable examples include potato and sweet potato among vegetables, dill and angelica among herbs, and nutmeg, star anise, and cumin among spices. Even in the denser nuts category, ingredients like almond and cashew exhibit identifiable signatures.
These results support our hypothesis that portable gas sensors capture chemically meaningful varia- tions, both across and within ingredient types, enabling ine-ggrined classification and motivating representation learning approaches.
Tab. 6 lists the top feature loadings for PCI and PC2 of the 6-channel sensor readings. PCI is driven primarily by NO2, C2H,OH, VOC, and CO, while PC2 is dominated by LPG and Alcohol. This separation indicates that PCA captures distinct modes of variation across the gas-sensor chan- nels, providing an interpretable summary of dominant censor-respone patterns for downstream analysis.
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Nuts PCA
Spices PCA
Ingredient
Ingredient
alispice
86.8% varfancee
brazil
PC2 (19.1% varfance)
cashew
cinnamon
chestnuts
covee
$ go state
hazelnut
cumin
peanuts
ginger
mustard
pili
nutmeg
pistachlos
25
PC1 (48.7% 7% varience)
100
125
walnats
PC1 (74.8% variance)
star, _anie
(a) Nuts
(b) Spices
Herbs PCA
Vegetables PCA
Ingredient
Ingredient
angelica
asparagus
PC2 (17 variance)
PC2 (28 9% variance)
avocado
chives
broccoli
corlander
prussel_sprouts
diil
cabbage
garlic
cauliflower
mugwort
oregano
sweet, potato
PC1 (71.% variance)
tumip
PC1 (46.1% variance)
tomato
(c) Herbs
(d) Vegetables
Figure 8: PCA projections ingredient-level sensor responses for each major category. Each point represents a time step of raw sensor readings, colored by ingredient. Clear clusters are observed, indicating that sensor signals encode discriminative chemical signatures within each food category.
B.2 FEATURE CORRELATION
Fig. 9 shows the Pearson correlation matrix for the 6 sensor channels. NO2, C2H5OH, and VOC exhibit strong positive correlations (r = 0.95-0.97), indicating closely co-varying response patterns. CO is moderately correlated with this group (r 0.63 to 0.67), while Alcohol and LPG show cluster and more independent behavior overall. These structured dependencies suggest partial redundancy among channels while still preserving complementary information for downstream modeling.
weaker correlations with the NO,/C2
C GC-MS DATA SOURCE AND REPRESENTATION
This appendix provides additional details on the GC-MS metadata used for modal supervision, including data source and coverage, construction of ingredient-level GC-MS embeddings, and how GC-MS enters training and inference.
C.I GC-MS SOURCE AND COVERAGE
We do not collect GC-MS measurements our sensing setup. Instead, we use FooDB derived volatile compound metadata as an ingredient-level chemistry prior. In our current experiments, all 50 base substances used in SMELLNET- BAS- were selected to ensure FooDB coverage of volatile-compound annotations and associated GC-MS descriptors, yielding 100% coverage for the base-substance label set.
As result, ablation that removes ingredients without GC-MS descriptors is not meaningful for the current SMELLNET: BASE subset, because the class set would remain unchanged. Accordingly, the relevant comparison in experiments is controlled ablation the same ingredients and evaluation protocol: sensor-only training (no GC-MS supervision) versus cross cross-mode modal training with GC-MS supervision.
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ICLR Data: Feature Correlation Matrix
NO2
1.00
0.97
0.96
0.83
0:20
0.06
C2H50H
0.97
1.00
0.95
0.67
0.22
0.11
VOC
0:96
0.21
CO
425
Alcohol
0:20
0.22
0.21
0.16
1.00
0.36
LPG
0.06
0.11
0.09
0.45
0.36
1.00
No2
C2HSOH
voc
CO
Alcohol
LPG
Figure Pearson correlation matrix sensor channels. NO,, C2HoOH, and VOC are strongly correlated,
while CO shows moderate correlation with cluster. Alcohol LPG are comparatively less correlated, indicating complementary sensor-response patterns.
m/z justification: cumulative intensity (mean over spectra)
Fraction of total intersity captured
0.6
0.4
0.2
Cumulative fraction inters by
Lower bound (40 miz)
Upper bound (500 mi)
100
200
300
400
500
(upper bound range)
Figure 10: Justification of the m/z range used for the binned mass-spectral embedding. We plot the cumulative fraction of total spectral intensity captured the upper m/z bound increases (mean retrieved El spectra). The rises rapidly low-to-mid m/z values and saturates near 1.0 by 500 m/z, indicating that the selected 40-500 m/z range preserves nearly intensity mass while keeping the emhedding dimension compact. Dashed/dotted vertical lines mark the lower and upper bounds used in Xapee-
C.2 GC-MS EMBEDDING CONSTRUCTION
For each ingredient, we precompute fixed GC-MS embedding from its FooDB linked volatile compounds. We consider two ingredient-level GC-MS encodings.
Binned mass-spectral embedding (Xepec: main-paper default). For each ingredient, we retrieve its annotated volatile compounds from FooDB and collect available experimental Electron Ionization (EI) mass spectra for those compounds. Each spectrum is converted into a fixed-length vector of binned intensities over 40-500 m/z using Da bins, and each spectrum is max max-normalized. We then average spectra for the same compound and subsequently average across compounds to obtain single fixed-length GC-MS embedding for the ingredient. We use the 40-500 m/z range because it captures
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PCA of GC-MS Features by Ingredient Category
Category
Nuts
Spices
Herbs
Vegetables
Fruits
PC2 (7.6% variance)
-5
-40
-30
-20
-10
0
10 20
PC1 (56.9% variance)
Figure 11: PCA of ingredient-level GC-MS fingerprints (XSspee, colored by category. Each point corresponds to one ingredient embedding constructed from averaged, binned El spectra. The first two principal components reveal both category- sategory-level organization and within-category spread, indicating that the binned mass mass-spectral representation captures meaningful chemical variation across foods, Axes report the explained variance of cach principal component.
the vast majority of signal intensity in our retrieved El spectra while keeping the representation compact shown in Fig. 10
iiemental-composition embedding (X atom: alternative encoding). As alternative descriptor, we also construct an ingredient-level embedding from elemental-composinn statistics derived from the molecular formulas of the ingredient's 's annotated volatile compounds. This representation captures coarse compositional trends but discards fine-grained spectral structure. We include it as an alternative encoding for robustness analysis and comparison with earlier versions of the paper.
C.3 How GC-MS IS USED IN CROSS-MODAL TRAINING
GC-MS information enters the model only through ingredient-level embeddings. During training, we apply the symmetric contrastive objective described App. F2 align wendorwwindow embeddings with the GC-MS embedding of the corresponding ingredient while pushing away mismatched ingredient embeddings. Thus, GC-MS acts as a chemistry-awar label representation (or prior) during training.
At inference time, new GC-MS measurement is required. We embed the sensor window using the trained sensor encoder and perform classification using the learned label-aligned representation. Therefore, GC-MS is not used additional per-sample input modality at test time.
C.4 QUALITATIVE STRUCTURE OF GC-MS EMBEDDINGS
We next examine the qualitative structure the ingredient-level GC-MS embeddings. For the main- paper representation Xspec we perform PCA on the binned mass-spectral embeddings. Figure [] shows the projection onto the first two principal components, with points colored by ingredient category. The projection exhibits both category-level organization and substantial within-categon variation, suggesting that Xspec captures meaningful chemical differences across ingredients while preserving fine- grained structure beyond coarse compositional summaries.
Fig. 12 shows examples of ingredient-leved GC-MS fingerprints (i.e., the averaged binned El spectra to construct Xspec). These examples illustrate that Xspec retains interpretable peak patterns across m/z bins while providing a fixed-length representation suitable for contrastive alignment with sensor embeddings.
