Monitoring the fermentation, post-ripeness and storage processes of set yogurt using voltammetric electronic tongue

Accepted Manuscript Monitoring the fermentation, post-ripeness and storage processes of set yogurt using voltammetric electronic tongue

Zhenbo Wei, Weilin Zhang, Yongwei Wang, Jun Wang PII:

S0260-8774(17)30028-6

DOI:

10.1016/j.jfoodeng.2017.01.022

Reference:

JFOE 8769

To appear in:

Journal of Food Engineering

Received Date:

27 July 2016

Revised Date:

20 December 2016

Accepted Date:

23 January 2017

Please cite this article as: Zhenbo Wei, Weilin Zhang, Yongwei Wang, Jun Wang, Monitoring the fermentation, post-ripeness and storage processes of set yogurt using voltammetric electronic tongue, Journal of Food Engineering (2017), doi: 10.1016/j.jfoodeng.2017.01.022

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ACCEPTED MANUSCRIPT Highlights

＞ The manufacture and storage processes of set yogurt were monitored by VE-tongue. ＞ Two types of chronoamperometric current methods, MRPV and MSPV, were employed. ＞ The visualization of PCM analyzed the correlation of the combination of EDs and PWs. ＞ The changing of samples in each process was exhibited clearly by chemometrics methods.

ACCEPTED MANUSCRIPT 1

Running title: Monitoring of set yogurt based on voltammetric electronic tongue

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Monitoring the fermentation, post-ripeness and storage processes of set yogurt using voltammetric electronic tongue

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Zhenbo Wei, Weilin Zhang, Yongwei Wang, and Jun Wang1

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Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058,China

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ABSTRACT:

A voltammetric electronic tongue (VE-tongue) was self-developed to monitor the

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fermentation, post-ripeness and storage processes of set yogurt. Multifrequency rectangular pulse

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voltammetry (MRPV) and multifrequency staircase pulse voltammetry (MSPV) were applied as potential

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waveforms, and the ‘area method’ was applied to extract feature data from the original responses. The

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ANOVA analysis was performed to evaluate the effects of electrodes and potential waveforms on the

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electrochemical responses, and the visualization of the Pearson Correlation Coefficient was performed to

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analyze the correlations among each combination of electrode and potential waveform. Discriminant function

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analysis worked better than principal component analysis based on the fusion data of MRPV- and MSPV-area

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data in classifying set yogurts in the stages of fermentation, post-ripeness and storage, respectively. VE-

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tongue with support vector machine (SVM) worked efficiently and stably in predicting the fermentation time,

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pH and viscosity of samples during the fermentation process; while the use of VE-tongue and partial least

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squares regression (PLSR) should be the first choice for the prediction work during the post-ripeness and

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storage processes. According to the classification and prediction results, the VE-tongue with different

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potential waveforms was proved a promising tool for monitoring the fermentation, post-ripeness and storage

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processes of set yogurt.

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Keywords: Voltammetric electronic tongue; Yogurt; Pattern recognition; Fermentation

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Tel.: +86-571-88982178; fax: +86-571-88982192. Email: [email protected] (Jun WANG)

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1. Introduction

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Set yogurt is produced by fermenting milk using thermophilic homofermentative lactic acid bacteria

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(usually Streptococcus thermophilus and Lactobacillus bulgaritus) that live symbiotically (Xu et al.,

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2008). Gel formation and acidification control, which have high correlation with the consistency of flavor

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and smooth texture, are key steps in fermentation, post-ripeness and storage of set yogurt (Riener et al.,

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2009). Structure formation is induced by acid gelation of proteins during the fermentation process: the

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acidification of the culture leads to the coagulation of milk caseins since three-dimensional casein gel

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networks are formed during the conversion of lactose to galactose (Cimander et al., 2002). Afterwards, the

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fermented milk product is cooled to 0-4 °C rapidly to prevent post-acidification through inhibiting

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activities of some microorganisms, which result in the incomplete conversion of lactose to galactose and

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the formation of flavor compounds (diacetyl, acetaldehyde, dimethyl sulfide, etc.) (Li et al., 1995). During

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the storage process, the metabolism of microorganisms can decompose protein and break the texture of set

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yogurt. Meanwhile, post-acidification increases the production of whey in set yogurt as storage time goes

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on, and shelf-life of yogurt products is directly affected by the post-acidification rate. Therefore,

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rheological (viscosity) and acidic (pH) properties are considered determining factors to textural and

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organoleptic qualities of set yogurt in past studies. However, the characteristics of set yogurt during each

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process can not be exhibited by either viscosity or pH (which is just a single parameter) completely,

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especially for some feature flavors. Therefore, a new approach, which can make quantitative and

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qualitative predictions about process variables simultaneously, should be presented for quality control in

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fermentation, post-ripeness and storage of set yogurt.

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Electronic tongue is a ‘soft’ measurement technique with which quality (e.g., a state of a process) can

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be measured, compared with the traditional measurement techniques with which a single parameter (e.g.

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temperature or conductivity) is measured (Ivarsson et al., 2001., Dias et al., 2015., Kraujalyte et al., 2015).

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Voltammetric electronic tongue (VE-tongue), first developed by Winquist (Winquist et al., 1997), The VE-

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tongue is usually composed of working electrodes, a reference electrode, and a counter electrode in a

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standard three-electrode configuration. With the potential waveforms applied to samples through working

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electrodes, the redox transient signals collected from working electrodes are applied for pattern recognition

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analysis (Tian et al., 2007., Olsson et al., 2008., Wei et al., 2011., Baldeóna et al., 2015). Due to its 2

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ruggedness, simplicity, and long service life, this device has already proven to be valuable in some

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monitoring applications, such as monitoring quality changes of water (Eriksson et al., 2011., Campos et

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al., 2013), milk (Winquist et al., 1998., Wei et al., 2011., Wei et al., 2013) and red wine (Cetó et al., 2014),

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grape ripeness (Campos et al., 2012) and fermentation processing of beer (Kutyła-Olesiuk et al., 2012).

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Unlike the samples detected before, set yogurt is a sticky fluid whose physical characteristics have deeper

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correlations with the chemical characteristics and the aggregated casein alone can affect the oxidation-

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reduction reaction happening in the surface of the VE-tongue working electrode. There is no application of

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VE-tongue for detecting the quality of set yogurt or for monitoring the fermentation, post-ripeness and

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storage processes.

