foods
Article
Matrix-Assisted Laser Desorption Ionization Time-of-Flight
Mass Spectrometry Combined with Chemometrics for Protein
Profiling and Classification of Boiled and Extruded Quinoa from
Conventional and Organic Crops
Rocío Galindo-Luján 1 , Laura Pont 1,2 , Fredy Quispe 3 , Victoria Sanz-Nebot 1 and Fernando Benavente 1,*
1 Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food
Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain; rgalindo@ub.edu (R.G.-L.);
laura.pont@ub.edu (L.P.); vsanz@ub.edu (V.S.-N.)
2 Serra Húnter Program, Generalitat de Catalunya, 08007 Barcelona, Spain
3 National Institute of Agricultural Innovation (INIA), Lima 15024, Peru; equispe@inia.gob.pe
* Correspondence: fbenavente@ub.edu; Tel.: +34-934021796
Abstract: Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free
food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration
with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize
the composition of quinoa, which is influenced not only by the variety type but also by the farming
and processing conditions. In this study, we present a rapid and straightforward method based on
matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to
generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated
under conventional and organic farming and processed through boiling and extrusion. The mass
spectra of the different protein extracts were processed using the MALDIquant software (version
Citation: Galindo-Luján, R.; Pont, L.; 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins
Quispe, F.; Sanz-Nebot, V.; Benavente, were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable
F. Matrix-Assisted Laser Desorption partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between
Ionization Time-of-Flight Mass farming and processing conditions, and the detected proteins that were critical for differentiation.
Spectrometry Combined with
They confirm the effectiveness of tracing the agricultural origins and technological treatments of
Chemometrics for Protein Profiling
quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted
and Classification of Boiled and
Extruded Quinoa from Conventional approach offers promising applications in food control and the food-processing industry.
and Organic Crops. Foods 2024, 13,
1906. https://doi.org/10.3390/ Keywords: boiling; conventional farming; extrusion; MALDIquant; MALDI-TOF-MS; multivariate
foods13121906 data analysis; organic farming; proteins; quinoa
Academic Editor: Cristina
Alamprese
Received: 14 May 2024 1. Introduction
Revised: 3 June 2024 Quinoa (Chenopodium quinoa Willd.) is an important crop originally from the Andes
Accepted: 11 June 2024 Mountains in Peru, Bolivia, and Chile. This “Golden Grain” is in global demand for
Published: 17 June 2024 its exceptional nutritional and immuno-nutritional properties [1–4]. Quinoa is a rich
source of gluten-free proteins containing all essential amino acids, important minerals,
omega-3 fatty acids, polyphenols, and vitamins, along with other interesting bioactive
Copyright: © 2024 by the authors. compounds [3,5]. Among these compounds, saponins exhibit haemolytic activity and
Licensee MDPI, Basel, Switzerland. induce bitterness. However, they are effectively removed from the seeds using various
This article is an open access article methods, such as washing and abrasion [6]. Quinoa is resilient to environmental stress
distributed under the terms and and poor soil, making cultivation a viable option worldwide [3,7,8]. In recent years, the
conditions of the Creative Commons cultivation of organic quinoa has experienced a dramatic increase, because it is perceived
Attribution (CC BY) license (https:// as safer, healthier, and more environmentally friendly than quinoa from conventional
creativecommons.org/licenses/by/ farming [9–11]. White quinoa, known for its high productivity, is the most widely cultivated
4.0/). commercial variety [12].
Foods 2024, 13, 1906. https://doi.org/10.3390/foods13121906 https://www.mdpi.com/journal/foods
Foods 2024, 13, 1906 2 of 24
The extensive exploration of technological approaches has been undertaken to improve
the nutritional and functional potential of quinoa-based products, aiming to enhance the
potential benefits of incorporating quinoa into the diet. The typical technological methods
described in the literature can be classified based on whether they involve heat energy input
during processing. Thermal treatment methods typically include extrusion, drying, and
boiling, under or without pressure. Conversely, nonthermal treatment methods involve
high hydrostatic pressure, atmospheric pressure, cold plasma, and sonication [2]. Among
thermal treatment methods, extrusion is considered a versatile and efficient technique
for processing instant foods with diverse textures and shapes. This process involves the
application of heat, mechanical energy, and pressure. During extrusion, the starch in
quinoa seeds undergoes gelatinization, and the proteins denature, improving digestibility.
However, it is noteworthy that the protein and lipid content in extruded quinoa typically
diminishes due to the formation of protein–lipid or starch–lipids complexes, resulting in
a remarkable decrease in solubility [2,13]. On the other hand, boiling can also enhance
digestibility and bioavailability while promoting sensory properties like palatability, taste,
flavour, and the development of soft and mushy textures. However, cooking also affects the
composition of numerous chemical constituents, including proteins, amino acids, vitamins,
and minerals [14].
Research on the impact of agricultural production methods and technological treat-
ments on quinoa proteins remains very limited [15–19]. These studies are a necessary
part of the quality control of quinoa grains and their derived products, which face the
threat of adulteration with cheaper cereals [20–23]. Food adulteration is a widespread
malpractice aimed at maximizing economic benefits, posing potential risks to human
health by either depriving consumers of vital nutrients or exposing them to allergenic or
toxic compounds [20,24–26]. Consequently, there is an urgent need to develop analytical
methods for quinoa characterization aimed at enhancing quality control, food-safety, and
fraud-prevention programs.
Different analytical techniques assisted by chemometrics for data deconvolution, mul-
tivariate data analysis, and classification have been described for the characterization of
quinoa [20–23,27–32]. Several authors have demonstrated the potential of using infrared or
fluorescence spectroscopic techniques to obtain global profiles of quinoa flour components
for tracing adulteration [20–23]. Other authors have targeted the volatile fraction of com-
pounds in quinoa flour for the same purpose, using headspace–gas chromatography–ion
mobility spectrometry (HS-GC-IMS) [27]. Alternatively, we have been focused on the
global profiling of quinoa proteins, which has proven to be an efficient way to character-
ize commercial varieties of quinoa grains [28–31]. We have developed different methods
based on capillary electrophoresis and liquid chromatography with ultraviolet absorp-
tion spectrophotometric detection (CE-UV and LC-UV, respectively) [28,29], shotgun pro-
teomics using label-free liquid chromatography coupled to tandem mass spectrometry
(LC-MS/MS) [30], and matrix-assisted laser desorption ionization time-of-flight mass spec-
trometry (MALDI-TOF-MS) [31]. In particular, the MALDI-TOF-MS method proved to be
highly convenient, enabling the rapid, straightforward, and reliable differentiation of com-
mercial quinoa grains based on the proteins detected in their characteristic mass spectra [31].
Additionally, the most relevant proteins for discriminating between different quinoa grains
were tentatively identified based on their molecular masses (Mr), comparing them with
the experimental proteome map obtained by LC-MS/MS shotgun proteomics [30]. Protein
identification not only enhances the reliability of differentiation but also provides valuable
information, such as the potential bioactivity of the present proteins [32].