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GC-MS fingerprint comparison: Star anise Apple (elstributtoo--noraaiieee))
0.040
Normalized intensity (sum=1)
Apple
0.025
0.020
0.005
0.000
0.002
4/
Difference
-0.002
-0.004
-0.005
Positive Star anise strenger Nagative Apple strangar
m/2
Figure 12: Example comparison of ingredient-level GC-MS fingerprints in the binned spectral space. Top: distribution normalized GC-MS fingerprints star anise and apple, shown averaged binned mass spectra over selected m/z range. Bottom: pointwise difference between two normalized fingerprints (star anise minus apple). Although the overall spectral envelopes are similar, the difference plot reveals localized peak-intensity deviations across multiple m/z bins, illustrating that visually similar fingerprints can still encode discriminative ingredient-specific structure in Xapoo-
distribution-norrmlliidd
PCA of GC-MS Features by Ingredient Category
Category
Spices
Nuts
Herbs
100000
Fruits
Vegetables
PC2 (0.4 4% variance)
-50000
-100000
-150000
PC1 (99.5% variance)
Figure PCA of GC-MS elemental composition across ingredient categories. PC1 accounts for 99.5% of variance, highlighting dominant compositional differences (e.g., carbon and hydrogen levels).
For completeness, we also examine the alternative elemental-composinn embedding Xatom- Al- though Xatom is substantially coarser than X it still exhibits non-trivial structure across ingre- dients, reflecting broad compositional trends in the underlying volatile compounds. Fig. [3 shows the PCA projection of X Xatoms which reveals category-level variation driven by dominant elemental
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GC-MS Correlation Heatmap of Elements
1:00
0.29
0:10
0.05
0.31
0.99
0.26 0.23
0.30
0.76
0.31 0.88 0.36 6:11 0.29 010
Ca
0.09
5.00
0:14
0.04
0.00
0.34
0.89
0.22 0.18 0:05
0.10 0.23 - 0. 0.04
0.30
3.02
0,02
0.02
1.00
6.02
0.48
0.35
0.82
810
0.03
0 02 0.00
Co
1.00
0.6
0.4
Mg
9.29
0.060.39
0.49
1.00
0.07
0.00
0.00
Mn
2.10-0
05
00.00
40.00
0.00
-0.52
.0.02
0.07
1.00
0..02-..00
0.2
0.76
0.10
0.10
0.30
0.09
0.21
0.77
0.25
0.11
0:09
1.00
0.30
0.47
9.40
0.10
0.09
Na
5.11
00000.000000
0.40
3.03-0.00
0.11
4.000
0.10
1:00
3.03
04
04
00.00.00000
0.58
0.02
0.00
0:31
0.07
0.03
0.63
0.26
0.34
0.20
0.47
0.03
1.00
0.29
02
0.20
0.0
0:30
00.000000
0.02
0.03-0.2
0.02
0.37
0.40
0.04
0.20
1.00
0.02
0.09
0.00
0.02
-0.2
Figure 14: GC-MS correlation heatmap of elemental counts. Strong positive correlations are observed between common organic elements (C, H, N, O), while many trace elements are uncorrelated.
statistics. Fig. further shows the glement-wiss correlation heatmap, highlighting interpretable dependencies among common elements organic molecules. These diagnostics help explain why Xatom can still provide useful cross- modsl supervision despite discarding fine- fine-grained spectral peak information. At the same time, its reduced expressiveness motivates our use of Xspor as the default GC-MS representation in the main paper.
D SMELLNET OVERVIEW
Table Descriptive statistics of sensor readings in the training dataset (150,711 samples).
NO₂
C2HşOH
VOC
Alcohol
LPG
Mean
97.80
138.33
195.94
792.94
3.41
30.33
Std
118.68
143.40
196.19
61.67
3.28
38.29
Min
13.00
39.00
26.00
705.00
0.00
2.00
25%
35.00
65.00
73.00
750.00
1.00
14.00
50%
46.00
77.00
106.00
776.00
2.00
23.00
75%
105.00
140.00
232.50
820.00
5.00
32.00
Max
753.00
863.00
953.00
1006.00
42.00
507.00
Table Descriptive statistics sensor readings in the testing dataset (29,423 samples).
NO2
C2H5OH
VOC
CO
Alcohol
LPG
Mean
92.33
134.08
187.80
791.69
3.47
33.31
Std
113.90
140.97
191.23
60.11
3.14
46.17
Min
15.00
41.00
26.00
710.00
0.00
3.00
25%
34.00
65.00
72.00
750.00
1.00
15.00
50%
45.00
73.00
93.00
774.00
2.00
23,00
75%
95.00
135.00
218.00
823.00
6.00
35.00
Max
775.00
850.00
947.00
1004.00
30.00
444.00
D.1 SMELLNET SUMMARY
This appendix provides descriptive statistics for all 6 sensor channels in both the training and testing datasets. Tab. 7 and Tab. 8 summarize key distributional properties, including mean, standard deviation, and range for each feature. The sensor readings exhibit substantial variability across
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samples, particularly in channels such VOC and NO2. These statistics highlight the diversity and dynamic range of the collected data, which underpin the challenges of robust model generalization in real-world settings. We include text description generated by LLMs of all substances we used for future experiments to enable alignment between text and smell modalities.
D.2 DATASET HIERARCHY
SmellNet Dataset Structure
TRA NING SET
TESTING SET
Alleric
Almond
Amgeelia
Apolee
Arparagur
Allepice
Almond
Argelice
Apple
Asparagux
CSV files
CSV files
CSV Files
files
files
Fie
CSV file
cSv
Figure 15: Hierarchical organization of the SMELLNET BASE dataset. Each ingredient folder contains multiple CSV files with raw sensor time series data.
Fig. 15 illustrates the hierarchical structure of the AMELLNET-BASH dataset. Each ingredient is represented as a folder containing multiple time series recordings in CSV format. The training set includes five CSV samples per ingredient to capture variation across trials, while the testing set contains one representative CSV file per ingredient. This structure ensures a consistent, per-ingredient organization and facilitates reproducible supervised learning.
E ADDITIONAL DETAILS FOR PROBLEM SETUP AND NOTATION
E.1 GC-MS DESCRIPTOR CONSTRUCTION
When a GC-MS readout is profiled for a substance class, we construct a fixed-length descriptor Rd by aggregating element counts over fixed set & (e.g., C, H, O, CI). We then apply per-dimension standardization using training-set statistics:
9
9 Pj
j= d'
Jj
and use ğ in all GC-MS-awan objectives.
E.2 MIXTURE-LABER CONSTRUCTION AND EVALUATION METRICS
Targets. Each example in SMELLNET-MIXTUS has ground-truth composition over K 12 odorants. Let ग e I2 denote raw proportions, we normalize to the probability:
π
Σ n;
The model predicts π (x()) via softmax head.
F BUILDING SCENTFORMER
F.1 DETAILED SCENTFORMER ARCHITECTURE
Backbone. Input windows x' oxd are linearly projected to dimension D. We use sinusoidal positional encodings and prepend learnable [CLS] token. The model comprises pre-norm Trans- former layers with attention heads, feed-forward width 4 4D, and dropout probability Parp-
Pooling and heads. Given encoder output H R='xD, apply masked mean pooling (default) or [CLS] embedding to obtain h E TR A two-layer MI.P projects to 50 logits. Linear heads predict mixture presence ú 122 and proportions & softmax (w,, h bm)) A
F.2 CONTRASTIVE LEARNING LOSS
Given N gas sensor embeddings 2 (c) and corresponding GC-MS embeddings (9) we mini- mize:
exp(sim (+) 22 ((7))/)
exp(sim(2 (2) ) log
Countrastvve
log Σ, exp(sim ())()))))))
Σ, (x.e(ssmm( (g) ; (+))))
(3)
where sim is cosine similarity and temperature.
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F.3 MIXTURE PREDICTION OBJECTIVE
Intuition. Our loss for mixture prediction blends three complementary terms to balance proportion accuracy, robustness, and class imbalance. (i) KL divergence encourages the predicted distribution f= softmax(2) to match the ground-truti proportions (ii) A hinge-{j penalty is applied only to components that are truly present, tightening errors beyond a small tolerance e. (ii)) Focal binary cross-entropy (Focal BCF) operates on presence/absence labels and down-weights easy negatives while focusing learning on hard positives. Scalars a and | balance the second and third terms relative to KL.
