Аннотация и ключевые слова
Аннотация (русский):
Wet chemistry methods are traditionally used to evaluate the quality of a coffee beverage and its chemical characteristics. These old methods need to be replaced with more rapid, objective, and simple analytical methods for routine analysis. Near-infrared spectroscopy is an increasingly popular technique for nondestructive quality evaluation called a green technology. Our study aimed to apply near-infrared spectroscopy to evaluate the quality of coffee samples of different origin (Brazil, Guatemala, Peru, and Kongo). Particularly, we analyzed the roasting time and its effect on the quality of coffee. The colorimetric method determined a relation between the coffee color and the time of roasting. Partial least squares regression analysis assessed a possibility of predicting the roasting conditions from the near-infrared spectra. The regression results confirmed the possibility of applying near-infrared spectra to estimate the roasting conditions. The correlation between the spectra and the roasting time had R2 values of 0.96 and 0.95 for calibration and validation, respectively. The root mean square errors of prediction were low – 0.92 and 1.05 for calibration and validation, respectively. We also found a linear relation between the spectra and the roasting power. The quality of the models differed depending on the coffee origin and sub-region. All the coffee samples showed a good correlation between the spectra and the brightness (L* parameter), with R2 values of 0.96 and 0.95 for the calibration and validation curves, respectively. According to the results, near-infrared spectroscopy can be used together with the chemometric analysis as a green technology to assess the quality of coffee.

Ключевые слова:
Spectroscopy, near-infrared spectroscopy, coffee, roasting, partial least squares analysis
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Near-infrared spectroscopy (NIRS) is an increasingly
popular technique used for non-destructive
quality evaluation in a variety of industries, including
the food, agricultural, pharmaceutical, and wood
industries [1–3]. It ensures rapid and easy measurements
without the need for multiple chemical reagents. Recent
NIRS methods include online measurement, portable
measurement, and imaging analysis [4–6]. NIRS is
continuously expanding its uses in food analysis and
becoming an important tool for food quality control.
The quality of coffee as a beverage is determined
by multiple factors such as the production system,
geographical origin, chemical composition of roasted
beans, and final beverage characteristics. Raw coffee
beans contain a wide range of chemical compounds
which interact amongst themselves at all stages
of coffee roasting, resulting in greatly diverse final
products [7–9]. For instance, the caffeine content, which
has a significant effect on the final quality of coffee
products, needs to be determined fast and reliably by
analytical techniques.
Wet chemistry methods are traditionally used to
evaluate coffee quality and chemical characteristics,
but these methods are destructive and time-consuming.
Therefore, it is in scientific interests to find rapid, more
objective, and simpler analytical methods for routine
coffee analysis to replace the old methods.
Recent research has shown that spectroscopy in nearinfrared
(NIR) and mid-infrared (MIR) radiation is
useful in coffee analysis [10–20]. Infrared spectroscopy
(especially NIRS) coupled with chemometrics has
been proposed as an analytical method to determine
the degree of coffee roasting, adulterants in ground
coffee, and sensory attributes [17, 18, 21]. It is also used
to distinguish between robusta and arabica varieties,
Wójcicki K. Foods and Raw Materials. 2022;10(2):295–303
discriminate coffee based on origin, and predict its
chemical composition [15, 20, 22–24].
The growing global demand for specialty coffee
increases the need for improved coffee quality
assessment. For this reason, Tolessa et al. proposed NIR
spectroscopy to predict specialty coffee quality [13].