In this study, we extend the previously developed MALDI-TOF-MS global profiling
approach to discriminate among commercial quinoa grain varieties, aiming to investigate
the impact of agricultural production methods and technological treatments on quinoa
proteins. We employ MALDI-TOF-MS to obtain global profiles of quinoa proteins from
white quinoa varieties cultivated under two distinct farming practices (organic and conven-
tional) and subjected to different processing methods (boiling and extrusion). Subsequently,
Foods 2024, 13, 1906 3 of 24
MALDIquant and chemometrics are applied for efficient data processing and multivari-
ate analysis. Additionally, the method tentatively identifies the most critical proteins for
discrimination within the analysed samples, providing insights that may have important
nutritional, functional, and technological implications. Ultimately, this information can
contribute to the improvement of agricultural and food production practices.
2. Materials and Methods
2.1. Chemicals
All the chemicals were of at least analytical reagent grade. Hydrochloric acid (37% (v/v)),
sodium hydroxide (≥99.0%, pellets), boric acid (≥99.5%), water (LC-MS grade), acetonitrile
(ACN, LC-MS grade), acetone (99.8%), sinapinic acid (SA, ≥99.0%), and trifluoroacetic acid
(TFA, 99.0%) were provided by Merck (Darmstadt, Germany). Milli-Q ultra-pure water system
(Millipore, Molsheim, France) was employed for water purification.
2.2. Samples
The investigation involved triplicate analysis of four distinct white quinoa variety
samples, including raw quinoa (seeds and grains), two crop conditions (conventional and
organic), and two processing conditions (boiling and extrusion). The quinoa varieties,
namely Quillahuaman INIA (Quillahuaman, V1), INIA 433-Santa Ana/AIQ/FAO (Santa
Ana, V2), INIA 431-Altiplano (Altiplano, V3), and Salcedo INIA (Salcedo, V4), were pro-
vided by the National Institute of Agrarian Innovation (INIA) from Lima, Peru. These
four quinoa varieties were cultivated in both conventional and organic conditions in La
Molina (Lima, Peru) (latitude 12◦04′36′′ S, longitude 76◦ 56′43′′ W, altitude 241 m above
sea level (masl)) and Omas (Lima, Peru) (latitude 12◦33′25.6′′ S, longitude 76◦19′9′′ W,
altitude 1227 masl), respectively. They were grown in the same year (2018) to minimize
environmental effects.
Conventional soil fertilization was performed using a mixture containing urea, potas-
sium chloride, and diammonium phosphate, while organic soil fertilization employed
‘Bokashi’, a fermented food-based fertilizer comprising organic materials such as animal
dung, yeast, and molasses. Quinoa seeds were processed using a scarifier machine (Vul-
cano, Lima, Peru) to separate the grain from the pericarp. To eliminate saponins responsible
for the bitter flavour, the obtained quinoa grains were washed three times for 5 min in
a quinoa-to-water 1:10 (m/v) bath at room temperature (rt). Finally, the washed quinoa
grains were dried at 40 ◦C in an oven (Memmert, Schwabach, Germany) and stored in a
dry environment at rt.
2.3. Extrusion Process
White quinoa grains from the four varieties, cultivated under both conventional
and organic farming methods, were preconditioned with water (12–14% moisture) to
achieve optimal heat transfer during the extrusion process and ensure starch gelatinization.
Extrusion took place in a co-rotating twin-screw extruder (Inbramaq, São Paulo, Brazil) with
a total barrel length of 960 mm, a screw diameter of 30 mm, and a cylindrical die diameter
of 10 mm. The extruder featured three independent zones: a feeding zone, a heating zone,
and a die zone. Temperature settings were as follows: the feeding zone was maintained
at 30 ◦C, gradually increasing to 40 ◦C and then 50 ◦C. The heating zone had variable
temperatures of 70 ◦C, 85 ◦C, and 100 ◦C, while the die zone was set at temperatures of
100 ◦C, 110 ◦C, and 125 ◦C. The grain feed rate was established at 14 kg/h, with a screw
speed of 800 rpm. The cut-off frequency was configured at 17 Hz, keeping the retention
time between 10 and 15 s. After the extrusion process, the extruded grains were cooled for
15 min and subsequently stored in polyethylene (PE) bags at rt until further analysis.
2.4. Boiling Process
Another batch of white quinoa grain samples was milled utilizing a laboratory ultra-
centrifugal mill (Restch, Schwabach, Germany) at 18,000 rpm for 30 s. The milling process
Foods 2024, 13, 1906 4 of 24
involved sieving through a mesh with a 0.5 mm opening. The resulting sieved flour was
dispersed in water before boiling to prevent lump formation, ensuring a homogeneous
mixture. This mixture was then boiled in a cooking pot at 100 ◦C for 20 min, maintaining
a flour-to-water mixture ratio of 1:20 (m/v) with continuous stirring. Finally, the boiled
quinoa was cooled for 20 min, dried at 40 ◦C for 72 h, and subsequently stored in PE bags
at rt until further analysis.
2.5. Sample Preparation
Protein extraction from raw (i.e., seeds and grains), boiled, and extruded quinoa from
conventional and organic farming was carried out in triplicate for each variety (V1, V2,
V3, and V4), resulting in a total of 96 quinoa protein extracts. The extraction protocol was
as described in our previous work [30], with some modifications. Briefly, 250 mg of each
sample was mixed with 2 mL of water and 39 µL of 1 M NaOH (final pH of 10.0) using
a vortex Genius 3 (Ika®, Staufen, Germany) for 3 h at rt. The resulting suspension was
centrifuged at 23,000× g for 60 min at 4 ◦C in a cooled Rotanta 460 centrifuge (Hettich
Zentrifugen, Tuttlingen, Germany). The supernatant was collected, and the pH value was
adjusted to 5.0 with 22 µL of 1 M HCl. After centrifugation at 30,000× g for 30 min at 4 ◦C,
precipitated proteins were resuspended in 1 mL of a solution of 60 mM H3BO3 (pH adjusted
to 9.0 with NaOH). The resulting solution was filtered through 0.22 µm nylon filters (MSI,
Westboro, MA, USA) before analysis. All pH measurements were made using a Crison
2002 potentiometer and a Crison electrode 52-03 (Crison Instruments, Barcelona, Spain).
The estimation of protein content in the quinoa extracts was determined spectropho-
tometrically utilizing a capillary electrophoresis (CE) instrument equipped with a diode
array detector (7100 CE, Agilent Technologies, Waldbronn, Germany). Three independent
replicates of samples, obtained from seed, grain, boiled, and extruded quinoa from conven-
tional and organic farming, were injected at 50 mbar for 10 s in a fused silica capillary of
58 cm total length (LT), 50 µm internal diameter (i.d.), and 365 µm outer diameter (o.d.)
