a r t i cl e s
doi: 10.1255/nirn.1469
Non-destructive near infrared-based chemical analyses
of agricultural products at Laimburg Research Centre—A
short report
Daniela Eisenstecken, Arnold Bodner, Aldo Matteazzi, Konrad Pixner, Andreas Putti, Peter Robatscher, Christof Sanoll, Angelo Zanella and Michael Oberhuber*
Laimburg Research Centre for Agriculture and Forestry, Laimburg 6—Pfatten (Vadena), 39040 Auer (Ora), BZ, Italy.
E-mail: [email protected]
Introduction
Since 1975, Laimburg Research Centre
has conducted research in the fields of
agriculture and forestry focusing on applied
genetics, crop production, plant protection
and, in particular, fruit quality. The main interest is the assessment of quality parameters
in fruit growing, viticulture and mountain
farming. Therefore, plant meta­bolomics and
sensory profiling approaches are applied to
study quality aspects in fruit, wine, milk and
dairy products as well as in livestock feed.
In recent years, increased effort has been
put into the development of more efficient
and faster non-destructive analytical methods for quality control of food and food
products.1 In this context, one of the most
promising in-line and on-line analytical tool
is near infrared (NIR) spectroscopy.2
Near infrared methods
established at Laimburg
Back in the 1980s, the Research Centre
started experimenting with infrared technologies on livestock feed and apples.
Now, NIR technology routinely complements wet chemical methods in feedstuff
analysis to measure quality parameters
Table 1. Quality parameters of mixed grass samples from South Tyrol which were analysed using the
NIRSystems 5000. Quality of calibration models is indicated by the correlation coefficient (R2) and the
standard error of calibration [SEP(C)].
Parameter
Number of
samples
Range
(g kg –1)
R
Dry matter (DM)
137
897–942
0.89 1.63
Drying
oven
Crude protein (CP)
134
48–174
0.89 8.16
Dumas
Crude fibre (CF)
136
219–376
0.89 10.51
Weender
Neutral detergent fibre (NDF)
119
437–707
0.93 15.77
Van Soest
Acid detergent fibre (ADF)
119
289–449
0.93 14.10
Van Soest
such as dry matter (DM), crude protein
(CP), crude fibre (CF) and fibre components (neutral detergent fibre NDF, acid
detergent fibre ADF, acid detergent lignin
ADL) in staple feed.3 The NIRSystems
5000 (Foss, Denmark) used for this purpose covers the wavelength range from
1100 nm to 2498 nm. Since the fodder analysis laboratory is a member of
the VDLUFA (Verband Deutscher Landwirtschaftlicher
Untersuchungsund
Forschungsanstalten) NIR network, the
acquired spectra are evaluated with
Laimburg Research Centre is situated 15 km south of Bolzano (Italy), in the middle of the South
Tyrolian fruit growing area.
2
SEP(C)
Reference
method
established calibration data from the
network database. The traditional wet
chemical analyses are applied in conjunction with the non-destructive measurements (Table 1) for the investigation of
mixed cultures, like grass or hay samples.
Some spectra of hay samples are shown
in Figure 1. Laimburg aims to develop its
own calibration data for mixed cultures
with particular focus on robustness of the
calibration set regarding the differences of
climatic and botanical conditions between
years. The main goal is to reduce analysis
time and efforts.
Non-destructive analytical methods
are highly desired for the determination
of maturity stage and quality parameters
of fruit; in particular of apples.4 Measurement of maturity stage is very important
to ensure the possibility of long storage
and shelf-life while measurement of quality parameters would guarantee a uniform
and standardised eating quality to the
consumer by in-line sorting of product
into pre-defined categories.
