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Application of Fourier transform-mid infrared reflectance spectroscopy for monitoring Korean traditional rice wine ‘Makgeolli’ fermentation
Accepted Manuscript Title: Application of Fourier Transform-Mid Infrared Reflectance Spectroscopy for Monitoring Korean Traditional Rice Wine ‘Makgeolli’ Fermentation Author: Dae-Yong Kim Byoung-Kwan Cho Seung Hyun Lee Kyungdo Kwon Eun Soo Park Wang-Hee Lee PII: DOI: Reference:
S0925-4005(16)30225-8 http://dx.doi.org/doi:10.1016/j.snb.2016.02.076 SNB 19736
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
28-10-2015 15-2-2016 17-2-2016
Please cite this article as: Dae-Yong Kim, Byoung-Kwan Cho, Seung Hyun Lee, Kyungdo Kwon, Eun Soo Park, Wang-Hee Lee, Application of Fourier Transform-Mid Infrared Reflectance Spectroscopy for Monitoring Korean Traditional Rice Wine ‘Makgeolli’ Fermentation, Sensors and Actuators B: Chemical http://dx.doi.org/10.1016/j.snb.2016.02.076 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Application of Fourier Transform-Mid Infrared Reflectance Spectroscopy for Monitoring Korean Traditional Rice Wine ‘Makgeolli’ Fermentation
Dae-Yong Kim1, Byoung-Kwan Cho1*, Seung Hyun Lee1, Kyungdo Kwon1, Eun Soo Park1, and Wang-Hee Lee1
Article note: 1
Department of Biosystems Machinery Engineering, Chungnam National University, 99
Daehak-ro, Yuseong-gu, Daejeon, 306-764, Republic of Korea
Corresponding Author: Byoung-Kwan Cho, Professor, Department of Biosystems Machinery Engineering, Chungnam National University, 99 Daehak-Ro, Yuseong-Gu, Daejeon, 305-764, Republic of Korea, Phone) +82-42-821-6715, Fax) +82-42-823-6246, Email) [email protected]
ABSTRACT This study was performed to examine the application of Fourier Transform-Mid Infrared (FTMIR) spectroscopy to rapidly and non-destructively measure the quality of Makgeolli during fermentation process. A model using the entire ranges of the spectra and a Partial Least Squares Regression (PLSR) model using specific wavelength ranges closely associated with Makgeolli quality were compared to investigate the appropriate non-destructive monitoring method for Makgeolli production. In the global PLSR model using the entire range of the spectra and the local PLSR model using two separate regions (functional group spectral region and fingerprint spectral region), the optimal prediction model for alcohol concentration was from 2nd derivative of Savitzky-Golay pretreatment. The coefficient of determination of the developed model was 0.984, and Standard Error of Prediction (SEP) was 0.595%. Reducing sugar was detected best with 1st derivative of Norris-Gap pretreatment, showing the determination coefficient of 0.983 and SEP of 0.579%. Titratable acidity was accurately predicted with 1st derivative of SavitzkyGolay pretreatment, showing the determination coefficient of 0.936 and SEP of 0.026%. This study showed that FT-MIR spectroscopy can be utilized to monitor the quality changes of Makgeolli. Keyword: Nondestructively measurement, Furrier Transform Infrared, Spectroscopy, Makgeolli quality, Fermentation monitoring
Highlights •
Nondestructive FT-MIR spectroscopy is efficient for monitoring the quality changes in Makgeolli during fermentation.
•
PSRL models were developed to predict Makgeolli quality.
•
Depending on spectral preprocessing methods, the quality index indicator of Makgeolli was differently predicted.
