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applied
sciences
Article
Evaluation of Yogurt Quality during Storage by
Fluorescence Spectroscopy
Haifeng Sun 1 , Ling Wang 1 , Hao Zhang 1, * , Ang Wu 1 , Juanhua Zhu 1 , Wei Zhang 1 and
Jiandong Hu 1,2
1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China;
haifengdreams@sina.cn (H.S.); wangling0351@126.com (L.W.); cwuang@163.com (A.W.);
zhujh88@sina.com (J.Z.); zw@henau.edu.cn (W.Z.); jiandonghu@163.com (J.H.)
2 State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 45002, China
* Correspondence: hao.zhang2016@hotmail.com; Tel.: +86-0371-6355-8040
Received: 10 December 2018; Accepted: 25 December 2018; Published: 2 January 2019
Featured Application: This work provides a potential for the evaluation of yogurt quality during
storage by fluorescence spectroscopy.
Abstract: The physico-chemical parameters including pH and viscosity, and the fluorescence signal
induced by fluorescent compounds presenting in yogurts such as riboflavin and porphyrin were
measured during one week’s storage at room temperature when five brands of yogurt samples were
exposed to ambient air. The fluorescence spectra of yogurt showed four evident emission peaks,
525 nm, 633 nm, 661 nm, and 672 nm. To quantitatively investigate the quality of yogurt during
deteriorating, a calculating method of the average rate of change (ARC) was proposed to study
the relative change of fluorescence intensity in the spectral range of 600 to 750 nm associated with
porphyrin and chlorin compounds. During the storage, the time evolution of two ARC, pH value,
and viscosity were regular. Moreover, the ARC showed a good linear relationship with pH value and
viscosity of yogurt. Further, multiple linear regression (MLR) models using two ARC as independent
variables were developed to verify the dependence of fluorescence signal with pH value and viscosity,
which showed a good linear relationship with an R-square of more than 85% for each class of
yogurt. The results demonstrate that fluorescence spectra have a great potential to predict the quality
of yogurt.
Keywords: fluorescence spectroscopy; yogurt quality; deteriorating; pH; viscosity
1. Introduction
Yogurt, as one kind of dairy products, has attracted more and more attention in recent years,
especially since various brands of yogurt now contain special strains of “probiotics” that can help
boost the immune system and promote a healthy digestive tract. Yogurt provides not only rich nutrient
elements from milk but also varieties of vitamins produced in the process of bacterial fermentation
of milk, which can contribute to health care and prevent diseases [1]. However, when exposed to
ambient air, yogurt is vulnerable to microbial contamination and, thus, spoiled. In addition, with the
fast development of the yogurt industry, various types of yogurt emerge in the market, followed by
the difference in yogurt quality and flavor. Therefore, the accurate identification of different yogurt
species and the quantitative evaluation of yogurt quality become very important for the development
of the dairy industry.
The traditional detection methods for yogurt quality include biological and chemical methods,
such as the standard plate colony counting method for bacterial plate counts and chromatography-mass
Appl. Sci. 2019, 9, 131; doi:10.3390/app9010131 www.mdpi.com/journal/applsciAppl. Sci. 2019, 9, 131 2 of 10
spectrometry for protein structure analysis [2–5]. However, these methods are time-consuming,
labor-intensive, cumbersome, and destructive. Optical spectroscopy techniques have been widely
used to study dairy products due to their advantages of being rapid, having high sensitivity, and being
non-invasive. Near-infrared spectroscopy (NIR) technique allows rapid and accurate determination
of typical chemical structures presenting in nutrients, such as C–H, N–H, and O–H, due to their
characteristic absorption spectrum in the near-infrared range (750–2500 nm) caused by the molecular
transition [6–10]. Based on visible/NIR spectroscopy, principal component analysis (PCA) and artificial
neural network (ANN) has been used for the discrimination of five kinds of yogurt, as well as for the
determination of the sugar content and acidity of yogurt [11,12]. Ultraviolet (UV)-visible spectroscopy
is a rapid and alternative technique that provides chemical information, which has been used to
monitor the stability of yogurt samples stored at 4 ◦ C up to 49 days [13]. Laser-induced fluorescence
(LIF) spectroscopy is a well-known noninvasive method for highly sensitive and selective analysis
of molecules, which has been used for the qualitative estimation of proteins in milk at different
milking times and for the freshness detection of milk, as well as for studies on deterioration of fresh
milk [14–16]. Spatially resolved diffuse reflectance spectroscopy has been used to non-invasively
evaluate the fat content in milk and yogurt by measuring the reduced scattering and absorption
properties [17]. Although several studies have been conducted, most of which were focused on the
quantitative analysis of nutritional components in milk, to our knowledge, few studies have been
developed to address the changes of the fluorescence signal and the physico-chemical parameters
(such as pH value and viscosity) during yogurt deterioration.