The suitability of NIR spectroscopy for
the non-destructive quality assessment
of apples was investigated by applying a
measuring system (Sacmi F5, Imola, Italy)
based on light transmittance (wavelength
range 650–970 nm) which provides higher
Vol. 25 No. 6 September/October 2014
11
a rti cl es
Figure 1. NIR spectra of 39 hay samples in the wavelength range
1100–2498 nm.
accuracy for fruits than the reflectance
mode; the instrument is available with a
mechanical set-up for commercial sorting-lines (five apples per second). In order
to optimise the reliability of NIR measurements, specific calibration models for
each of eight apple cultivars were created. Best results were obtained for the
prediction of total soluble solids. Relevant
factors such as cultivar, origin, degree
of ripening and storage conditions only
marginally affected these results. Internal
tissue alterations such as flesh browning,
watercore and core flush could be reliably predicted. In addition, in this work
we demonstrated that a prediction of the
firmness of apples is possible under certain conditions. However, compared to
the other parameters, firmness depended
much more on the specificity of the calibration.5
Recently, NIR spectroscopy was also
utilised to investigate quality parameters
in grapes for which sufficiently accurate
methods are still lacking. Grapes (n = 102;
cv. Vernatsch) were harvested on 15
October 2013 at one site in South Tyrol
and measured immediately. Data pretreatment and partial least squares (PLS)
analysis led to a standard error of prediction (SEP) of 0.6°KMW (Klosterneuburger
Mostwaage) with a residual predictive
deviation (RPD) of 2.2°KMW for determination of the total soluble solids content
(TSS) shown in Figure 2.
12
Vol. 25 No. 6 September/October 2014
Figure 2. PLS plot for total sugar content (TSS) in berries using 102
Vernatsch grapes. Each sample was measured twice in reflectance mode.
For all spectra, a mean normalisation and de-trending of second
polynomial order was applied prior to PLS analysis.
Other non-destructive
methods
In order to improve the determination
of apple maturity, the suitability of timeresolved reflectance spectroscopy (TRS)
was investigated; this technique separately
measures the two optical properties of
absorption and scattering. TRS was used
to evaluate the usefulness of the absorption coefficient measured at 630 nm (µa 630)
for the assessment of apple quality at
harvest and after storage. By using TRS,
fruit of different maturity and quality were
distinguishable within the same batch.
For apple fruit, the state-of-the-art method
for maturity determination is assessment of
the starch degradation index on fruit halves
which have been dipped in starch–iodine
solution. It was demonstrated that the
normally employed visual evaluation could
be replaced by a non-staining, hyperspectral NIR image analysis in the wavelength
range of 1000–1700 nm, determining starch
and starch-free areas comparably to the
RGB colour images of the stained fruit.
For the non-destructive assessment
of apple firmness, three optical maturity
indices for two cultivars (“Braeburn” and
“Cripps Pink”) were compared based on
chlorophyll content:6 the absorption coefficient measured at 670 nm (µa 670) by TRS,
the IAD measured by a DA-Meter (Sinteleia,
Bologna, Italy) and the NDVI measured
by a Pigment Analyzer (PA1101, Control
in Applied Physiology, Berlin-Falkensee,
Germany). While TRS probes the inner fruit
tissue at a depth of ~2 cm, the DA-Meter
and Pigment Analyzer probe skin and the
outer cortex of the apple fruit. IAD and NDVI
showed high linear correlation for both cultivars, indicating that they assessed the
properties of the same tissue (skin) while
µa 670 explored deeper tissue layers, i.e.
the pulp. In “Braeburn” apples, firmness
correlated well with µa 670 and, with lower
r, with IAD and NDVI. In contrast, for the
variety “Cripps Pink” no correlations were
found between firmness and the optical
indices, possibly indicating the influence
of the genotype on these predictive methods. Future investigations will focus on
parameters describing the physical texture
(firmness, stiffness, energy-to-rupture) and
sensory attributes (firmness, crispness,
juiciness, mealiness). Current approaches
are focused on establishing sensor-based
and spectroscopic methods, among which
NIR is an important element, for the nondestructive quality assessment of food,
using apple fruit as a model.
Mid infrared (MIR) spectroscopy is
applied for wine analyses using the WineScan SO2 instrument (Foss, Denmark).7 After
accurate calibration, up to 20 parameters
such as alcohol, density, total acidity, pH
value, reducing sugar, glucose, fructose,
total dry extract, sugar-free extract and
continued on page 18
a rti cl es
continued from page 12
glycerine can be routinely determined in a
single measurement.