(Polymicro Technologies, Phoenix, AZ, USA). A calibration curve was established using
BSA standard solutions at 100 to 1000 mg·L−1. Flow injection experiments were performed
without voltage, with the sample plug mobilized through applications of 50 mbar pressure
after the injection. Absorbance measurements were taken at 214 nm within the region of
the detected protein peaks.
2.6. MALDI-TOF-MS
For the preparation of the protein extracts for MALDI-TOF-MS analyses, MF-Millipore®
membrane filters (Merck) and Milli-Q water were employed for desalting [31]. Briefly, 10 µL
of protein extracts were deposited onto the membrane filter, and desalting was achieved by
dialyzing with water for 45 min at rt. The dialyzed extracts were then collected and stored
at −20 ◦C until the analyses.
A 4800 MALDI TOF/TOF mass spectrometer (Applied Biosystems, Waltham, MA,
USA) was employed to acquire mass spectra in mid-mass positive mode within a
3000–25,000 m/z range. Data acquisition and processing were conducted using the 4000 Se-
ries ExplorerTM (Applied Biosystems, version 3.5) and Data Explorer® (Applied Biosystems,
version 4.5) software. Sample-MALDI matrix mixtures were freshly prepared as described
in our previous work [31]. Briefly, the procedure involved manually spotting, droplet-
by-droplet, onto a steel MALDI plate 1 µL of a 27 mg·mL−1 SA solution in 99:1 (v/v)
acetone:water, 1 µL of dialyzed sample solution, an additional 1 µL of dialyzed sample
solution (for enhanced homogeneity), and finally 1 µL of a 10 mg·mL−1 SA solution in
50:50 (v/v) ACN:water with 0.1% (v/v) of TFA. Between each droplet addition, spots were
allowed to dry at rt. The resulting layer-by-layer spots ensured maximal homogeneity and
reproducibility in the MALDI-TOF-MS analyses. Each of the 96 quinoa protein extracts was
spotted and analysed in triplicate.
Foods 2024, 13, 1906 5 of 24
2.7. Data Analysis
The MALDI-TOF mass spectra were processed and analysed employing MALDIquant
and multivariate data analysis [31].
2.7.1. MALDIquant Data Processing
The raw mass spectra were initially converted to text format (.txt) using Data Explorer®
software (version 4.5, accessed on 1 December 2023). Afterward, the raw mass spectra were
imported into the R platform (version 4.0.4, http://www.R-project.org/, accessed on 1 Jan-
uary 2024) [33] with the MALDIquantForeign package (version 0.12) [34]. MALDIquant
(version 1.19.3) [35] was then employed to detect protein peaks in the mass spectra based
on their characteristic m/z and intensity values. Imported data from the 96 protein ex-
tracts (3 spots c/u) were first transformed for variance stabilization through a square root
transformation [36]. Smoothing was applied to enhance the signal-to-noise ratio (SNR)
and reduce noise in the mass spectra using the Savitsky–Golay algorithm filter in profile
mode [37]. Subsequently, the baseline was subtracted using the sensitive nonlinear iterative
peak (SNIP) algorithm [38]. The denoised data were then normalized, setting the total
ion current to one [39]. After that, alignment was achieved using the warping algorithm
facilitated by locally weighted scatterplot smoothing (LOWESS) [40]. Following alignment,
the mass spectra from replicates were averaged to derive a mean mass spectrum for each
of the 96 protein extracts. Then, a peak detection algorithm based on the median absolute
deviation (MAD) was applied to detect features of potential proteins [41]. Finally, a peak
binning procedure, using the binpeaks function, was implemented to compensate for small
variations in the m/z values.
2.7.2. Multivariate Data Analysis
Multivariate data analysis was conducted using the PLS Toolbox (Version 9.0, Eigen-
vector Research Incorporated, Wenatchee, WA, USA) in Matlab R2016a (The MathWorks
Incorporated, Natick, MA, USA). Principal component analysis (PCA) and partial least
squares discriminant analysis (PLS-DA) were performed using the scaled intensities of
the proteins detected with MALDIquant. PCA served for the unsupervised assessment of
general clustering trends among different farming and treatment conditions, as well as for
detecting potential outliers. Subsequently, PLS-DA was used to maximize the separation
between observed sample classes, constructing a classification model [42,43]. For model op-
timization, a leave-one-out cross-validation model was performed [44]. Membership within
each class was examined within a 95% confidence ellipse in the PLS-DA score plot [45].
Variable importance in the projection (VIP) scores [44,46] was also calculated to investigate
the degree of influence of each individual protein on discrimination. Finally, the most
relevant proteins for discriminating between the sample classes were tentatively identified
based on their Mr, comparing them with the experimental proteome map of the Salcedo
white quinoa grains obtained in a separate study by LC-MS/MS shotgun proteomics [47].
3. Results and Discussion
3.1. MALDI-TOF-MS Analysis
To obtain characteristic mass spectra profiles of protein extracts from quinoa, we em-
ployed a reliable and reproducible sample preparation method described in our previous
study [31]. This sandwich method, previously used on raw commercial quinoa grains,
was applied to the preparation of sample-MALDI matrix mixtures and spot deposition.
Figures 1 and 2 present representative mass spectra for the protein extracts of seed, grain,
boiled, and extruded V4 quinoa varieties from conventional and organic farming, respectively.
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,  1906  6  off  22  
 
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In particular, Figures 1 and 2a,b display the mass spectra of the protein extracts from
seeds and grains under both farming conditions. As can be observed, characteristic mass
  
Foods 2024, 13, 1906 7 of 24
spectra rich in proteins were obtained within the scanned range of 3000 to 25,000 m/z
in all cases. Moreover, the differences in mass spectra were more pronounced when
comparing seeds and grains for a specific farming type. This could be attributed to the
technological treatments applied to quinoa seeds to prepare grains, including scarification
and, particularly, washing and drying, aimed at reducing the high levels of saponins.
On the other hand, Figures 1 and 2c,d illustrate the mass spectra of the protein extracts
from boiled and extruded quinoa under both farming conditions. As can be observed, both
boiling and extrusion treatments led to a reduction in the detected proteins. Indeed, the
total amount of protein in these processed quinoa extracts was lower compared to the raw
quinoa (i.e., seeds and grains), with, for example, 1.1% and 5.5% (m/m) for extruded and
seed V4 quinoa varieties from conventional farming, respectively. Such behaviour can be
attributed to the effect of heat and pressure treatments on the reduction in protein solubility,
resulting in protein denaturation, oxidation, and aggregation [14,48–50].
To generate a reliable large data set for multivariate data analysis, mass spectra
were collected in triplicate (n = 288 spots (96 × 3)) for all the protein extracts of seed,
grain, boiled, and extruded quinoa samples from the four varieties grown under both
conventional and organic farming methods. However, direct peak detection for protein
fingerprinting was challenging due to the complexity of the mass spectra, which exhibited
numerous overlapped protein peaks with varying intensities. Consequently, we employed
MALDIquant software (version 1.19.3) for the efficient quantitative processing of the
mass spectra, as described in our previous work [31]. This approach facilitated improved
peak detection, reliably providing distinctive protein features with characteristic m/z and
intensities. Such accuracy was essential for subsequent multivariate data analysis by PCA
and PLS-DA to discriminate between quinoa samples.