They examined the NIR spectra of 86 green Arabica
bean samples of various quality. To create a model
that correlates spectral data to cupping score data,
they applied the partial least squares (PLS) regression
method. The high correlation coefficient between the
measured and predicted cupping scores (R2-values of
90, 90,78, 72 and 72) indicate that NIR spectroscopy
coupled with chemometric analysis could be a promising
tool for fast and accurate prediction of coffee quality and
for classifying green coffee beans into different specialty
The sensory analysis of espresso coffee with
the attenuated total reflectance-Fourier transform
infrared spectroscopy (ATR-FTIR) was proposed by
Belchior et al. [10]. The authors evaluated the potential
of ATR-FTIR and chemometrics in discriminating
espresso coffees with different sensory characteristics
reported by a panel of coffee tasters. They performed
partial least-squares discriminant analysis (PLS-DA)
based on spectroscopic data to classify the coffee
samples according to their sensory qualities,
demonstrating the potential of FTIR and chemometric
analysis in assessing coffee quality.
In another study, Magalhaes et al. proposed FT-NIR
spectroscopy and PLS regression as a non-destructive
and rapid tool to assess the content of three main
phenolics (caffeic acid, (+)-catechin, and chlorogenic
acid) and methylxanthines (caffeine, theobromine, and
theophylline) in spent coffee grounds [11]. The best PLS
model was obtained for caffeine content (0.95) followed
by caffeic acid (0.92), (+)-catechin (0.88), theophylline
(0.84), and chlorogenic acid (0.71), indicating FT-NIR
spectroscopy as a suitable technique to screen spent
coffee grounds.
Mees et al. identified coffee leaves using FT-NIR
spectroscopy and soft independent modelling by class
analogy (SIMCA) [12]. In particular, they investigated
nine taxa of Coffea leaves harvested over nine years
in a tropical greenhouse of the Meise Botanic Garden
(Belgium). The FT-NIR coupled with SIMCA allowed
the authors to discriminate the spectral profile by
taxon, aging stage, and harvest period with a correct
classification rate of 90, 100, and 90%, respectively.
NIRS, PLS, and variable selection were used by
Ribeiro et al. to predict concentrations of a wide range
of compounds in raw coffee beans [15]. The authors
proposed NIR spectroscopy coupled with chemometrics
as a low-cost, rapid, and eco-friendly method in both
off-line and on-line analyses of coffee beans and coffee
beverages. The obtained values of root mean square
error of prediction (RMSEP) (0.08, 0.07 and 0.27) and
rcv (0.98, 0.96, and 0.96) showed linear relations of
PLS models for quantifying caffeine, trigonelline, and
5-caffeoylquinic acid, respectively.
Near-infrared spectroscopy was used by Macedo et al.
to evaluate the chemical properties of intact green coffee
beans based on PLS regression models [25]. The highest
determination coefficients obtained for the samples
in the validation set were 0.810, 0.516, 0.694, and 0.781
for moisture, soluble solids, total sugar, and reducing
sugars, respectively. These results indicate that the
NIR technology can be applied routinely to predict the
chemical properties of green coffee.
In another study, Baqueta et al. investigated the
use of NIR spectroscopy in conjunction with the PLS
approach to identify the sensory properties of coffee [21].
The coffee samples varied in species, production region,
variety, drying conditions, transit, postharvest procedure,
storage times, coffee blend, coffee composition, and
roasting process. The performance of PLS models was
verified with the following merit parameters: sensitivity,
accuracy, linearity, residual prediction deviation, fit,
quantification, and detection limits. Since all the
sensory qualities were predicted with acceptable values
compatible with the merit criteria, the created models
were suitable for quantifying, detecting, differentiating,
and predicting the sensory features of coffee samples.
Kyaw et al. reported encouraging findings about
utilizing NIR spectroscopy to forecast the moisture
content of ground unroasted coffee beans [26]. The
spectral data processed with second derivative and
Kubelka-Munk (K/S) data yielded good accuracy for
moisture prediction (r = 0.87 and accuracy = 99%).
In view of the above, we aimed to develop a simple,
rapid, and accurate method for evaluating the quality
of coffee samples by NIR spectroscopy, especially to
investigate changes in the coffee spectra during roasting.
Samples. Our study objects were arabica coffee
samples roasted by the Cafe Creator in Poznań, Poland.