The basic aim of this work was to explore the potential for fast and accurate evaluation of yogurt
quality by measuring the fluorescence spectra, pH value, and viscosity during storage. A total of five
typical brands of yogurt were investigated. Linear discrimination analysis (LDA) was first attempted
to distinguish different yogurt species. To quantify the fluorescence spectra related to porphyrin
and chlorin compounds, a calculating method of the average rate of change (ARC) was proposed.
Subsequently, the time-dependent ARC, pH value, and viscosity in the process of yogurt storage were
analyzed. In the end, multiple linear regression (MLR) models were built to evaluate the relationship
between the fluorescence signal with the physico-chemical parameters pH value and viscosity.
2. Materials and Methods
2.1. Yogurt Samples
Five typical brands of yogurt in China indicated with the same manufacture date and the same
shelf life were purchased at a local super-market in Zhengzhou, China. The five brands are HuaHuaNiu
(from Zhengzhou, China), JunLeBao (from Shijiazhuang, China), MengNiu (from Neimenggu, China),
YiLi (from Neimenggu, China), and GuangMing (from Shanghai, China), which are named by classes
A, B, C, D, and E, respectively. The ingredients in yogurt mainly consist of raw milk (≥80%),
food additives, and lactobacillus. For each type of yogurt, 20 samples were selected for fluorescence
spectral measurements. A total of 100 samples were obtained and placed in glass cups. For the quality
measurement of yogurt during storage, all samples were stored in a compartment with the temperature
maintained at 23 ◦ C by means of an air conditioner.
2.2. Experimental Setup
A typical LIF spectrum measurement system was constructed with a diode laser, a high-pass
filter, three biconvex lenses, a multi-mode fiber, and a fiber-optical spectrometer. The diode laser with
an emission wavelength of 405 nm and an output power of 50 mW shot the light onto the surface
of the yogurt sample by a focusing lens. The excited fluorescence was collected by an optical unit
consisting of a high-pass filter as well as two focusing lenses with the same focusing lens. To decrease
the directly reflected light from the surface of the yogurt sample, the optical unit was arranged with a
45-degree angle. After that, the scattered excitation light was further suppressed by using a high-passAppl. Sci. 2019, 9, 131 3 of 10
filter with the cutoff wavelength of 420 nm. The sample fluorescence was focused into the port
of a multi-mode optical fiber with a core diameter of 600 µm and then transported to a portable
spectrometer (USB2000+, Ocean Optics, USA). In the end, the spectral data were stored by a personal
computer (PC) for further analysis.
2.3. pH and Viscosity Measurements
The pH value and viscosity of the yogurt samples were measured using a pH-meter (pH-100,
Lichen Instruments, Shanghai, China) and a viscometer (LND-1, Lichen Instruments, Shanghai, China),
in the condition of room temperature. Before measurements, the pH-meter was calibrated using
standard buffer solutions of pH = 4.00, pH = 6.86, and pH = 9.18 at room temperature (about 25 ◦ C).
After each measurement, the pH-meter and viscometer were washed with ultra-pure water. For each
sample, three independent measurements were performed to obtain the average value.
2.4. Statistical Analysis
In this work, linear discriminant analysis (LDA) based on the principal component analysis (PCA)
was firstly used for identifying five species of yogurt. Principal component analysis (PCA) is an
unsupervised dimension-descending method, which reduces the dimensions of the original spectral
data matrix with the minimal loss of information by decomposing the data matrix into a structure part
and a noise part. The decomposing process is expressed by [18]
X = TP T + E (1)
where P is the transformation matrix (or loading matrix) between the original variable space and the
new data space decided by the principal components (PCs). The loading vectors in the matrix P are the
representations of the principal components in the original variables. T is the score coefficient matrix,
which are the coordinates of all data points in the new principal component space. E represents the
residual noise.
The principle of LDA is to find out a so-called discriminant function which best separates
the classes by minimizing the distance of within-class samples and maximizing the distance
of between-class samples. The LDA is also a projection method by projecting the data into a
multidimensional straight line and then selecting an appropriate line to separate the different data
points. The projection function is given as:
y j = w Tj x + w j0 (2)
where, x ∈ X j , Xj is the jth sample set. j = 1, 2, . . . , k, k is the class number of the total samples.