Following this data processing strategy, a total of 49 proteins were detected across the
different quinoa samples, including the four varieties, the two raw materials (seeds and
grains), the two farming conditions (conventional and organic), and the two processing
methods (boiling and extrusion). To tentatively identify these proteins based on their Mr,
we compared them with the experimental proteome map of the Salcedo samples obtained
in a separate study by LC-MS/MS shotgun proteomics [47]. Table 1 lists the experimental
Mr calculated for the detected proteins, their theoretical Mr, the accession number (ID), and
the names of the 31 tentatively identified proteins out of the 49 detected proteins. Note that
in many cases, several possible identifications were provided because the mass accuracy
and resolution of the full-scan MALDI-TOF mass spectrometer was not enough for an
unequivocal identification.
3.2. Multivariate Data Analysis
3.2.1. Discrimination of Conventional and Organic Quinoa
Multivariate data analysis was carried out, considering the intensities of the 49 detected
proteins in the different protein extracts. To simplify data interpretation for differentiating
conventional and organic quinoa samples, only the protein fingerprints from the 48 raw
samples corresponding to seeds and grains grown under both farming conditions were
considered. Initially, unsupervised PCA was employed to visualize trends and identify
outliers from the scores plot (Supplementary Figure S1) [28,31]. Two principal components
(PCs) explained a total variance of 52.9% (Supplementary Figure S1). Given the absence
of distinct trends in the scores plot across samples from the four different white quinoa
varieties, even when increasing the number of components, the representation of the
samples was solely based on farming conditions. As can be observed, PC1 (33.9% of the
explained variance) revealed differential clustering between conventional and organic
quinoa samples, while PC2 (19.0% of the explained variance) separated samples within
these two groups. Additionally, only two samples corresponding to the V2 quinoa variety
from conventional farming appeared outside the 95% confidence ellipse of the scores plot
and were identified as outliers, thus excluding them from the supervised PLS-DA analysis.
Foods 2024, 13, 1906 8 of 24
Table 1. List of proteins detected by MALDI-TOF-MS used as variables for multivariate data analysis with their corresponding experimental Mr, PLS-DA VIP score
values for discrimination between farming and processing conditions, and tentative identifications. Theoretical Mr, accession number (ID), and protein name were
based on an experimental proteome map of the Salcedo samples obtained in a separate study by LC-MS/MS shotgun proteomics [47].
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C f fraw Oraw Raw Boiled Extruded
1 4231 0.45 0.45 1.10 0.54 0.74 -
2 4445 0.67 0.67 0.06 0.48 0.41 -
3 4650 0.91 0.91 0.78 0.40 0.53 -
4 4951 1.17 1.17 0.87 0.43 0.59 -
5 5180 1.07 1.07 1.18 0.61 0.81 -
6 5304 0.85 0.85 1.45 * 0.76 1.00 5307 XP_021736943.1 wound-induced basic protein-like
7 5460 0.69 0.69 0.44 0.78 0.70 -
8 5587 1.31 1.31 0.12 0.63 0.54 -
9 5767 0.96 0.96 0.19 1.53 1.30 -
10 5934 0.59 0.59 0.47 1.29 1.13 -
11 6184 1.52 1.52 0.20 1.70 1.45 -
12 6391 0.78 0.78 1.31 * 0.70 0.91 6413 XP_021764391.1 40S ribosomal protein S29
13 6818 1.80 1.80 1.06 0.58 0.75 -
14 7063 1.08 1.08 0.46 1.84 1.58 -
15 7435 0.84 0.84 0.54 1.04 0.93
16 7730 0.73 0.73 0.67 1.32 * 1.17 * 7747 XP_021714409.1 uncharacterized protein LOC110682385
17 7983 1.17 * 1.17 * 1.06 * 1.66 * 1.52 * 7974 XP_021773921.1 metallothionein-like protein 4B
18 8221 0.51 0.51 0.94 1.81 1.62 -
19 8465 0.62 0.62 1.06 1.01 1.02 -
Foods 2024, 13, 1906 9 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C f fraw Oraw Raw Boiled Extruded
20 8635 0.50 0.50 0.84 0.79 0.81 -
21 8840 1.11 * 1.11 * 1.08 * 0.53 0.73 8806 XP_021718956.1 protein DELETION OF SUV3 SUPPRESSOR 1(I)-like
8814 XP_021729007.1 uncharacterized protein LOC110696048
8823 XP_021762602.1 defensin-like protein
22 9054 0.42 0.42 0.55 0.84 0.77 9076 XP_021768105.1 late seed maturation protein P8B6-like
23 9314 0.57 0.57 0.74 1.39 1.24 -
24 9695 1.26 * 1.26 * 0.95 0.47 0.64 9692 XP_021747091.1 sm-like protein LSM5
25 10,106 0.43 0.43 0.29 0.57 0.51 -
26 10,703 0.75 0.75 0.37 0.37 0.37 10,689 XP_021714810.1 uncharacterized protein LOC110682782
10,724 XP_021756490.1 sm-like protein LSM8
10,736 XP_021756753.1 60S ribosomal protein L37-3
10,750 XP_021768220.1 sm-like protein LSM7
27 10,947 0.99 0.99 1.00 0.69 0.79 10,920 XP_021761138.1 mitochondrial import inner membrane translocasesubunit Tim9
10,928 XP_021746329.1 probable steroid-binding protein 3
10,989 XP_021761862.1 peamaclein-like
28 11,274 1.04 * 1.04 * 0.25 0.86 0.75 11,270 XP_021771595.1 sm-like protein LSM3A
11,308 XP_021716413.1 60S acidic ribosomal protein P2-4-like
29 11,489 0.84 0.84 0.56 1.21 * 1.07 * 11,449 XP_021727941.1 NADH dehydrogenase
11,458 XP_021766637.1 non-specific lipid-transfer protein-like
11,517 XP_021754863.1 thioredoxin M-type, chloroplastic-like isoform X2
Foods 2024, 13, 1906 10 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C f fraw Oraw Raw Boiled Extruded
30 11,779 0.84 0.84 0.47 0.79 0.72 11,723 XP_021772578.1 RNA polymerase II transcriptional coactivatorKIWI-like isoform X1
11,772 XP_021755694.1 uncharacterized protein LOC110720913
11,797 XP_021763483.1 small ubiquitin-related modifier 1-like