The coffee samples were divided into four groups based
on their origin, namely (1) Brazil, (2) Guatemala, (3)
Peru, and (4) Congo. Their roasting parameters included
the roaster power and roasting time (Table 1).
Color measurements. The color of 41 samples
of coffee beans was measured by the L* a* b* method
using a Konica Minolta Chroma Meter CR-310
trichromatic colorimeter. Each sample was measured
10 times. Before the measurements, the device was
calibrated against a white standard with the following
parameters: Y = 93.00, x = 0.3170, y = 0.3330. The entire
analysis was carried out using a D65 light source, i.e. the
daylight phase and the CIE L* a* b* color system.
Near-infrared (NIR) measurements. NIR spectra
were performed on a MPA/FT-NIR spectrometer
(Bruker). Single beam spectra of the coffee samples
were collected and rationed against the background of
air. For each sample, the NIR spectra were recorded
from 12500 to 400 cm–1 by co-adding 16 interferograms
at a resolution of 4 cm–1. Each sample was measured five
Wójcicki K. Foods and Raw Materials. 2022;10(2):295–303
times. The coffee was ground in an electric grinder for
measurements. Between the measurements, the samples
were mixed in order to obtain reliable results. The
measurements were registered in the OPUS software
(Bruker, USA).
Partial Least Squares (PLS) regression. The PLS
regression method was used to determine relations
between the spectra and the roasting time. Independent
variables (X) were the NIR spectra and dependent
variables (Y) were the color parameter or the roasting
time. Full cross-validation was applied to the regression
model. The regression models were evaluated using the
adjusted R2 and the root mean-square error of crossvalidation.
The quality models were evaluated by the
ratio of the standard deviation of reference data to the
root mean-square error of prediction, or the ratio of
performance to deviation. The PLS analysis was carried
out using the Unscrambler X software (CAMO, Oslo,
Color measurements. Table 2 shows the color
measurements of coffee beans in the L* a* b* system.
The L* parameter is responsible for the brightness
of color in the tristimulus model. The higher it is, the
greater the brightness of the tested sample. Among the
coffees under study, the green coffee beans from Peru
had the highest L* value, i.e., the highest brightness.
The Congo coffee, which was roasted at the power of
80% for 12 min, had the lowest L* parameter, i.e., the
lowest brightness. All the samples had positive a* and
b* values, with their shades varying between red and
As we can see in Table 2, the green coffee beans
showed the greatest brightness, followed by the samples
roasted for 8 min. With the increasing degree of roasting,
the color of coffee beans became darker, which is
consistent with literature [27, 28].
Spectral characteristics of coffee samples. Figure 1
shows the absorption spectra of the coffees from Brazil,
Congo, Guatemala, and Peru roasted for 12 min (80%
roasting power). The spectral range was recorded
throughout the region of 12500–4000 cm–1. The most
intense absorption bands were recorded in the range
of 8230–4440 cm–1. The spectra were characterized by
seven bands with maximum absorption at 8238, 6819,
5800, 5700, 5100, 4700, and 4440 cm–1. These bands
corresponded to the C-H, N-H, and O-H vibrations [29].
The spectral range of 4545–4000 cm–1 corresponded to
the C-H stretching vibrations. The bands in the region
of 5000–4545 cm–1 were assigned to the combination
of the N-H and O-H stretching vibrations. The range
of 6060–5555 cm–1 corresponded to the first tone of the
C-H stretching vibration. In the 7142–6666 cm–1 region,
it was associated with the first shade of the N-H and
O-H stretching vibrations, while the absorption band
in the 7692–7142 cm–1 range was derived from the C-H
stretching vibrations. The band in the region of 9090–
8163 cm–1 originated from the second tone of the C-H
stretching vibrations [30]. Specific chemical compounds
can be described with the following wavenumbers:
caffeine (8865, 7704, 5981, 5794, and 5171 cm–1),
trigonelline (8865 cm–1), chlorogenic acid (6770, 5794,
5171, and 4699 cm–1), lipids (6770, 5794, 5171, and 4699
cm–1), hydrocarbons (6770, 5171, and 4699 cm–1), sucrose
(5794, 5405, and 5171 cm–1), proteins and amino acids
(5171 cm–1), and water (5171 cm–1) [9, 14, 31]. Table 3
presents the origin of the bonds occurring at the given
wavenumbers for the tested coffee beans.
