foods
Article
Evaluation of the Miscibility of Novel Cocoa Butter
Equivalents by Raman Mapping and Multivariate Curve
Resolution–Alternating Least Squares
Efraín M. Castro-Alayo 1,2,3,* , Llisela Torrejón-Valqui 1, Ilse S. Cayo-Colca 4 and Fiorella P. Cárdenas-Toro 2,3
1 Facultad de Ingeniería y Ciencias Agrarias, Instituto de Investigación, Innovación y Desarrollo para el Sector
Agrario y Agroindustrial de la Región Amazonas (IIDAA), Universidad Nacional Toribio Rodríguez de
Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas 01001, Peru;
llisela.torrejon@untrm.edu.pe
2 Sección de Ingeniería Industrial, Departamento de Ingeniería, Pontificia Universidad Católica del Perú,
Av. Universitaria 1801, San Miguel 15088, Peru; fcardenas@pucp.pe
3 Programa de Doctorado en Ingeniería, Departamento de Ingeniería, Pontificia Universidad Católica del Perú,
Av. Universitaria 1801, San Miguel 15088, Peru
4 Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez
de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas 01001, Peru;
icayo.fizab@untrm.edu.pe
* Correspondence: efrain.castro@untrm.edu.pe; Tel.: +51-98-637-6463
Abstract: Cocoa butter (CB) is an ingredient traditionally used in the manufacturing of chocolates,



 but its availability is decreasing due to its scarcity and high cost. For this reason, other vegetable