31 12,043 0.84 0.84 0.07 0.41 0.35 11,992 YP_009380236.1 ribosomal protein S18 (chloroplast)
12,050 XP_021776279.1 peptidyl-prolyl cis-trans isomerase FKBP12-like
12,055 XP_021765385.1 NADH dehydrogenase
12,064 XP_021774292.1 huntingtin-interacting protein K-like
32 12,362 0.64 0.64 1.36 * 1.19 * 1.24 * 12,301 XP_021760438.1 gibberellin-regulated protein 9-like
12,315 XP_021716755.1 uncharacterized protein At2g27730,mitochondrial-like
12,332 XP_021765334.1 V-type proton ATPase subunit G 1-like
12,375 XP_021720641.1 60S ribosomal protein L30
12,407 XP_021761775.1 uncharacterized protein LOC110726608
12,413 XP_021773050.1 60S ribosomal protein L36-2-like
12,420 XP_021738644.1 40S ribosomal protein S25-like
33 12,801 1.01 * 1.01 * 1.58 * 0.78 1.06 * 12,827 XP_021769120.1 60S ribosomal protein L35a-3
12,849 XP_021757241.1 nodulin-related protein 1-like
12,855 XP_021718430.1 nodulin-related protein 1-like
34 13,220 0.43 0.43 1.50 * 1.21 * 1.30 * 13,205 XP_021758336.1 thioredoxin H-type 1-like
13,231 XP_021759897.1 thioredoxin H-type 1-like
Foods 2024, 13, 1906 11 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C fraw O fraw Raw Boiled Extruded
35 16,215 0.70 0.70 1.06 * 0.54 0.72 16,134 XP_021716351.1 ferredoxin, root R-B2-like
16,149 XP_021747601.1 uncharacterized protein LOC110713466
16,165 XP_021730244.1 outer envelope pore protein 16-2, chloroplastic-likeisoform X2
16,200 XP_021717733.1 high mobility group B protein 3-like
16,215 XP_021716749.1 ferredoxin, root R-B2-like
16,216 XP_021754488.1 high mobility group B protein 3-like
16,239 XP_021762815.1 uncharacterized protein At5g48480-like
16,250 XP_021766528.1 40S ribosomal protein S14-2
16,289 XP_021721762.1 oleosin 1-like
36 16,514 1.13 * 1.13 * 1.35 * 0.68 0.92 16,458 XP_021733518.1 uncharacterized protein At5g48480-like
16,464 XP_021761922.1 uncharacterized protein LOC110726743
16,469 XP_021746531.1 60S ribosomal protein L27a-3-like
16,474 XP_021769235.1 glycine cleavage system H protein 2,mitochondrial-like
16,524 XP_021768671.1 60S ribosomal protein L27a-3-like
16,568 XP_021762909.1 uncharacterized protein LOC110727639
16,570 XP_021732568.1 uncharacterized protein LOC110699354
37 16,693 1.34 * 1.34 * 1.41 * 0.75 0.98 16,616 XP_021755504.1 2S albumin-like
16,624 XP_021751394.1 60S ribosomal protein L26-1
16,625 XP_021730224.1 probable calcium-binding protein CML13
Foods 2024, 13, 1906 12 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C f fraw Oraw Raw Boiled Extruded
16,636 XP_021760375.1 eukaryotic translation initiation factor 1A
16,651 XP_021731588.1 glycine-rich RNA-binding, abscisic acid-inducibleprotein-like
16,685 XP_021735190.1 ubiquitin-conjugating enzyme E2 variant 1D-like
16,693 XP_021774210.1 60S ribosomal protein L28-1-like
16,702 XP_021717270.1 blue copper protein-like isoform X2
16,742 XP_021720407.1 17.4 kDa class III heat shock protein-like
16,758 XP_021766054.1 uncharacterized protein LOC110730552
38 16,897 1.56 * 1.56 * 1.20 * 0.86 0.97 16,833 XP_021733717.1 40S ribosomal protein S16-like
16,834 XP_021776507.1 calmodulin-7-like
16,860 XP_021754554.1 calmodulin
16,877 XP_021749775.1 peptidyl-prolyl cis-trans isomerase FKBP15-1-like
16,884 XP_021716580.1 17.4 kDa class III heat shock protein-like
16,933 XP_021735458.1 probable prefoldin subunit 5
16,942 XP_021731073.1 thiosulfate sulfurtransferase 16, chloroplastic-likeisoform X2
16,946 XP_021743153.1 uncharacterized protein LOC110709246
16,962 XP_021758167.1 transcription initiation factor TFIID subunit 15b-like
39 17,101 1.74 * 1.74 * 0.72 1.16 * 1.05 * 17,026 XP_021751891.1 NADH dehydrogenase
17,040 XP_021771944.1 DNA-directed RNA polymerases II, IV and Vsubunit 8B-like
17,048 XP_021739940.1 uncharacterized protein LOC110706342
Foods 2024, 13, 1906 13 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C f fraw Oraw Raw Boiled Extruded
17,111 XP_021765383.1 40S ribosomal protein S13-like
17,129 XP_021766190.1 uncharacterized protein LOC110730679
17,131 XP_021740721.1 MLP-like protein 423
17,143 XP_021769150.1 17.8 kDa class I heat shock protein-like
40 17,326 1.62 * 1.62 * 0.20 1.56 * 1.33 * 17,290 XP_021770408.1 outer envelope pore protein 16-3,chloroplastic/mitochondrial-like
17,301 XP_021729636.1 NADH dehydrogenase
17,330 XP_021717756.1 uncharacterized protein LOC110685525
17,340 XP_021747441.1 eukaryotic translation initiation factor 5A-4-like
17,350 XP_021764293.1 40S ribosomal protein S15-4-like
17,355 YP_009380273.1 ribosomal protein S7 (chloroplast)
17,366 XP_021747435.1 eukaryotic translation initiation factor 5A-like
17,376 XP_021720177.1 ubiquitin-NEDD8-like protein RUB2
17,385 XP_021748235.1 60S ribosomal protein L23A
41 17,617 1.00* 1.00* 0.32 1.03 * 0.89 17,532 XP_021765685.1 glycine cleavage system H protein, mitochondrial
17,543 XP_021768154.1 glycine cleavage system H protein,mitochondrial-like
17,560 XP_021731505.1 oleosin 1-like
17,562 XP_021736891.1 peroxiredoxin-2B-like
17,572 XP_021732018.1 peroxiredoxin-2B-like
Foods 2024, 13, 1906 14 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C fraw O fraw Raw Boiled Extruded
17,592 XP_021756471.1 putative 4-hydroxy-4-methyl-2-oxoglutaratealdolase 3
17,604 XP_021735589.1 nascent polypeptide-associated complex subunitbeta-like
17,622 XP_021733122.1 protein mago nashi homolog 2
17,652 XP_021743932.1 histidine-containing phosphotransfer protein 1-like
17,665 XP_021753630.1 uncharacterized protein LOC110719020
17,675 XP_021759953.1 nascent polypeptide-associated complex subunitbeta-like
17,700 XP_021745442.1 40S ribosomal protein S11-3