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In this study, the VE-tongue developed in Zhejiang University was employed to monitor the

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manufacture and storage processes of set yogurt. Principal component analysis (PCA) and discriminant

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function analysis (DFA) were applied to evaluate the ability of VE-tongue to classify set yogurt samples in

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the different stages of fermentation, post-ripeness and storage. Partial least squares regression (PLSR) and

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support vector machine (SVM) were applied to evaluate the ability of VE-tongue to predict the rheological

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(viscosity), acidic (pH), and time characteristics in different periods (fermentation, post-ripeness and

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storage stages) of set yogurt. The main purposes of this study were: (1) to investigate whether the

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fermentation, post-ripeness and storage processes of set yogurt could be presented accurately by the VE-

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tongue on the basis of multi-frequency rectangular pulse voltammetry (MRPV) and multi-frequency

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staircase pulse voltammetry (MSPV) in PCA and DFA score plots; (2) to investigate whether the changing

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trends of viscosity and pH of set yogurt in fermentation, post-ripeness and storage processes could be

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evaluated accurately by VE-tongue using PLSR and SVM.

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2. Materials and methods

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2.1. Voltammetric electronic tongue (VE-tongue)

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The voltammetric electronic tongue (Fig. 1), which was self-developed in Zhejiang University,

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contained four working electrodes of gold, silver, platinum and palladium (all with 99.9% purity, length of

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5 mm, and diameter of 2 mm), an Ag/AgCl (3 M saturated KCl, diameter 2 mm) reference electrode, and a

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platinum auxiliary electrode (length 5 mm, diameter 2 mm). MRPV and MSPV, which were generated by

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a pulse scanner (built at Institute of Automatic and Modern Agriculture Equipment, Zhejiang University), 3

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were applied between auxiliary and working electrode as scanning potential waveform. After scanned by

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each potential waveform, the working electrode was switched to the next one until all the four working

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electrodes had been run through. The shift controlled by the relay array in the pulse scanner which was

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also used for collecting the transient current responses obtained by working electrodes. However, the

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current signals could not be measured by the pulse scanner directly. NI-6009 card (National Instruments,

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USA), the key unit of the pulse scanner, could only receive the voltage signals, so, a Current - Voltage

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Conversion Unit was added into the circuit, then the voltage signals were received by NI-6009, and the PC

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was used for handling the measurements and data storage.

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2.2. The manufacture and storage processes of set yogurt

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All yogurt manufacturing uses the same basic procedure, and the herein developed models for set

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yogurt production were similar to industrial settings and were used for evaluation on the manufacture and

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storage processes of set yogurt products. Skim milk powder (14.6% w/w), stabilizer (HM or LM pectin,

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0.01% w/w), and saccharose (1% w/w) were the main ingredients to formulate yogurts. Those ingredients

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were reconstituted to their final concentration in de-ionized water. The mixtures were shaken and stored

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overnight in a refrigerator to obtain complete hydration, then put through pasteurization (95 °C for 10 min)

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and cooled to 40 °C. Fermentation was conducted with a starter culture (which contains Streptococcus

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thermophilus and Lactobacillus delbrueckii subsp. Bulgaricus) at 40 °C in a SNJ-5091 Bear incubator for

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six hours. When the fermentation procedures finished, the yogurts were cooled for post-ripeness using ice

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water for twelve hours and then stored at 4 °C for eight days.

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The monitoring of set yogurt was throughout the fermentation, post-ripeness and storage processes.

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The fermentation process lasted six hours, and the set yogurt samples were detected once an hour during

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the process; the post-ripeness process lasted twelve hours, and the samples were detected every other hour

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beginning with the second hour; the storage time lasted eight days, and the samples were detected every

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other day beginning with the second day. Thirty-five samples were detected each time.

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2.3. Experimental procedures

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2.3.1. The measurement of pH

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The pH of the samples was tested through the whole experiment. The changes of pH during 4

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fermentation were recorded at one-hour intervals during the fermentation, two-hour intervals during the

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post-ripeness and one-day intervals during the storage using a pH meter (FE20 – FiveEasy Plus™,

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METTLER TOLEDO, Switzerland. All pH measurements were performed in triplicate for each sample.

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2.3.2. The measurement of viscosity

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Rheological measurements of the set yogurt followed the pH tasting with carrying out in triplicate for

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each sample, at 40 °C (controlled by a circulating bath), using a controlled stress rate rheometer (Anton

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paar Company, type Rheolab QC, Austria), with a conical concentric cylinder. Each measurement was

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composed of three intervals: the first interval, the samples were stirred about 40 s with the shear rate of 5

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s−1 to homogenize yogurt in the outer cylinder (50 mm diameter, 118 mm depth); the second interval, the

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samples were left to rest for 50 s, allowing the stress induced to diminish; the third interval, the viscosity

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of yogurt samples were tested by the shear rate from 2 s−1 to 200 s−1 in 300 s, and sampling frequency was

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12 points min−1. The data and flow curves of shear rate-dependent viscosity and shear stress were recorded

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by the Anton paar software. As shown in Fig. 2, yogurt behaves as a non-Newtonian, shear thinning and

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pseudoplastic fluid. The viscosity decreased with the increasing of shear rates, and finally stabilized; the

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shear stress increased with the shear rates to a ‘certain value’, then it decreased gradual. The ‘certain

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value’ (18.4 Pa in the Fig. 2) could be taken as the inflection point of the response curve, and the

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corresponding viscosity (0.12 Pa∙s in the Fig. 2) was analyzed in the experiment.

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2.3.3 The measurement of VE-tongue

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After rheological measurements, the samples were tested by the VE-tongue immediately. The set

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yogurt samples were detected by four working electrodes on the basis of MRPV and MSPV respectively.

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After a testing circle (in which the yogurt samples were tested by the combination of one electrode and one

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potential waveform), the electrodes were cleaned with a sand cloth for 5 s and then rinsed with de-ionized

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water for 15 s. For each sample, three replicates were prepared for analyzing and the average values of the

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replicates were used in data evaluation. All the experiments were performed at 40 °C.

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2.4. PCA, DFA, PLSR, and SVM

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In the study, the responses of the VE-tongue were analyzed by the following four pattern recognition

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methods: principal component analyses (PCA) and discriminant function analysis (DFA) were applied for 5

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the classification analysis, partial least squares regression (PLSR) and support vector machine (SVM) were

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applied for the prediction analysis.

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PCA is a typical technique can convert the original correlated variables to the new uncorrelated and

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ordered ones, with linear combinations of the original ones (Tian et al., 2013). The results from the PCA

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calculation were presented in score plots, displaying groups and trends among the measurements, and

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loading plots showing correlation and information content of the sensors (Zhang et al., 2008).

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DFA is a supervised learning technique, which classifies the sample by developing a model and then

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identifies the unknown samples (Armitage et al., 2010). DFA requires prior knowledge about the samples

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during the training, and it was used to determine whether it is possible to separate two or more individual

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groups, given measurements for these individuals from several variables (Siripatrawana et al., 2006).

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PLSR is a method for constructing predictive models when many factors exist and are significantly

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redundant like the results from a VE-tongue (Sohna et al., 2008). The PLSR was used to model the

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relationships between the observable variables (Y-variables) to the variation of predictors (X-variables),

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through finding a linear regression model by projecting the predicted variables and the observable

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variables to a new space (Gil et al., 2008).