Then the discriminant function is given by [19]:
∏ W T Sb W
diag d wiT Sb wi
J (W ) = =∏ (3)
∏ WTS wW i =1 wiT Sw wi
diag
where W is the projection matrix composing of the maximum eigenvalue of the matrix, d is the number
of the maximum eigenvalue. Sw and Sb are the within-class scatter matrix, and the intra-class scatter
matrix, respectively, which are expressed by:
k
Sb = ∑ Nj (µ j − µ)(µ j − µ)T (4)
j =1
where µ is the mean value of the total samples, µj is the mean value of the jth class samples, Nj is the
number of the j-class samples.Appl.
Appl. Sci.
Sci. 2019,9 9,
2019, FOR131 PEER REVIEW 4 of 10 4
In this study, after performing PCA to reduce the dimensionality of variables, LDA was carried
3. Results and Discussion
out to develop a linear discrimination model by using the score values of the first six PCs that gave
more than 99.9% explained percentage.
3.1. Spectral Investigation
3. Results and Discussion
Fluorescence spectra were obtained in the wavelength range of 339.98 nm to 1028.70 nm with a
resolution of 0.38 nm. Each spectrum includes 2048 data points which are decided by the linear CCD
3.1. Spectral Investigation
array of USB 2000+ spectrometer. To reduce the number of data points available for multivariate
Fluorescence
analysis, the spectra spectra
in thewere obtained in
wavelength the wavelength
range of 410 to 910 range
nm,ofwhere
339.98 all
nmavailable
to 1028.70fluorescence
nm with
a resolution
signals of 0.38 nm.
are included, wasEach spectrum
selected. includes fluorescence
The average 2048 data points which
spectra are decided
measured from byfive
the different
linear
CCD array of USB 2000+ spectrometer. To reduce the number of data points
kinds of yogurt are shown in Figure 1. As can be observed clearly, there are several spectralavailable for multivariate
analysis, thebands
wavelength spectrathat
in the wavelength
might range ofinteresting.
be particularly 410 to 910 nm,Thewhere all available
prominent emissionfluorescence
band with signals
a peak
are included, was selected. The average fluorescence spectra measured from five different kinds of
wavelength of 525 nm located in the wavelength range of 500 to 600 nm has almost the same spectral
yogurt are shown in Figure 1. As can be observed clearly, there are several spectral wavelength bands
shape for five kinds of yogurt, which means that the emission spectra in this range should be
that might be particularly interesting. The prominent emission band with a peak wavelength of 525 nm
attributed by the fluorophores presenting in raw milk, such as tryptophan, nicotinamide adenine
located in the wavelength range of 500 to 600 nm has almost the same spectral shape for five kinds of
dinucleotide (NADH), vitamin A, riboflavin, and Maillard products [20,21]. Another interesting
yogurt, which means that the emission spectra in this range should be attributed by the fluorophores
region is from 600 to 750 nm, where five different narrow emission bands occur. The first emission
presenting in raw milk, such as tryptophan, nicotinamide adenine dinucleotide (NADH), vitamin A,
band appears approximately at 633 nm, which has a similar spectral shape for different kinds of
riboflavin, and Maillard products [20,21]. Another interesting region is from 600 to 750 nm, where five
yogurt samples.
different narrowAn apparent
emission double
bands occur.peak
The appearing
first emission at about 661 nmapproximately
band appears and 672 nm have at 633anm,slight
difference
which hasinathe spectral
similar intensity,
spectral shape for
for example, the peak
different kinds intensity
of yogurt at 661 nm
samples. of class Ddouble
An apparent is far less
peak than
that
appearing at about 661 nm and 672 nm have a slight difference in the spectral intensity, for example, be
at 672 nm. These narrow emission peaks in the red and near-infrared (NIR) region might
produced by the atpresence
the peak intensity of porphyrin
661 nm of class D is far less and chlorin
than that at 672compounds
nm. These narrowin yogurt,
emissionmostpeakslikely
in
protoporphyrin, hematoporphyrin, chlorophyll a and chlorophyll b [22,23]. In
the red and near-infrared (NIR) region might be produced by the presence of porphyrin and chlorin addition, a small peak
appearing
compounds at about 420 most
in yogurt, nm islikely
the same for both thehematoporphyrin,
protoporphyrin, spectral shape and intensity afor
chlorophyll andthechlorophyll
five kinds of
yogurt, which
b [22,23]. should be
In addition, attributed
a small to the fluorescence
peak appearing at about 420 signal
nm isfrom the high-pass
the same for both the filter whenshape
spectral excited
with
and405 nm laser
intensity so this
for the five peak
kindscan be ignored
of yogurt, which inshould
the present study. to the fluorescence signal from
be attributed
the high-pass filter when excited with 405 nm laser so this peak can be ignored in the present study.