42 17,879 0.80 0.80 0.26 1.11 * 0.96 17,803 XP_021769395.1 40S ribosomal protein S11-like
17,855 XP_021773311.1 60S ribosomal protein L12-1
17,916 XP_021748317.1 desiccation protectant protein Lea14 homolog
17,939 XP_021749487.1 MLP-like protein 43
17,969 XP_021715429.1 universal stress protein PHOS34-like
43 18,311 0.77 0.77 0.84 0.62 0.69 18,221 XP_021738830.1 oleosin 16 kDa
18,224 XP_021737967.1 MFP1 attachment factor 1-like
18,238 XP_021765145.1 60S ribosomal protein L24-like
18,240 XP_021753128.1 peptidyl-prolyl cis-trans isomerase 1-like
18,252 XP_021763237.1 pathogenesis-related protein STH-21-like
18,254 XP_021775867.1 peptidyl-prolyl cis-trans isomerase 1
Foods 2024, 13, 1906 15 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C fraw O fraw Raw Boiled Extruded
18,258 XP_021769094.1 18.3 kDa class I heat shock protein-like
18,271 XP_021730326.1 universal stress protein PHOS32
18,276 XP_021744114.1 17.3 kDa class II heat shock protein-like
18,317 XP_021752091.1 probable NADH dehydrogenase
18,348 XP_021738936.1 17.3 kDa class II heat shock protein-like
18,348 XP_021732306.1 pathogenesis-related protein STH-21-like
18,348 XP_021725562.1 deoxyuridine 5-triphosphate nucleotidohydrolase
18,366 XP_021774711.1 50S ribosomal protein L18, chloroplastic
44 20,349 0.46 0.46 1.72 * 0.85 1.16 * 20,301 XP_021763546.1 30S ribosomal protein 3, chloroplastic
20,406 XP_021734303.1 HMG-Y-related protein A-like
45 20,556 0.81 0.81 2.06 * 1.08 * 1.42 * 20,466 XP_021727144.1 21 kDa seed protein-like
20,499 XP_021763320.1 photosystem II reaction center Psb28 protein-like
20,522 XP_021744010.1 succinate dehydrogenase assembly factor 2,mitochondrial-like
20,523 XP_021729294.1 uncharacterized protein LOC110696308
20,557 XP_021766022.1 PLAT domain-containing protein 3-like
20,565 XP_021741243.1 putative H/ACA ribonucleoprotein complexsubunit 1-like protein 1
20,592 XP_021769990.1 ADP-ribosylation factor 1-like
20,619 XP_021752903.1 thioredoxin-like protein CITRX, chloroplastic
46 20,780 1.25 * 1.25 * 1.83 * 0.93 1.25 * 20,736 XP_021773813.1 adenylate kinase isoenzyme 6 homolog
Foods 2024, 13, 1906 16 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C f fraw Oraw Raw Boiled Extruded
20,739 XP_021740322.1 protein CutA, chloroplastic-like
20,778 XP_021761077.1 peroxiredoxin-2F, mitochondrial-like isoform X1
20,799 XP_021753718.1 60S ribosomal protein L11-1
20,801 XP_021772257.1 HMG-Y-related protein A-like
20,844 XP_021763208.1 60S ribosomal protein L18-3-like
20,848 XP_021738998.1 protein OPI10 homolog
20,852 XP_021763370.1 monothiol glutaredoxin-S10-like
47 21,075 1.19 * 1.19 * 1.48 * 0.76 1.01 * 21,027 XP_021750037.1 uncharacterized protein LOC110715738
21,031 XP_021730777.1 thioredoxin O2, mitochondrial-like isoform X2
21,055 XP_021766443.1 lactoylglutathione lyase isoform X2
21,077 XP_021756715.1 uncharacterized protein LOC110721825
21,107 XP_021727997.1 50S ribosomal protein L27, chloroplastic
21,121 XP_021733985.1 glycine-rich RNA-binding protein 3,mitochondrial-like
21,149 XP_021736893.1 probable inactive nicotinamidase At3g16190
21,170 XP_021763161.1 uncharacterized protein Os08g0359500-like
21,172 XP_021732021.1 probable inactive nicotinamidase At3g16190
48 21,343 1.20 * 1.20 * 1.09 * 0.61 0.78 21,241 XP_021720070.1 ankyrin repeat and SAM domain-containing protein6-like isoform X2
21,268 XP_021771518.1 uncharacterized protein LOC110735639
21,294 XP_021764214.1 cyclic phosphodiesterase-like
Foods 2024, 13, 1906 17 of 24
Table 1. Cont.
Multivariate Data Analysis Protein Variables a Tentative Identifications b
PLS-DA VIP Scores d
Protein Experimental M cr Farming Processing Theoretical Mr Accession Number (ID) e and Protein Name
C fraw O fraw Raw Boiled Extruded
21,326 XP_021763910.1 60S ribosomal protein L18a-2
21,364 XP_021754795.1 50S ribosomal protein L24, chloroplastic-like
21,376 XP_021718085.1 60S ribosomal protein L18a
21,433 XP_021730369.1 probable prefoldin subunit 3
49 22,079 1.01 * 1.01 * 1.30 * 1.36* 1.34 * 21,984 XP_021772119.1 RNA-binding protein Y14-like
22,018 XP_021763572.1 40S ribosomal protein S7-like
22,034 XP_021761714.1 histone H1-like
22,040 YP_009380239.1 ClpP (chloroplast)
22,088 XP_021766393.1 50S ribosomal protein L9, chloroplastic-like
a PLS−DA variables correspond to the protein peaks detected by MALDIquant. b The experimental proteome map of the same samples obtained in a separate study by LC-MS/MS
shotgun proteomics [47] was used as a reference for the tentative identification. A mass error ±0.5% between the theoretical and experimental Mr was considered acceptable for
proposing an identity. This threshold value was established considering the mass error observed for the analysis of a ribonuclease A standard (from a bovine pancreas) under the same
instrumental conditions, Mr = 13,690). c Experimental Mr were calculated from the m/z values considering the formation of single-charged molecular ions by MALDI-TOF-MS. d VIP
scores > 1 were considered important for discrimination and are marked in bold, and tentatively identified scores were marked with an asterisk (*). e Accession numbers (IDs) of the
identified proteins correspond to the IDs of the indicated LC-MS/MS shotgun proteomics work [47]. The tentatively identified proteins fulfilling the acceptance criterium are ordered by
the Andromeda score values obtained by LC-MS/MS, which are a measure of the reliability of their identification. f Craw and Oraw quinoa correspond to raw (seeds and grains) quinoa
from conventional and organic farming, respectively.