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SVM, which is essentially a kernel-based procedure, is one of these promising and attractive

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techniques for pattern classification and regression problems (Cortes et al., 1995). SVM can adopt kernel

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functions which make the original inputs linearly separable in mapped higher dimensional feature space

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(Liu et al., 2007). Moreover, SVM can simultaneously minimize estimation errors and model dimensions

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(Papadopouloua et al., 2013).

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In the paper, PCA and DFA was performed by using SAS v8, and PLSR and SVM were performed by

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using MATLAB 7.0.

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3. Results and discussions

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3.1. Feature data extraction

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As shown in Fig. 3, MRPV and MSPV were applied to set yogurt samples in the six hours of

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fermentation through gold and platinum electrodes, and the responses ranged from −1.0 V to 4.0 V

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(obtained by platinum electrode), respectively. Single electrode could only obtain partial characteristic

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information of the samples, and obviously, the signals obtained by the same working electrode on the basis 6

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of MRPV and MSPV presented different response patterns (in terms of size and shape) (Fig. 3(b) and (e)).

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Furthermore, the responses obtained by different combinations of electrode and potential waveform could

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enrich the samples’ characteristic information for further analysis.

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The sampling frequency of the VE-tongue was 1000 Hz during operation, and thousands of redundant

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data which highly correlated with each other were obtained by the VE-tongue in a complete test cycle. To

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improve the working efficiency of the VE-tongue, the “total areas” method was employed to reduce those

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redundant data and to extract the characteristic data from the original responses. As shown in Fig. 3(c) and

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(f), the total areas under the whole corresponding curves were taken as the feature data, and a total 1 area

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datum × 4 electrodes = 4 data were obtained from a sample by the VE-tongue. These areas data were

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applied as the input data of pattern recognition techniques for monitoring fermentation, post-ripeness and

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storage of set yogurt.

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The ANOVA analysis based on the feature area values, which was obtained from the yogurt samples

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of the first fermentation hour, was performed to evaluate the influences of the electrodes (ED: Ag, Au, Pt,

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and Pd) and the shapes of potential waveform (PW: MRPV and MSPV) on the response signals (Table 1).

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The ANOVA analyses of the feature data obtained from the samples of other process time were quite

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similar; therefore, we omit its results. It was obvious that the type of electrode showed a strong influence

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on the electrochemical responses (P < 0.001); the shape of potential waveforms performed little worse (P =

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0.030); but the interaction of the electrode and shape also had an obvious effect on the responses (P <

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0.001). In the paper, the classification results based on the feature data obtained by the electrodes with

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either MRPV or MSPV were compared with that based on the feature data obtained by the electrodes with

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the fusion data of MRPV and MSPV, the most effective feature data would be chose for the future

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analysis.

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Multicollinearity among independent variables hinders the regression analysis, making it difficult to

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estimate the distinct effect of certain independent variables on a dependent variable precisely (Qiu et al.,

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2015). Fig. 4 visualizes the correlations between all the combinations of four working electrodes and two

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types of potential waveform based on Pearson Correlation Matrix (PCM). The Pearson correlation

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coefficient of two variables X and Y is formally defined as the covariance of the two variables divided by

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the product of their standard deviations and it can be equivalently defined by

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rxy =

∑(𝑥𝑖 - 𝑥)∑(𝑦𝑖 - 𝑦) ∑(𝑥𝑖 - 𝑥)

7

2

∑(𝑦𝑖 - 𝑦)

2

(1)

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1 N

1 N

Where x = n∑i = 1xi denotes the mean of x. y = n∑i = 1yi denotes the mean of y. The coefficient rxy ranges from -1 to 1 and it is invariant to linear transformations of either variables.

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In case of the whole fermentation process, the multicollinearity among the combination of sensors

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with MRPV (Au/MRPV and Pt/MRPV) and of sensors with MSPV (Pd/MSPV and Au/MSPV) was very

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high except for the Ag/MSPV and Ag/MRPV (Fig. 4(a)); in case of the last two fermentation hours, the

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multicollinearity among the combinations of the sensors with MRPV was very high (such as Au, Pt and Pd

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with MSPV), and the correlations among the combinations of the sensors with MSPV were lower than that

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in case of the whole fermentation process (Fig. 4(b)); in case of the post-ripeness process, the correlations

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among the combinations of the sensors with MRPV and MSPV was very high except for Ag/MRPV and

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Ag/MSPV (Fig. 4(c)); in case of the storage processes, just the correlations among the combinations of the

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sensors with MRPV was high (Fig. 4(d)). It could be concluded that the correlations among the different

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combinations of sensor and potential waveform were relative lower in case of the storage process and

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higher in case of the post-ripeness process, and combination of sensor and potential would work more

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efficiently for the classification and prediction in the storage process.

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3.2. The monitoring of set yogurt during the fermentation process

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3.2.1. The monitoring of the whole fermentation process.

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In this study on yogurt fermentation the purpose was to monitor the acidification of the culture that

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leads to the coagulation of milk caseins through the conversion of lactose to galactose and the formation of

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lactate, which bring about a decrease in the pH of the culture. During the six hours of fermentation, the

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detection interval was one hour, and the set yogurt samples were detected by pH meter, rheometer and VE-

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tongue sequentially once every hour during the fermentation process.

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(1) The classification analysis for the set yogurt on the basis of PCA and DFA during the

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fermentation process. The set yogurt was analyzed on the basis of the VE-tongue with PCA during the

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fermentation process, and the area feature data obtained by the VE-tongue on the basis of MRPV and

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MSPV were taken as input values. The PCA score plots for the classification of set yogurt in different

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fermentation times by the VE-tongue with MRPV are shown in Fig. 5(a). All the samples were mixed with

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each other, the distribution of the samples in PCA score plots was chaos and the positions of the samples

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of the same fermentation time in the plot grouped badly. Therefore, the feature information obtained by the 8

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VE-tongue with MRPV was not enough to describe the differences of set yogurt in different fermentation

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times. The PCA score plots on the basis of the VE-tongue with MSPV were presented in Fig. 5(b).

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Although those samples cannot be separated from each other completely, the classification result was

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better than that in Fig. 5(a). According to the spacing between each sample group, the samples were

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divided into four groups in Fig. 5(b): group 1 contained samples of the first three hours of fermentation;

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group 2 contained samples of the fourth hour; group 3 contained samples of the fifth hour; group 4

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contained samples of the sixth hour. Furthermore, the time for fermentation of samples increased along the

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PC1 axis (X axis) from left to right. The pH and viscosity were tested during the fermentation process. As

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shown in Fig. 6(a), the pH and viscosity values changed slowly at the first three hours, there were

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significant changes in pH and viscosity from the fourth hour, and a faster rate of pH reduction and

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viscosity increase was observed at the last two hours of fermentation. The acidification of milk during

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yogurt production results in gel formation, and the fermentation process consisted of two steps: first, the

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gel formation is caused by the unfolded whey proteins that interact with each other when the pH is close to

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the iso-electric point (pH 5.2-5.3) (Lucey et al., 1997); second, the gel network is rearranged due to the

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aggregation of casein particles as they reach their iso-electric pH (pH 4.6) (Lucey et al., 1997., Lucey et

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al., 1998). Therefore, the feature data obtained by the VE-tongue on the basis of MSPV worked more

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efficiently, and the PCA presented a more precise classification results on the basis of MSPV-area data.

