You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Food safety
VideometerLab for
seed and grain applications

The DiTECT project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 861915.

LED band-sequential spectral imaging
Camera and lens
Emission filter changer
Integrating sphere
LEDs of multiple
wavelengths
Sample is placed in
target opening
Backlight or background

• LEDs: Stable, durable, large selection, rapidly developing technology
• Up to 20 different high-resolution bands acquired sequentially in 0.5-1.0 seconds
• May be combined with emission filters, backlight, and darkfield illuminant
• Combined reflectance spectral imaging and fluorescence spectral imaging possible!
Videometer Imaging Technology, www.videometer.com

Spectral Imaging
ultraviolet
(UV)

near-infrared
(NIR)
nm

200

300

400

500

600

700

800

900

1000

N images
obtained at
N specific
wavelengths

Videometer Imaging Technology, www.videometer.com

Spectral imaging
Spectroscopy

Spectral
imaging
or
imaging
spectroscopy

Machine
learning, AI

Imaging

aka
• multispectral imaging
• hyperspectral imaging
Videometer Imaging Technology, www.videometer.com

Primary attributes for single grains and seeds
• Color of grain or any grain part
• Spectral indices of grain or any seed part
• Shape of grain or any grain part
• Texture of grain or any grain part
• Size of grain or any grain part
• Relative size of any grain parts
• Contour
• Position and relative position
• Orientation and relative orientation
Videometer Imaging Technology, www.videometer.com

Primary features for grain and populations
• Count and TSW (given sample weight)
• Conditional counts
• Count-weighted distributions
• Area-weighted distributions
• Volume-weighted distributions (solid of
revolution)
• Optical weight distribution (assume volumetric
mass density) as an estimate og physical weight
distribution used in physical purity
Videometer Imaging Technology, www.videometer.com

Secondary features for single grains and seeds
• Physical purity class (crop, other crop, inert, weed)
• Damage (mechanical, insect, mold)
• Seed health (check for specific pathogens)
• Germination
• Early emergence/priming/preharvest sprouting
• Seed treatment (coating, disinfection, pelleting)
Secondary attributes are based on a calibration
model and a reference blob collection
Videometer Imaging Technology, www.videometer.com

Basmati rice vs long grain rice

Basmati rice

Long grain rice

Videometer Imaging Technology, www.videometer.com

Sorted by width, red is basmati

Videometer Imaging Technology, www.videometer.com

Sugar beet seeds

Videometer Imaging Technology, www.videometer.com

Sugar beet seeds segmented

Videometer Imaging Technology, www.videometer.com

Sugar beet seeds aligned

Videometer Imaging Technology, www.videometer.com

Sugar beet seeds sorted by area

Fines can be removed

Videometer Imaging Technology, www.videometer.com

Sugar beet seeds sorted by chlorophyll

Videometer Imaging Technology, www.videometer.com

Purity analysis of spinach samples
Class
Spinach

Example Images

Features
Sensitivity
Color, shape, 99.9%
texture

Cleavers / Galium
aparine

Primarily
shape and
texture

99.5%

Black bindweed/
Polygonum
convolvulus

Texture,
shape and
color for

99.7%

Videometer Imaging Technology, www.videometer.com

Purity analysis of spinach samples
Radish/ Raphanus
radicula

97.6%
Color and
smoothnes of
surface.

Rapeseed/ Brassica
napus

Color and
shape

97.9%

Hemp-nettle/
Galeopsis

Texture and
color

98.2%

Cereal

Shape, size,
99.7%
color of
broken kernel
and
presence of
furrow on
ventral side

Videometer Imaging Technology, www.videometer.com

Second level attributes - Seed classification
Classifier performance on test set with 57115 seeds

Videometer Imaging Technology, www.videometer.com

Classification Spinach vs Cleavers
Spinach

Cleavers
Cleavers classified
as cleavers

Class
Spinach classified
as Spinach

Example Images

Cleavers classified
as spinach
Spinach classified
as Cleavers

Videometer Imaging Technology, www.videometer.com

Pure samples 1 and 2 in sRGB

Durum

Common wheat
Videometer Imaging Technology, www.videometer.com

Pure samples 1 and 2 after nCDA

Videometer Imaging Technology, www.videometer.com

Sample 3

All 100 seeds automatically segmented and sorted
according to likelihood of being durum