 oils, known as cocoa butter equivalents (CBE), are used to replace CB partially or wholly. In the
Citation: Castro-Alayo, E.M.; present work, two Peruvian vegetable oils, coconut oil (CNO) and sacha inchi oil (SIO), are proposed
Torrejón-Valqui, L.; Cayo-Colca, I.S.; as novel CBEs. Confocal Raman microscopy (CRM) was used for the chemical differentiation and
Cárdenas-Toro, F.P. Evaluation of the polymorphism of these oils with CB based on their Raman spectra. To analyze their miscibility, two
Miscibility of Novel Cocoa Butter types of blends were prepared: CB with CNO, and CB with SIO. Both were prepared at 5 different
Equivalents by Raman Mapping and concentrations (5%, 15%, 25%, 35%, and 45%). Raman mapping was used to obtain the chemical
Multivariate Curve Resolution–
maps of the blends and analyze their miscibility through distribution maps, histograms and relative
Alternating Least Squares. Foods 2021,
standard deviation (RSD). These values were obtained with multivariate curve resolution–alternating
10, 3101. https://doi.org/10.3390/
foods10123101 least squares. The results show that both vegetable oils are miscible with CB at high concentrations:
45% for CNO and 35% for SIO. At low concentrations, their miscibility decreases. This shows that it
Academic Editor: Lili He is possible to consider these vegetable oils as novel CBEs in the manufacturing of chocolates.
Received: 10 November 2021 Keywords: cocoa butter; coconut oil; sacha inchi oil; confocal raman microscopy; raman mapping;
Accepted: 10 December 2021 multivariate curve resolution–alternating least squares; chocolate
Published: 14 December 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in 1. Introduction
published maps and institutional affil- In the manufacture of chocolates, one of the main ingredients is cocoa butter (CB),
iations. which is the main contributor to the high fat content of chocolate. In total, 30 to 40%
of the weight of chocolate is fat [1]. CB remains as a solid at 20 ◦C (with hard texture
and snap) and melts rapidly at 33 ◦C, leading to the release of flavor and soft mouth-feel
texture [2,3]. Cost and technical limitations have increased CB demand, although it is the
Copyright: © 2021 by the authors. main lipid phase in chocolate manufacturing. In addition, it is also used in combination
Licensee MDPI, Basel, Switzerland. with other vegetable oils, such as hydrogenated or partially hydrogenated soybean oil
This article is an open access article and palm oil [2–4]. Therefore, researchers have been searching for cheaper alternatives
distributed under the terms and with similar characteristics to CB [2]. These alternatives should improve the physical
conditions of the Creative Commons properties and bloom resistance and reduce the health risks of the final product [3]. CB
Attribution (CC BY) license (https://
can be substituted with vegetable fats by blending to produce chocolate or replace CB
creativecommons.org/licenses/by/
either partially or wholly [2]. These are the so-called cocoa butter equivalents (CBE), which
4.0/).
Foods 2021, 10, 3101. https://doi.org/10.3390/foods10123101 https://www.mdpi.com/journal/foods
Foods 2021, 10, 3101 2 of 17
should be compatible with CB without presenting any eutectic behavior [5]. According
to Norazlina et al. [2], CBEs are nonlauric fats that are obtained from the fractionation,
interesterification and blending of fats and oils. CBEs have physicochemical, thermal and
sensory attributes similar and compatible with those of CB, so they can be miscible in any
proportion without changing the characteristics of CB (that is, they are fully compatible
with CB properties) [2,6–8]. CBEs also possess TAGs similar to those in CB but are produced
from low-cost vegetable oils [9]. Some countries allow the use of noncocoa vegetable fats
or oils at a defined maximum level to improve the properties of chocolate [10]. The current
legislation of the European Union allows the incorporation of up to 5% of CBE in the
total weight of the chocolate [11], while the United States legislation does not specify
this value [12–14]. European regulation only allows six vegetable oils to be used as CBE,
specifically illipe, palm oil, sal, shea, kokum gurgi and mango kernel [11,13].
In the Peruvian amazon, sacha inchi (Plukenetia huayabambana L.) is cultivated. This
plant is known as the Inca peanut and is an important source of phenolic compounds and
high antioxidant capacity [15,16]. Sacha inchi oil (SIO) is emerging as a functional food
due to its rich composition of polyunsaturated fatty acids, tocopherol and sterols. These
compounds have shown multiple human health benefits [17]. Coconut oil (CNO) is used
in food manufacturing. It is a saturated fat rich in small and medium chain fatty acids,
comparable to animal fat [18]. The scientific and nonspecialized literature promotes the
consumption of CNO based on the assumption that it is beneficial for health because it is
low in cholesterol, reduces the risk of cardiovascular diseases, encourages weight loss, and
improves cognitive functions, among others [19]. In Peru, these natural vegetable oils are
produced, and could be good candidates to be considered novel CBEs.
During food manufacturing, some ingredients, even when they may be macroscopi-
cally miscible, can show microscopic heterogeneities that would cause instability and phase
separation during storage [20]. This is a problem in the manufacture of pharmaceutical
tablets, but is also a problem in the manufacture of chocolates. To ensure the quality of the
final product, analytical techniques are used to create chemical maps [21] and determinate
their miscibility. Some researchers are developing methodologies based on Raman map-
ping (or Raman imaging) for the evaluation of miscibility between ingredients [20,22–24].
Raman spectroscopy can offer widespread food safety assessments in a nondestructive,
easy to operate, sensitive, and rapid manner [25]. Raman mapping assimilates two impor-
tant technologies, imaging and Raman spectroscopy, to simultaneously provide an image
of, and spectral information regarding, food products [26]. The purpose of Raman mapping
is to visualize the distribution of components by chemical properties in a sample [25] and
to study heterogeneous materials, since it provides submicron spatial resolution with high
sensitivity [27].
In Raman mapping, each pixel in the image corresponds to a Raman spectrum, which
is compared to an established Raman database to determine the specific analytes or spectral
background measurements in this location [25]. The spectral information obtained is
complex, so multivariate analysis is necessary to unravel complex spectral data from
Raman mapping data [21]. Multivariate curve resolution–alternating least squares (MCR–
ALS) is a self-modeling curve resolution method that offers the possibility of extracting
physically meaningful spectra associated with pure components from the mixed Raman
spectra of real biological samples with the benefit of not requiring prior information about
the nature of the sample [28]. Using MCR–ALS, Mitsutake et al. [22] found that CB and
CNO present intermediate miscibility at concentrations of 75% and 25%, respectively, when
used in the manufacture of pharmaceutical tablets.
Raman mapping is expected to be a useful tool for the food industry to assess the
quality and safety of food [26]; however, there is scarce information on this topic. The
present work focuses on two important approaches for the food industry: the search for
natural sources of Peruvian origin for use in the chocolate industry as novel CBEs, and the
use of a new non-destructive analysis technique to evaluate the miscibility of these natural
sources with CB as a first step for the development of new CBEs. Thus, the objective of this
Foods 2021, 10, 3101 3 of 17
work was to study the miscibility of CNO, SIO, and CB using confocal Raman microscopy
and MCR–ALS to propose novel CBEs for the chocolate industry.
2. Materials and Methods
2.1. Materials
The vegetable oils used were pure CNO and SIO, which were purchased from a local
market in Chachapoyas, Peru. CB was provided by the Cooperativa de Servicios Múltiples
Aprocam (Bagua, Amazonas, Peru).
2.2. Sample Preparation
Following Mitsutake et al. [22], the samples were prepared by heating them to 10 ◦C
above the CB melting point; the materials were added while stirring until a visually
homogeneous blend was obtained. Two batches were prepared: the first was composed