Coffee roasting. Many physical and chemical
changes take place during coffee roasting. The method
of roasting depends on the origin of coffee beans and
consumer preferences. Heavily roasted coffee has a
lower nutritional value than light coffee [32].
Numerous efforts have already been made to
use NIR spectroscopy as an alternative technique to
determine coffee quality during roasting and analyze its
chemical composition. According to Ribeiro et al., NIR
spectroscopy can be used to determine the relationship
Table 1 Roasting parameters of coffee samples
Origin Power of the roaster, % Roasting time, min
Brazil Green –
75 8, 10, 12, 13
80 8, 10, 12
95 8, 10, 12
Guatemala Green –
75 8, 10, 12, 15
80 8, 10, 12, 14
95 8, 10, 12, 13
Peru Green –
75 8, 10, 12, 14
80 8, 10, 12, 14
95 8, 10, 12, 14
Congo Green –
80 8, 10, 12
Figure 1 Absorption spectra of ground coffee in near-infrared
region (12500–4000 cm–1)
12000 11000 10000 9000 8000 7000 6000 5000 4000
Wavenumber, cm–1
Wójcicki K. Foods and Raw Materials. 2022;10(2):295–303
Table 2 Color measurements of green and roasted coffee beans
Origin Power of the roaster, % Roasting time, min L* average b* average a* average
Guatemala (green beans) – – 51.670 ± 0.20 0.870 ± 0.120 11.700 ± 0.141
Guatemala (roasted beans) 95 8 45.440 ± 0.163 5.980 ± 0.057 12.650 ± 0.013
10 42.410 ± 0.233 5.240 ± 0.064 9.810 ± 0.177
12 38.050 ± 0.099 3.550 ± 0.099 6.250 ± 0.085
13 36.390 ± 0.318 3.210 ± 0.042 4.760 ± 0.086
80 8 45.480 ± 0.255 6.060 ± 0.156 12.290 ± 0.383
10 42.460 ± 0.283 5.300 ± 0.106 9.980 ± 0.163
12 39.720 ± 0.191 4.150 ± 0.077 7.380 ± 0.205
14 36.770 ± 0.282 3.230 ± 0.071 4.550 ± 0.085
75 8 48.120 ± 0.184 6.280 ± 0.163 14.470 ± 0.134
10 43.210 ± 0.269 5.600 ± 0.106 10.490 ± 0.185
12 40.620 ± 0.184 4.570 ± 0.099 7.860 ± 0.120
15 36.460 ± 0.141 3.300 ± 0.064 4.160 ± 0.141
Peru (green beans) – – 53.440 ± 0.042 0.880 ± 0.049 12.840 ± 0.078
Peru (roasted beans) 95 8 42.540 ± 0.120 5.560 ± 0.085 10.430 ± 0.148
10 38.940 ± 0.099 4.330 ± 0.064 7.280 ± 0.057
12 38.060 ± 0.410 3.710 ± 0.121 6.440 ± 0.234
14 36.860 ± 0.155 3.390 ± 0.064 5.270 ± 0.049
80 8 45.010 ± 0.057 6.360 ± 0.064 12.620 ± 0.042
10 42.240 ± 0.078 5.510 ± 0.092 10.420 ± 0.127
12 39.910 ± 0.156 4.160 ± 0.020 7.510 ± 0.106
14 37.350 ± 0.120 3.200 ± 0.049 5.360 ± 0.099
75 8 45.350 ± 0.092 6.440 ± 0.057 12.930 ± 0.106
10 40.240 ± 0.157 4.850 ± 0.085 8.290 ± 0.142
12 39.270 ± 0.099 4.120 ± 0.049 7.130 ± 0.071
14 37.610 ± 0.134 3.530 ± 0.021 5.440 ± 0.071
Congo (green beans) – – 51.440 ± 0.134 0.610 ± 0.085 11.050 ± 0.071
Congo (roasted beans) 80 8 42.150 ± 0.141 4.620 ± 0.041 9.270 ± 0.078
10 39.240 ± 0.141 3.810 ± 0.078 6.680 ± 0.078
12 36.120 ± 0.156 2.670 ± 0.085 3.950 ± 0.041
Brazil (green beans) – – 52.360 ± 0.205 1.140 ± 0.057 13.080 ± 0.058
Brazil (roasted beans)
8 46.240 ± 0.092 6.810 ± 0.064 13.910 ± 0.099