Full objective. Let = 1[p > 0] indicate presence, and S {i: 1} the set of present components. The loss
f KL(Pl)
Σ E
--il max( (0: - -pil - 0) + +3- FocalBCE(s,r),
|S|
with ý softmax(2), and FocalBCE using (0y=0.75,772.00 in our experiments. Here, (T promotes globally accurate proportions, the hinge-l1 term tightens emror present components beyond tolerance e, and the focal term addresses class imbalance in presence prediction.
G ADDITIONAL EXPERIMENTAL SETUP
G.1
MODEL DETAILS
This section specifies input/shape conventions, pooling/masking semantics, and per-architecture hyperparameters used in our code. We purposefully omit high-level architecture and objective overviews that appear the main text and earlier appendices.
Input shapes. Unless noted otherwise, models consume windows BBTxx (batch, time, features). For variable-length batches, we pass sequence lengths e N When layer expects channel-first we convert C,T, internally.
Mask semantics.
Where masks are used, True marks padding. For mean pooling over valid tokens
we use
E. m, hag
max Σx mb,t, 10-6)² Mbbt = 1[ 6s),
and apply the same m to exclude pad tokens from attention or max max-pool operations.
SCENTFORMER
Input stem: Linear(F D) then LayerNorm, optional sinusoidal positional encodings added in-place.
Tokenization: Optional [CLS] (O0, Pooling either masked mean (default) or [CLS].
Encoder: L pre-norm layers with H heads, FFN width 4D, dropout p. activation gelu.
Head: Linear( D Dri-
Defaults:
H=8, L=4, 10.1 activation=gelu, positional enc.=on, [CLS]=off,
pool=mean.
LSTMNet
Core: LSTM(F-H) with L layers, bidirectional by default. Dropout p only if L 1.
Pooling: last (concat final fwd/bwd), masked mean, or masked max.
Variable length: Uses pack__aadee_seeeueeee hptppeacpeaeeeeeeeeeeee....
Projection head: Linear(-- Demb) then Linear(Demt CC).
Defaults: L=1, bidirectional, 0.11
0.1, pool=mean.
CNNID classifier
Layout: Stack of
Convid Couts padding via k//2) RacchNor--RellU--Dppooll
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Head: Global average pooling over T then Linear(C'+
Channel order: Accepts (B, (B,,CC) (channel_last-te (B, C,T). We coerce (B, C, 7) internally.
Defaults:
c4,
kernel size k=5, Párop 0.2, BatchNorm on,
channel_last=trre
MLP classifier
Pooling: If input is (B,T,C) or (BC,, pool over T via mean/max (default: mean). Optionally flatten requires fixed T with input dim CxP.
Backbone: Repeated (BatchorBa
blocks. Head is Linear- C.
Defaults: hidden sizes (256,256), BatchNorm
Párop
0.2, pool=mean,
channel_last=trre
GC-MS MLP encoder
Stem: Optional LayerNorm on input, then MLP with ReLU and optional Batch- Norm/Dropout, endinng in Linear-> D.
Normalization: Optional & normalization of the final embedding when used in contrastive objectives.
Defaults:
hidden (512,256), D-256, Pirop = 0.1, LayerNorm on, BatchNorm off, l2 off.
Hyperparameters
Component
Key defaults toggles
Transformer
H=8, L=4, FFN 4D, P0rop=0.1. =0.1, GELU, PE on, CLS off, pool=mean L=1, bi=True, Parop =0.1, pool (asan,,lst,,ax), Demh set channels=(64,) 128,256), k=5, BN on, Parop 0.2, baannnellass=ttrus hidden=(256.256), BN on, Pdro = 0.2, pool { mlatten
LSTMNet
CNNID
MLP
GC-MS enc.
hidden=(512,256), D=256, LayerNorm on, BN off, dropout 0.1, l off
G.2 TRAINING IYPPRRAAAMETERS
We standardize training across baselines and SCENTFORMER to ensure fair comparison and to avoid overfitting to any one configuration.
Temporal differencing. To quantify the value of short-range dynamics, we evaluate fixed lags p {0, 25) when forming first-order temporal differences Az, Ir Tt-p (Sec. 4.2). This follows prior evidence in our setting that differencing can substantially improve discriminability.
Wliding-window segmentation. We segment streams into overlapping windows of length (50, 100) (Sec. 4.2). The shorter window (w 50) increases the number training and evaluation samples, whereas the longer window (w 100) trades sample count for more stable temporal context (App. 1.1).
Learning rate selection. To keep model comparisons robust and reproducible, we tune over small, fixed grid shared by all methods: (3 10-4, 10-3, 3 10-3). We evaluate final checkpoint of each rum the validation set and choose the best final checkpoint. Limiting the grid prevents "hyperparameter fishing" and reduces variance attributable to optimizer settings.
Epoch budgets and batching. For SMELLNET- BASE classification we train for 90 epochs with batch size 32. For SMELLNET MIXTURE distribution prediction we train for 60 epochs with batch size 64. Using fixed epoch budgets across models minimizes variance due to training length. The best checkpoint chosen by validation Top-1.
Randomization.
We fix the Python-level random seed to 42 for reproducibility across all experi-
ments.
GC-MS supervision. Contrastive alignment with GC-MS is used only for the ingrediett classification setting, where per-ingredient GC-MS signals available. We do not apply GC-MS supervision to mixture prediction because reliable GC-MS profiles for arbitrary mixtures are not available at scale.
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Table GC-MS encoding ablation SMELLNET-BASE (Acc@1, %). We compare sensor-only training (S). pross-modal training with elemental- composiiionn GC-MS embedding (Xatoo). cross-modal training with a binned mass-speddd paastsspectaal GC-MS embedding (Xepec). The sensor encoder, training protocol, and evaluation split are unchanged across columns for each row. Atom and Aspec denote the Acc@ change relative to S under the same model and preprocessing setting.
Acc@1 (%)
A S
Model
Xatom
Xspec
Atomm
Aepec
50
21.9
23.6
24.1
+1.7
+2.2
50
25
18.2
23.8
24.7
+5.6
+6.5
MI.P
21.0
25.6
21.3
+4.6
+3.3
100
25
26.8
28.0
30.8
+1.2
+4.0
50
25.5
28.4
27.7
+2.9
+2,2
50
25
46.9
45.9
45.7
-1.0
-1.2
CNN
100
29.5
31.0
26.7
+1.5
-2.8
100
25
52.7
57.1
58.9
+4.4
+6.2
50
29.3
29.7
31.9
+0.4
+2.6
50
25
50.6
53.3
50.7
+2.7
+0.1
LSTM
100
28.8
33.7
35.2
+4.9
+6.4
100
25
57.9
56.1
56.9
-1.8
-1.0
50
35.1
36.2
34.7
+1.1
004
50
25
50.6
50.9
52.6
+0.3
+2.0
Transformer
100
39.9
41.0
39.5
+1.1
-0.4
100
25
56.1
58.5
63.3
+2.4
+7.2
Note. Xapoe denotes the binned mass-spectral GC-MS embedding.
Design choice for mixtures (no temporal differencing). For SMELLNET-MIXTURE sensor streams are recorded at Hz on a four-channel array. At this sampling rate, small lags yield negligible signal change, whereas larger lags substantially reduce the effective number windows. We therefore train mixture models on raw (non- (oon-differenced) windows.
H ADDITIONAL EXPERIMENT RESULTS AND ABLATION STUDY
H.1 ENCODING ABLATION: Xatom VS. Xapoe
To test whether our RQ2 conclusions depend on the specific GC-MS encoding, we compare three settings SMELLNET- BASE: sensor-only training (denoted S, no GC-MS supervision), cross-modal training with the elemental- composiiioon GC-MS embedding (Xatom), and cross- moodal training with the binned mass mass-spectral GC-MS embedding (XSpee). The sensor encoder, training schedule, and evaluation protocol are unchanged. Only the GC-MS-side ingredient embedding is replaced.
Tab. reports Acc@ for each architecture, window size w. and temporal differencing lag We also report Astom and Dapec defined the change in Acc @ relative the sensor-only baseline S under the same architecture and preprocessing setting.