of CB and CNO (CB-CNO), and the second was composed of CB and SIO (CB-SIO). Each
batch contained 5 concentrations of vegetable oils, ranging from 5% to 45% (Table 1), and
3 replicates of each concentration were produced. The samples were placed in a chocolate
mold and cooled to 4 ◦C for easy removal of the tablets. A piece of 1 × 1 cm2 was removed
from each tablet for Raman mapping.
Table 1. Composition of the samples.
Sample Cocoa Butter (%) Coconut Oil (%) Sacha Inchi Oil (%)
CB55-CNO45 55 45 —
CB65-CNO35 65 35 —
CB75-CNO25 75 25 —
CB85-CNO15 85 15 —
CB95-CNO05 95 05 —
CB55-SIO45 55 — 45
CB65-SIO35 65 — 35
CB75-SIO25 75 — 25
CB85-SIO15 85 — 15
CB95-SIO05 95 — 05
2.3. Raman Mapping
Following the process of Mitsutake et al. [22] with some modifications, the samples
were mapped using a Raman confocal microscope system (Horiba Scientific, XploRA plus,
Montpellier, France). Chemical maps were obtained by a 532 nm laser as an excitation light
with a 50% filter. The experimental conditions were as follows: 100 nm slit width, pinhole
100 µm, x50/0.90 NA Vis-LWD air objective, and 1 s acquisition time with 2 accumulations.
The Raman signal was obtained using a 600 lines/mm grating centered between 800
and 3100 cm−1. The acquired spectra were corrected in a range from 1000 to 1800 cm−1,
smoothed, and baseline corrected using LabSpec 6 Suite software. Each sample generated
a cube of data with dimensions of 25 × 25 × 761, where 25 was the number of pixels at the
x and y axes and 761 was the number of spectral variables.
2.4. Data Analysis of Chemical Maps
According to Vajna et al. [29], before chemometric evaluation, all spectra were base-
line corrected (this was done by using the same baseline points for all maps and pure
component spectra). Then, the spectral range from 1000 to 1800 cm−1 was used for the
corresponding evaluation. Raman chemical map data were analyzed by using Solo+MIA
software (Eigenvector, Research, Inc. Wenatchee, WA, USA). The raw 3-dimensional data
were unfolded into a 2-dimensional matrix. The estimation of pure component spectra
from the Raman chemical maps was carried out by MCR-ALS. This technique is based on
the following bilinear model (Equation (1)):
X = CST + E (1)
Foods 2021, 10, 3101 4 of 17
where X (p ∗ λ) is the matrix containing the mapping spectra, ST (k ∗ λ) is the set of pure
component spectra, and C (p ∗ k) contains the vectors of spectral concentrations (each
row in C contains the concentrations of the k ingredients). The matrix E represents the
residual noise. MCR–ALS generated both the concentration matrix C (scores) and recovered
spectrum matrix ST (loadings) from the dataset X in an iterative manner, using an initial
estimation for either C or ST and appropriate constraints.
Preprocessing and Constraints
The preprocessing technique was normalized (1-norm, area = 1). The normalization of
concentration profiles or resolved spectra or use of reference concentration values within
the optimization helps to suppress the rotational ambiguity [30]. The applied constraints
were non-negativity, which forces the profiles to be formed by positive values and can
be implemented replacing negative values by zeros [30], and equality, which makes the
concentration profile and/or spectra of a component equal to a certain known predefined
shape [30]. Pure CB, CNO, and SIO spectra were used as equality constraints.
2.5. Miscibility
To determinate the miscibility of the vegetable oils proposed as novel CBEs with
CB, the homogeneity of the samples was quantitatively and qualitatively determined.
The relative standard deviation (RSD) is a commonly used tool in the pharmaceutical
industry to estimate the homogeneity of a component within a blend [31] and describe the
distribution of the components quantitatively. The RSD was calculated from the ratio of the
standard deviation (σ) and the mean (µ) of each measured image score. Using RMarkdown
software, the RSD of the scores within a chemical image was calculated. A lower RSD of
the chemical image corresponded to a more homogeneous distribution of the respective
ingredient [29,32,33] and, therefore, its miscibility. Qualitatively, the homogeneity of the
samples was analyzed using histograms. According to Gendrin et al. [33], a histogram
showing a symmetric distribution with a narrow base and sharp peak is representative of a
low-contrast image and therefore of a homogeneous sample. Conversely, an asymmetric
histogram with a wide base and flatter peak or several modes is representative of a more
contrasted image, i.e., a heterogeneous sample.
3. Results
3.1. Characterization of the Spectra of Cocoa Butter and Vegetable Oils
Figure 1 shows the characteristics of the Raman spectra of the pure components (CB,
CNO, and SIO) in the full range (1000–3100 cm−1) at room temperature (20 ◦C). In the
CB spectra, the C–H stretching region shows peaks at 2885.7 and 2886.1 cm−1 which are
assigned to alkyl-chain methylene symmetric (νs) and antisymmetric (νas) stretching. A
peak at 2936.5 cm−1 associated with the terminal methyl νs(CH3) stretch was observed.
In the C=O stretching region, we can see 2 peaks at 1745.4 and 1733.8 cm−1, which are
representative of forms III and IV at room temperature (Figure 2). The full width at half
maximum (FWHM) of the peak at 1745.4 cm−1 was 17.38 cm−1. In the C=C stretching re-
gion [ν (C=C)] of the olefinic band, we can see a peak at 1662.7 cm−1
s (Table 2), representing
the solid state of CB form IV at room temperature. This peak is of greater intensity in SIO
due to its liquid state and is also related to the proportion of oleic acid. A total of 2 peaks at
1445.9 and 1462.9 cm−1 in the CH2 and CH3 deformation regions can also be seen. In the
CH2 twisting region, we can also see a peak at 1301.3 cm−1, which is related to the degree
of coupling of the alkyl chains in the lipids. The CB spectra also reveal 3 characteristic
peaks of the CB polyforms, located in the C–C stretching range (1030–1183 cm−1). The
peak at 1102.21 cm−1 indicates the existence of CB in its solid state at room temperature.
This peak is not present in CNO and SIO. The pure spectra of CB, CNO, and SIO look
similar (Figure 1); however, we can find some differences in some peaks. CNO has a peak
at 1662.7 cm−1 whose intensity is lower. SIO has high intensity peaks at 1276.7 cm−1 and
3020.2 cm−1 that the others do not have. These peaks are assigned to plane =CH deforma-
Foods 2021, 10, 3101 5 of 17
tion in an unconjugated cis C=C and asymmetric C=H stretch group. From Figure 2, we
can see that the peak at 1745.3 cm−1 in CB is shown at 1747.93 cm−1 in CNO and at 1746.8
in SIO, and the peak at 1733.84 cm−1 in CB is shown at 1734.83 cm−1 in SIO. We found
differences between the area ratios of the peaks at 1733.84 and 1745.43 cm−1, which can be
used as differentiation patterns between CB, CNO, and SIO.
Table 2. Raman peaks for CB and vegetable oils.
Assignments 1 Cocoa Butter (cm−1) Coconut Oil (cm−1) Sacha Inchi Oil (cm−1)
Foods 2021, 10, x FOR PEER REVIEW νas(C–C)T 1066.2 1069.3 Nd 5 of 17 
 ν(C–C)G 1102.9 1092.4 Nd
νs(C–C)T 1132.3 1132.3 1125.7
τ(CH2) Nd 1268.8 Nd
the alkτy(Cl cHh2a)ins in the lipids. NTdhe CB spectra also rNedveal 3 characteristic 1p2e7a6.k7s of the CB 
polyfoτr(mCsH, 2l)ocated in the C–C13 0s1tr.3etching range (1013300–31.4183 cm−1). The pea1k3 a0t8 .18102.21 cm−1 
indicatδe(sC tHh2e) existence of CB 1in44 i5t.s9 solid state at roo1m44 t5e.1mperature. This pe1a4k4 9is.9 not present 
in CNδOa ((a
CnHd3 )C=C)SIO. The pure sp14e62
166c2t
.
.r
9
3a of CB, CNO, an N
1d66 S
d
2I.2O look similar (Fig N
1u6r
d
6e2 .17); however, 
ν
we canν 
s
(fCin=dO s)ome differences1 7i3n3 .s8ome peaks. CNO h −1
Nads a peak at 1662.7 cm173 4w.8hose inten-
sity is νlo(Cw=eOr). SIO has high in17te45n.s4ity peaks at 12761.774 7c.m9 −1 and 3020.2 cm−117 t4h6a.8t the others 
do nνo(Ct Hha3–vCe.H T2h) ese peaks ar2e7 a2s8s.7igned to plane =C27H30 d.8eformation in an u27n3c3o.9njugated cis 
C=C aνnsd(C aHsy2m) metric C=H st2re85tc5h.7 group. From Fig2u8r5e6 .27, we can see that th2e86 p3e.4ak at 1745.3 
cm−1 iνna sC(CBH is2 )shown at 1747.29838 c6m.1 −1 in CNO and a2t8 18764.16.8 in SIO, and the2 9p0e7a.6k at 1733.84 
cm−1 inν sC(CBH i3s)2 shown at 1734.28933 c6m.5 −1 in SIO. We fou29n3d2 .3differences betweenN thde area ratios 
(=CH) Nd Nd 3020.2
of the peaks at 1733.84 and 1745.43 cm−1, which can be used as differentiation patterns 
1
bAesstiwgnemeenn CtsBac,c CorNdiOng, taonBdre sSsIoOn .e t al. [34], and Jiménez-Sanchidrián et al. [35].
 