10 41.650 ± 0.128 5.360 ± 0.640 9.310 ± 0.064
12 36.980 ± 0.085 3.460 ± 0.085 4.780 ± 0.064
8 45.600 ± 0.071 6.040 ± 0.084 13.110 ± 0.099
10 42.290 ± 0.134 5.800 ± 0.099 10.290 ± 0.065
12 37.930 ± 0.092 3.910 ± 0.071 5.860 ± 0.042
8 43.570 ± 0.184 6.190 ± 0.057 11.300 ± 0.064
10 39.340 ± 0.128 4.600 ± 0.057 7.340 ± 0.014
12 37.440 ± 0.170 3.710 ± 0.058 5.160 ± 0.085
13 36.690 ± 0.134 3.080 ± 0.057 4.210 ± 0.071
between the quality of a coffee cup and the chemical
composition of roasted coffee beans [9]. In addition, the
authors created a model from roasted beans to predict
the quality attributes of a coffee cup (e.g. acidity, body,
and flavor).
The relationship between some coffee roasting
variables (weight loss, density, and moisture) and
near-infrared spectra of original green and differently
roasted coffee samples was investigated by
Alessandrini et al. [14]. They developed separate
calibration and validation models based on partial
least square (PLS) regression, correlating NIR spectral
data of 168 representatives and suitable green and
roasted coffee samples with each roasting variable.
As a result, the authors constructed robust and reliable
models to predict roasting variables for unknown
roasted coffee samples, considering that measured vs.
predicted values showed high correlation coefficients
Pires et al. used multivariate calibration and NIR
spectroscopy to correctly predict roasting degrees
in ground coffee and coffee beans as a substitute for
the Agtron method [18]. The mathematical models
for predicting Agtron values of new coffee samples
using the PLS approach were based on the association
between NIR spectra data and Agtron reference results.
All Agtron roasting characteristics were investigated
in order to create representative models. With RMSEP
Wójcicki K. Foods and Raw Materials. 2022;10(2):295–303
Table 3 The origin of bonds occurring at given wavenumbers
for tested coffee beans [31]
Bond type Wavenumber,
CH3; second overtone; stretching symmetric 8545–8042
CH 7020–6562
CH3; first overtone; stretching asymmetric 5841–5751
CH2; first overtone; stretching asymmetric 5725–5654
OH; stretching 5234–5000
CH; stretching 4954–4509
CH3; stretching 4358–4302
values of 4.48 and 3.67, respectively, the proposed
models showed promising results in predicting roasting
characteristics in roasted whole coffee beans and ground
Yergenson and Aston investigated the use of in situ
NIR spectroscopy in the prediction of cracking events
(start and end) during coffee roasting in order to develop
a more robust method of roasting based on cracks [33].
Two sets of popping sounds (first and second cracks) that
occur during coffee roasting are essential indicators for
establishing the roasting endpoint. The coffee samples
were roasted using various time-temperature profiles.