1. Introduction Makgeolli is rich in protein, carbohydrates, and lactic acid bacteria, and also has a small amount of organic compounds such as the physiologically active substance [1]. There has been a noticeable increase in the consumption of Makgeolli in Korea because of its nutritional and functional values [2]. Nuruk containing yeast and several types of fungi is essential for subsequent Makgeolli fermentation [3,4]. Although Makgeolli is fermented under identical condition, its consistent quality is rarely achieved due to the complex biochemical mechanism of alcohol fermentation. For overcoming this issue, process analytical technology (PAT) has been spotlighted in alcoholic liquor industry. In order to apply and develop the PAT method for the fermentation process of alcoholic liquors, several elements that should be concerned in the PAT method are exact experimental design, the selection of sensors and technology, and process sampling [5]. It is particularly crucial to select appropriate sensor type in accordance with the characteristics of experimental methods and materials. As an alternative to conventional sensor technique based on chemical analysis, the spectroscopic detection methods have been recently developed to analyze complex structural information associated with biochemical reaction occurred during alcoholic fermentation [6]. The spectroscopy using near infrared (NIR, reciprocal wavelength range between 12000 and 4000 cm-1) and mid infrared (MIR, 4000-400 cm-1) has been employed in agro-food industry. MIR can be classified into four regions: C-H, O-H, N-H stretching region (4000 to 2500 cm-1), the triple bond region (2500 to 2000 cm-1), the double bond region (2000 to 1500 cm-1), and the fingerprint region (1500 to 400 cm-1). Since spectral libraries for the identification of food major components i.e., carbohydrates and proteins, are well established depending upon the spectral information provided from those regions, MIR spectroscopy can be used to qualitatively analyze the components of unknown samples with the measured spectra. MIR
spectroscopic method using the concept of chemomtrics is technically feasible for quantitative analysis of unknown samples [7,8]. Since MIR and NIR spectral data encompass overlapping information derived from the organic components of agricultural products, chemometrics could be used to determine distinctive spectral features of complex and high-dimensional data measured during MIR and NIR spectroscopy [9,10]. Representative analytical methods of chemometrics include principle component analysis, principle component regression, Partial Least Squares Regression (PLSR). The qualitative and quantitative quality prediction model using spectral information can be established by the aforementioned statistical techniques. Therefore, MIR spectral information obtained from chemometrics can be utilized to measure the quality factors (alcohol content, reducing sugar, and titratable acidity) which are closely associated with the taste of Makgeolli. This study was conducted to (1) monitor the quality change in Makgeolli during fermentation process by using FT-MIR spectroscopy and (2) develop a rapid and non-destructive detection technique to evaluate quality factors for Makgeolli.
2. Material and Methods 2. 1. Sample preparation and Makgeolli brewing procedure Non-glutinous rice of 20 kg and five packs of glutinous rice of 4 kg were purchased from a local market. Wheat Nuruk produced at a brewery (Gwangju, Korea) was used for further Makgeolli fermentation. The mixing ratios of rice to water for making Makgeolli used in this study were 1 (Rice flour of 500 g, steamed rice of 2 kg):1 (water of 2.5 L), 1:1.5 (3.75 L), and 1:2 (5 L) in the table 1. Two different type of rice (non-glutinous rice and glutinous rice) was used as steamed rice. In addition, Makgeolli was brewed and produced as following the protocol used in the study done by Kim and Cho (2015) (Fig. 1).
2. 2. Fourier Transform Infrared equipment FT-MIR spectroscopy (Nicolet 6700, Thermo Scientific Inc., Pittsburgh, PA, USA) equipped with MB-ATR (multi-bounce attenuated total reflectance; ZnSe) was used to obtain the spectra of Makgeolli samples. OMNIC software (OMNIC 9.2.41, Thermo Scientific Inc., Pittsburgh, PA, USA) was used for achieving the spectra of Makgeolli samples with the resolution of 4 cm-1. The spectra of Makgeolli samples were scanned for 32 times. The average value of 32 scans was used as mean spectrum, and the obtained spectra were converted to absorbance, and acquired spectra of Makgeolli were analyzed by multivariate analysis software (Unscrambler 9.7 Ver., Camo Co., Oslo, Norway).
2. 3. Wet chemistry 2.3.1. Alcohol concentration measurment High performance liquid chromatography (HPLC) that can measure a very small amount of samples was used in this study. For the measurement of alcohol concentration in Makgeolli, the sample was diluted in 0.005 N sulfuric acid and filtered using a 0.2 μm Syringe filter (25HP020AN, Advantech, Japan). The experimental condition and the information for column and device used in HPLC analysis are listed in Table 2.
2.3.2. Reducing sugar measurement Copper–bicinchoninate (CBC) method was used to measure reducing sugar in Makgeolli. After two stock solutions (reagent A and reagent B) were prepared as shown in Table 3, copper reagent was made by mixing the reagent A and B at the proportion of 1:1 in CBC method. The calibration curve was established with maltose (1–20 μg/mL at the rate of standard amount of 1 μg/mL) used as reducing sugar standard because maltose has the stable reducing sugar value. Reducing sugar concentration in Makgeolli sample was estimated from the calibration curve
and maltose concentration in Makgeolli sample was calculated as reducing sugar.