FiFgiugurere1 .1R. Ramamanans pspecetcrtarao fotfh tehep upruereC CBBa nadndv evgeegteatbalbeloei olsilisn itnh tehfeu flul rlla nragneg(e1 0(10000–301–03010c0m c−m1−1) a) tart oroomom 
tetmemppereartauturere(2 (020◦ C°C).). 
Table 2. Raman peaks for CB and vegetable oils. 
Assignments 1 Cocoa Butter (cm−1)  Coconut Oil (cm−1) Sacha Inchi Oil (cm−1) 
νas(C–C)T 1066.2 1069.3 Nd 
ν(C–C)G 1102.9 1092.4 Nd 
νs(C–C)T 1132.3 1132.3 1125.7 
τ(CH2) Nd 1268.8 Nd 
τ(CH2) Nd Nd 1276.7 
τ(CH2) 1301.3 1303.4 1308.8 
δ(CH2) 1445.9 1445.1 1449.9 
δa(CH3) 1462.9 Nd Nd 
νs(C=C) 1662.3 1662.2 1662.7 
ν(C=O) 1733.8 Nd 1734.8 
ν(C=O) 1745.4 1747.9 1746.8 
ν(CH3–CH2) 2728.7 2730.8 2733.9 
 
Foods 2021, 10, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/foods 
Foods 2021, 10, x FOR PEER REVIEW 6 of 17 
 
νs(CH2) 2855.7 2856.7 2863.4 
νas(CH2) 2886.1 2886.1 2907.6 
Foods 2021, 10, 3101 νs(CH3) 2936.5 2932.3 Nd 6 of 17
(=CH)2 Nd Nd 3020.2 
1 Assignments according to Bresson et al. [34], and Jiménez-Sanchidrián et al. [35]. 
 