In situ NIR spectroscopy proved to be a reliable tool in
forecasting the start and finish times of first and second
crack occurrences based on the PLS regression (PLSR)
with audio recordings from coffee roasting.
The NIR spectra of coffees (beans and ground)
roasted under different conditions are shown in Fig. 2.
The obtained spectra were similar to each other,
although varying in intensity. Longer roasting time
lowered the intensity of the bands in all the ranges.
This was due to decreased values of coffee components,
as well as their volume and weight [34–36]. We found
that the samples with the shortest roasting time
(8 min) showed the highest absorbance, while those with
the longest roasting time (12 min) showed the lowest
absorbance at the same wavelength. We also noticed
that the intensity of the spectrum bands decreased
with increasing roasting time. The NIR spectra
obtained during the roasting assays were similar to the
spectra reported in other studies [37, 38]. According
to the authors, the main changes in the spectra of the
roasting process were an absorbance decrease in the
water band region (5200–5000 cm−1), which was due
to moisture loss, and an absorbance increase in the
combination band region (5000–4000 cm−1). A more
detailed discussion of the main wavelength intervals
and their relationships to chemical and physical changes
in coffee during roasting can be found in the work by
Santos et al. [37]. Our results were also consistent with
those reported by Catelani et al. [38]. The roasting
process degraded coffee compounds, namely
chlorogenic acid, coffee sugar, fat, and water. Literature
data shows that the roasting time also affects the
caffeine content in coffee [39]. The longer the coffee
is roasted, the lower its caffeine content. All the
samples showed a lower intensity with an increase in
the roasting time. We concluded that regardless of the
origin, the roasting time caused a decrease in the coffee
components. The most intense bands occurred in the
coffees roasted for the shortest time, which means that
they lost the least of their components and nutritional
The partial least squares (PLS) analysis was
performed to determine the time of roasting. The
PLS models were obtained for the entire spectral
range (12500–4000 cm–1) and sub-regions without
mathematical transformations (Table 4).
We found good correlations between the spectra
and the roasting time for all the coffee samples. The
R2 values for the calibration and validation curves
were 0.94 and 0.78, respectively. The root mean-square
errors (RMSE) were low – 0.39 and 0.76 for calibration
and validation, respectively. The obtained models were
improved when analyzing each type of coffee samples
separately. Also, the sub-regions were used to improve
the model quality.
There was a weak correlation between the spectra
and the roasting power for all the coffee samples. For
this reason, we analyzed the samples separately. The
most accurate model for Guatemala coffee was obtained
in the spectral region of 6813–5332 cm–1. The R2 was
0.97 for calibration and 0.64 for validation. For Peru
coffee, the spectral range of 5374–4954 cm–1 gave the
best quality model, with R2 values of 0.97 and 0.84 for
calibration and validation, respectively. There was no
correlation between the spectra and the roasting power
for Brazil coffee. The coffee from Congo was not
analyzed (only one power condition – 80%).
The degree of coffee roasting can be assessed by the
color: the longer the roasting, the darker the beans. We
studied a possibility of estimating the roasting time on
the basis of the NIR spectra by using the PLS analysis
to correlate the NIR spectra (coffee beans) with the
L* parameter (Table 4). By analyzing the values of the
calibration (R2 = 0.96) and validation (R2 = 0.95) curves,
as well as the RMSE values (0.92 for calibration and
1.05 for validation), we assumed that the coffee roasting
time could be determined based on the PLS regression
analysis and the brightness parameter (L*).
Our study indicates the potentiality of NIR
spectroscopy in evaluating coffee quality. Based on the
changes of spectra, it is possible to monitor changes
during roasting. Chemometric analysis also delivered
very promising results. The PLS models (for roasting
time and power conditions) hold potential as a rapid
and reliable method which could be helpful in coffee
manufacturing. Our next step will be to determine the
chemical composition of the coffee samples and identify
the potential of NIR spectroscopy in correlating roasting
Wójcicki K. Foods and Raw Materials. 2022;10(2):295–303
conditions (time and power) with the chemical changes
in order to select optimal roasting conditions for the final
Our study aimed to apply near-infrared spectroscopy
to evaluate the quality of the coffee samples from Brazil,
Guatemala, Peru, and Congo. We investigated their
composition based on the spectral bands and vibrations.