2.3.3. Titratable acidity measurement Organic acid concentration in Makgeolli samples was measured by using 5 different types of acids (acetic acid, citric acid, lactic acid, malic acid, and succinic acid) and was determined by the accessible literature concerning physicochemical content analysis of Makgeolli (Kim et al., 2008). Five different organic acid concentrations of the samples were measured from Makgeolli fermentation day 1, day 5, and day 10. The concentrations of 5 different acids were determined and compared. The organic acid with the highest acid concentration among 5 different acids was selected as the representative acid for titratable acidity measurement of Makgeolli. The titratable acidity was calculated from the following equation: Titratable acid %
N
100
(1)
where N is the volume (mL) of 1 N NaOH added to sample, f is 0.059 of the tiiter of organic acid using 1 N NaOH solution, and S is the sample amount (mL)
2. 4. Procedures of modeling global and local PLSRs The concentrations of alcohol, reducing sugar, and titratable acid that significantly affected the taste of Makgeolli were determined as quality factor. Therefore, partial least squares regression analysis (PLSR) was used to develop a monitoring method for prediction of quality factors using a spectrum variable. PLSR was regression analysis decreasing or solving the problems of multicollinearity among numerous variables such as spectra [11]. PLSR method exploits inner correlation of a dependent variable, score vector of the spectrum and an independent variable, score vector of Y, and created a regression model with the relationship between the
spectra and actual measured values [11]. Due to the recent active research on online monitoring technology, studies on Partial Least Squares Regression (PLSR) models using specific wavelength ranges related to the quality rather than models using the entire ranges of the spectra were conducted to develop the monitoring model. The information of the constituents in the models using the entire ranges of the spectrum was redundant; as a result, unnecessary information, noise, and background information were included which result in decreasing of prediction performance [12]. Thus, the developed a model using local PLSR was divided into two groups: fingerprint region in MIR region to trace the changes of reducing sugar and titratable acidity, and O-H region to detect the changes of alcohol concentration. The local PLSR model was compared to global PLSR model using the entire ranges of the spectrum.
2. 5. Data preprocessing When there was a significant difference between standard error of calibration (SEC) and standard error of prediction (SEP) in the developed model, a number of latent variables could be generated and the model could become unstable due to undesirable noise [13]. SEC, SEP, and bias are given by:
∑
∑
∑
(2)
(3)
(4)
where
is the measured value,
is the predicted value of each observation, and m is
observation number in the calibration and the validation set To establish a good prediction model, the pretreatment of spectra could be used to eliminate the environmental noise during spectra measurement and to amplify the minor spectra (Kim et al., 2010). In this study, the differential pretreatment of Savitzky-Golay and Norris-Gap was employed for increasing the effect of minor components of critical wavelength [14,15].
3. Results and Discussion 3.1. Quality changes The mixing ratios of rice to water for making Makgeolli were 1 (Rice flour of 500 g, steamed rice of 2 kg):1 (water of 2.5 L), 1:1.5 (3.75 L), and 1:2 (5 L). Since the fixed amounts of rice flour and steamed rice were used and the amount of water was varied depending on the mixing ratio, the generated alcohol, reducing sugar, and titratable acidity during fermentation were diluted with the water. Therefore, the amounts of alcohol, reducing sugar, and titratable acidity in the Makgeolli that was produced by using one to one ratio and has low amount of water were higher than those by using 1:1.5 and 1:2. The amounts of alcohol and titratable acidity were stable at the final stage of Makgeolli fermentation; however, the amount of reducing sugar was low regardless of material and water because of enzymatic hydrolysis.
3.1.1. The changes of alcohol concentration Figure 2 shows the changes in the concentration for ten days after adding the steamed rice (a: steamed non-glutinous rice, b: steamed glutinous rice). After fermentation day 3, there was a significant difference in alcohol concentrations depending on the concentrations of ingredients and water. Alcohol concentration was substantially increased from the day of adding the
steamed rice to day 2; however, after day 6 it was gradually increased. Alcohol concentration in Makgelli could be affected by the difference in the ingredients and brewing method. From initial fermentation to the completion of fermentation, alcohol concentration with steamed non-glutinous rice was in a range of 0.16 ~ 17.94% and the one with steamed glutinous rice was in a range of 0.15 ~ 17.73%. Among the brewed samples with steamed non-glutinous rice, alcohol concentration of the sample made with the mixing ratio of ingredient to water 1:1 was lowest at the initial stage of fermentation, but highest at the completion of fermentation. In addition, alcohol concentration of Makgeolli was increased with decrease in ratio of water.
3.1.2. The changes of reducing sugar Since yeast consumed sugar for the metabolism of alcohol, reducing sugar in Makgelli was decreased over the fermentation time as shown in Figure 3. The amount of reducing sugar is significantly changed at initial stage of Makgeolli fermentation due to high potential activity of microbial enzyme. Thus, the generated amount of reducing sugar at initial stage of Makgeolli fermentation was varied and has the highest variation. However, the variation of alcohol and titratable acidity were low because the amounts of alcohol and titratable acidity generated by the conversion of reducing sugar were low. The highest concentration of reducing sugar was observed on the second day after adding the steamed rice because the rice starch was actively converted to sugar at initial stage. Since sugar played a considerable part in alcohol fermentation, the samples (G1 and R1) with highest alcohol concentration also contained high sugar concentration [16].