FFigiguurere2 .2C. Carabrobonnyyl ls tsrtertecthchininggr ergeigoinon( 1(710700–01–7187080c mcm−−11)) ooff CCBB aannddv veeggeetatabbleleo oilisls: :( a(a))C CBB; ;( b(b) )C CNNOO; ; 
anandd( c()c)S SIOIO. . 
TTabablele3 3 sshoowss tthee aarreeaa rraatitoioss anandd FWFWHHMM of oCfBC, BC,NCON, aOn,da SnIdOS iInO thien mthoedem oofd veiborfa-
vtiibornasti oνn(Cs=νO(C).= TOh)e. Tlohweelor wFWerHFWM HvMaluveasl uinedsiicnadteic aa tbeeattbere taterrraanrgraenmgeenmt eonf tthofe tchreysctraylsst aalnsd 
atnhdeitrh seoirlisdo slitdatset;a tthee; rtehfeorreefo, aret ,raotormoo tmemtpemerpaeturarteu, rthe,et chreycsrtaylsst aolfs CoBf  C(1B7.(3187 .c3m8 −c1m) w−o1)uwldo hualdve 
ha vbetatebre attrerranagrreamngenemt tehnatnt hthaonsteh ofs Ce NofOC N(2O7.5(2 7c.m52−1c),m d−e1m),odnesmtraotninstgr aittisn sgoiltids  ssotalitde.s tate.
  
Table 3. FWHM and area ratios of the components of the Gaussian function of Raman spectra of CB,
CNO, and SIO at room temperature (T = 20 ◦C).
1733.84 cm−1 1745.43 cm−1 Area Ratio
Component
Area FWHM Area FWHM A1733.84/A1745.43
Sacha inchi oil 288.19 3.99 2751.58 9.04 0.11
Coconut oil Nd Nd 8112.33 27.52 Nd
 Cocoa butter 2448.96 9.85 5366.85 17.38 0.46
Foods 2021, 10, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/foods 
Foods 2021, 10, x FOR PEER REVIEW 7 of 17 
 
Table 3. FWHM and area ratios of the components of the Gaussian function of Raman spectra of 
CB, CNO, and SIO at room temperature (T = 20 °C). 
1733.84 cm−1 1745.43 cm−1 Area Ratio 
Component 
Area FWHM Area FWHM A1733.84/A1745.43 
Foods 2021, 10, 3101 Sacha inchi oil 288.19 3.99 2751.58 9.04 0.11 7 of 17
Coconut oil Nd Nd 8112.33 27.52 Nd 
Cocoa butter 2448.96 9.85 5366.85 17.38 0.46 
33.2.2. .M Misisccibibiliiltityyo offC CooccooaaB Buutttteerra annddV VeeggeettaabblleeO Oiillss 
FFigiguurere3 3s shhoowwsst thhees sppeeccttrraallr raannggee uusseedd ffoorr tthhee mmiisscciibbiilliittyy aannaallyyssiiss bbeettwweeeenn CCBB,, CCNNOO,, 
aannddS SIOIO. .T ThheeR Raammaanns sppeeccttrraao offt thheep puurreec coommppoonneennttss aarree ssiimmiillaarr dduuee ttoo tthhee ssiimmiillaarriittyy iinn 
ththeeirirc chheemmicicaallc coommppoossititioionn.. HHoowweevveerr,,t thheerreea arree iimmppoorrttaanntt ddiiffffeerreenncceess tthhaatt aarree ccoonnssiiddeerreedd 
foforrt htheea annaalylyssisisb byyM MC
−C
R
1R
––AALLSS..T Thheessee ddiiffffeerreenncceess aarree mmaaiinnllyy ffoouunndd iinn tthhee CC––CC ssttrreettcchhiinngg 
rereggioionn( 1(1000000––11220000c mcm−1), t
−1), thhee CC==CC ssttrreettcchhiinngg [[ννs(
s(CC==CC))]],, aann−dd1  tthhee ccaarrbboonnyyll CC==OO ssttrreettcchhiinngg 
rereggioionn( (11770000––11880000 ccmm−1)). .TThhee ppeaekak lolcoactaetde data 1t616626.727.7 c7mcm−1 hash aa gsraeagtreera itnetreinnstietny siinty thine SthIOe  
StIhOanth iann CinB CanBda CndNOCN, aOn,da insd chisarcahcatrearcistetirci sotfi citosf liiqtsuliidq ustiadtes.t ate.
 
FFigiguurere3 3.. RRaamaann ssppeeccttrraal lraranngge eusuesde dfofro arnaanlyasliyss oisf tohfe tmheismcibisilciitbyi loift yCBof aCndB vaengdetvabeglee otailbsl ebyo MilsCbRy-
MACLRS-. ALS.
FFigiguurree 44 sshowss tthee eefffefecct tofo tfhteh 1e-n1o-nrmor mprepprreopcreoscseinssgi ntegchtencihquneiq oune thone rtahwe draawta fdraotma  
frsoampslaems pClBe7s5C–BC7N5O–C25N (OFi2g5u(rFei g4ua)r ean4ad) CanBd75C–SBI7O52–5S I(OFi2g5u(rFei g4cu)r eob4tca)inoebdta binye dSobloy +S oMloIA+ 
M(oIArig(ionraigl idnaatlad inat aSuinppSluepmpelnemtaeryn tMarayteMriatle).r iTahl)i.sT mheisthmoedt hwoads wabalse atbol ceotroreccotr rthecet nthoeisen oainsde  
asncdatstecraitntegr cinogntcroibnuttriiobnust iionn tshien rathwe drawta (dFaitgau(rFe i4gbu,rde) 4bbe,fdo)reb feiftotirnegfi tthtein dgattha etod tahtea MtoCthRe–
MACLRS –mAoLdSeml. odel.
Table 4 shows the MCR–ALS quality parameters and correlation coefficients between
the original spectra and the spectra recovered (ST) by MCR–ALS. The quality parameter
related to the fit of the model was the percentage of explained variance, whose values
were between 94.59 and 98.46%, acceptable for our work. The use of the spectra of the
 pure compounds as equality constraints produced correlation coefficients between 0.9999
Foods 2021, 10, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/foods 
and 0.9993. With these values, it can be verified that component 1 was related to CB and
component 2 was related to vegetable oils (CNO or SIO) according to the analyzed samples.
Foods 2021, 10, 3101 8 of 17
Foods 2021, 10, x FOR PEER REVIEW 8 of 17 
 