Videometer Imaging Technology, www.videometer.com

Result sheet from spectral info only
Sample
number
Sample name
100% Durum control
1
100% Aestivum control
2
10% adulterated
3
3% adulterated
4
100% adulterated
5
3% adulterated
6
2% adulterated
7
5% adulterated
8
10% adulterated
9
0.5% adulterated
10
0% adulterated
11
0.5% adulterated
12
5% adulterated
13
5% adulterated
14
2% adulterated
15
10% adulterated
16
0% adulterated
17
100% adulterated
18
0.5% adulterated
19
2% adulterated
20
100% adulterated
21
0% adulterated
22
3% adulterated
23

REAL # of EST # of REAL # of EST # of
Durum
Durum Aestivum Aestivum Total
wheat
wheat
wheat
wheat number
seeds
seeds
seeds
seeds
of seeds
300
0
300
0
300
0
300
0
300
300
89
11
90
10
100
95
5
97
3
100
0
100
0
100
100
95
5
97
3
100
98
3
98
2
100
94
6
95
5
100
90
11
90
10
100
195
4
199
1
200
98
2
100
0
100
198
2
199
1
200
95
5
95
5
100
95
5
95
5
100
87
3
88
2
90
91
9
90
10
100
99
1
100
0
100
0
100
0
100
100
197
0
199
1
200
98
2
98
2
100
0
100
0
100
100
100
0
100
0
100
96
4
97
3
100

Videometer Imaging Technology, www.videometer.com

Incoming barley: skinning test

No skinning (left) – skinned kernels (right)
Videometer Imaging Technology, www.videometer.com

Heatmap for skinning
23

Incoming barley: Red Fusarium Gray mold test
2 step test:
1. Present the barley in a 90
mm petri dish (single layer)
2. Press F12
Output will show the
1. a color image of the sample
2. a segmented image with red
type Fusarium marked with
red color and gray type molds
with black color
3. an area fraction of red type
Fusarium and of gray type
molds

Videometer Imaging Technology, www.videometer.com

Heavily infected sample
Red color:
red, orange or purple
areas on kernels

Black color:
Gray and black mold
areas on kernels

Videometer Imaging Technology, www.videometer.com

Microdochium detection
Artificially infected
malt
Fusarium Culmorum
Fusarium Avenaceum
Fusarium
avenaceum/tricinctum
Lewia infectoria
Microdochium bolleyi
Cladosporium
Fusarium poae

52 ”red” kernels analyzed with NGS after spectral imaging
Videometer Imaging Technology, www.videometer.com

26

Tomato seed viewed in sRGB (D65)
Lot 1 (low chlorophyll)

Lot 2 (high chlorophyll)

Videometer Imaging Technology, www.videometer.com

Tomato seed chlorophyll A fluorescence
Lot 1 (low chlorophyll)

Lot 2 (high chlorophyll)

Videometer Imaging Technology, www.videometer.com

Feeding most granular products
Analysis time for complete analysis
Throughput varies with the computational
demand of the models, coverage on the
belt, and TSW.
Examples:
• 300 g of corn in 5 minutes
• 100 g of spinach seed in 8.5 min
• 50 g of barley/wheat in 5 min
• 50 g of OSR/canola in 5 min
• 1 kg of kibbles in 18 min
Videometer Imaging Technology, www.videometer.com

29

High reproducibility and sensitivity
Same 560 g sample measured
repeatedly over several months
using the autofeeder option

Adding few infected and partially infected seeds to the 560 g
sample gives a signifikant rise in detected infection level
Videometer Imaging Technology, www.videometer.com

Spectral imaging troughout the
seed and grain quality chain
• Breeding and genetic resources
• Screening, phenotyping, ploidy, genebank management (off-type, phenotype query)
• Seed technology
• Seed coating, seed priming, seed pelleting, seed disinfection, seedborne disease control
• Sowing
• Germination, vigor, hydration, root and shoot analysis
• Growing
• Field and greenhouse phenotyping, stressors, resistance
• Harvesting
• Maturity assessment, preharvest sprouting, combine harvester control
• Trading
• Product appraisal
• Cleaning
• Physical purity, broken, high value seed sorting, self-adjusting cleaning machines
• Refining
• Milling, mixing, malting
Videometer Imaging Technology, www.videometer.com
</reference>

<statements>
1. The centralized master controller coordinates the spatial trajectory of the direct-drive permanent magnet synchronous motor (PMSM) with hardware-level digital transistor-transistor logic (TTL) triggers, simultaneously firing camera exposure shutters and high-current LED strobe drivers
2. When driving high-power light-emitting diode (LED) arrays continuously, rapid junction heating induces thermal droop, resulting in exponential reductions in radiant flux and spectral shifts across acquisition cycles
3. Driving the illuminator in pulsed over-drive synchronization with camera exposure windows circumvents junction overheating while generating high instantaneous irradiance, allowing microsecond exposure times that freeze structural vibration
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.