25
A
B
C
20 D
E
Relative intensity (a.u.)
15
10
5
0
410 510 610 710 810 910
Wavelength (nm)
Figure 1. Average fluorescence spectra of five brands of yogurt (HuaHuaNiu, JunLeBao, MengNiu,
Figure 1. Average fluorescence spectra of five brands of yogurt (HuaHuaNiu, JunLeBao, MengNiu,
YiLi, and GuangMing).
YiLi, and GuangMing).
3.2. Yogurt Classification Based on LDA
Based on the PCA score coefficient matrix, LDA was used to improve the classification accuracy
between different brands of yogurt that were difficult to separate using only PCA. Since the numberAppl. Sci. 2019, 9, 131 5 of 10
3.2.Appl.
Yogurt Classification
Sci. 2019, Based
9 FOR PEER on LDA
REVIEW 5
Based on the PCA score coefficient matrix, LDA was used to improve the classification accuracy
of PCs selected for the LDA model will influence the classification result, the first six PCs accounting
between different brands of yogurt that were difficult to separate using only PCA. Since the number of
for over 99.99% of the total variance of the data matrix were chosen as the optimal variables to
PCs selected for the LDA model will influence the classification result, the first six PCs accounting for
develop the LDA discrimination model. Thus, the 100 × 1459 data matrix of fluorescence spectra was
over 99.99% of the total variance of the data matrix were chosen as the optimal variables to develop the
transformed to a new dataset consisting of a 100 × 6 data matrix. To establish a suitable LDA
LDA discrimination model. Thus, the 100 × 1459 data matrix of fluorescence spectra was transformed
discrimination model and then evaluate the model, the new dataset was divided into a training set
to a new dataset consisting of a 100 × 6 data matrix. To establish a suitable LDA discrimination model
and a prediction set. Each set consists of 50 fluorescence spectra (a 50 × 6 data matrix), where the
and then evaluate the model, the new dataset was divided into a training set and a prediction set.
training set was used to establish the LDA discrimination model, and the prediction set was used to
Each set consists of 50 fluorescence spectra (a 50 × 6 data matrix), where the training set was used to
predict the feasibility of discrimination model.
establish the LDA discrimination model, and the prediction set was used to predict the feasibility of
Figure 2a depicts the discrimination results of different yogurt brands by means of 2D scatter
discrimination model.
plot of the first two most important discriminant functions, an 85% confidence ellipse was used to
Figure 2a depicts the discrimination results of different yogurt brands by means of 2D scatter
describe the accuracy of predicted discrimination. As can be obserrved clearly, the yogurt samples
plot of the first two most important discriminant functions, an 85% confidence ellipse was used to
are reasonably well discriminated. The samples of the classes A, B, and D show a good separation,
describe the accuracy of predicted discrimination. As can be obserrved clearly, the yogurt samples are
while the samples of the classes C and E have several overlaps, which might produce some
reasonably well discriminated. The samples of the classes A, B, and D show a good separation, while the
misclassifications.
samples of the classes C and E have several overlaps, which might produce some misclassifications.
Figure 2. (a) Linear discriminant analysis for five different brands of yogurt samples (A, B, C, D,
Figure 2. (a) Linear discriminant analysis for five different brands of yogurt samples (A, B, C, D, and
and E) based on the first two discriminant functions; (b) Bar plot of LDA discrimination model for the
E) based on the first two discriminant functions; (b) Bar plot of LDA discrimination model for the
predication set from five different kinds of yogurt.
predication set from five different kinds of yogurt.
The predicted classification result of the LDA model is shown in Figure 2b, where the classes A,
B, C, D,The
andpredicted classification
E are indicated resultofofblue,
by the color the LDA
green,model is shown
red, cyan, andin Figurerespectively.
mauve, 2b, where the It classes
can be A,
B, C, D, and E are indicated by the color of blue, green, red, cyan, and mauve,
observed clearly that almost all the fluorescence spectra of yogurt samples (47 samples) except respectively. It five
can be
observed
samples fromclearly
class Ethat almost
in the all the fluorescence
prediction set were correctlyspectra of yogurt samples
discriminated. (47 samples)
For these except five
five misclassified
samples from class E in the prediction set were correctly discriminated. For these
samples from class E, one was identified as the class A, and the other four were identified as the class five misclassified
samples from
C. Therefore, theclass
classesE, one
A, B,was
andidentified
D have aas the class
good A, and the other
discrimination, four were identified
the misclassification of theasclasses
the class
C. Therefore,
C and E are mostlythecaused
classes byA, the
B, and D have
overlap a good discrimination,
of discriminant the misclassification
functions shown in Figure 2a. Theof the classes
results
C and E are mostly caused by the overlap of discriminant functions shown in
demonstrate the LDA discrimination model can be successfully employed for the accurate classificationFigure 2a. The results
demonstrate
of yogurt brands.the LDA discrimination model can be successfully employed for the accurate
classification of yogurt brands.