 
FFigiguurere4 .4.R Rawawd daatata( a(,ac,)c)a nanddp prerpeprorocecsesesdedd daatata( b(b,d,d) )f rforommC CBBs asmampplelsesm mixixededw witihth2 52%5%C CNNOO( a(,ab,b) ) 
anandd2 52%5%S ISOIO( c(,cd,d).). 
Table 4. MCR–ALS quality resultsTanbdlec 4o rsrheloawtiosn thcoee MffiCcieRn–tAs bLeStw qeueanlirteyc opvaerraemd espteercstr aanbdy ctoherrmeloadtieolna ncdoereffailcsiepnectstr ba.etween 
the original spectra and the spectra recovered ( ) by MCR–ALS. The quality parameter 
Sample Numberreslof Explain
Factor ated to theV afriita onfc eth
ede model waMs CR-ALS
(%) Comthpeo npeenrtcentage oCfo ecoxaplBauinttedr  varianVce,g ewtahbolseeO vilalues 
were between 94.59 and 98.46%, acceptable for our work. The use of the spectra of the 
CB55-CNO45 2 pure compounds9 6a.s7 4equality constrCaionmtsp p1roduced corre0l.a9t9i9o9n coefficients be0.t9w39e6en 0.9999 
and 0.9993. With these values, it caCno bmep v2erified that com0.9p3o8n4ent 1 was rela0te.9d9 9to9  CB and 
CB65-CNO35 2 component 2 wa9s6 r.6e0lated to vegetaCbolem opil1s (CNO or SIO0). 9a9c9c7ording to the a0n.9a5l4y6zed sam-
ples. Comp 2 0.9397 0.9996
Comp 1 0.9998 0.9378
CB75-CNO25 2 98.14
Table 4. MCR–ALS quality results andC ocmorpre2lation coefficien0t.s9 4b0e1tween recovered0 s.9p9e9c9tra by the 
model and real spectra. Comp 1 0.9996 0.9377
CB85-CNO15 2 98.46 Comp 2 0.9408 0.9999
Sample Numbers of Explained Variance MCR-ALS 
Factor (%C) omp 1 Componen0t.9 999Cocoa Butter V0.e9g3e8t6able Oil 
CB95-CNO05 2 92.76 Comp 2 Comp 1 0.9404 0.9999 0.9909.89396 
CB55-CNO45 2 96.74 
Comp 1 Comp 2 0.9998 0.9384 0.5904.49999 
CB55-SIO45 2 94.59 Comp 2 Comp 1 0.6117 0.9997 0.9909.39546 
CB65-CNO35 2 96.60 
Comp 1 Comp 2 0.9999 0.9397 0.5907.69996 
CB65-SIO35 2 97.46 Comp 2 Comp 1 0.6099 0.9998 0.9909.59378 
CB75-CNO25 2 98.14 
Comp 1 Comp 2 0.9998 0.9401 0.9909.59999 
CB75-SIO25 2 97.99 Comp 2 Comp 1 0.6121 0.9996 0.5907.69377 
CB85-CNO15 2 98.46 
Comp 2 0.9408 0.9999 
Comp 1 0.9999 0.5924
CB85-SIO15 2 98.01 Comp 2 Comp 1 0.6147 0.9999 0.9386 
CB95-CNO05 2 92.76 0.9993
Comp 2 0.9404 0.9998 
Comp 1 Comp 1 0.9997 0.5914
CB95-SIO05 2 0.9998 0.5944 
CB55-SIO45 97.329 94.C59o mp 2 0.6140 0.9995
Comp 2 0.6117 0.9993 
Comp 1 0.9999 0.5976 
CB65-SIO35 2 97.46 
Comp 2 0.6099 0.9995 
 
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Foods 2021, 10, x FOR PEER REVIEW 9 of 17 
 
Comp 1 0.9998 0.9995 
CB75-SIO25 2 97.99 
Comp 2 0.6121 0.5976 
Comp 1 0.9999 0.5924 
CB85-SIO15 2 98.01 
Foods 2021, 10, 3101 Comp 2 0.6147 0.99939 of 17
Comp 1 0.9997 0.5914 
CB95-SIO05 2 97.39 
Comp 2 0.6140 0.9995 
FFiigguurree 55 sshhoowwss aa ccoommppaarriissoonn bbeettwweeeenn tthhee ssppeeccttrruumm rreeccoovveerreedd (
T
(S )) bbyy tthhee MMCCRR––AALLSS 
mmooddeell aanndd tthhee oorriiggiinnaall ssppeeccttrruumm ooff tthhee ppuurree ccoommppoonneennttss.. TThhee rreessttrriiccttiioonnss uusseedd aalllloowweedd 
ffoorr aallmmoosstt iiddeennttiiccaall ssppeeccttrraa wwiitthh aa ggoooodd ccoorrrreellaattiioonn.. WWee ccaann nnoottee tthhaatt tthhee ssppeeccttrruumm ooff 
ccoommppoonneenntt 11 rreeccoovveerreedd bbyy MMCCRR––AALLSS iiss iiddeennttiiccaall ttoo tthhee rreeaall ssppeeccttrraa ooff CCBB ((FFiigguurree 44aa)) aanndd 
ccoommppoonneenntt 22 iiss iiddeennttiiccaall ttoo tthhee rreeaall ssppeeccttrraa ooff CCNNOO ((FFiigguurree 44bb)).. 
 
FFiigguurree 55.. CCoommppaarrisisoonnb beetwtweeeennt htheer eraelasl psepcetrcatroaf otfh ethpeu preurceo mcopmonpeonnteannt danitds  rietss preecstpivecetsivpee cstpruecmtrruemco vreecroedvebryedM bCyR M-ACLRS-:
(Aa)LrSe:a (laa) nrdearle acnodv erreecdovspereecdtr aspoefcCtrBa; o(fb C) rBe;a (lba)n rdearle aconvde rreecdosvpeerecdtr aspoefcCtrNa Oof. CNO. 
Fiigurree 66 sshowss tthee ddiissttrriibbuttiion maappss aanndd ththeeirir ccoorrrreessppoonnddiningg hhisistotoggrraammss.. Theessee 
mapss arre cconssttrrucctted by tthe MCR–ALS modell ffrrom tthee ccoonncceennttrraattiioonn maattrriixx C and sshow 
tthe diisttriibuttiion off tthe compounds iin tthe bllend.. The reddestt areas correspond tto hiigher 
concenttrattiionss aannddt htheeg rgereeneensetsat raearesacso rcroersrpeospndontdo  tthoe tlhoew leoswt ceosnt cceonntcraetniotrnast.ioWnes.c Wanen coaten 
tnhoatte, itnhagte, niner agle,nthereahl,i stthoeg hraismtosgcroarmress pcornrdeisnpgontoditnhge dtois thrieb udtiisotnribmuatiposna mreappesa kar-seh paepaekd-
ashnadpseydm amndet sryicm, wmheitcrhici, nwdhicicahte isntdhiactabteost hthtahte bCotNhO th(eF CigNurOe (5Fai–geu)raen 5da–the)e aSnIOd t(hFeig SuIOre (5Ffi–gj)-
fuorrem 5fa–jh) ofomrmog aen heomusogbelennedouws ibthlenthde wCiBth.  Hthoew CeBv.e Hr, othweerveear,r ethdeirfefe arreen cdeisffeinretnhcees hina pthese 
bsheatwpese nbetawcheehni setaocghr ahmistcoagursaemd bcayutsheedd bifyf etrheen tdcifofnercentt rcaotniocnesntorfatvieognest aobf lveeogieltuasbeled oinil 
euascehd sianm eapcleh. sample. 
Table 5 shows the quantitative analysis performed on the samples. The lowest RSD
value of each component indicates its most homogeneous distribution in the sample.
The RSD results show that CNO is more homogeneously distributed in the CB when
its concentration is 45%, while its distribution is less homogeneous at 15% or 5%. The
distribution of SIO in the sample is more homogeneous at 35% and less homogeneous at
15% or 5%. With these results, we can affirm that the CNO is more miscible with CB than
SIO and that the miscibility of both oils improves by increasing their concentrations in
the sample.
 
