1. Development of PAT tools using guided microwave spectroscopy and chemometrics for meat and dairy processing applications
Ming Zhao,¹ Bhavya Panikuttira,¹ and Colm O’Donnell¹
¹ UCD School of Biosystems and Food Engineering & UCD Institute of Food & Health, University College Dublin, Dublin 4, Ireland
Introduction
The adoption of a process analytical technology (PAT) approach in food processing would facilitate real-time process monitoring. In this study the potential of guided microwave spectroscopy (GMS) in combination with chemometric methods was investigated for the development of PAT tools for meat and dairy processing applications. The current study on GMS was carried out in a static manner to mimic the relative static measurement moments during in-line monitoring. Specifically the potential of GMS technology to predict the fat and moisture composition of ground beef and milk samples was investigated using microwave spectra recorded from 100 to 2200 MHz at 4 MHz increments. Partial least squares regression (PLSR) models were developed using a modified ensemble Monte Carlo uninformative variable elimination (EMCUVE) procedure PLS to predict both fat and moisture content of ground beef; PLSR models based on all the spectral variables and variable importance on projection (VIP) selected spectral variables were also developed to predict the coagulum strength of cheese milk by storage modulus (G′ (Pa)).
Figure 1. The ε scan in-line process analyser (Thermo Fisher Scientific Inc., Takkebijsters Breda, The Netherlands).
Figure 2. The general work flow.
Methods & Materials
Study I. Forty five ground beef samples (2.2 kg per sample) were measured using the ε scan in-line process analyser (Thermo Fisher Scientific Inc., Takkebijsters Breda, The Netherlands) under the static condition at constant density. Each sample was compressed into a ‘U’ shaped stainless steel mould designed to produce a sample size fitting to the measurement field (7.5×10×22.6 cm³) in the chamber of the analyser to form a brick shape with density of 1497 kg/m³. Guided microwaves were generated over MHz by the electro-magnetic field and trespassed the distance between the transmitter and the acceptor (Figure 3).
Study II. Cheese milk samples were prepared in three different coagulation conditions by adding rennet [0.20 g , 0.28 g and 0.36 g] into 1000 ml of low fat milk respectively. For each sample, GMS measurements were taken at 5 th , 15 th, 25 th, 35 th, 45 th and 55 th minutes after the addition of rennet with constant temperature control (~ 32 °C). Guided microwave spectral data over MHz were collected (Figure 4).
All the electronic signals were collected by the acceptor and saved as data form in the embedded computer system. For calibration of the system, 12 normalised spectra were collected for each sample and saved as .csv files. These data were imported into Matlab 2014a (The Mathworks, Natick, MA, USA) for chemometric approaches.
For ground beef samples, Partial least squares regression (PLSR) models were developed on the full range of spectral data (X) and chemical reference data (Y) for the predictions of both fat and moisture content in ground beef. In-house produced samples (n=36) were used as the calibration data set while the commercial samples (n=9) formed a validation set. A modified ensemble Monte Carlo uninformative variable elimination (EMCUVE) was also employed to improve the performance of all PLSR models and to increase the consistency of prediction results using the most informative spectral variables (Esquerre et al., 2011). For cheese milk, PLSR models were developed using leave-one-out cross validation of the whole sample set for the prediction of G’ (Pa) of cheese milk coagulation conditions; the most informative spectral variables were decided using variable importance on projection (VIP).
Figure 3 a) a ‘U’ shaped stainless steel mould designed to adapt the size of the measurement field (7.5×10×22.6 cm3) of the ε scan analyser chamber and the moulded ground beef samples; b) Bench measurement using the ε scan analyser: 1-measurement chamber, 2- transmitter, 3-receiver, 4-temperature probe.
Figure 4 Bench measurement of the ɛ scan analyser on cheese milk: 1- measurement chamber, 2-transmitter, 3- receiver, 4-temperature probe.
Results
Study I.
For ground beef samples, PLSR models were developed using full spectral variables (n= 226) over the microwave frequency range of MHz (Table 1). PLSR models were also developed using the least number of spectral variables (i.e. n=16, 17 or 22 and n= 72, 73 or 74 for fat and moisture contents respectively (Table 2 ) which were decided by EMCUVE with a threshold value around For both fat and moisture content prediction, the retained informative spectral variables were almost fixed. Generally, less than 4 or 6 PLS loadings were involved to develop prediction models. The values of R²CV and R²P were stabilized at 0.93 and 0.90 respectively for calibration and prediction of fat content, while their corresponding values of RMSECV and RMSEP were around %w/w and %w/w. For the moisture content prediction, the values of R²CV and R²P were fixed at 0.89 and 0.82 with a RMSECV of 1.93%w/w and a RMSEP of 1.77 %w/w. The best performed PLSR model were highlighted in red colour in Table 2 and shown in Figure 5.