Overall, Xapec shows the same qualitative behavior as Xatom: GC-MS supervision yields the largest gains for weaker models (especially MLP), while stronger temporal models (CNN, LSTM, and SCENTFORMER) exhibit smaller and sometimes mixed effects depending on preprocessing. This confirms that our main RQ2 conclusions are robust to the choice of GC-MS encoding. In both cases, GC-MS functions complementary chemistry-aware prior that modestly regularizes the sensor embedding, while the dominant performance gains come from modeling the sensor time series itself.
H.2 ADDITIONAL MIXTURE METRICS AND FULL RESULTS
Each SMELLNET-MIXTUSE sample is defined by volumetric recipe over 12 base odorants, nor- malized to a 12-dimensional target vector. The model predicts this continuous mixture distribution directly from the sensor time series, with the goal of recovering the intended recipe proportions rather than exact gas phase concentrations, which additionally depend on volatility and headspace dynamics.
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Table 10: Mixture proportion prediction performance SMELLNET-MINTURE We report KL divergence. MAE, and cosine similarity between predicted and ground-truth 12-D mixture distributions seen and unseen mixtures across models and window sizes.
Split
Model
KT,1
MAE
Cosinet
50
MLP
0,450
0,0428
0.861
50
CNN
0.370
0.0404
0.867
LSTM
0,416
0.0399
0.878
50
SCENTFORMER
0.480
0.0395
0.863
Seen
MLP
0.615
0.0586
0.799
100
CNN
0.425
0.0476
0.849
100
LSTM
0.503
0.0430
0.856
SCENTFORMER
0.386
0.0417
0.874
MLP
2.919
0.1190
0.860
50
CNN
2.649
0.1
0.860
LSTM
2.532
0.1160
0.848
50
SCENTFORMER
2.618
0.1100
0.863
Unseen
100
MLP
2.302
0.1110
0.799
CNN
2.488
0.1
0.824
100
LSTM
2.441
0.1150
0.856
100
SCENTFORMER
2.454
0.1110
0.895
Table 11: Single-channel mask ablation (SCENTFORMER, window=50, temporal difference p=25). Entries are negative point changes in Acc@ when masking a channel (masking always reduces accuracy). Baselines: Cross Cross-modall modal: 52.60%, Sensor-only: 50.60%.
Cross-modal
Sensor-Only
Channel (gas)
A Acc@1
Channel (gas)
Acc@1
LPG
-28.07
LPG
-28.90
VOC
224.65
Alcohol
-26.50
Alcohol
-23.92
VOC
-24.10
CO
-18.93
CO
-22.07
OH
-17.36
NO₂
-19.76
NO₂
-15.60
ON
-10.25
In addition to Top- 10.1 and dynamic Top-K accuracy, report KL divergence, MAE, and cosine similarity between the predicted and ground- boound-rruth 12-D mixture distributions. We use Top- @0.1 because mixture ratios defined on 0.1 grid. A +0.1 tolerance therefore corresponds to exactly one step of the recipe resolution.
@0.1
Tab. 10 reports full mixture prediction results for seen and unseen mixtures across models and window sizes. Cosine similarity remains relatively high in both settings, while KL divergence and MAE increase substantially on unseen mixtures. This divergence between metrics indicates that models tend recover the direction of the mixture vector, while still incurring substantial absolute errors in the predicted proportions for unseen combinations. We therefore treat cosine similarity as useful complementary diagnostic, whereas MAE, KI. divergence, and Top-1 @0.1 remain the primary metrics for evaluating ratio accuracy.
H.3 CHANNEL IMPORTANCE THROUGH MASKING
All reported values are negative AAcc@ 1, i.c. masking any single channel reduces Top-1 accuracy. With temporal differencing (p=25, Tab. [T]. the largest drops occur for LPG (-28.9% to -28, 1%). followed by VOC and Alcohol, indicating these channels carry the most decisive information under different preprocessing. Without differencing (p=0. Tab. 12 NO, and CO dominate the impact (up to -25.2%) suggesting the raw-signal model leans more on oxidizing and CO-related responses.
Contrastive training slightly reshapes importance but preserves the main ordering within each pre- processing regime. Overall. the strictly negative AAco@) I across all cells reinforces that each gas channel contributes uniquely. The magnitude pattern shifts with temporal preprocessing and contrastive alignment.
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Table 12: Single-channel mask ablation (SCENTFORMER, window=50. temporal difference p=0). Entries are negative point changes in Acc@ when masking a channel (masking always reduces accuracy). Baselines: closs-modal: 34.70%, Sensor-only: 35.13%.
Cross-modal
Sensor-only
Channel (gas)
A Acc@1
Channel (gas)
A Acc@)
NO
-21.27
CO
-25.15
CO
-88.80
NO₂
-22.77
OH
-18.71
C₂H₂OH
-21.01
VOC
-16.15
VOC
-14.03
Alcohol
-9.36
Alcohol
-12.44
LPG
-8.91
LPG
-10.41
Table 13: Leave-one-day-on performance of SCENTFORMER on SMELLNET- BASE for p 25. We report mean standard deviation over the 6 held-out days for each window size. All values are percentages.
Acc@1
Acc@5 (%)
F1 (macro,
50
49.4 + 8.0
84.2 6.3
48.2 + 7.9
100
53.0 + 6.0
85.5 5.0
51.6 6.0
H.4 LEAVE-ONE-DAY-OUTT EVALUATION ON SMELLNET-BAL BASE
To assess temporal robustness, we evaluate SCENTFORMER on SMELLNET BASE using a leave-one- day-out protocol. Each day in SMELLNET- BASE corresponds separate acquisition session on different calendar day with different ambient conditions. In each split, we hold out all samples from one day as the test set and train on the remaining five days. This protocol therefore measures generalization across day-to-day distribution shifts, rather than relying i.i.d. resampling.
Tab. 13 reports w (50, 100). Tab. 14 provides the per-day breakdown. Across both window sizes, Day is the most challenging split and consistently yields the lowest Ace@ I and macro-F1, suggesting larger day-specific distribution shift (e.g., ambient conditions or sensor state). Importantly, performance remains well above chance even on the hardest day (2% Acc@1 1 for 50 classes), and performance on the remaining days clusters substantially higher. Overall, the mean + std across days reflects genuine between-day domain differences and indicates that SCENTFORMER generalizes robustly across realistic day-to-day variations in SMELLNET-BASH
standard deviation across the 6 held-out days for p 25 and window sizes
H.5 TIMESTAMP SIZE ANALYSIS
To justify our 10-minute recording interval, we conducted an ablation that varies the number of initial time steps fed to SCENTFORMER(windON size w=50) under differencing lags p e {0, 25) (Tab. 15). Using only the first 200 steps yields lower accuracy. As the number of steps increases, performance improves and then plateaus around 400-600 steps. With temporal differencing (p=25), @cc@I increases from 45.7 at 200 steps to 53.2 at 500 steps and remains essentially unchanged at =600 steps (50.6/52.6 for sensor/cross-modal)) This saturation suggests that a 10-minute window captures sufficient temporal dynamics for robust recognition, while longer acquisitions provide diminishing returns. Based on these preliminary findings and practical data-collection constraints, we therefore adopt 10-minute intervals throughout.
H.6 RUNTIME AND MEMORY ANALYSIS
All experiments use SCENTFORMER on NVIDIA L40S (compute capability 8.9, driver 550.54. 14, CUDA 12.4, 46,068 MiB VRAM) with FP32, batch size 32, and a ---aaaee-rrrr model (live memory 18.9626 MB, peak GPU 75.4966 MB). Inference latency is extremely low across settings: mean windww latency ranges from 8.0191-0.044 ms, and throughput remains high at 20.892-52,371 windows per second. As measurements are forward-only, temporal differencing is irrelevant. At this batch size, both the presence contrastive training and the window size have negligible practical impact on latency.