3.3. The Quality Evaluation
3.3. The Quality Evaluation
Each sample was regularly measured over a period of one week to monitor the deterioration
process Each sample
of yogurt was regularly
samples, measured
measurements wereoverperformed
a period of at one week to
a regular timemonitor
every the
daydeterioration
since the
process of
porphyrins andyogurt
chlorinssamples, measurements
naturally were both
existing in yogurt performed
can be at a regular
acted time every day
as photosensitizers, which sincearethe
porphyrins
highly sensitiveand chlorins
to light naturally
and easy existing
to suffer from in yogurt bothdegradation.
light-induced can be actedBy as selecting
photosensitizers, which are
the fluorescence
highlyband
spectral sensitive
in the to light andrange
wavelength easy ofto 600
suffer from
to 650 nm,light-induced degradation.
where five narrow By selecting
shape emission peaksthe
fluorescence spectral band in the wavelength range of 600 to 650 nm, where five narrow shape
emission peaks attributed to the porphyrins and chlorins are presented, the normalized fluorescence
spectra of five brands of yogurt are shown in Figure 3A–E. During the successive measurements
from one to seven days, these emission peaks exhibit major changes due to the photodegradation ofAppl. Sci. 2019, 9, 131 6 of 10
attributed to the porphyrins and chlorins are presented, the normalized fluorescence spectra of five
Appl. Sci. 2019, 9 FOR PEER REVIEW 6
brands of yogurt are shown in Figure 3A–E. During the successive measurements from one to seven
days, these emission peaks exhibit major changes due to the photodegradation of the porphyrins and
the porphyrins and chlorins. Therefore, these fluorescence peaks can be used as indicators to
chlorins. Therefore, these fluorescence peaks can be used as indicators to estimate the changes of the
estimate the changes of the porphyrins and chlorins during yogurt deterioration.
porphyrins and chlorins during yogurt deterioration.
1.5 1.5 1.5
A B C
Normalized intensity (a.u.)
Normalized intensity (a.u.)
Normalized intensity (a.u.)
1 1 1
0.5 0.5 0.5
0 0 0
600 650 700 750 600 650 700 750 600 650 700 750
Wavelength (nm) Wavelength (nm) Wavelength (nm)
1.5 1.5
D E
Normalized intensity (a.u.)
Normalized intensity (a.u.)
Day 1
Day 2
1 1
Day 3
Day 4
Day 5
0.5 0.5 Day 6
Day 7
0 0
600 650 700 750 600 650 700 750
Wavelength (nm) Wavelength (nm)
Figure 3. Normalized fluorescence spectra of each kind of yogurt during the measurements over
Figure
one 3. Normalized fluorescence spectra of each kind of yogurt during the measurements over one
week.
week.
To extract the spectroscopic information related to time-dependent deterioration process of yogurt,
a calculating method
To extract of the average information
the spectroscopic rate of change (ARC)to
related was proposed, which
time-dependent can be used to
deterioration evaluate
process of
the relative
yogurt, change ofmethod
a calculating fluorescence
of the intensity
average rate in a of
given spectral
change range.
(ARC) The formula
was proposed, for ARC
which can becan be
used
expressed
to evaluateby:
the relative change of fluorescence intensity in a given spectral range. The formula for
ARC can be expressed by: ∆I Iλ − Iλ2
Kλ1 /λ2 = = 1 (5)
∆λ λ1 − λ2
ΔI I λ1 − I λ2
where Iλ1 and Iλ2 are the fluorescence intensity K λ1 / λ2 = at the
= wavelength of λ1 and λ2 . Considering the (5)
Δλ λ1 − λ2
fluorescence change related to porphyrin and chlorin compounds in the wavelength range of 600 to
I622 − I633
750 nm,IK
where and I=
λ 622/633 the was
are−633
λ 622
defined for
fluorescence
1
estimating
intensity thewavelength
at the 2
of λ1 and λpeak
change of fluorescence at 622 nm and
2 . Considering the
661 − I672
633 nm, K661/672
fluorescence change = I661related
−672 to was defined and
porphyrin for evaluating the peak change
chlorin compounds at 661nm and
in the wavelength 672ofnm.
range 600 to 750
The time evolution
I 622 − I 633 of calculated K 622/633 and K 661/672 for these five types of yogurt samples are
nm, K 622/ 633in=Figure
displayed 4. A was definedtendency
decreasing for estimating
of K the change
and anof fluorescence
increasing peak
tendency at
of 622
K nm and
are
622 − 633 622/633 661/672
clearly found during the whole
I 661 − I 672 measurements of seven days. As suggested by Wold [22], the peak
633 nm, K
at 633 nm 661/ could
672 = was defined for evaluating the peak change at 661nm
be attributed to protoporphyrin, while the double peak at 661 nm and 672 nm and 672 nm.