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Foods 2021, 10, 3101 10 of 17
Foods 2021, 10, x FOR PEER REVIEW 10 of 17 
 
 
FiFgiugruere6 .6D. Disistrtirbibuutitoionnm maaps of the samples and their different concentrations: (a) CB55-CNO45; (b) CB65-CNO35; (c) CB75-
CNO25; (d) CB85-CNO1p5s; (oef)t hCeB9sa5m-CpNleOs0a5n; d(ft)h CeiBr5d5i-fSfIeOre4n5t; c(ogn) cCeBn6tr5a-tSiIoOn3s5: ;( a(h) )C CBB557-5C-SNIOO2455;; ((ib) )CCBB8655-S-CION1O5;3 5(j;) (Cc)BC9B5-75-
CNSIOO2055;. (d) CB85-CNO15; (e) CB95-CNO05; (f) CB55-SIO45; (g) CB65-SIO35; (h) CB75-SIO25; (i) CB85-SIO15; (j) CB95-SIO05.
TableT5a. bMlei s5c isbhiloitwy so fthvee gqeutaabnlteitoaitlisvwe iatnhacloycsoisa pbeurtfteorrmateddi fofenr etnhtec soanmcepnltersa.t iTohnes dloewteermst iRneSdDb y
tvhaeilruRe SoDf .each component indicates its most homogeneous distribution in the sample. The 
RSD results show that CNO is more homogeneously d1 istributed in the CB when its 1con-
centration iSs a4m5p%l,e while its distributCioonco ias Bleustst ehroRmSoDgeneous at 15V%e goert a5b%le. TOhileR dSiDstribu-
tion of SICOB 5in5- CthNeO s4a5mple is more homo0g.1e2n±eo0u.0s1 aatb 35% and less homo0g.0e9n±eo0u.0s 2abt 15% or 
5%. WithC tBh6e5s-eC rNesOu3l5ts, we can affirm th0a.t1 t7h±e C0.N03Oa bis more miscible wi0t.h2 1C±B t0h.0a9na Sb IO and 
that the mCBis7c5i-bCilNO a
ity 2o5f both oils improves0 .b2y3  ±inc0r.0e6asing their concentra0ti.o29ns± in0. 0th9ea bsample. 
CB85-CNO15 0.21 ± 0.12 a 0.47 ± 0.13 a
ab a
Table 5. MCBis9c5ib-CiliNtyO o0f5 vegetable oils with c0o.1c8oa± b0u.t0t4er at different concentra0t.i4o4ns± d0e.t2e3rmined by 
their RSD.C B55-SIO45 0.12 ± 0.02 ab 0.25 ± 0.03 ab
CB65-SIO35 0.10 ± 0.01 ab 0.15 ± 0.04 b
CSBa7m5-pSlIeO 25 Coco0a. 1B0u±tte0.r0 R4 SabD 1 Vege0ta.1b8le± O0.i0l4 RabSD 1 
CBC5B58-C5-NSIO4155 0.01.20 7±± 0.00.10 1abb 00..0294 ± 00..0023 ba b
CBC6B59-C5-NSIOO3055 0.01.70 7±± 0.00.30 2abb 00.2.119 ±± 00.0.093 aabb 
1 DifferenCt Ble7tt5er-sC(Na,bO) 2in5t he same column repre0s.e2n3t s±i g0n.i0fi6c aan t differences (p ≤ 0.05)0. .29 ± 0.09 ab 
CB85-CNO15 0.21 ± 0.12 a 0.47 ± 0.13 a 
4. DiscuCsBs9io5-nCNO05 0.18 ± 0.04 ab 0.44 ± 0.23 a 
4.1. CharCaBct5e5r-izSaIOtio4n5 of the Spectra of Co0co.1a2B ±u 0tt.e0r2 aanb d Vegetable Oils 0.25 ± 0.03 ab 
AccCorBd6i5n-gSItoOC35a rmona et al. [36], th0e.s1p0e ±c t0r.a0w1 aebr e examined separate0l.y15in ±t h0.e0w4 ba venumber
region frCoBm751-0S0I0Ot2o53 100 cm−1 to find d0i.f1f0e r±e n0c.0e4s abbe tween them. Then0,.a1l8t h±o 0u.0g4h atbh e spectra
for edibCleBv8e5g-SeItOab1l5e oils were similar0(.F0i7g ±u r0e.01) b, it could be seen th0a.2t4t h±e 0y.0e3x ahb ibit some
differencCeBs9w5-hSiIcOh0a5r e small but enable0th.0e7i r±d 0i.s0c2r ibm ination [35]. Wan0g.1e9t  a±l .0[.0337 ]abr eported a
p1 eDaikffearten3t0 l1e6ttecrms (−a,1b)i nint hthee Csahmine ecsoelu-smpne rceifiprcespeenot nsiygnsiefiecdanot idlisffpeerecntrcuesm (p. ≤T 0h.0is5)p. eak is located
in the region =C–H stretching vibration of the methyl linoleate group (cis, cis diene) of
RCH=CHR, and it is used in the evaluation of oils with different unsaturation degrees. The
 