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However, those samples of different fermentation times cannot be separated completely, and the

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differences among the set yogurt samples of the last three hours of fermentation were not distinct. The

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different responses, which were obtained by one sensor based on MRPV and MSPV, contained partly

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feature information, respectively. To improve the classification results, the MRPV- and MSPV-area data

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were fused and the fusion data were taken as the input data of PCA and DFA for further classification

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analysis. As shown in Fig. 5(c), all the samples, which could be well separated by PCA on the basis of the

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fusion data, were divided into five groups: group 1 contained samples of the first hour of fermentation,

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group 2 contained samples of the second and third hours; group 3 contained samples of the fourth hour;

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group 4 contained samples of the fifth hour; group 5 contained samples in the sixth hour. Use of topology

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in sample positions on the PCA score plots was located regularly, and the fermentation times of those

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samples rotated clockwise from short to long as marked by arrow in the figure. Obviously, the fusion data

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worked better than the solo usage of either MRPV- or MSPV-area data in classifying the set yogurt 9

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samples of different fermentation times. The fusion data was also analyzed by DFA (Fig. 5(d)). All the

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samples could be completely separated by DFA, and the positions of the samples of the same fermentation

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time grouped better. The yogurt samples that were close to one another shared more similar characteristics

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in DFA score plots, according to which all the samples were divided into four groups: group 1 contained

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samples of the first hour of fermentation, group 2 contained samples of the second and third hours; group 3

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contained samples of the fourth and fifth hours; group 4 contained samples of the sixth hour. The

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distribution of sample positions on the DFA score plot was clearer, and the topology of the samples

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matched the fermentation process more accurately: the acidification of set yogurt accelerated the changes

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of pH and viscosity during the fermentation process, and the samples of the first three hours of

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fermentation were better separated with the samples of the last three hours in the DFA score plot.

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Furthermore, both PCA and DFA are unsupervised techniques. PCA treats each replicate samples as

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individual data, but DFA assumes that replicate samples are clustered (Winquist et al., 1999). Therefore,

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DFA always worked more efficiently in classifying samples than PCA.

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The fusion data of MRPV- or MSPV-area data worked well in classifying set yogurt samples during

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the fermentation process, and the topology of the samples in the PCA and DFA plots reflected the

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fermentation characteristics of set yogurt accurately. Therefore, the fusion data was employed as the

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independent data of pattern recognition methods throughout the study.

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(2) The prediction analysis for, pH, viscosity, fermentation hours values on the basis of PLSR

264

and SVM. In this study, PLSR with leave-one-out (LOO) and SVM with radial basis function (RBF)

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techniques were used to establish a correlation between the fusion data and the fermentation parameters of

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fermentation hours, pH and viscosity. 210 samples (35 samples of each fermentation hour) were detected,

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of which 120 samples (20 samples of each fermentation hour) were randomly selected as the training set

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and the rest 90 samples (15 samples of each cultivar) were used as the testing set. The VE-tongue based on

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the fusion data with DFA showed the best results in classifying those samples. The accumulative

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reliabilities of the first three DFs were 99.19%, and the first three DFs contained the majority information

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and features of the yogurt samples. Therefore, the first three DFs were used as regression factors analyzed

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by PLSR and SVM. Prediction parameters, correlation coefficient (R2) between the predicted and

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experimental values and relative standard deviation (RSD) of the prediction values (larger R2 and lower

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RSD indicating higher efficiency of the prediction model), were employed to estimate the efficiency of 10

ACCEPTED MANUSCRIPT 275

PLSR and SVM.

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The calibration and prediction results on the basis of PLSR are shown in Table 2. The correlation

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coefficients between the actual and predicted values were very good on the basis of the training sets (all

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the R2 were over 0.95), and all the correlation coefficients on the basis of the testing sets were also over

279

0.90. However, the RSD of the prediction values of viscosity and time were very high (all the RSD >

280

20%), and the RSD of the prediction values of viscosity on the basis of the testing set was even close to

281

50%. Therefore, on the basis of either training sets or testing sets, the PLSR performed well in predicting

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pH (higher R2 (0.9679) and lower RSD (1.3%)), bad in predicting time (R2 = 0.9426 and RSD = 38.57%)

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and worst in predicting viscosity (R2 = 0.9172 and RSD = 44.47%) on the basis of the testing sets. During

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the fermentation process, the decrease in pH was followed by simultaneous increase in titratable acidity

285

(expressed as lactic acid content), and those acidic compounds are electrolytes and therefore can be easily

286

detected by the VE-tongue with electrochemical technique; although the changes of set yogurt viscosity

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are induced by the chemical changes of set yogurt (i.e. the changes of acid content), the changes of

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viscosity have high correlation with the formation process of casein gel and mainly reflected the physical

289

characteristics of set yogurt. Therefore, the fusion data obtained by the VE-tongue have a higher

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correlation with pH than with viscosity. Both chemical and physical characteristics of set yogurt, which

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changed all through the fermentation process, had correlations with the time for fermentation, and the

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prediction results of the fermentation time were lower than those of pH, and higher than those of viscosity

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(Table (2)).

294 295

The first step is to optimize parameters gam (γ) and sig2 (σ2) for obtaining the optimal SVM models. The SVM model can be expressed as 𝑦(𝑥) = ∑

296 297 298 299 300 301 302

𝑛

𝑎 𝐾 𝑖=1 𝑖

(𝑥,𝑥𝑖) + 𝑏

(2)

where K(x, xi) is the kernel function, xi is the input vector, ai is Lagrange multipliers called support value, b is the bias term. During the application of LS-SVM, the mostly used kernel is the radial basis function (RBF) kernel, also applied in this paper. The function can be expressed as K (x, xi) = exp (

‒ ∣∣𝑥 ‒ 𝑥𝑖∣∣2 𝜎2

)

(3)

where xi is the input data. Sigma is the RBF kernel parameter, and σ2 was the bandwidth and implicitly 11

ACCEPTED MANUSCRIPT 303

defined the nonlinear mapping from input space to some high dimensional feature space. The two

304

important parameters in LS-SVM with RBF kernel were the regularization parameter gam (γ) and the

305

width parameter sig2 (σ2). The regularization parameter γ determined the tradeoff between minimizing the

306

training error and minimizing model complexity. The parameter σ2 was the bandwidth and implicitly

307

defined the nonlinear mapping from input space to some high dimensional feature space. So, optimizing

308

parameters γ and σ2 is the first step in obtaining the optimal LS-SVM models. The contour plot of the

309

optimization the parameters γ and σ2 is shown in Fig. 7(a), b and c. The grids ‘·’ in the first step is 10 × 10,

310

and the searching step in the first step is large. The optimal search area is determined by error contour line.