661 − 672
might mostly be due to the chlorophyll residues. Therefore, the decrease of K622/633 indicates the
The time evolution of calculated K622/633 and K661/672 for these five types of yogurt samples are
photodegradation of protophyrin, and the increase of K661/672 represents the photodegradation of
displayed in Figure 4. A decreasing tendency of K622/633 and an increasing tendency of K661/672 are
chlorophyll residues. The results show that the characteristic fluorescence intensity is highly sensitive
clearly found during the whole measurements of seven days. As suggested by Wold [22], the peak at
to the changes of yogurt composition during the deterioration process, and therefore the fluorescence
633 nm could be attributed to protoporphyrin, while the double peak at 661 nm and 672 nm might
mostly be due to the chlorophyll residues. Therefore, the decrease of K622/633 indicates the
photodegradation of protophyrin, and the increase of K661/672 represents the photodegradation of
chlorophyll residues. The results show that the characteristic fluorescence intensity is highly
sensitive to the changes of yogurt composition during the deterioration process, and therefore theAppl. Sci. 2019, 9 FOR PEER REVIEW 7
Appl. Sci. 2019, 9, 131 7 of 10
Appl. Sci. 2019, 9 FOR PEER REVIEW 7
fluorescence peaks of photodegradation of protophyrin could be used as indicators to quantitatively
estimate the quality
fluorescence peaks ofofphotodegradation
yogurt during storage.
ofcould
protophyrin
peaks of photodegradation of protophyrin be usedcould be used to
as indicators as quantitatively
indicators to quantitatively
estimate the
estimate
quality ofthe quality
yogurt of yogurt
during during storage.
storage.
Figure 4. K622/633 and K661/672 values calculated for yogurt samples (A, B, C, D, and E) over one week.
Figure 4. K622/633 and K661/672 values calculated for yogurt samples (A, B, C, D, and E) over one week.
Figure 4. K622/633 and K661/672 values calculated for yogurt samples (A, B, C, D, and E) over one week.
For comparison purposes, the physico-chemical parameters pH value and viscosity of yogurt
For comparison
samples purposes,during
were also measured the physico-chemical
the deterioratingparameters
process. AspH valuein
shown and viscosity
Figure of yogurt
5, a decreasing
For
samples comparison purposes, the physico-chemical parameters pH value and viscosity of yogurt
tendencywere
samples wasalso
were found
also
measured
both forduring
measured pH value
during
theand
the
deteriorating
deteriorating
process.
viscosity during
process.
As shown
a period
As of 7 in
shown
Figure
days,
in while
Figure
5, aindividually,
5, a
decreasing
decreasing
tendency
each typewas found sample
of yogurt both forhaspHavalueslightand viscosityThe
difference. during a period
reduction of 7in
of pH days, while
yogurt individually,
could be mostly
tendency
each type was
of foundsample
yogurt both forhaspHa value
slight and viscosity
difference. The during a period
reduction of of 7indays,
pH yogurtwhile
could individually,
be mostly
caused by
each type the production
of yogurt sample of lactic
has acid acid
a slight as a result
difference. of
The the action
reduction of the starter
of pHbacteria, bacteria,
in yogurt could which was
beactually
mostly
caused by
actuallyby the production
apparent of
in the caselactic
of room as a result of the action of the starter which was
caused
apparent inthe
theproduction
case of room acid temperature
of temperature
lactic as storage. ofstorage.
a result The the Theofpronounced
action
pronounced the viscosity-decreasing
starter bacteria,
viscosity-decreasing which was
behavior
behavior
actually might
apparent beinattributed
the case to
of the
room protein denaturation
temperature storage. and
The the destruction
pronounced of protein colloid
viscosity-decreasing
might be attributed
structure in the room to temperature.
the protein denaturation
Therefore, and
the the destruction
reduction of pH of protein
value colloid structure
and viscosity determinatein
behavior
the might be attributed to the protein denaturation and the destruction of protein colloid
theroom temperature.
quality
structure of yogurt. Therefore, the reduction of pH value and viscosity determinate the quality
of yogurt. in the room temperature. Therefore, the reduction of pH value and viscosity determinate
the quality of yogurt.