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Foods 2021, 10, 3101 11 of 17
SIO spectrum has a peak at 3020.2 cm−1 (Figure 1, Table 2) that is not present in CB and
CNO. This peak demonstrates the degree of unsaturation of the SIO. In the characterization
of the Raman spectra of CB at 22 ◦C carried out by Bresson et al. [34], 2 peaks were reported
at 1744 and 1732 cm−1, which are representative of forms III and IV. In the present work,
these peaks were identified at 1745.4 and 1733.8 cm−1 (Figure 2, Table 2) and show that the
existing conformational differences depend on the CB polymorphism. Bresson et al. [38]
observed 3 components between 1750 and 1725 cm−1, 1730, 1735, and 1744 cm−1, which
were attributed to the peak at 1735 cm−1 in CB or the peak at 1736 cm−1 in CBE for form V
or VI. This peak was not observed in the CB, CNO, and SIO spectra (Figure 2, Table 3), so
we can deduce that the V form was not present in the CB; therefore, the previous statement
is corroborated.
In the olefinic band in the C=C stretching range (1200–1800 cm−1), Bresson et al. [34]
attributed the liquid form of CB to the intensity of the peak located at ~1661 cm−1, as well
as to the functional group present in oleic acid. That is, the higher intensity of this peak
characterizes the liquid state of CB, and the lower intensity characterizes the solid state
(form IV). This coincides with what is observed in Figure 2a,c, in which this peak is located
at 1662.7 cm−1 in CB, CNO, and SIO. There is a noticeable difference in the intensity of this
peak, since it is higher in the SIO, which determines its liquid state at room temperature
and its proportion of oleic acid. Likewise, this peak was observed at 1658 cm−1 by De
Géa Neves et al. [18] in the Raman spectrum of CNO and was used as a differentiating
pattern between CNO and other vegetable oils. The peak at 1445 cm−1 is related to the
C–H deformation vibration, and the peak at 1658 cm−1 is assigned to cis C=C bonds, both
of which provide the degree of unsaturation value [35,37]. Therefore, these peaks can be
useful to determine the degree of unsaturation of CB, CNO, and SIO.
In the CB spectra, the stretching C-C region (1030–1183 cm−1) allows its different
polyforms to be identified. According to Bresson et al. [34], the existence of three peaks
(1066.26, 1102.21, and 1133.10 cm−1) (Figure 3) at room temperature allows us to recognize
that it is the IV or V form and the solid state of CB. The physical state of these ingredients
must also be taken into account; that is, at room temperature, the semisolid CNO only
presented a peak at 1133.10 cm−1, and the liquid SIO did not present any peak in this
region. This characteristic of the SIO spectrum agrees with the spectra of peony seed oil,
soybean oil, and extra virgin olive oil reported by Wang et al. [37]. Bresson et al. [34] affirms
that the peak at 1100 cm−1 is representative of the solid state of CB and is not shown in the
liquid state. This statement agrees with our results, since these peaks are not seen in CNO
and SIO, which are not solid at room temperature.
To find differentiation patterns between CB and CBE, Bresson et al. [38] identified
peaks at 1744, 1735, and 1730 cm−1 and found notable differences between the area ratios
of these peaks. The results in Table 3 indicate that only two peaks were identified at 1733.84
and 1745.43 cm−1, which correspond to the peaks mentioned above. The area ratios of
CB and SIO are very different, making their differentiation possible. It was not possible
to calculate the area ratio for the CNO because the peak at 1733.84 cm−1 was not found.
FWHM is also an indicator of polymorphism in the sense that a decrease in this value
indicates the transition of CB from a liquid to a solid due to the better arrangement of the
crystals [34]. This statement agrees with the results of Table 3, since the semisolid state
of CNO will produce a higher FWHM than CB, which is solid at room temperature. The
same behavior does not occur with SIO, since this vegetable oil is completely liquid at
room temperature.
4.2. Miscibility of Cocoa Butter and Vegetable Oils
Following Mitsutake et al. [22], to start the analysis by MCR–ALS, the Raman range
from 1000 to 1800 cm−1 was chosen, because it contains those peaks that allow differences
to be found between the three materials studied. Therefore, Figure 3 shows the range of
analysis and the main peaks that differentiate CB from CNO and SIO. The most striking dif-
ference is the high intensity of the peak of the SIO spectrum located at 1662.77 cm, which is
Foods 2021, 10, 3101 12 of 17
related to its liquid state at room temperature. On the other hand, De Géa Neves et al. [18]
reported the existence of peaks at 1264 and 1658 cm−1 in the CNO spectrum that differenti-
ated it from other vegetable oils. In the present work, these peaks were shown at 1268.8 and
1662.2 cm−1, and their intensity was very low with respect to CB and SIO. According to
Castro et al. [39], to remove noise signals and optimize the results of the MCR–ALS model,
the data were preprocessed using the Savitzky–Golay filter from LabSpec 6 and the 1-norm
normalization method from Solo + MIA. Figure 4b,d shows the preprocessed data showing
low scattering contributions, with which they were corrected, obtaining acceptable results
according to Zhang et al. [39,40].
The analysis of mixtures has been a constant concern in any scientific domain [41].
MCR–ALS solves this problem by providing a chemically (scientifically) significant additive
bi-linear model of pure contributions from an original data matrix [30]. This bilinear model
could produce several solutions, known as rotational ambiguity, which is the primary
source of uncertainty. Thus, selecting the appropriate constraints is essential to obtain
optimal solutions [30,41,42]. Once the optimization process has been finished, the MCR–
ALS results are the set of concentration profiles, spectra and quality parameters (explained
variance) related to the model [30]. Therefore, nonnegativity and equality restrictions were
applied to our data. With these considerations, the explained variance of the MCR–ALS
model for all samples was between 94.59 and 98.46% (Table 4), which means that this
model is capable of representing the original data with high precision. Mitsusake et al. [22]
reported explained variance percentages between 98.9 and 99.6% in MCR–ALS when it
was applied to blends formulated using natural excipients, and Zhang et al. [40] reported
values between 99.43 and 99.56% in the analysis of the constituents of commercial chocolate
samples. The authors conclude that their results are well adjusted, and therefore, the
MCR–ALS model is capable of constructing chemical maps of the samples. Although
these values are higher than the results found in our work, we can say that our data fit the
MCR–ALS model in such percentages.
Zhang et al. [40] worked with white chocolates, making a comparison between the
spectrum of the pure components such as sucrose, lactose, butter and whey. The correlation
coefficients between the pure spectra and those recovered by MCR–ALS with data prepro-
cessing were between 0.6701 and 0.9910, which were considered satisfactory. The equality
constraint fixes the recovered spectra or concentrations to specific known values [43]. This
is the reason why the values of the correlation coefficient between the recovered spectra
and the pure compounds are high (Table 4). Additionally, Figure 5 shows that there is
no rotational ambiguity, because the recovered spectra (ST) are identical to the original
spectra. The same results were obtained for the other samples. Based on these results, we
can affirm that the first spectrum recovered by MCR–ALS (component 1) corresponds to
CB, while the second spectrum (component 2) corresponds to vegetable oil, according to
the sample analyzed.
The mathematical analysis of each image allows for the extraction of parameters
that are helpful in the interpretation of the images and in understanding of the blending
process studied [31]. The data provided by Raman mapping contain spectral and spatial
information; then, MCR–ALS can be used to visualize the concentration distribution
maps of the different components present in a sample based on their individual spectral
signals [44]. However, the quality of the Raman mapping is limited by the spectra of the
individual compounds and their concentration, so the analysis becomes complex if the
spectra of the components have common peaks [21], as is the case with CB, CNO and
SIO (Figure 3). This was another reason why we decided to use the spectra of the pure
compounds as equality constraints. It is important to mention that the values in matrix C
are related to the concentrations, but they are not the real concentrations of the components
of the blends, so the mean value should not be compared with the real concentration
of each component [22]. Figure 6 shows the distribution maps of CB, CNO, and SIO in
all the samples analyzed constructed from the matrix of concentrations C obtained by
MCR–ALS. Figure 6a shows a better distribution of CB and CNO at concentrations of 55
Foods 2021, 10, 3101 13 of 17
and 45%, respectively. Similarly, Figure 6g shows a better distribution of CB and SIO at
concentrations of 65 and 35%, respectively. Both figures show better distribution than the
others. Similar results were obtained by Scoutaris et al. [21] when analyzing mixtures of
paracetamol (PMOL) and compritol 888 (C-888).
We consider that the miscibility of two components can be determined by their
distribution in a chemical map, and homogeneous distribution is an indicator of good
miscibility. The homogeneity of the samples is also analyzed using the histograms of the
distribution map. According to Gendrin et al. [45], a histogram that exhibits a symmetric
distribution with a narrow base and a sharp peak is representative of an image with a
low contrast, and therefore a homogeneous sample. The histograms corresponding to
each distribution map show a symmetric shape in all cases (Figure 6), with differences
according to the actual concentration of each sample. Lyon et al. [46] prepared tablets
composed of furosemide and excipients at five different degrees of mixing, reporting that
the most homogeneous distribution was obtained in those samples whose histograms
were symmetric.
The homogeneity of the samples can be quantitatively analyzed using the RSD. Ac-
cording to Scoutaris et al. [21], RSD has been used to compare the homogeneity of a sample;
a low RSD value is interpreted as signifying higher homogeneity. Mitsusake et al. [22]
showed that the standard deviation of histograms (STD) is used to evaluate the miscibility
for the preformulation stage of pharmaceutical tablets. Therefore, we consider that both
parameters are comparable. Lyon et al. [46] used the RSD of the histograms generated
by the image scores to determine the homogeneity of the distribution of furosemide in
tablets, observing a progressive increase in RSD as the degree of homogeneity decreased.
Mitsutake et al. [22] observed an intermediate miscibility (STD = 6.9) between CB and CNO
at real concentrations of 75 and 25%, respectively. Similar results were obtained in the
present study (Table 5), in which it is shown that blends containing 45% CNO and 35% SIO
have the lowest RSD; therefore, they form a more homogeneous blend with CB and are
more miscible at those concentrations. In the elaboration of CBEs, the candidate vegetable
oil must have an SOS triglyceride concentration similar to that of CB. To achieve this, it
undergoes a fractionation process [8]. In the case of CNO and SIO, they were used in their
natural state, showing good miscibility with CB; therefore, they would be good candidates
to be used as CBE.
Food products are complex mixtures of heterogeneous nature; obtaining chemical
and spatial information from them is crucial for food safety and quality control [47]. For
this, food detection technologies play a fundamental role [25]; therefore, it is urgent to
develop rapid methods of nondestructive analysis to control the quality and safety of food
and thus control its circulation in the market [47]. Chemical Raman imaging (CRI), in
combination with chemometrics, can provide spectral information and spatial distributions
of specific chemicals, analyzing them non-destructively [42,47–50]. However, in the food
field, only a few investigations on the application of CRI have been reported [50]. CRM
allows the application of Raman mapping with MCR-ALS to obtain the chemical charac-
teristics of CB, CNO, and SIO through their Raman spectral fingerprint (Figure 1) and to
identify the miscibility of these three components (Figure 6, Table 5), which demonstrates
the usefulness of this methodology in initiating the development of new products in the
chocolate industry. Some authors have also used this methodology, such as Liu et al. [51],
who used chemical Raman mapping to study the compatibility between hydroxypropyl
methylcellulose (HPMC) and gelatin. It was found that HPMC was easily adapted to form
continuous and intermediate phases from the molecular interactions between both compo-
nents. Mitsutake et al. [52] studied the miscibility and structural changes (polymorphism)
in mixtures of natural and synthetic beeswax (BW) with copaiba oil using Raman mapping
and MCR-ALS. Structural changes were found in the synthetic BWs, and the miscibility
between both BWs with copaiba oil was not significantly different. It was also observed
that the differences between the freshly prepared mixtures and those with three months of
storage were more significant when the amount of oil was increased. On the other hand,
Foods 2021, 10, 3101 14 of 17
Rodríguez et al. [12] studied the compatibility of shea butter and CB mixtures using the
isosolid diagram but did not use Raman mapping.
Lauric acid and partially hydrogenated fats are not recommended in the chocolate
industry because they can increase LDL cholesterol levels [53]. Norazlina et al. [2] reported
the following CBE candidates: mean fraction of palm oil, mango seed fat, shea stearin,
kokum fat, illipe butter, high oleic and stearic sunflower oil, palm stearin, and bambangan
kernel fat. However, this author does not report CNO and SIO. The results of the present
work show CNO and SIO as possible candidates for novel CBE, as they demonstrate some
advantages, such as their high degree of unsaturation, which makes them healthy fats, as
well as their excellent molecular compatibility with CB demonstrated by their miscibility.
However, it is necessary to carry out some additional studies, such as the investigation
of their thermal and rheological properties at different concentrations of solid and liquid
lipids and the investigation of their behaviors over time.
5. Conclusions
In the present work, the usefulness of the confocal Raman microscopy (CRM) tech-
nique to identify the chemical properties of cocoa butter, coconut oil and sacha inchi oil is
demonstrated. These latter vegetable oils are proposed as candidates to be cocoa butter
equivalents in the manufacture of chocolates. The main differences are in the physical
state and the degree of unsaturation, which are differentiated by the intensity of the peaks
in the Raman spectra. Likewise, the usefulness of the chemometric technique known as
multivariate curve resolution–alternating least squares to analyze the miscibility of these
vegetable oils with cocoa butter is demonstrated. We conclude that coconut oil is more
miscible with cocoa butter at a 45% concentration, and sacha inchi oil is more miscible at a
35% concentration. Between the two vegetable oils, coconut oil is more miscible than sacha
inchi oil. We consider that this work is the first step in finding novel CBEs for developing
new chocolates. Further work is necessary to evaluate their thermal, rheological, and
sensorial properties.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10
.3390/foods10123101/s1, map blend: CB–CNO; CB–SIO and pure spectrum: CB; CNO; SIO.
Author Contributions: Conceptualization, E.M.C.-A. and F.P.C.-T.; methodology, E.M.C.-A., F.P.C.-T.
and L.T.-V.; software, E.M.C.-A. and L.T.-V.; validation, E.M.C.-A.; formal analysis, E.M.C.-A., F.P.C.-T.
and I.S.C.-C.; investigation, E.M.C.-A.; resources, I.S.C.-C.; data curation, E.M.C.-A.; writing—original
draft preparation, E.M.C.-A., F.P.C.-T. and I.S.C.-C.; writing—review and editing, E.M.C.-A., F.P.C.-T.
and I.S.C.-C.; visualization, E.M.C.-A. and I.S.C.-C.; supervision, E.M.C.-A. and F.P.C.-T.; project
administration, I.S.C.-C.; funding acquisition, I.S.C.-C. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the Consejo Nacional de Ciencia, Tecnología e Innovación
Tecnológica-Concytec (Project: Equipamiento Científico 2018-01/E044-2018-01-BM, Contrato N◦ 012-
2018-Fondecyt/BM) of the Peruvian Government, The World Bank Group, the Universidad Nacional
Toribio Rodríguez de Mendoza de Amazonas and the Pontificia Universidad Católica del Perú. The
APC was funded by the Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica-Concytec.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The authors thank the Cooperativa de Servicios Múltiples APROCAM for the
facilities provided during the execution of this work.
Conflicts of Interest: The authors declare no conflict of interest.
Foods 2021, 10, 3101 15 of 17
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