26

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Published as a conference paper at ICLR 2026
Table 14: Per-day leave-one day-out performance of SCENTFORMER SMELLNET BASE for p 25 and e {50, 100). Each row holds out one day as test set. values are percentages.
w
Day
Acc@1
Acc@5
(macro)
50
34.8
73.5
33.3
50
2
54.7
90.7
52.8
55.7
89.4
54.2
50
54.5
85.9
53.2
50
45.9
80.7
46.3
50.6
85.0
49.5
100
43.6
80.8
41.4
100
56.0
89.6
54.9
100
89.5
54.1
100
58.0
88.0
56.6
100
47.2
77.9
47.0
56.1
87.4
55.5
Table 15: Ablation the number initial time steps used (SCENTFORMER) window size 5=55). denotes the temporal differencing lag (in samples). Accuracies Fl are reported in Increasing beyond 400-600 steps yields diminishing returns.
Sensor (SCENTFORMER, w=50)
Cross Cross-Modal (GC-MS)
Timestamps
Acc@
Acc@5
F1
Acc@1
Acc@5
F1
200
33.1
78.2
30,9
34.5
32.6
200
25
45.7
80.8
43.1
35.6
66.1
30.7
300
35.2
70.1
31.1
36.2
69.4
32.8
300
25
48.3
84.3
46.7
46.7
77.0
44.5
400
35.4
73.7
32.0
37.8
68.4
33.4
400
50.5
83.3
49.2
50.5
48.6
500
34.6
78.2
32.0
43.9
77.1
41.5
500
25
53.2
87.4
51.2
50.9
83.7
49.4
=600
35.1
70.9
33.1
34.7
69.0
31.4
x600
50.6
85.0
49.5
52.6
82.5
51.7
I
MATH
1.1 NUMBER OF WINDOWS
Let T be the number of time steps in a recording, w the window length (in steps), and the stride. Using valid (no-padding) windows, the number of extracted windows is
T w
1,
T w;
N NT,,
0,
T w.
We use 50% overlap, i.e., s 2 yields
Thus a 10-minute recording at Hz has T 600 steps and
600
50
600 100
N6600, 50, 25)
+1 22+1 23, N(600, 100, 50)
10+10++ 11.
25
50
With sampling rate fa (Hz) and duration L (s), T and each window spans w/fa seconds. right-padding is used include final partial window, replace the floor in (1) with ceiling.
I.2 TOP-K PERFORMANCE CALCULATION
For each example .I....NN with predicted class probabilities ((") softmax(s(")) [0, 1] let R, = (c: yc (m) > 0} denote the nonempty set ground-truth present classes and P,, R., its cardinality. Let ,, (1). n (C') index classes in descending order of p(*), and let II, (k) (1,, n (k)) denote the set of top-k predicted classes. The dynamic Top-K metric is defined
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Published as a conference paper at ICLR 2026
Table 16: Latency and resource metrics for SCENTFORMER (batch=32, FP32)
NVIDIA L40S.
Variant
Win
Mean (ms)
WPS
Eval (ms)
GPU MB
Live MB
Params (M)
No contrastive
50
0.0191
$2371
0.0189
63.3540
18.9626
2,4109
Contrastive
0.0203
49237
0.0310
61.0024
18.9626
2.4109
No contrastive
100
0.0479
20892
0.0218
73.6172
18,9626
2.4109
Contrastive
100
0.0420
23821
0.0151
75.4966
18.9626
2.4109
as the label recall under per-example cutoff K, = Pn:
N Σ Rm n 11,
DynTopk
x 100%.
(2)
Σ Rm
In the SMELLNET-MIXTURE evaluation, = 12 odorant classes are considered. Defining H E12 RR, (P.)) and M Σ [R,,, the metric reduces Dyn DynTopK (H/M) 100%. In the special case where each example contains exactly one present class (i.e., R2, = for all n), the denominator simplifies to M N, and DynTopK is equivalent to standard Top-1 accuracy over the N examples.
J DETAILS OF SMELL MIXTURES
We define 43 unique ingredient combinations, and instantiate 126 unique recipes by assigning multiple ratios to each combination. For the collection of mixture data, each base odorant was treated as a distinct class, yielding 12 base classes. Beyond these bases, we constructed binary and ternary mixtures of the base odorants at fixed volumetric ratios, resulting in total of 126 unique mixture combinations (54 in training, 45 in test-seen, 27 in test-unseen). The dataset exhibits the following mixture distribution:
Base odorants: 24.7% of training sessions (168 sessions), 22.3% of test-seen (48 sessions), 0% of test-unseen
Binary mixtures: 71.3% of training sessions (484 sessions), 73.5% of test-seen (158 sessions),
83.2% of test-unseen (153 sessions)
Ternary mixtures: 4.0% of training sessions (27 sessions), 4.2% of test-seen (9 sessions), 16.8%
of test-unseen (31 sessions)
The composition of odor readings is shown Fig. Binary mixture ratios in the training set span multiple combinations including 20/80 (160 sessions). 50/50 (174 sessions), and 80/20 (120 sessions), with additional ratios spanning from 10/90 to 90/10. Ternary mixtures include both balanced (33/33/33) and asymmetric (10/30/60) distributions. This comprehensive ratio coverage enables the model learn ratio prediction across the full spectrum of possible combinations.
K
LLM USAGE
We disclose limited use of large language model (LLM) during development and manuscript preparation.
Manuscript editing. We used an LLM for light copy-editing of the paper text (e.g., grammar, phrasing, and clarity improvements). All technical content, experiments, analyses, figures, tables, and claims were written, checked, and approved by the authors.
Limited technical use in an early prototype. During an early prototype the element-count GC-MS descriptor pipeline (an alternative encoding reported for comparison in App. C) some FooDB entries did not include chemical formulas. In a small number such cases, we queried an LLM once to suggest candidate molecular formula. These suggestions formula was manually verified against public chemistry databases and our own parsing and validation code before inclusion.
not used directly. Each chemical
28

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Training Data: Alcohol Distribution by Category
Category
0.6
Spices
Nurt
Herba
0.5
Density
0.2
0.1
Alcohol
Figure 16: KDE distribution of Alcohol Sensor Category. These numbers represent raw, unfiltered data readings. Normalizations filtering abnormal channels are performed as preprocessing steps before modeling. provide standard kits for preprocessing, but the raw values are provided to preserve as much information as possible.
Training Data: C2H5OH Distribution by Category
Category
Spices
huts
leerbs
0.08
Vepetables
0.06
Density
0.04
C2H50H
Figure 17: KDE distribution of C2H5OH Sensor by Category.
29

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Published as a conference paper at ICLR 2026
Training Data: CO Distribution by Category
Category
Spices
Nuts
Herbs
Vegetables
Pruits
0.025
Density
0.010
1050
Figure 18: KDE distribution of CO Sensor by Category.
Training Data: LPG Distribution by Category
0.05
Category
Spices
Vegelables
Fruits
Density
0.03
0.02
0.00
100
200
300
400
500
LPG
Figure 19: KDE distribution of LPG Sensor by Category.
Training Data: NO2 Distribution by Category
0.10
Category
Spices
Nuts
Herbs
0.06
Density
0.02
NO2
Figure 20: KDE distribution of NO2 Sensor by Category.
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Table 17: Offactory materials used in SMELLNET, organized alphabetically by label. We distinguish essential oils (volatile natural extracts with identifiable key odorants), flavor extracts (culinary preparations in or alcohol carriers), fragrance oils (synthetic or proprietary blends), and cosmetic oils (carrier oils with an odor).
Label
Type
Material
Vendor
Almond
Flavor Extract
Pure Almond
CADIA
Extract
Apple
Fragrance
Apple
P&J Trading
Fragrance Oil
Banana
Cosmetic Oil
Banana Oil
The Aromatherapy Shop
Ltd
Clove
Essential Oil
Clove Bud
365 Whole Foods Market
Essential
Coriander
Essential Oil
Coriander
Skylara Essentials
Essential Oil
Cumin
Essential Oil
Cumin
Silky Scents
Essential Oil
Garlic
Essential Oil
Garlic Essential
Skylara Essentials
Oil
Mango
Essential Oil
Mango
The Aromatherapy Shop Ltd
Essential Oil
(Egypt)
Orange
Flavor Extract
Orange Flavor
Frontier Co-op Store P&J Trading
Peach
Fragrance Oil
Peach
Fragrance Oil
Pear
Fragrance Oil
Pear Fragrance
P&J Trading
Strawberry
Fragrance Oil
Strawberry
P&J Trading
Fragrance Oil
Training Data: VOC Distribution by Category
Category
Spices
Natts
Herbs
0.04
Vegetazles
Fruis
0.03
Density
0.02
0.01
0.00
200
400
600
800
1000
VOC
Figure 21: KDE distribution of VOC Sensor by Category.