Foods 2024, 13, 1906 18 of 24
A PLS-DA model considering two classes was established to enhance discrimination
and identify the protein variables significantly contributing to the differentiation between
quinoa farming conditions. The scores plot of the PLS-DA model, with two latent variables
(LVs), accounting for 45.5% of the explained variance and illustrated in Figure 3a, effectively
demonstrated discrimination between conventional and organic quinoa samples, suggest-
ing that farming conditions induced differences at the protein level [51]. The loadings
plot depicted the contribution of the different protein variables to the LVs (Figure 3b),
while the VIP scores provided additional information to reveal the relevant contribution of
these variables for discrimination (Figure 4). As shown in Figure 4, 20 of the 49 detected
proteins were found to be the most important for discriminating between conventional and
organic quinoa (VIP > 1) [44]. The Mr values of this subset of 20 proteins ranged between
5000 and 25,000. Additionally, 14 of these relevant proteins were tentatively identified, as
summarized in Table 1. Notably, several of the tentatively identified proteins ranked at
the top of VIP values (VIP > 1.5), including protein 38 (VIP value of 1.56), protein 39 (VIP
value of 1.74), and protein 40 (VIP value of 1.62), which emerged as primary discriminants
Foods 2024, 13, 1906 between conventional and organic quinoa (see Table 1 for the identities). Overall, the16s eof 22 
 20 proteins, selected based on their discriminatory potential, could be considered critical
markers for discriminating quinoa grown under varying agroecological conditions.
 
Fiigurre 3.. PPLS−DDAA ssccoorreess pplolott ((aa)) aanndd llooaadiingss pllott ((b)) deerriiveed ffrrom tthee anallyyssiiss ooff 4466 pprrooteteinin ex-
terxatcrtasc tfsrofrmom cocnonvvenentitoionnaal laannd organic rraawq quuininooaav avraireiteietise(ss e(seedeadn adngdr aginra)iuns)i nugsitnhge tinhtee ninstiteinesiotifes of 
the 49 protteeiinnp peeaakkssd deetetcetcetdedb ybyM MAALDLIDqIuqaunat.nt. 
 
Figure 4. VIP scores of the different protein variables when considering the separation between con-
ventional and organic raw quinoa sample classes. Protein variables with a VIP score greater than 
one are numbered, and those tentatively identified are marked with an asterisk (*) (as in Table 1). 
  
 
Foods 2024, 13, 1906 16 of 22 
 
 
Figure 3. PLS−DA scores plot (a) and loadings plot (b) derived from the analysis of 46 protein ex-
Foods 2024, 13, 1906 tracts from conventional and organic raw quinoa varieties (seed and grain) u1s9ionfg24 the intensities of 
the 49 protein peaks detected by MALDIquant. 
 
FFiigguurere4 .4.V VIPIPs csocroesreosf othfe thdeif fderieffnetrpernott epinrovtaeriinab vleasriwahbelnesc wonhsiedne rcinognsthide esreipnagra tthioen sbeeptwareaention between con-
cvoennvteinotnioanla laanndd oorrggaannicicra wraqwu iqnouainsaomap sleamclapsslees .cPlarostseeins. vParrioatbeleisnw viathriaaVbIlPess cworiethgr eaa tVerIPth asncore greater than 
oonneea areren unmubmerbeedr, eandd, athnodse ttheonstaet itveenlytaidtievnetilfiye didaerentmifiarekde dawrei tmh aanrkasetder iwskit(h*) a(ans iansTteabrilsek1 )(.*) (as in Table 1). 
3.2.2. Discrimination of Raw and Processed Quinoa
 In order to differentiate between r aw and processed quinoa, a PCA was conducted
considering the intensities of the 49 detected proteins in the different protein extracts of
seed, grain, boiled, and extruded quinoa samples from conventional and organic farming.
As can be observed in the scores plot of Supplementary Figure S2, two PCs explained a total
variance of 38.3%. To focus on the impact of boiling and extrusion on raw quinoa proteins
and clarify the evaluation of sample trends and clustering, samples were represented in the
 score plot without considering the different varieties and farming conditions. PC2 (16.4% of
the explained variance) facilitated the separation of boiled and extruded quinoa from grain
and seed samples, predominantly located along the positive axis of PC2. Furthermore,
PC1 (21.9% of the explained variance) led to a very slight separation between boiled and
extruded quinoa samples, while grain and seed samples were distributed and overlapped
along the axis of this component. Since no clear clustering was observed for seed and
grain samples, we decided to consider raw quinoa samples as a single class for subsequent
PLS-DA analysis (Figure 5).
A PLS-DA model considering three classes (i.e., raw (seed and grain), boiled, and
extruded quinoa samples) was established for improved discrimination between raw
and processed quinoa samples, as well as to identify the most relevant variables for the
discrimination. Figure 5a displays the scores plot of the PLS-DA model with two LVs
(accounting for 30% of the explained variance), revealing a complete separation with a
clear division between boiled, extruded, and raw quinoa samples. This suggested that
quinoa processing affected raw quinoa proteins, as well as demonstrating a differential
effect of boiling and extrusion. VIP values (Figure 6) were calculated to assess the level of
contribution of the different protein variables represented in the loadings plot of Figure 5b
for the discrimination of the three classes of quinoa samples.
Figure 6 shows, in each case, the VIP plots for the discrimination of raw, boiled,
and extruded quinoa samples from the other two sample classes. Analysing Table 1 and
Figure 6, it can be concluded that 38 of the 49 detected proteins were significant for the
discrimination of quinoa sample classes (VIP > 1), constituting critical markers of quinoa
processing. It is important to note that this protein set included the 20 proteins necessary to
distinguish between farming practices. Additionally, 25 of the 38 relevant proteins were
tentatively identified (5000 < Mr < 25,000), as summarized in Table 1 and marked with
an asterisk in Figure 6. Notably, several of the tentatively identified proteins exhibited
high VIP values (VIP > 1.5), underscoring their significance in distinguishing between
Foods 2024, 13, 1906 17 of 22 
 
3.2.2. Discrimination of Raw and Processed Quinoa  
In order to differentiate between raw and processed quinoa, a PCA was conducted 
considering the intensities of the 49 detected proteins in the different protein extracts of 
seed, grain, boiled, and extruded quinoa samples from conventional and organic farming. 
As can be observed in the scores plot of Supplementary Figure S2, two PCs explained a 
total variance of 38.3%. To focus on the impact of boiling and extrusion on raw quinoa 
proteins and clarify the evaluation of sample trends and clustering, samples were repre-
sented in the score plot without considering the different varieties and farming conditions. 