Figure 6. a) GMS spectra of low fat milk and milk samples with rennet additions measured at 5th min of coagulation; mean spectrum at each measurement time of b) Trial 1 (0.28 g rennet in 1000 ml low fat milk), c) Trial 2 (0.36 g rennet in 1000 ml low fat milk), d) Trial 3 (0.20 g rennet in 1000 ml low fat milk).
Figure 5. GMS mean spectrum of ground beef with spectral variable selection using EMCUVE for a) fat content prediction; d) water content prediction; regression coefficient plots of the seclected variables for b) fat content; e) moisture content; PLSR plots based on microwave spec tral variables using EMCUVE - predicted value vs. reference value: c) fat content ; f) moisture content.
Table 1. Summary of PLSR model performances (ɛ scan – MHz) for fat and moisture content in ground beef.
Figure 7. Rheometer data (G’ (Pa)) over milk coagulation time (min).
Table 3. Summary of PLSR model performances for the prediction of storage modulus (G’ (Pa)) in cheese milk.
ɛ scan ( MHz)
cal. Set n=36
val. Set n=9
(spectral variables, v=226)
PLS loadings
R²CV
RMSECV
R²P
RMSEP
Bias
fat
normalisation 6
0.91
2.63
1.56
-0.35
normalisation+ SNV 3
0.87
3.12
0.89
1.77
0.56
normalisation+ MSC 1
0.82
3.59
0.73
2.78
-0.99
moisture 4
0.86
2.21
0.85
2.34
0.02
normalisation+SNV
0.84
2.18
2.36
1.55
-0.58
ɛ scan
cal. set n=15
Data pre-treatment
Spectral variables retained
PLS loadings
R²C
RMSEC
R²CV
RMSECV
Bias
normalisation (norm.)
460 5
0.93
2.63
0.74
5.4
-0.293 8 4
0.94
2.49
0.81
4.54
-0.139
norm. + SNV
0.97
1.59
0.92
3.01
-0.101 6
0.96
1.87
2.98
-0.038
norm. + MSC
0.98
1.36
2.64
-0.169
2.04
0.91
2.92
0.003
Study II.
Spectral plots of cheese milk samples of 3 trials ( i.e g (T1) , 0.36 g (T2) and 0.20 g (T1) of rennet in 1000 ml of milk ) obtained at coagulation time points were shown in Figure 6. The measurements of rheological parameter (G’) were shown in Figure 7. These results were used as reference values (Y) of PLSR modelling. In the summary of PLSR results (Table 3), the best performed PLSR prediction model was developed using normalization and multiplicative scatter correction (MSC) pre-treated spectral data over MHz. The value of R²CV was at with a RMSECV of 2.28 G’(Pa) (Figure 8a). The model developed using four spectral variables at 592, 940, 1340, 1984 MHz selected using VIP algorithm also shows satisfactory performance (R²CV-0.91, RMSECV- 2.92) of G’ (Pa) prediction (Figure 8b). a) b)
Table 2. Summary of PLSR model performances (ɛ scan spectral variables retained using EMCUVE) for fat and moisture content in ground beef.
ɛ scan
cal. Set n=36
val. Set n=9
Data pre-treatment with EMCUVE
Spectral variable retained
PLS loadings
R²CV
RMSECV
R²P
RMSEP
Bias
fat
normalised (v=22) 22 6
0.93
2.18
0.90
1.79
-0.46
normalised (v=17) 17
2.13
0.89
1.83
-0.51
normalised (v=16) 16 4
1.72
-0.33
moisture
normalised(v=72) 72
1.93
0.82
1.77
-0.05
normalised (v=74) 74
normalised (v=73) 73
Figure 8. PLSR plots for G’ (Pa) prediction based on normalization and MSC pre-treated a) 460 spectral variables and b) 4 spectral variables .
References
Conclusion
Esquerre, C., Gowen, A. A., Downey, G., & O’Donnell, P. C. (2011). Selection of variables based on normalised partial least squares regression coefficients in an ensemble Monte Carlo procedure. Journal of Near Infrared Spectroscopy. 19,
This study demonstrated the potential of GMS for PAT applications in meat and dairy processing. The results also showed that spectral variable selection method can efficiently eliminate non-informative variables in order to improve consistency of model prediction and can also be implemented to a low-cost GMS system design.
Acknowledgement
The authors acknowledge funding for this work from the Irish Department of Agriculture, Food and the Marine under the Food Institutional Research Measure (FIRM) programme over