The regression results confirmed the possibility
of applying the NIR spectra to predict the roasting
conditions. There was a correlation between the spectra
and the roasting time, with the R2 of 0.94 and 0.78 for
calibration and validation, respectively. The RMSEs
were low – 0.39 and 0.76 for calibration and validation,
respectively. We also obtained a linear relation between
the spectra and the roasting power. The quality of the
models differed based on the coffee’s origin and subregion.
All the coffee samples showed a good correlation
between the spectra and the brightness (L* parameter).
The R2 values were 0.96 and 0.95 for the calibration and
validation curves, respectively.
The results proved that NIR spectroscopy coupled
with chemometrics could be a promising tool to predict
Figure 2 Changes in near-infrared spectra in coffee roasted at different power and time: (a) Guatemala coffee, (b) Peru coffee,
(c) Brazil coffee, (d) Congo coffee. Full range spectrum (12500–4000 cm–1)
12000 11000 10000 9000 8000 7000 6000 5000 4000
12000 11000 10000 9000 8000 7000 6000 5000 4000
Wavenumber, cm–1
Guatemala 75% 10′
Guatemala 75% 12′
Guatemala 75% 15′
Guatemala 80% 10′
Guatemala 80% 12′
Guatemala 80% 14′
Guatemala 95% 10′
Guatemala 95% 12′
Guatemala 95% 13′
Wavenumber, cm–1
Peru 75% 10′
Peru 75% 12′
Peru 75% 14′
Peru 80% 10′
Peru 80% 12′
Peru 80% 14′
Peru 95% 10′
Peru 95% 12′
Peru 95% 14′
12000 11000 10000 9000 8000 7000 6000 5000 4000
Wavenumber, cm–1
Brazil 75% 10′
Brazil 75% 12′
Brazil 75% 13′
Brazil 80% 10′
Brazil 80% 12′
Brazil 95% 10′
Brazil 95% 12′
12000 10000 8000 6000 4000
Wavenumber, cm–1
Congo 80% 10′
Congo 80% 12′
0.3 0.3
Wójcicki K. Foods and Raw Materials. 2022;10(2):295–303
Table 4 Partial least squares (PLS) regression analysis
PLS model Samples Spectral region, cm–1 Root mean-square error R2
Calibration Validation Calibration Validation
Roasting time All coffee samples 12500–4000 0.39 0.76 0.94 0.78
Guatemala 12500–4000
Peru 12500–4000
Brazil 12500–4000 0.22 0.30 0.96 0.95
Congo – – – – –
Roasting power All coffee samples – – – – –
Guatemala 12500–4000
Peru 12500–4000
Brazil – – – – –
Congo – – – – –
Color (L* parameter) All coffee samples 12500–4000 0.92 1.05 0.96 0.95
the roasting conditions of coffee samples. However, the
models developed in this study need to be further tested
on independent data sets from other coffee varieties
to assess their stability and accuracy. Because of its
characteristics, NIR spectroscopy has been applied in
different production stages in the coffee industry: from
green coffee beans to the end product. The growing
interest in NIR spectroscopy is primarily due to the
technique’s numerous advantages over other analytical
techniques. In addition, this technique is nondestructive
and noninvasive, with a minimal or non-sample
preparation. NIR spectroscopy is also fast, low-cost,
and robust, so it can be used in different environments
such as laboratories and industrial plants. In the future,
the availability of portable instruments will also allow
its use in the field. For these reasons, NIR spectroscopy
could be named a “green technology”.
The author declares that there is no conflict of

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