311

The grids ‘·’ in the second step is 10 × 10, and the searching step in the second step is smaller. The optimal

312

search area is determined on the basis of the first step. After automatic test, γ = 26.8730 and σ2 =

313

237.0546, γ = 40.5845 and σ2 = 235.6148, γ = 6.8768 and σ2 = 350.2504 were picked out as optimal

314

parameters in the prediction of pH, viscosity, and fermentation time. As shown in Table 2, the SVM

315

performed better than PLSR on the basis of either training sets or testing sets in predicting the three

316

fermentation parameters, and the correlation coefficients of predicting pH, viscosity, and fermentation time

317

on the basis of testing set were R2 = 0.9738, R2 = 0.9351, and R2 = 0.9527, respectively. Meantime, all the

318

RSD values of the prediction values became lower. The prediction results of the three fermentation

319

parameters on the basis of SVM presented similar features to those on the basis of PLSR: the prediction

320

results of pH were the best, followed by the prediction results of fermentation time, and the prediction

321

results of viscosity stood as the worst. SVM model gave a clear indication of the VE-tongue ability, and

322

SVM was a powerful tool for the prediction of the three fermentation parameters on the basis of VE-

323

tongue signals.

324

3.2.2. The monitoring of the last two fermentation hours.

325

As discussed in section 3.2.1, the formation of casein gel and rearrangement of gel network happened

326

in the last two hours of fermentation, and a faster rate of pH reduction and viscosity increase was observed

327

in the process. Therefore, the changes of chemical and physical characteristics of set yogurt during the last

328

two hours were studied by the VE-tongue on the basis of the fusion data. During the last two hours, the

329

detection interval was 20 minutes, and the set yogurt samples were detected by pH meter, rheometer and

330

VE-tongue sequentially at the 20th, 40th, 60th, 80th, 100th and 120th minutes.

12

ACCEPTED MANUSCRIPT 331

(1) The classification analysis for set yogurt on the basis of PCA and DFA during the last two

332

hours of fermentation. Firstly, PCA was conducted on VE-tongue fusion data (Fig. 5(e)). Although the

333

separation amongst samples was not complete, in the two-dimensional PCA score plot, all the samples

334

could be divided into two groups: group 1 contained samples of the 20th, 40th and 60th minutes and group 2

335

contained samples of the 80th, 100th and 120th minutes, with the fermentation time of samples increasing

336

along the PC1 axis (X axis) from left to right. The DFA score plot on the basis of the VE-tongue fusion

337

data are shown in Fig. 5(f). All the samples could be well separated except for the samples of the 80th and

338

120th minutes, and the positions of the samples of each fermentation time in the plot grouped better.

339

According to the distance among the samples of each fermentation time, they could be divided into three

340

groups in the DFA score plot: group 1 contained samples of the 20th minute, group 2 samples of the 40th

341

and 60th minutes, and group 3 samples of the 80th, 100th and 120th minutes. The fermentation time of

342

samples increased along the PC2 axis (X axis) from bottom to top. The changes of pH and viscosity of set

343

yogurt during the last two hours of fermentation are presented in Fig. 6(b). The changing rate of pH from

344

the 20th to the 60th minute was faster than that from the 80th to 120th minute, and the changing rate of

345

viscosity was slower. The acidification process became lower during the last fermentation process which

346

resulted in the lower pH values, but the total acid content became greater which accelerated the formation

347

of casein gel. Therefore, the classification results of both PCA and DFA can reflect the changing trends of

348

pH and viscosity during the last two hours, and the classification results, distribution and topology of the

349

samples on the basis of DFA were better.

350

(2) The prediction analysis for the pH and viscosity values on the basis of PLSR and SVM

351

during the last two hours of fermentation. PLSR - LOO and SVM - RBF were employed to estimate the

352

changes of pH and viscosity during the last two hours. 35 samples were detected every 20 minutes, of

353

which 20 samples were randomly selected as the training set and the rest 15 samples were used as the

354

testing set. The accumulative reliabilities of the first three DFs were 96.78%, and the first three DFs

355

contained the majority information and features of the yogurt samples. Therefore, the first three DFs were

356

used as regression factors analyzed by PLSR and SVM.

357

The PLSR predictions of pH and viscosity values on the basis of training set and test set are shown in

358

Table 2. The PLSR performed well in predicting the pH values, and both correlation coefficients on the

359

basis of training set and testing set were over 0.94, and all the RSD were lower than 1%. Although PLSR 13

ACCEPTED MANUSCRIPT 360

on the basis of training set (R2 = 0.9401, RSD = 17.65%) performed better than that on the basis of test set

361

(R2 = 0.8272, RSD = 13.09) in predicting the viscosity values. During the last two hours, pH and viscosity

362

were predicted by SVM on the basis of training set and test set are shown in Table 2. The contour plot of

363

the optimization of the parameters (γ and σ2) to predict pH is shown in Fig. 7(d), and γ = 20.1913 and σ2 =

364

683.0535 were picked out as the optimal parameters. SVM worked efficiently in predicting pH on the

365

basis of training set, and the correlation coefficients were over 0.95, and the RSD were lower than 1%. The

366

SVM result on the basis of test set was not as good as that on the basis of training set, and the correlation

367

coefficients R2 = 0.8530. However, according to the previous study (R2 over 0.85 being taken as a

368

meaningful result), although SVM didn’t work as well as PLSR in predicting pH, the combination of VE-

369

tongue and SVM still was a good tool for the prediction. The contour plot of the optimization of the SVM

370

parameters (γ = 7.7039 and σ2 = 307.9543) to predict viscosity is shown in Fig. 7(e). SVM worked more

371

efficiently on the basis of either training set or testing set than PLSR in predicting viscosity, the correlation

372

coefficient on the basis of testing set was R2 = 0.8630, and the RSD was decreased to 11.70%.

373

As discussed above, both correlation coefficients of SVM results for the prediction of pH and

374

viscosity were over 0.85, and the performance of SVM model was more stable in the prediction work. On

375

the basis of testing set, The PLSR model presented a better result in predicting pH, but the PLSR

376

predictions of viscosity had no sense (the correlation coefficient below 0.85). Therefore, the VE-tongue

377

together with SVM was also a powerful tool that could clearly notice a positive trend in predicting pH and

378

viscosity during the last two hours of fermentation.

