HFQA - Hyperspectral Imaging for Fish Quality Assessment: A Spatial-Spectral Benchmark Dataset for Non-Destructive Fish Freshness Analysis

This Zenodo record contains a representative subset of the full dataset: Pack No. 5, captured daily across all 16 storage days. The subset is released to let users inspect data quality, file formats, and tooling before working with the complete collection.

HFQA (Hyperspectral Imaging for Fish Quality Assessment) is a hyperspectral imaging (HSI) dataset capturing the visual and spectral degradation of Atlantic salmon (Salmo salar) fillets during cold storage. A single fillet pack was imaged once per day for 16 consecutive days under controlled refrigeration, producing a time series that documents freshness loss across the visible and near-infrared (VNIR) range.

The dataset is intended for research in:

• Non-destructive food freshness / shelf-life estimation
• Spectral regression and ordinal classification of storage day
• Hyperspectral band selection and dimensionality reduction
• Domain adaptation and few-shot / transfer learning across packs and days
• General hyperspectral image processing method development

Each acquisition is provided in two interchangeable formats so users can adopt whichever fits their pipeline:

Both formats encode identical pixel data and wavelengths — they are provided purely for convenience.

This record contains Pack No. 5, all 16 days, in both formats:

HFQA_pack05/
├── README.md                     <- this file
├── bip/                          <- ENVI BIP format
│   ├── day_01.bip
│   ├── day_01.hdr
│   ├── day_02.bip
│   ├── day_02.hdr
│   ├── ...
│   ├── day_16.bip
│   └── day_16.hdr
├── npy/                          <- NumPy format
│   ├── day_01.npy
│   ├── day_02.npy
│   ├── ...
│   ├── day_16.npy
│   ├── wavelengths.npy           <- shared 1-D array of band centre wavelengths (nm)
│   └── metadata.json             <- acquisition + pack metadata
└── scripts/                      <- visualisation utilities (see Section 6)
    ├── visualize_npy.py
    ├── visualize_bip.py
    └── visualize_bip.m

Note on day ordering: files are named day_01 … day_16 so that simple alphabetical sorting yields correct chronological order. day_01 is the freshest (day of purchase / packing) and day_16 is the most degraded.

The values below are templated placeholders — please replace the bracketed fields with your actual acquisition parameters before publishing.

Calibration: R = (raw − dark) / (white − dark).

• day_XX.npy — a 3-D array of shape (lines, samples, bands) = (H, W, B), dtype float32.
• wavelengths.npy — a 1-D float64 array of length B giving the centre wavelength (in nanometres) of each band, shared across all 16 days.
• metadata.json — acquisition metadata. Example structure:

{
  "dataset": "HFQA",
  "pack": 5,
  "day": 1,
  "species": "Salmo salar",
  "sensor": "VNIR",
  "lines": 600,
  "samples": 800,
  "bands": 120,
  "wavelength_unit": "nm",
  "wavelength_range": [400.0, 1000.0],
  "reflectance_scale": "[0,1]",
  "storage_temperature_c": 4.0,
  "acquisition_date": "2025-11-25"
}

Minimal load in Python:

import numpy as np, json
cube = np.load("npy/day_01.npy")              # (H, W, B) float32
wl   = np.load("npy/wavelengths.npy")          # (B,) nm
meta = json.load(open("npy/metadata.json"))

• day_XX.bip — raw binary cube in Band-Interleaved-by-Pixel layout. On disk, for each pixel the full spectrum is stored contiguously, scanning samples then lines.
• day_XX.hdr — ENVI ASCII header. Key fields:

ENVI
samples = 800
lines   = 600
bands   = 120
data type = 4            ; 4 = 32-bit float (see ENVI codes)
interleave = bip
byte order = 0          
wavelength units = Nanometers
wavelength = { 400.0, 405.0, ..., 1000.0 }

ENVI data type codes used here: 1=uint8, 2=int16, 4=float32, 12=uint16 (full mapping handled by the provided scripts).

The cube can be opened directly in ENVI, in MATLAB via the provided script (or hypercube/multibandread), or in Python via the spectral (SPy) package or the bundled visualize_bip.py parser.

pip install numpy matplotlib            # core requirements
# pip install spectral                  # optional, for --use-spectral in the BIP script

# Visualise a NumPy cube
python scripts/visualize_npy.py --input npy/day_01.npy --outdir figures

# Visualise an ENVI BIP cube
python scripts/visualize_bip.py --input bip/day_01.bip --outdir figures

% From the dataset root, with scripts/ on the path:
addpath('scripts');
visualize_bip('bip/day_01.bip', 'figures');     % writes PNGs into figures/

@dataset{hfqa_2026,
  author       = {Alam, Kazi Nabiul, Sheikh-Akbari, Akbar, Bagheri Zadeh, Pooneh, Leeds Beckett University},
  title        = {{HFQA - Hyperspectral Imaging for Fish Quality Assessment: A Spatial-Spectral Benchmark Dataset for Non-Destructive Freshness Analysis}},
  year         = {2026},
  publisher    = {Zenodo},
  version      = {1.0 (Pack 05 subset)},
  doi          = {10.5281/zenodo.20508624},
  url          = {https://doi.org/10.5281/zenodo.20508624}
}