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Non-destructive measurement of mandarin orange quality

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Non-destructive measurement of mandarin orange quality
VI.P8
Non-destructive measurement of mandarin
orange quality parameters with visible near infrared spectroscopy
L. Paulo, M. Resende, A. Nunes , C. Miguel Pintado C, P. Antunes
CATAA- Associação Centro de Apoio Tecnológico Agro-Alimentar de Castelo Branco, Zona Industrial de Castelo
Branco, Rua A, 6000-459 Castelo Branco, Portugal.
Introduction
The development of sensors to measure fruit internal quality variables is one of the challenges
of post-harvest technology. Visible-near infrared spectroscopy (Vis/NIRS) has been a
promisable technique for non-destructive fruit quality assessment. This study was focused to
evaluate the use of Vis/NIRS in measuring the quality parameters of intact mandarin orange fruit
(Citrus clementina).
Material and Methods
• Vis/NIRS - Spectra were collected with a contact probe (ASD Labspec 4.0 NIR)
• Fruit colour, L*, a* and b* (Minolta CR-400)
• Firmness (TA.XT Plus, Stable Microsystems) Compression until 5 % deformation
•
Soluble solids content, SSC (Atago PR32α)
•
Titratable acidity, TA (Titromatic 2S -Crison)
Results
Parameter
Minimum
Maximum
Parameter
L*
a*
45.77
71.82
Minimum
SSC
(%)
8.20
Maximum
11.6
pH
b*
-10.24
41.99
33.62
69.74
peel
thickness
(mm)
1.6
5.8
3.2
TA
(mEq. 100 g-1)
9.47
Firmness
(N)
4.84
3.9
17.5
14.29
Parameter
L*
a*
b*
peel thickness
SSC
pH
TA
Firmness
L*
a*
b*
peel thickness
SSC
TA
pH
Firmness
Calibration
preprocess
Min-max
Min-max
SNV
SNV
SNV
Min-max
1der+SNV
2der
Validation
preprocess
Min-max
Min-max
SNV
SNV
SNV
1der+SNV
Min-max
2der
Rank
3
4
4
7
7
7
r2
0.923
0.963
0.852
0.909
0.749
0.535
RMSEC
0.876
1.780
1.580
0.382
0.314
0.095
Bias
-4.54e-8
8.51e-7
-4.09e-7
-1.56e-8
-2.72e-7
-7.95e-8
7
5
0.629
0.650
1.101
1.030
-4.77e-7
1.14e-7
Rank
3
4
4
7
7
7
7
5
r2
0.939
0.971
0.893
0.791
0.747
0.648
0.517
0.516
RMSEP
0.871
1.803
1.536
0.625
0.312
1.073
0.094
1.536
Bias
-0.066
0.192
-0.218
0.082
-0.019
0.044
0.016
-0.136
Rank, number of factors used in prediction models; r2, coefficient of determination; RMSEC, root mean
square error of calibration; RMSEP, root mean square error of prediction; Bias, estimate of the test error at
that same value of the tuning parameter; 1der, first derivative; 2der, second derivative; SNV, vector
normalization; Min-max, minimum-maximum normalization.
Conclusions
 The models presented a good prediction for color parameters, peel thickness, and SSC.
 pH and acidity showed low variability in the samples reducing the correlation and accuracy
of the model.
 The results indicate that Vis/NIRS technique could provide an accurate, reliable and nondestructive method for assessing the internal quality indices.
Acknowlegments
This research was supported by MITTIC. The MITTIC Project, Technological Modernization and Innovation based on ICT in strategic and traditional
sectors, is financed jointly by the European Regional Development Fund (ERDF), through the Operational Programme of Cross-border Cooperation
Spain - Portugal (POCTEP) 2007-2013.
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