379

3.3. The monitoring of set yogurt during the post-ripeness process

380

After the fermentation process, set yogurt needed cooling down to 0 – 4 oC immediately to prevent

381

over-acidification and then were stood for twelve hours at 4 oC before storage. The low temperature can

382

inhibit the activity of the acid bacterium, and lower the conversion of lactose to galactose and the

383

formation of lactate. Under the activity of lactic acid bacteria, the incomplete conversion of proteins can

384

produce some flavor compound which could make set yogurt taste good. During the post-ripeness process,

385

the detection interval was two hours, and the set yogurt samples were detected by pH meter, rheometer and

386

VE-tongue sequentially every other hour beginning with the second hour.

387

3.3.1. The classification analysis for set yogurt on the basis of PCA and DFA during the post-ripeness 14

ACCEPTED MANUSCRIPT 388

process.

389

PCA was mapped using the relative fusion response of VE-tongue sensors to set yogurt during the

390

post-ripeness process. As shown in Fig. 8(a), all the samples could not be separated completely. Samples

391

of the first eight hours shared more similar characteristics, and samples of the second, fourth, and eighth

392

hours overlapped each other. All the samples could be divided into three groups: group 1 contained

393

samples of the second, fourth, sixth, and eighth hours, group 2 contained samples of the tenth hour, group

394

3 contained samples of the twelfth hour. Although there was no distinct regular about the topology of those

395

samples, the samples of the first eight hours could well be separated from those of the last four hours. DFA

396

worked more efficiently than PCA in classifying samples in the post-ripeness process, and the positions of

397

samples of each post-ripeness time grouped better (Fig. 8(b)). Although all the samples could be separated

398

with each other, it might be reasonable that the samples were divided into three groups which included the

399

same samples as the groups on the basis of PCA. The changes of pH and viscosity were presented in Fig.

400

6(c). The pH and viscosity values almost had no changes in the first eight hours, but the pH became

401

slightly lower and the viscosity became slightly higher during the last four hours. These samples were

402

stood for twelve hours at 4 oC, which could inhibit the activity of lactic acid bacteria and bifidobacteria

403

and prevent the yogurt over acidification. However, the persistent metabolic activity of acid bacteria can

404

slightly decrease the pH values, inducing more junctions and more bonds per junction between micelle

405

particles which result in the increase of viscosity. Moreover, some flavor compounds (such as acetone),

406

which can offer good mouth feel, was produced by lactic acid bacteria during the post-ripeness process.

407

Except for the changes of pH and viscosity, the changes of flavor also could affect the responses of VE-

408

tongue. The changes of the complex chemical and physical characteristics of set yogurt might not be

409

reflected in the PCA and DFA plots regularly, but those changes of flavor, pH and viscosity were

410

continuous during the post-ripeness process, and all the samples could be completely separated with each

411

other in the DFA plot. Therefore, the combination of VE-tongue and DFA was proved a powerful tool for

412

the classification of set yogurt in the post-ripeness process.

413

3.3.2. The prediction analysis for the time of post-ripeness on the basis of PLSR and SVM during the post-

414

ripeness process.

415

The pH and viscosity values had almost no changes during the post-ripeness process, and therefore

416

only the time for post-ripeness of set yogurt was analyzed by PLSR and SVM on the basis of the first three 15

ACCEPTED MANUSCRIPT 417

DFs (The accumulative reliabilities of the first three DFs were 99.89%, and the first three DFs contained

418

the majority information and features of the yogurt samples). The results obtained for the PLSR model can

419

be seen in Table 2, where it might be seen that a satisfactory trend was obtained. Moreover, PLSR

420

performed well on the basis of either training set or testing set for the prediction (both correlation

421

coefficients over 0.85). The regression results of the SVM methods are presented in Table 2. The

422

parameters of the SVM, γ = 14.4561 and σ2 = 270.9939, were optimized automatically (Fig. 9(a)). SVM

423

worked less efficiently than PLSR in the prediction: the correlation coefficient of SVM predictions on the

424

basis of testing time was just 0.8118. By contrasting the prediction results, both the model did not work

425

well in predicting the standing time during the post-ripeness process (lower R2 and Higher RSD). As we

426

discussed in section 3.1, higher correlation among the feature values obtained by each combination of the

427

sensor and potential waveform in the post-ripeness process, which might lead to the lower prediction

428

results.

429

3.4. The monitoring of set yogurt during the storage process

430

The set yogurt samples should be stored at 4 oC after the manufacturing process (including

431

fermentation and post-ripeness). With acidification in progress, the pH values became lower as the storage

432

time went on, and the changing rate accelerated during the last four days of storage (Fig. 6(d)). The pH

433

values went down to 4.24 at the eighth day, water coming out from the casein gel network structures, and

434

the whey became more with the viscosity getting lower. Since the mouth feel and body in the texture in

435

yogurt gels result from a protein network formed by casein strings or clusters which entrap serum and fat

436

globules, the changes of pH and viscosity already affected the gustatory qualities of set yogurt, and the

437

storage process was ended at the eighth day. During the eight days, the detection interval was two days,

438

and the set yogurt samples were detected by pH meter, rheometer and VE-tongue sequentially at the

439

second, fourth, sixth and eighth days.

440

3.4.1. The classification analysis of the samples on the basis of PCA and DFA during the storage process.

441

Fig. 8(c) displays the distribution of the set yogurt samples in the storage process along the first two

442

new PCA coordinates. It can be seen that all the samples could be separated well in the PCA score plot,

443

and the samples could be divided into four groups: group 1 contained the samples stored for two days,

444

group 2 contained the samples stored for four days, group 3 contained the samples stored for six days, and 16

ACCEPTED MANUSCRIPT 445

group 4 contained the samples stored for eight days. The samples of the first six days were very stable, and

446

the positions of the samples grouped well in the PCA score plot. However, there was an uncertain reason

447

affecting the responses of VE-tongue obtained from the samples stored for eight days, and the positions of

448

the samples were more scattered than those with different storage times. Meanwhile, the topology of the

449

positions of the samples showed same regular distributions: the storage time of samples increased along

450

the PC1 axis (X axis) from left to right. Fig. 8(d) shows the DFA results for the classification of set yogurt

451

in different storage times. All the samples were well separated in the DFA score plot, and the positions of

452

the samples grouped better than that in the PCA score plot (especially the position of the samples stored

453

for eight days). The storage time of samples also increased along the PC1 axis (X axis) from left to right,

454

and the differences between the samples of the first four days of storage and the samples of the last four

455

days became greater. The changing trends of pH and viscosity of set yogurt during the storage process are

456

shown in the Fig. 6(d). The changing trend of viscosity was divided into two parts during the storage: the

457

viscosity values became higher with the storage time going on during the first four days, and then the

458

viscosity values became lower to the end of the storage time. As discussed in section 3.3, the cold

459

temperature could not completely inhibit the activity of the acid bacteria, and over acidification made the

460

pH value decrease as the storage time passed. The viscosity values increased at the beginning of the

461

storage process because partial micelle particle fusion and inter-particle rearrangement were induced by

462

the lower pH values. However, the rearrangements together with particle fusion produce stresses that were

463

closely related to the strands becoming thinner with the lapse of time, which could decrease the rigidity of

464

the gel. Therefore, the viscosity values became lower to the end of the storage time.