No LLM use in model development or evaluation. We did an LLM design experiments, select models hyperparameters, generate results for model training or evaluation, analyze outcomes, or produce scientifie conclusions. No LLM outputs were used as training data, labels, inputs the reported sensor models.
Reproducibility. To ensure reproducibility without any LLM dependency, we release the final deterministic scripts used construct the GC-MS descriptors, together with the finalized compound- formula table and processed GC-MS embeddings. Reproducing the reported results does not require calling any LLM.
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Table 18: Statistics of Gas Sensor Measurements These numbers represent raw, unfiltered data readings. Normalizations and filtering of abnormal channels performed preprocessing steps before modeling. We provide standard kits for preprocessing, but the values provided preserve much information
possible.
NO2
C2H50H
VOC
CO
Alcohol mean
LPG
Ingredient
mean
std
mean
std
std
mean
std
std
mean
std
allspice
257.35
47.64
298.64
46.79
493.35
72.72
835.91
23.98
2.42
0.52
34.23
1.75
almond
58.10
15.99
112.52
26.03
164.75
61.27
735.84
12.07
4.38
0.66
20.64
1.09
angelica
143.16
41.41
175.46
32.63
316.82
69.57
769.97
9.79
1.07
0.73
8.91
1.89
apple
45.38
3.91
66.93
2.40
126.27
28.85
750.08
7.97
2.25
0.44
10.58
0.53
asparagus
54.49
6.42
87.34
7.87
117.50
15.95
780.18
14.74
4.26
1.84
18.93
3.44
avocado
31.56
1.11
69.69
1.31
77.40
3.22
860.57
14.95
1.72
0.45
24.55
3.32
banana
58.15
7.60
89.30
7.99
154.88
34.11
804.62
18.02
2.71
0.52
31.82
3.16
brazil nut
32.12
3.84
58.70
3.63
61.27
8.54
727.97
5.98
2.42
0.59
23.91
1.10
broccoli
35.14
3.87
69.14
5.73
72.25
11.89
855.23
22.45
1.05
0.23
34.33
4.26
brussel sprouts
42.56
5.18
65.20
3.23
84.28
8.67
9.68 744.62
3.73
2.66
20.03
3.83
cabbage
37.25
1.86
70.06
1.48
82.76
3.74
776.35
6.04
1.67
0.48
25.44
1.11
cashew
34.52
1,28
66.05
0.84
67.58
2.70
757.08
1.83
5.59
2.94
13.81
2.51
cauliflower
36.75
1.08
69.59
0.90
81.63
1.98
777.96
6.62
1.29
0.46
30.06
1.66
chamomile
47.79
5,60
69.86
7.89
104.80
14.10
752.26
19.14
1.41
0.73
12.40
1.73
chervil
38.36
3.65
62.39
1.86
77.55
5.74
718.41
7.32
5.65
2.72
33.60
5.43
chestnuts
34.51
5.07
60.54
2.88
67.40
8.48
819.79
26.96
11.49
3.23
54.55
9.19
chives
46.26
4.93
80.56
6.78
114.11
16.46
801.50
11.77
7.37
1.23
22.74
2.96
cinnamon
163.05
37.50
182.66
31.10
374.53
96.46
787.42
15.21
3.20
1.01
23.16
2.55
cloves
121.25
34.45
105.63
25.27
238.72
69.35
778.28
7.02
11.29
1.66
43.08
4.02
coriander
237.54
38.93
275.84
53.13
481.83
70.99
830.32
16.57
1.42
0.52
23.82
4.30
cumin
322.36
64.14
454.09
62.83
608.59
75.88
848.05
16.92
5.22
1.63
31.79
3.09
dill
305.26
75.62
472.43
85.59
660.98
105.97
927.31
29.28
9.29
4.52
33.76
5.21
garlic
75.73
11.98
111.27
23.07
149.23
32.19
826.53
26.66
1.36
0.73
15.95
1.90
ginger
281.14
53.82
281.85
45.31
585.09
90.61
810.70
17.01
5.19
0.50
20.41
0.76
hazelnut
35.27
1.92
62.76
0.71
71.75
2.57
727.42
2.93
3.62
1.15
26.62
1.85
kiwi
30.99
2.33
63.09
2.98
63.09
6.45
771.93
2.86
1.02
0.15
20.80
1.12
lemon
373.19
108.93
497.90
109.86
672.55
120.62
877.10
44.48
2.89
0.50
14.61
3.64
mandarin
111.15
35.02
145.58
33.67
246.67
74.52
872.39
35.47
1.59
0.53
23.36
3.30
mango
49.58
6.39
82.23
8.90
115.03
13.03
757.49
7.69
2.04
0.22
11.91
0.86
mint
60.52
4.89
81.03
6.27
141.44
17.75
745.44
4.56
0.17
0.38
12.38
1.36
mugwort
116.18
19.94
171.32
33.38
264.83
41.12
786.64
6.50
1.19
0.43
18.76
1.02
mustard
29.07
3.12
65.48
3.93
63.17
8.33
754.74
3.97
1.22
0.44
10.43
1.88
nutmeg
647.16
108.49
789.17
94.66
850.61
99.73
985.72
29.25
6.76
1.80
66.55
25.78
oregano
176.32
33.02
245.84
51.77
344.29
48.64
794.02
14.98
7.09
3.08
45.97
7.72
peach
64.74
23.37
153.68
67.61
224.51
86.09
956.07
27.04
5.98
2.01
262.50
63.55
peanuts
35.84
2.00
64.09
2.16
63.25
5.72
753.14
2.78
1.60
1.18
8.60
1.62
pear
40.39
3.34
67.87
2.88
98.02
9.45
802.24
16.63
2.32
0.48
18.23
2.90
pecans
25.14
3.14
49.67
3.58
47.55
8.22
726.83
4.23
7.28
1.64
30.81
3.52
pili nut
33.74
1.63
57.68
0.52
67.48
1.88
726.42
3.57
7.07
1.58
36.68
2.01
pineapple
47.09
5.87
82.72
6.47
102.16
12.14
897.35
26.27
0.00
0.00
48.32
19.73
pistachios
39.79
4.90
56.79
3.38
69.73
9.04
720.86
6.71
0.00
0.00
4.78
0.81
potato
32.79
5.49
56.15
4.86
65.47
14.22
745.50
11.72
5.53
1.48
40.54
4.37
radish
46.52
8.50
82.89
9.08
102.48
17.90
787.99
21.02
0.00
0.00
32.07
17.94
saffron
L10.59
13.60
150.79
12.46
220.78
22.85
763.06
4.90
1.99
0.12
25.14
2.42
star anise
91.89
21.29
115.33
17.30
173.25
33.78
744.94
6.98
0.00
0.00
4.47
0.60
strawberry
54.26
6.89
91.86
10.47
108.25
12.18
749.20
10.58
0.00
0.00
3.67
0.50
sweet potato
38.58
2.85
73.99
4.61
98.93
9.31
763.77
11.52
8.83
2.51
107.87
16.80
tomato
39.76
10.09
73.82
6.74
90.18
19.60
809.73
13.25
1.60
0.49
22.61
1.92
turnip
30.31
0.79
67.94
1.42
71.80
2.71
821.85
9,80
2.00
0.75
22.17
1.69
walnuts
32.67
5.04
63.04
3.21
59.40
8.73
762.96
2.53
2.38
1.89
12.39
2.28
32


Reviews:
[{"review_id": "yZ2ikdsCIa", "reviewer": "Reviewer_WutW", "summary": "This paper presents SMELLNET, a dataset of gas sensor recordings collected from 50 natural substances along with mixtures among them. The dataset contains approximately 828,000 time-series data points and is paired with preexisting GC-MS data. The authors also introduce SCENTFORMER, a Transformer-based model designed to process these temporal signals for substance classification and mixture ratio prediction. The authors claim that SMELLNET can serve as a benchmark for research in olfactory AI and AI-based smell recognition", "questions": "- What concrete research questions in olfactory AI can this dataset help answer? \n- How is the mixture dataset can be used in the further research\n-How are “mixtures” defined chemically?\n- Could the authors provide comparisons between SMELLNET and other gas-sensor or GC–MS datasets?\n- How do you envision connecting SMELLNET to actual human olfactory perception in future work?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper is well-written and clearly structured, making it easy to follow.\nIt explores an understudied and potentially impactful area, bridging olfactory perception and machine learning.\n- The introduction of a new dataset and benchmark is a valuable contribution, especially in a domain with limited data resources.", "weaknesses": "- The dataset includes no human perceptual data (e.g., semantic descriptors, intensity or pleasantness ratings, similarity judgments, or brain recording). Without human responses, the dataset cannot be meaningfully linked to olfactory AI studies as claimed in the paper. Labeling the dataset as “SMELLNET” and positioning it as central to “olfactory perception” is overstated. At best, the dataset captures chemical sensing information, not perceptual smell data.\n- In Table 1, the comparison with human-evaluated datasets (e.g., Dravnieks, DREAM, Snitz, Ravia) is misleading. Other datasets include both stimulus (odorants) and response (human judgments). SMELLNET only includes the stimulus component (gas sensor signals), not perception-based responses. Comparing against other gas-sensor or e-nose datasets that exist in related areas would probably be more relevant and better contextualize the scale and novelty of SMELLNET.\n- Although the paper describes the dataset as “large-scale,” 50 odorants with six repetitions each is modest by modern machine learning standards. Summing the total number of time-series data points to claim large-scale is not meaningful. A more relevant metric would include the number of unique odorants and repetitions. \n- The definition of “mixture” is ambiguous and the motivation behind that is not clear. In olfactory research, mixtures typically refer to combinations of mono-molecular odorants, while here it seems to involve mixing natural extracts (e.g., “apple + banana”), which inherently are mixtures themselves. Predicting this kind of mixture composition from sensor data, without any perceptual correspondence, does not provide meaningful insight into olfactory AI.\n- The dataset provides low-resolution sensor data, yet it later depends on pairing with high-resolution GC–MS data. It is unclear why the low-resolution data is necessary or what advantages it offers. No ablation is provided to show how a model trained solely on high-resolution data would perform compared to one using sensor data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DCvHlWyMY4", "reviewer": "Reviewer_uEQe", "summary": "The authors propose SmellNet, a database of olfactory properties for both single-object and mixtures related to food. The authors gather this data for these objects placed in a controlled environment and use a metal oxide gas sensor to detect specific compounds, and track other ambient parameters alongside the data collection.  