Foods 2024, 13, 1906 PC2 (16.4% of the explained variance) facilitated the separation of bo20iloef d24 and extruded 
quinoa from grain and seed samples, predominantly located along the positive axis of 
PqCui2n.o Fa uprrothceesrsmingormee, tPhoCd1s .(S2p1e.c9i%fic aollfy ,tphreo teexinpsl3a3in(VedIP vvaarluiaenocfe1).5 l8e),d4 4to(V aI Pvvearlyu esolifght separation 
b1e.7t2w),e4e5n(V bIoPivleadlu eanofd2 e.0x6t)r, uandde4d6 q(VuIiPnoval useamofp1.l8e3s), ewmherigl e dgaras ipnr iamnadry sdeiesdcr ismaimnapnltess were distrib-
ubteetwd eaenndra owvaenrdlabpopileed/ aelxotrnugd etdheq uainxoisa .oSfi mthiliasr lcyo, pmroptoeinnesn17t. (SViInPcvea lnuoe ocfle1a.6r6 )calundstering was ob-
s4e0r(vVeIdP fvoarlu seeoefd1 a.5n6)dw gerraeipni vsoatmalpinledsi,s wcriem dineactiidngedb ettow ceoennsbiodileerd raanwd  rqauwi/neoxatr suadmedples as a single 
cqluainextsrus
o fao, rw shuilbesperqotein 17 (VIP vded and raw/ubeonilte dPLquSi-nDoaA
al uaenoafly1.s5i2s) (aFlsioguprlaey 5ed). a crucial role in differentiating.
 
FFiigguurree5 .5P. LPSL−SD−ADsAco rsecsoprloets( ap)laontd (lao)a dainndgs lpolaotd(ibn)gdse rpivleodt f(rbom) dtheeraivnaeldys fisroofm96 tphreo taeinnaelxytrsaicst sof 96 protein ex-
tfrraocmtss eferdo,mgr asiene, dbo, iglerda,iann,d beoxitlreudde, danqudi neoxatvruardieetide sqfuroimnocaon vvaenriteiotnieasl  afnrdomorg caonnicvfearnmtiiongnauls ianng d organic farm-
itnhge iunsteinnsgit itehseo finthteen49siptireoste oinf ptheaek s49d eptercotetedibny pMeAakLsD dIqeutaenctt.ed by MALDIquant. 
A PLS-DA model considering three classes (i.e., raw (seed and grain), boiled, and 
extruded quinoa samples) was established for improved discrimination between raw and 
processed quinoa samples, as well as to identify the most relevant variables for the dis-
crimination. Figure 5a displays the scores plot of the PLS-DA model with two LVs (ac-
counting for 30% of the explained variance), revealing a complete separation with a clear 
 
Foods 2024, 13, 1906 18 of 22 
 
division between boiled, extruded, and raw quinoa samples. This suggested that quinoa 
processing affected raw quinoa proteins, as well as demonstrating a differential effect of 
boiling and extrusion. VIP values (Figure 6) were calculated to assess the level of contri-
Foods 2024, 13, 1906 bution of the different protein variables represented in the loadings plot of Figure 5b for 21 of 24
the discrimination of the three classes of quinoa samples.  
 
FFiigguurree6 .6.V VIPIPsc osrceosroefst hoef dtihffee rdenifft perroetnetin pvraortiaebinle svwarhieanbcleosn swidhereinng ctohne ssiedpearraintigon tohfe( as)erpaawration of (a) raw 
((sseeeedda andndgr gairna),in(b)), b(boi)le bdo, ailnedd(,c a) enxdtr u(cd)e dexqturiunodaesda mqupliencolaas sseasmfropmlet hcelaostsheesr  tfwroomsa mthpele octlahsesers t.wo sample clas-
sPerso.t ePinrovtaeriianb lvesarwiaitbhlaesV IwP istcho rae gVreIaPt esrctohraen ognreeaarteern uthmabner oedn,ea nadreth nousemtebnetarteivde,l yaindden tthifioesdea treentatively identi-
fimeadrk aerdew mitharakneadst ewriistkh( *a)n(a assinteTraisbkle (1*)). (as in Table 1). 
4. Conclusions
IFnitghuisrset u6d syh, wowe psr,e isnen etaedcha rcapsied, atnhde sVimIPp lpe lcohtesm fomr etthriec sd-aisscirsitmedinMaAtiLoDnI -oTfO rFa-w, boiled, and 
eMxStrmudetehdo dqutoinaossae sssamtheplinefls ufreonmce  tohf ec oontvheenrt itownaol saanmd porlgea cnliacsfsaersm. iAngn,ablyoisliinngg, Taanbdle 1 and Figure 
6, it can be concluded that 38 of the 49 detected proteins were significant for the discrimi-
nation of quinoa sample classes (VIP > 1), constituting critical markers of quinoa pro-
cessing. It is important to note that this protein set included the 20 proteins necessary to 
 
Foods 2024, 13, 1906 22 of 24
extrusion on protein profiles across various white quinoa grain varieties. Once the raw mass
spectra had been acquired appropriately, we employed MALDIquant for data processing,
enabling the resolution of complexities within the mass spectra and the reliable detection
of proteins. A total of 49 proteins were detected, with 31 tentatively identified. The global
fingerprints, comprising the intensity values of these proteins, were subsequently subjected
to multivariate data analysis. Our results revealed a PLS-DA model for distinguishing
between conventional and organic farming samples, with 20 out of the 49 detected proteins
proving critical for differentiation (14 of which were identified). These 20 proteins were
also relevant for discriminating between raw and processed samples, which required a
total of 38 proteins for an effective differentiation by PLS-DA (25 of which were identified).
This global profiling approach allows protein fingerprinting and chemometrics analysis
to evaluate differences at the protein level in quinoa grains, facilitating the assessment of
farming practices and quality changes during food processing. Further research will be
needed to assess the impact of these differences at the nutritional and immunonutritional
levels. Additionally, the potential application of the presented approach extends to other
areas of food analysis, especially when dealing with complex mass spectra with highly
overlapped peaks.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/foods13121906/s1, Supplementary Figure S1. PCA scores plot
derived from the analysis of 48 protein extracts from conventional and organic raw quinoa varieties
(seed and grain) using the intensities of the 49 protein peaks detected by MALDIquant.; Supplemen-
tary Figure S2. PCA scores plot derived from the analysis of 96 protein extracts from seed, grain,
boiled, and extruded quinoa varieties from conventional and organic farming using the intensities of
the 49 protein peaks detected by MALDIquant.
Author Contributions: R.G.-L.: Methodology, Investigation, Writing original draft. L.P.: Con-
ceptualization, Supervision, Investigation, Writing—review and editing, F.Q.: Conceptualization,
Writing—review and editing. V.S.-N.: Conceptualization, Supervision, Writing—review and editing.
F.B.: Conceptualization, Supervision, Writing—review and editing, Funding acquisition. All authors
have read and agreed to the published version of the manuscript.
Funding: This study was supported by grant PID2021-127137OB-I00, funded by MCIN/AEI/10.13039/
501100011033, and by “ERDF A way of making Europe”. The Bioanalysis group of the University of
Barcelona is part of the INSA-UB Maria de Maeztu Unit of Excellence (Grant CEX2021-001234-M)
funded by MCIN/AEI/FEDER, UE.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The original contributions presented in the study are included in the
article/Supplementary Materials, further inquiries can be directed to the corresponding author.
Acknowledgments: R.G. thanks the Ministry of Education of Peru for a PhD fellowship.
Conflicts of Interest: The authors declare no conflicts of interest.
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