Figure5.5.pH
Figure pHvalue
valueand
andviscosity
viscosityof
ofyogurt
yogurtsamples
samples(A,
(A,B,
B,C,
C,D,
D,and
andE)
E)measured
measuredover
overone
oneweek.
week.
Figure 5. pH valuewas
and selected
viscosity of
to yogurt samples (A, B, C, D, and E) measured over spectra
one week.
TheKK622/633
The value find out the relevance between fluorescence
622/633 value was selected to find out the relevance between fluorescence spectra and the
and the
physico-chemical
physico-chemical parameters
parametersduring
during yogurt
yogurtdeterioration
deterioration since it had
since a similar
it had timetime
a similar evolution withwith
evolution pH
value The
and Kviscosity.
622/633 value was selected to find out the relevance between fluorescence spectra and the
As displayed in Figure 6, good linear relationship is achieved both for pH value
pH value and viscosity.
physico-chemical As displayed
parameters during in Figure
yogurt 6, good linear
deterioration relationship
sincecorrelation is achieved
it had a similar time both forwith
evolution pH
and viscosity,
value andand which
viscosity,indicates
which that K 622/633 value
indicatesin that shows
K622/633 a strong
value shows a strong with the physico-chemical
correlation with pHthe
pH value
parameters viscosity.
of yogurt. TheAsphysico-chemical
displayed Figure 6, good
parameters linear
pH andrelationship
viscosity is regarded
are achieved both
as twofor
most
physico-chemical parameters of yogurt. The physico-chemical parameters
value and viscosity, which indicates that K622/633 value shows a strong correlation with the pH and viscosity are
physico-chemical parameters of yogurt. The physico-chemical parameters pH and viscosity areAppl. Sci. 2019, 9 FOR PEER REVIEW 8
regarded
Appl. as9two
Sci. 2019, FORmost common
PEER REVIEWindicators for the evaluation of yogurt quality, which further verifies 8
Appl. Sci. 2019, 9, 131 8 of 10
that fluorescence signals can be used as one indicator to quantitatively estimate the quality of
regarded
yogurt. as two most common indicators for the evaluation of yogurt quality, which further verifies
that
commonfluorescence
indicatorssignals can be used
for the evaluation as onequality,
of yogurt indicator to further
which quantitatively estimate
verifies that the quality
fluorescence signalsof
yogurt.
can A
be used as one indicator B
to quantitatively C quality of yogurt.
estimate the -3 D -3 E
-0.009 -0.011 -0.009 10 10
-4 -6
A -0.012
B C -3 D -3 E
-0.009 -0.011 -0.009 10 10
-4 -6
-0.013 -0.0115 -8 -9.5
-0.013
-0.012
-0.013 -0.0115 -8 -9.5
-0.017 -0.014 -0.014 -12 -13
4.08 4.28 4.48-0.0134.08 4.23 4.38 4.16 4.26 4.36 4.46 4.06 4.16 4.26 4.36 3.9 4 4.1 4.2
pH value pH value pH value pH value pH value
-0.017 -0.014 -0.014 -12 10-3 -13 10
-3
-0.009
4.08 4.28 4.48 -0.011
4.08 4.23 4.38 -0.009 4.06 4.16 4.26 4.36 -6
4.16 4.26 4.36 4.46 -4 3.9 4 4.1 4.2
pH value pH value pH value pH value pH value
-0.012 10
-3
10
-3
-0.009 -0.011 -0.009 -4 -6
-0.013 -0.0115 -8 -9.5
-0.013
-0.012
-0.013 -0.0115 -8 -9.5
-0.017 -0.014 -0.014 -12 -13
20 60 100-0.013 50 100 150 10 50 90 130 20 60 100 130 40 70 100 130
Viscosity (mm2 /s) Viscosity (mm2 /s) Viscosity (mm2 /s) Viscosity (mm2/s) Viscosity (mm2 /s)
-0.017 -0.014 -0.014 -12 -13
Figure 20
6. The 60relevance
100 50 100
of fluorescence 150 10 50 90 130
peak ratio K622/633 with pH20 60 100 130
value 40 70 100 130
and viscosity of yogurt
Viscosity (mm2 /s) Viscosity (mm2 /s) Viscosity (mm2 /s) Viscosity (mm2/s) Viscosity (mm2 /s)
during the whole measurement series.