Using this data, the authors train a series of models for identifying the objects or mixture compositions via its sensory and ambient fingerprints. The authors evaluate the model’s performance to generalize across temporal domains for single objects, and also on unseen mixture compositions. Olfaction has long been a data-scarce domain, and I commend the authors’ efforts in bridging this gap for machine learning. However, from my perspective, there are some serious issues with the quality of the data, the methodology that the authors adopted, and the evaluation of the models proposed. Therefore, I am rejecting this paper, but am willing to raise my score if the authors address the numerous concerns I will detail below.", "questions": "1) Do you have an explanation on why you chose to use gas sensors, and the specific sensors within your platform?\n2) Please provide an ablation where either the sensory data or the environmental data is removed from the training data.\n3) Why are the sensor outputs said to be qualitative (Section 4.2), but quantitative sensor readings are provided? \n4) Please provide leave-day-out model performance metrics at least for SmellNet-Base.\n5) What is the proportion of the GC-MS data that you could gather from FooDB? Is there an ablation where molecules that do not have the GC-MS data are eliminated, and the performance metrics are reported on a model trained with and without the GC-MS data?\n6) Please show the performance in terms of cosine similarity for the mixture evaluation. \n7) Is it surprising to you that the models can’t learn the time lag and that it is a requirement for you to inject this human-engineered feature into the model for increased model performance?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 2, "strengths": "I think the writing is clear, and that the authors have posed their research questions in a structured format that makes it easy to understand the motivations for performing the experiments throughout the work. \n\nThe authors design a hardware platform for the collection of this data. Recognizing the limitations of sensor data, they attempt to pair sensory data up with high-quality GC-MS data for cross-modal learning. The authors also consider the role of sequential and non-sequential architectures for model performance.", "weaknesses": "I think the authors need to provide a stronger motivation for adopting gas sensors over other platforms beyond “portability”. Additionally, the authors justify the choices for the sensors as “common odors found in food, drinks and other common substances”. I am not convinced that typical food items contain large amounts of alcoholic, petrochemical and nitrogen oxide vapors, and it would be worrying if this was the case. For many of these natural products, the volatiles are mostly relatively complex organic compounds, which I am also not confident that the VOC sensor is able to capture beyond the fact that all these volatiles would be crammed into the same signal channel. Figure 9 in the SI also shows strong correlations for the NO2, C2H5OH and VOC sensors. Because the prior for choosing these sensors is not strong, I am worried that there is significant overfitting present.\n\nOther evidence that points towards overfitting/data leakage exists in Table 16. Based on what I can see it seems possible to already build a reasonable classifier (e.g. tree-based model) just from temperature, pressure, humidity, gas resistance and altitude. Angelica for example can be classified by temperatures greater than >~26 and <33.59. \n\nThe GC-MS processing seems strange -- are the top 10 ingredients being completely broken up into its constituent atoms to form a representation? I had thought that the raw GC-MS data (i.e spectral data) was being used, but this form of considering only the atomic counts appears to be rather strange because the molecular structure will clearly affect its olfactory properties (and its ability to bind to the sensors). The PCA performed on top of the GCMS representation shows an extremely large PC1 importance -- and this could be because the ingredients are clearly separated by the presence/absence of certain elements, or atomic counts. Another slight weakness is that the GC-MS data from FooDB does not directly correspond to the same substance’s measurement under the same conditions (given natural variations in food products), so the gathered gas sensor data could potentially be incongruent with the GC-MS traces.\n\nIn terms of the utility of the models trained, querying requires measurement under the same pristine conditions and with the same sensor setup as well -- limiting its widespread use for classification. It would have been interesting to see this coupled to some data modality that is easily accessible to increase the scope of things that can possibly be predicted. \n\nIn alignment with the points above, I find it concerning that the authors claim that this dataset is relevant for the real-world when 1) the data was gathered in controlled environments to minimize environmental factors, which is not representative of the objects in the real-world, and 2) despite their efforts to control the environment they report wildly varying metrics for their environments.\n\nI’m not entirely sure of the cost of model training, but testing the model could have been done across all other combinations of the leave-one-day-out approach the authors used for SmellNet-Base to show the robustness of their data modelling efforts.\n\nThe authors also report the limited generalizability of their approach in Table 4 for unseen combinations, but it’s not clear to me how these ratios were constructed, given that different masses of compounds lead to different concentrations and proportions of volatiles in the headspace, etc.\n\nFinally, the authors evaluate the mixture proportion prediction task using a Top-x@0.1 metric. I’m not sure how 0.1 was decided, and if 0.1 is even an acceptable tolerance for error. The authors evaluate binary and ternary mixtures, and it’s not clear to me if weights are also predicted for more components than there are in the training set. The mixture ratios detailed in Appendix J are also rather limited and can almost be decomposed into a classification problem where the relevant odorant is identified within the mixture and its subsequent ratio-class is predicted. I think cosine similarity would be a more appropriate metric for this purpose.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DjX23NczQ3", "reviewer": "Reviewer_eaQT", "summary": "This paper introduces a new olfactory dataset, combining temporal sensor data with with object labels, and proposes several models to decode single- and multi-source odorants to establish the baseline results for these data.", "questions": "- What is the source of the temporal dynamics in the data?\n\nWhile one of the results here regards the usefulness of the remporal data, it's unclear from the text where the temporal variations stem from. Is it because the sensor is brought closer to and then further from the odor sourse to mimic the inhalation? Or is it sue to the variability of the sensor readout at the constant distance from the odor source? In ether case, what do the temporal changes represent and why may they be important? What has motivated the collection of the temporal data in the first place?\n\n- Could you please further comment on the utility of this type of data?\n\nAs this dataset abstracts form the brain and the processing witin it, the scope of the use case for this dataset differs from that of conventional olfactory datasets. Could you please detail the potential use cases for your type of data (some of them are mention in the introduction) and discuss the differences with the use cases for the conventional data?\n\n- Minor: Why are C2H5OH and Alcohol different channels?\n\nSeem to be the same thing to me.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- A new dataset\n\nThe paper offers a new large-scale dataset connecting the odorants and the objects emitting them. The dataset has a reasonable number of odorant (50; typical for olfaction; compare to ~100 in the Leffingwell dataset) and a large number of data point (recorded by the sensors), thus enabling the application of larger machine-learning models than previously (e.g., GNNs and CNNs).