465 466

3.4.2. The prediction analysis for the pH, viscosity and storage time on the basis of PLSR and SVM during the storage process.

467

The PLSR plots of the prediction of pH, viscosity and storage time were presented in Table 2. The

468

accumulative reliabilities of the first three DFs were 100%, and the first three DFs contained the majority

469

information and features of the yogurt samples. The PLSR performed best in the storage process during the

470

study. The correlation coefficients of all PLSR predictions on the basis of training set were over 0.96, and

471

the correlation coefficients of the predictions of pH and storage time were almost one, R2 = 0.9916 and R2

472

= 0.9977, respectively. Although the PLSR predictions on the basis of testing set were a little lower, all the

473

correlation coefficients were still over 0.94 and RSD were lower than 5%, and the PLSR results for the 17

ACCEPTED MANUSCRIPT 474

prediction of pH, viscosity and storage time were R2 = 0.9406, R2 = 0.9836 and R2 = 0.9878, respectively.

475

The changes of chemical and physical characteristics of set yogurt were very slow during the storage

476

process, and therefore the responses obtained by the VE-tongue could reflect the feature information more

477

accurately. The SVM was also employed for the prediction analysis during the storage process. After

478

automatic test, γ = 15.3870 and σ2 = 507.2886, γ = 16.7336 and σ2 = 311.7086, γ = 15.4627 and σ2 =

479

280.2017 were picked out as the optimal parameters to predict pH, viscosity, and storage time (Fig. 9(b)-

480

(d)). SVM did not perform as well as PLSR in the prediction work, with the correlation coefficients of

481

SVM predictions on the basis of either training set or testing set over 0.90, and all the RSD were lower

482

than 5%. Moreover, SVM on the basis of testing set worked more efficiently. In the storage process, some

483

interesting prediction results were presented: both PLSR and SVM worked more efficiently in predicting

484

viscosity than in predicting pH. The predictions could not be explained reasonably on the basis of the

485

present study, and further work about the usage of the VE-tongue to detect changes of chemical and

486

physical characteristics of set yogurt during the storage process will be carried out. As discussed above,

487

both PLSR and SVM worked efficiently in predicting the pH, viscosity, and storage time during the

488

storage process, and PLSR performed better than SVM. Therefore, the combination of VE-tongue and

489

PLSR should be the first choice for the prediction work during the storage process. Moreover, both the

490

PLSR and SVM perform stable in the storage process (high R2 (all the R2 > 0.9) and low RSD (all the RSD

491

< 5%)). As we discussed in the section 3.1, the correlation among the feature values obtained by each

492

combination of the sensor and potential waveform in the storage process was very low, and the great

493

differences among those feature values gave positive effects on the prediction results.

494

4. Conclusions

495

In the study, the changes of rheological and acidic characteristics of set yogurt were monitored by

496

using VE-tongue during fermentation, post-ripeness and storage processes. The responses, which were

497

obtained by the VE-tongue on the basis of MRPV and MSPV, were processed by PCA and DFA for

498

classification and processed by PLSR and SVM for prediction. The main results were as follows:

499

(1) The fusion data of MRPV- or MSPV-area data worked better than the sole usage of either the

500

MRPV- or MSPV-area data in classifying the set yogurt samples of different fermentation times. DFA

501

worked more efficiently than PCA in classifying those set yogurt samples of different fermentation, post18

ACCEPTED MANUSCRIPT 502

ripeness and storage times. Although not all samples of the three processes presented clear PCA

503

topologies, some regular distributions of the positions of set yogurt samples were presented in the PCA

504

plot: almost all the samples in fermentation, post-ripeness and storage processes increased along the PC1

505

axis (X axis) from left to right.

506

(2) SVM and PLSR worked more efficiently during the storage process, and all the correlation

507

coefficients on the basis of either training set or testing set were over 0.90, and all the RSD were lower

508

than 5%; the performance of SVM and PLSR was not good during the post-ripeness process, with the

509

correlation coefficients on the basis of testing set below 0.85. The VE-tongue together with SVM

510

performed better in predicting the fermentation time of samples, pH and viscosity during the fermentation

511

process, and the combination of VE-tongue and PLSR should be the first choice for the prediction work

512

during the post-ripeness and storage processes. Furthermore, the changing trends of pH and viscosity

513

presented clearly in PLSR and SVM fitting plots.

514

Those classification and prediction results showed that the VE-tongue could be applied to monitor the

515

process state of set yogurt culture and storage, and the pH and viscosity of each process could be evaluated

516

accurately by the VE-tongue. It was envisaged that some other physical and chemical characters (such as

517

stress, protein content, total sugar content, etc) of the set yogurt samples also changed during

518

manufacturing and storage processes, which could also affect the dynamic-response characters of the VE-

519

tongue. To improve the monitoring results, a further study would be done to clarify changing principles of

520

those physical and chemical characters and to clarify the relationship between those physical and chemical

521

characters of set yogurt and dynamic-response characters of the VE-tongue.

522

Acknowledgment

523

The authors acknowledge the financial support of the Chinese National Foundation of

524

Nature and Science through Project 31570005, the Fundamental Research Funds for the

525

Central Universities 2015QNA6004.

526

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Winquist, F., Wide, P., Lundstrom, I., 1997. An electronic tongue based on voltammetry, Anal. Chim. Acta. 357, 21-31.

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Xu, Z.M., Emmanouelidou, D.G., Raphaelides, S.N., Antoniou, K.D., 2008. Effects of heating temperature and fat content 21

ACCEPTED MANUSCRIPT 597 598 599

onthe structure development of set yogurt, J. Food. Eng. 85, 590-597. Zhang, H.M., Wang, J., Ye, S., 2008. Predictions of acidity, soluble solids and firmness of pear using electronic nose technique, J. Food Eng. 86, 370-378.

600

22

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Table 1. Table 1 Result of the VAOAN for the three variances

602

Table 2 Table 2. The prediction results of PLSR and SVM in different production processes

603

Fig. 1. The set-up of the VE-tongue: (a) the working VE-tongue; (b) software system of the VE-tongue; (c) the socket

604

connectors between the sensor array and NI acquisition card; (d) the sensor array embedded in a

605

polytetrafluoroethylene and stainless steel tube; (e) the configuration of the four working electrodes, one

606

counter electrode, and one reference electrode.

607

Fig. 2. Flow curve obtained by the rheometer from set yogurt of the 5th fermentation hour.