Figure 6.6. The
Figure The relevance
relevance ofoffluorescence
fluorescencepeak ratioKK
peakratio with pH
622/633 with
622/633 pH value and viscosity
viscosity of
of yogurt
yogurt
To further
during
during the validate
the whole
whole the dependence
measurement
measurement series. of fluorescence signal with pH value and viscosity, multiple
series.
linear regression (MLR) models were developed, where K622/633 and K661/672 value were used as
To
Tofurther
further
independent validate
validate the
variables, the dependence
pH dependence
value and viscosity of
of fluorescence
fluorescence signal
signal with
were selected with
as pH
pH value
value and
dependence and viscosity,
viscosity,
variables, multiple
multiple
respectively.
linear
linear regression
As shownregression (MLR)
(MLR)
in Figure models were
= p1 ⋅ K 622/
7, Kmodels were developed,
+ p 2 ⋅ K
developed, where
, where
where K KK is the
622/633
622/633 and
and KK
normalized
661/672
661/672 value
value
value were
were used
used as
dependent as
on
633 661/ 672
independent
independent variables,
variables, pH
pH value
value and
and viscosity
viscosity were
were selected
selected as
as dependence
dependence
both K622/633 and K661/672 value, p1 and p2 are the coefficients. Each MLR regression model presents a variables,
variables, respectively.
respectively.
As
As shown
correlation Figure7,7,K =
shownininFigure
coefficient K 2p1
(R ·1K⋅ K
=) pof 622/633
more ++pp2
622/ 633 than 2 ⋅ ·KK661/
661/672
0.85, , where
, where
672where KKisisthe
classes the normalized
B normalized
and value
E havevalue dependent
dependent
a preferable on
on
linear
both
both K 622/633 and
K622/633 and
relationship, K 661/672
K661/672
which value,
value,
again p1 and
p1 andthat
indicates p2
p2 are are the coefficients.
the coefficients.
fluorescence Each MLR
Each MLRhave
measurements regression
regression model
model to
the potential presents
presents
estimate aa
correlation coefficient (R 2 ) of more than 0.85, where classes B and E have a preferable linear relationship,
correlation coefficient (R ) of more than 0.85, where classes B and E have a preferable linear
the yogurt quality. 2
which again indicates
relationship, that fluorescence
which again indicates that measurements
fluorescencehave the potentialhave
measurements to estimate the yogurt
the potential quality.
to estimate
the yogurt quality.
Figure 7. Predicted pH value and viscosity of yogurt samples based on MLR model.
Figure 7. Predicted pH value and viscosity of yogurt samples based on MLR model.
Figure 7. Predicted pH value and viscosity of yogurt samples based on MLR model.Appl. Sci. 2019, 9, 131 9 of 10
4. Conclusions
The present work demonstrates that fluorescence spectroscopy can be used rapidly and
non-invasively for the evaluation of yogurt quality during deteriorating. The characteristic fluorescence
spectra of yogurt consist of a broadband spectrum in the wavelength region of 500 to 600 nm with
a peak at 525 nm attributed mainly by the riboflavin and Maillard products of raw milk and several
narrow emission peaks in the region of 600 to 750 nm caused by the porphyrin and chlorin compounds
existing naturally in yogurt. Based on a total of 100 fluorescence spectra from five brands of yogurt,
LDA discrimination model was carried out to classify the yogurt brands, which showed a preferable
classification result. To realize the prediction of yogurt quality, two ARC formulas K622/633 and K661/672
were utilized to extract the spectral information related to yogurt deterioration, particularly in the
600 to 750 nm region. The decreasing tendency of K622/633 and the increasing tendency of K661/672
indicate the photodegradation of porphyrin and chlorin compounds. Moreover, the fluorescence peak
ratio K622/633 shows a good linear relationship with the measured pH value and viscosity of yogurt,
which further verifies the physico-chemical change related to the quality of yogurt. Based on these two
fluorescence peaks ratios K622/633 and K661/672 , MLR models were established to verify the dependence
of fluorescence signal with pH value and viscosity. A more than 85% correlation coefficient is obtained
for each class of yogurt, which further demonstrates the potential and effectiveness for the evaluation
of yogurt quality by using fluorescence spectroscopy.
Author Contributions: H.S. and H.Z. performed the fluorescence spectral measurements. L.W. and A.W.
performed the measurements of pH value and viscosity. J.Z. and W.Z. were involved in the data processing and
data analysis. H.Z. and J.H. were involved in writing and revising the paper.
Funding: This work was financially supported by the China Postdoctoral Science Foundation (No. 2017M612399),
the Science and Technology Project of Henan Province (No. 182102110427), the Science and Technology Innovation
Project of Henan Agricultural University (No. KJCX2018A09), the National Natural Science Foundation of China
(No. 31671581), the Natural Science Foundation of Henan Province (No. 162300410143), and the Science and
Technology Project of Henan Province (No. 182102110250).
Conflicts of Interest: The authors declare no conflict of interest.
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