\n\n- Complementary approach to olfactory data\n\nWhile conventionally the olfactory datasets were focused on psychophysical processing of the smell (exposing human participants or animals to smells and recording their semantic or similarity reports respectively), this dataset fully avoids the perceptual properties of the smell, going directly from sensor readouts to object classification. While this choice shifts the scope of what's can be learned from the olfactory dataset, it also enables the large-scale data collection that scales easily and thus, in turns, enables the training of larger models.\n\n- Thorough considerations for the data collection\n\nA lot of thought has been put into the ways these data were collected, resulting in a dataset that contains sufficient information to learn from and guiding us in the ways how such data can be collected and scaled. In particular, the Authors have made the informed choices of the sensors (NO2, C2H5OH, VOC, CO, Alcohol, LPG), data collection modalities (temporal-difference), and standardization (controlling for the external air parameters; adding repetitions). All these choices were guided by direct data analyses.\n\n- A comprehensive set of baseline models and metrics\n\nThe Authors have tested a comprehensive set of standard baseline models (MLP, CNN, RNN, Transformer) and metrics (top-1 and top-5 accuracy; F1-score) on three tasks (sensor-only; cross-modal; mixture) showing that (1) the labels are decodeable from the data and (2) that they are not saturated near 100%, leaving room for improvement. Both these properties are highly important for a benchmark dataset.\n\n- Text clarity\n\nThe text is written clearly and structured nicely. The figures and the tables illustrate the concepts in the text well.", "weaknesses": "- The results are somewhat overstated\n\nI found the results of the comparisons between the Transformer and the other models here to be overstated. While the Transformer surely shows some of the best results here, it's outperformed by the LSTMs and CNNs on several tasks (e.g. in Table 2). Likewise, I wouldn't say that \"GC-MS supervision strongly boosts weaker models\" -- I found the numbers that this statements refers to to be quite similar to each other, which is typical for transfer learning. Finally, I wouldn't focus on the claim that the dataset is much larger than the existing olfactory datasets, as these are two very different types of data: the latter involve psychophysics while the former one doesn't.\n\n- Minor: The emphasis on the mixtures is unclear with this type of data\n\nTypically, mixtures receive separate consideration in olfaction due to the differences in mass transfer between the molecules, differences in their affinity for the olfactory receptors, and synergetic effects in their perceptual qualities. None of these properties apply to the sensors or other the odor-emitting objects. Thus further discussion may be helpful to determine why the detection properties for the mixtures cannot be modeled as linearly dependent on the properties of individual smells.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Lu09V5V5XP", "reviewer": "Reviewer_wU3A", "summary": "In this paper the authors contribute SmellNet, a novel large-scale dataset of chemical data for olfactory recognition tasks. In particular, the authors collect over 68 hours of gas sensor data, pertaining to 50 different substance and 43 artificial mixtures of compounds. Furthermore, the authors present SmellFormer, a transformer-based architecture for odor prediction from sensor data. Finally, the authors extensively evaluate ScentFormer on single-component and mixture odor recognition data, highlighting how their transformer architecture outperforms other standard architectures (MLP, CNN, LSTM) and the potential of their dataset for future applications in olfactory machine learning.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "- **Originality**: While large-scale datasets of olfactory phenomena exist and have been extensively used by the community for olfactory prediction tasks ([1, 2]), the dataset presented here distinguishes itself by using directly sensor readings, collected using consumer-available hardware (instead of GC-MS and other specialized equipment).\n\n- **Quality**: I found the paper to be of high quality, with minimal typos, high-quality figures and text. The authors extensively evaluate the discriminative power of the collected dataset, both in the main text and in appendix. Furthermore, the authors evaluate different experimental setups, in terms of preprocessing steps, additional modalities and model architectures, for single and mixture odor prediction.\n\n- **Clarity**: Overall, the authors describe in extensive detail their data collection setup (hardware used and processing pipeline) and model architecture (preprocessing steps, architecture and training objectives). I also deeply appreciated the structure of the evaluation section, with clear research questions and highlighting the main findings for each of them.  \n\n- **Significance**: The lack of large-scale, diverse, olfactory data, collected in-the-wild, is currently a major bottleneck for the olfactory machine learning community. While being part of ongoing data collection efforts by the community, the use of consumer-available hardware to collect SmellNet makes this work a potential interesting resource for the community.\n\n**References**:\n\n- [1] Lee, Brian K., et al. \"A principal odor map unifies diverse tasks in olfactory perception.\" Science 381.6661 (2023): 999-1006.\n- [2] Taleb, Farzaneh, et al. \"Can transformers smell like humans?.\" Advances in Neural Information Processing Systems 37 (2024): 72032-72060.", "weaknesses": "- I would suggest the authors to refrain from over claiming in certain statements. For example, statements such as \"smell is a new data modality for AI (line 51)\", or \"large-scale AI for smell is completely unexplored (line 91)\" do not only overlooks important advances from the chemical and neuroscience community (some of them, already cited in the paper) on using AI for olfactory prediction tasks, but it is also factually incorrect: \"large-scale AI\" (whatever that means) have been used for olfactory prediction tasks, see [1], [2], for example.\n\n- I found it quite interesting that throughout the work the authors state that they collect sensory gas data in 12-channels, and evaluate their dataset under these conditions (Section 3.4), yet the first step in data preprocessing (Section 4.2) is to drop 6 of those channels, due to potential malfunctioning of these sensors. From a practical point of view, the authors should only release the 6-channel dataset (which I don't think would significantly decrease the novelty or significance of the work), or redo the data collection with functioning sensors. Releasing the full 12-channel dataset, where 6 of those are malfunctioning, can lead to future issues when people may employ the dataset without removing the channels. Is there any reason to release the full dataset, beyond the ones stated in Line 268-270, which are not particularly beneficial considering the previously discussed danger?\n\n- While the authors describe extensively the statistics and experimental apparatus to collect SmellNet, I found the description of the GC-MS pairings (which, accordingly to the results in Section 5, improves the performance of the predictive models) to be lacking and confusing. GC-MS is a high-precision measurement of the compounds present in a sample and, as such, varies across samples. Moreover, to the best of my knowledge, there exists no single GC-MS measurement/spectrum for some of the samples employed in your dataset (e.g., Apple, Pineapple). How did the authors collect this data? Moreover, how did the authors take the spectrum data and get the atomic counts (which is an unusual representation of GC-MS data, what about concentration for example)? The Appendix C mentions the use of an LLM for this purpose, yet it's unclear what is the process. Also, this use of LLMs for technical content goes against what is claimed in Appendix K, regarding the use of LLMs, which the authors to be used \"solely for light copy-editing\".\n\n**References**:\n\n- [1] Lee, Brian K., et al. \"A principal odor map unifies diverse tasks in olfactory perception.\" Science 381.6661 (2023): 999-1006.\n- [2] Taleb, Farzaneh, et al. \"Can transformers smell like humans?.\" Advances in Neural Information Processing Systems 37 (2024): 72032-72060.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)