Laimburg Research Centre has not only
implemented NIR measurements, but
also participates in cooperation projects
with national and international partners
in order to widen the scope of NIR spectroscopy. “OriginAlp”, for instance, a multipartner project funded by the Interreg IV
Italy–Austria programme, aims at applying
NIR for the traceability and quality assessment of alpine products (e.g. milk, cheese,
meat and apples). Lead partner of the
project is Professor Christian Huck from
the Analytical and Radiochemical Institute
of the Leopold Franzens University Innsbruck. His research group performs the
NIR analyses of the regional food samples
in order to incorporate new findings in the
field of non-invasive analyses into routine
analysis. The research group at the University of Bolzano uses stable isotope measurement to trace the origin.8 Agrarmarketing Tyrol and the South Tyrolean alpine
dairy association provide the samples. For
calibration of the NIR method, reference
analyses of fatty acid profiles in milk and
cheese by gas chromatography (GC) and
quality parameters such as firmness, total
18
Vol. 25 No. 6 September/October 2014
soluble solids and total acidity in apples
are carried out at Laimburg.
Acknowledgement
Laimburg Research Centre for Agriculture
and Forestry is funded by the Autonomous
Province of Bolzano. Financial support by
the Interreg IV Italy–Austria programme is
gratefully acknowledged.
References
1. B.G. Osborne, “Near-infrared spectroscopy
in food analysis”, in Encyclopedia of Analytical Chemistry, Ed by R.A. Meyers. John
Wiley & Sons (1986). doi: http://dx.doi.
org/10.1002/9780470027318.a1018
2. B.M. Nicolaï, K. Beullens, E. Bobelyn, A.
Peirs, W. Saeys, K.I. Theron and J. Lammertyn, “Nondestructive measurement of
fruit and vegetable quality by means of NIR
spectroscopy: A review”, Postharvest Biol.
Tec. 46, 99–118 (2007). doi: http://dx.doi.
org/10.1016/j.postharvbio.2007.06.024
3. A. Bodner and C. Klotz, “Grundfutter unter
die Lupe nehmen”, Südtiroler Landwirt No. 5,
63–64 (2011).
4. S. Jha and R. Garg, “Non-destructive prediction of quality of intact apple using near
infrared spectroscopy”, J. Food Sci. Tech-
nol. 47, 207–213 (2010). doi: http://dx.doi.
org/10.1007/s13197-010-0033-1
5. A. Zanella, M. Cecchinel, P. Cazzanelli, M.
Coser, A. Panarese and O. Rossi, “Nondestructive NIRS-assessment of apple quality and ripening parameters, compared to
conventional analysis by an appropriate statistical procedure”, Acta Horticulturae 682,
1505–1512 (2005). http://www.actahort.org/
books/682/682_202.htm
6. A. Zanella, M. Vanoli, A. Rizzolo, M. Grassi,
P. Eccher Zerbini, R. Cubeddu, L. Spinelli
and A. Torricelli, “Correlating optical maturity
indices and firmness in stored ‘Braeburn’ and
‘Cripps Pink’ apples”, Horticulturae 1012,
1173–1180 (2013). http://www.actahort.org/
books/1012/1012_158.htm
7. D. Cozzolino, W. Cynkar, N. Shah and P.
Smith, “Technical solutions for analysis of
grape juice, must, and wine: the role of infrared spectroscopy and chemometrics”, Anal.
Bioanal. Chem. 401, 1475–1484 (2011).
doi: http://dx.doi.org/10.1007/s00216-0114946-y
8. M. Scampicchio, T. Mimmo, C. Capici, C.
Huck, N. Innocente, S Drusch and S. Cesco,
“Identification of milk origin and processinduced changes in milk by stable isotope
ratio mass spectroscopy”, J. Agric. Food
Chem. 23(8), 11268–11273 (2012). doi:
http://dx.doi.org/10.1021/jf302846j