608

Fig. 3. the potential waveforms applied in the paper and their response obtained by platinum electrode from set

609

yogurt at the 8th storage day, (a) and (b) is the MRPV and its response, (d) and (e) is the MSPV and its

610

response; the response curves obtained by the platinum electrode with MRPV (c) and MSPV (f). The ‘total area

611

method’ was also presented on the (c) and (f).

612

Fig. 4. Visualization of the Pearson correlation matrix composed of all sensors based on the MSPV and MRPV: (a)

613

the first hour of the whole fermentation process, (b) the 20 minutes of the last two fermentation hours process,

614

(c) the first hour of the post-ripeness processes, and (d) the first day of the storage processes. Values close to -1

615

and 1 correspond to low and high correlation of sensors’ signals, respectively.

616

Fig. 5. The set yogurt samples of different fermentation hours were analyzed by using PCA on the basis of MRPV (a)

617

and MSPV (b), using PCA (c) and DFA (d) on the basis of the fusion data. Meanwhile, the set yogurt samples of

618

different fermentation minutes (during the last two fermentation hours) were analyzed by using PCA (e) and

619

DFA (f) on the basis of the fusion data.

620 621

Fig. 6. The changes of pH and viscosity during the manufacturing and storage process for the fermentation process (a), the last two fermentation hours process (b), the post-ripeness process (c), and the storage process (d).

622

Fig. 7. The SVM contour plot for the prediction results of pH (a), viscosity (b) and fermentation time(c) during the

623

whole fermentation process, and the SVM contour plot for the prediction results of pH (d) and viscosity (e)

624

during last two fermentation hours process.

625 626 627 628

Fig. 8. The set yogurt samples of different post-ripeness time were analyzed by using PCA (a) and DFA (b) and different storage time were analyzed by using PCA (c) and DFA (d) on the basis of the fusion data, respectively. Fig. 9. The SVM contour plot for prediction results of post-ripeness time (a) during the post-ripeness process, and for the prediction results of pH (b), viscosity (c) and fermentation time (d) during the storage process.

Source

Type III

df

Mean

Sum of

Square 23

F

Sig*.

Partial Eta

ACCEPTED MANUSCRIPT Squares

Squared

Corrected Model

2.87E8

7.00

4.10E7

5.27E4

0.000

1.00

Intercept

7.70E8

1.00

7.70E8

9.90E5

0.000

1.00

Electrode (ED)

2.86E8

3.00

9.55E7

1.23E5

0.000

1.00

Potential waveform (PW)

3.95E3

1.00

3.95E3

5.08

0.030

0.14

ED * PW

3.00E5

3.00

1.00E5

128.76

0.000

0.92

Error

2.49E4

32.00

777.81

Total

1.06E9

40.00

Corrected Total

2.87E8

39.00

629

Table 1. Result of the VAOAN for the three variances

630 631

Response signal of individual electrode with single potential waveform obtained from the yogurt samples of the first

632

fermentation hour. Potential waveforms: MRPV and MSPV; electrodes: gold, silver, platinum and palladium.

633

*Significant

634

at the probability ≤ 0.05 level.

Highly s ignificant at the probability ≤ 0.001 level.

24

635

Table 2. The prediction results of PLSR and SVM in different production processes

PLSR

Different production

SVM

Training sets

processes

Testing sets

Training sets

Testing sets

R2

RSD (%)

R2

RSD (%)

R2

RSD (%)

R2

RSD (%)

pH

0.9876

1.03

0.9679

1.30

0.9895

0.86

09738

1.13

viscosity

0.9694

33.17

0.9172

44.47

0.9445

6.57

0.9351

9.73

fermentation time

0.9713

9.43

0.9426

14.19

0.9794

21.50

0.9527

38.57

pH

0.9708

0.87

0.9410

0.85

0.9388

0.98

0.8530

0.99

viscosity

0.9401

17.65

0.8271

13.09

0.9342

16.07

0.8630

11.70

post-ripeness time

0.9213

12.73

0.8720

17.83

0.8627

14.91

0.8118

21.05

pH

0.9642

0.50

0.9406

0.67

0.9042

0.34

0.9214

0.37

viscosity

0.9916

1.08

0.9836

1.48

0.9805

1.12

0.9903

1.22

storage time

0.9977

2.56

0.9878

5.72

0.9799

2.47

0.9837

3.12

The whole fermentation process

The last two fermentation hours process The post-ripeness process

The storage process

636 25

ACCEPTED MANUSCRIPT

637 638 639

Fig.1 The schematic of the VE-tongue

26

640 641

Fig. 2. Flow curve obtained by the rheometer from set yogurt of the 5th fermentation hour.

27

643 644

Fig. 3. the potential waveforms applied in the paper and their response obtained by platinum electrode from set yogurt at the 8th storage day, (a) and (b) is the MRPV and its

645

response, (d) and (e) is the MSPV and its response; the response curves obtained by the platinum electrode with MRPV (c) and MSPV (f). The ‘total area method’ was also presented on

646

the (c) and (f).

28

ACCEPTED MANUSCRIPT

647 648

Fig. 4. Visualization of the Pearson correlation matrix composed of all sensors based on the MSPV and MRPV:

649

(a) the first hour of the whole fermentation process, (b) the 20 minutes of the last two fermentation hours process, (c)

650

the first hour of the post-ripeness processes, and (d) the first day of the storage processes. Values close to -1 and 1

651

correspond to low and high correlation of sensors’ signals, respectively.

29

ACCEPTED MANUSCRIPT

653 654

Fig. 5. The set yogurt samples of different fermentation hours were analyzed by using PCA on the basis of

655

MRPV (a) and MSPV (b), using PCA (c) and DFA (d) on the basis of the fusion data. Meanwhile, the set yogurt

656

samples of different fermentation minutes (during the last two fermentation hours) were analyzed by using PCA (e)

657

and DFA (f) on the basis of the fusion data.

30

ACCEPTED MANUSCRIPT

659 660

Fig. 6. The changes of pH and viscosity during the manufacturing and storage process for

661

the fermentation process (a), the last two fermentation hours process (b), the post-ripeness process (c), and the

662

storage process (d).

31

663 664 665

Fig. 7. The SVM contour plot for the prediction results of pH (a), viscosity (b) and fermentation time(c) during the whole fermentation process, and the SVM contour plot for the prediction results of pH (d) and viscosity (e) during last two fermentation hours process.

32

ACCEPTED MANUSCRIPT

666 667 668

Fig. 8. The set yogurt samples of different post-ripeness time were analyzed by using PCA (a) and DFA (b) and different storage time were analyzed by using PCA (c) and DFA (d) on the basis of the fusion data, respectively.

33

669 670

Fig. 9. The SVM contour plot for prediction results of post-ripeness time (a) during the post-ripeness process,

671

and for the prediction results of pH (b), viscosity (c) and fermentation time (d) during the storage process.

34