3.1.3. The changes of titratable acidity Figure 4 represents the changes in titratable acidity for 10 days, showing significant differences based on mixing ratios of ingredient and water. The concentration of titratable acidity was
significantly increased by day 2 but remained constant until the completion of fermentation. The highest titratable acidity was observed Makgeolli sample having the ratio of 1:1, and depending on the mixing ratios there wave notable differences.
3.2. Total absorbance using FT-MIR Reflectance spectra using FT-MIR spectroscopy were obtained once a day for 10 days from the day of adding steamed rice to the completion of fermentation. Six samples based on the ingredients and mixing ratio of Makgeolli to water were used, and 11 spectra of each sample measured at the same time of the day were extracted (total 66 spectra). The measurements were conducted in a duplicate, and total 132 spectra were obtained. Among the total spectra, 129 spectra excluding three outliers were converted into absorbance and used for the analysis. The developed model was randomly divided. 75% of them (96–97 spectra) were used for developing a model to predict the concentrations of alcohol, reducing sugar, and titratable acidity and 25% of them (32–33 spectra) were utilized for validation. Table 4 shows the basic statistical values of the divided data for model development and validation. Three major peaks were observed in the entire spectra of Makgeolli (Fig. 5). The high peak related to water was observed at 3500-3200 cm-1, and two peaks at the fingerprint region, 18001500 cm-1 and 1100-900 cm-1 were observed [8]. The main absorbance of wines in MIR spectra appeared prominently by alcohol and water, and the fingerprint region depending on water, ethanol, sugar, and organic acid was observed at 1800-900 cm-1 [17]. Absorbance of Makgeolli spectra showed similar pattern with the spectra obtained from wine. Concentration changes in alcohol, sugar, and organic acid during fermentation showed two peaks: fingerprint region and functional group (O-H stretch) region. As shown in Figure 6, the O-H region, 3650-2600 cm-1 in the spectra of Makgeolli during fermentation was used to develop a prediction model for alcohol concentration, and the
fingerprint region, 1800-950 cm-1 was used for prediction of reducing sugar and titratable acidity (Schindler et al., 1998). That is, local PLSR model to predict the quality of Makgeolli was developed with the spectral data, dividing functional group region (2600 ~ 3650 cm-1) and fingerprint region [18].
3.3. Global PLSR results Table 5 represents the PLSR analysis results of alcohol concentration. The spectra at 4000-650 cm-1 were only measured because material of cell window was ZnSe. By eliminating front end noise and section without absorption spectra, only region from 750 to 3950 cm-1 was used for PLSR analysis. In both spectra with/without pretreatment, the coefficient of determination was over 0.9. The model having 2nd derivatives of Savitzky-Golay showed excellent performance. The validation results obtained from the model with 1st derivatives of Norris-Gap showed R2 value of 0.955 and SEP of 1.061% and the developed model had R2 value of 0.983 and SEC of 0.585% (Table 6). The model having 1st derivatives of Savitzky-Golay was practical to analyze titratable acidity by using PLSR (Table 7). The coefficient of determination was 0.930, and SEP was 0.035%. The developed model had R2 value of 0.947 and SEC of 0.027.
3.4. Local PLSR results Local PLSR analysis results of alcohol concentration are presented on Table 8. The model having 2nd derivatives of Savitzky-Golay resulted in good performance. The developed model provided R2 value of 0.991 and SEC of 0.449%. Table 9 represents the PLSR results of reducing sugar, and the model having 1st derivatives of Norris-Gap showed excellent performance. The developed model had R2 value of 0.983 and SEC of 0.617%. Table 10 shows the PLSR results of titratable acidity, and the model having 1st derivatives of Savitzky-Golay showed excellent performance.
R2 value slightly increased when using the specific wavelength ranges in the development of the model for alcohol concentration and titratable acidity prediction. R2 value increased more when using local PLSR model in the development of the model for reducing sugar. Local PLSR model showed better results than global PLSR model in the same pretreatment. Figure 7 shows the regression coefficient of alcohol, reducing sugar, and titratable acidity used in prediction. C-H stretching peak related to CH2 and CH3 was observed at the range between 2985 and 2912 cm-1 in the regression coefficient of alcohol [19]. The large coefficient value from 2800 to 3100 cm-1 was caused by the influence of the amplified small peak on the alcohol prediction model due to the derivative pretreatment. The regression coefficient of reducing sugar was shown in Figure 7 (b). Region for polysaccharides was 950-1200 cm-1 which was associated with C-O-C and C-O-H link positions, and the region for pseudo polysaccharide component was 1045 cm-1 [20]. In Figure 7 (c), results of regression coefficient showed many peaks at these regions. Peak in the C=O stretching involving acid from the region for organic acid, 1700-1725 cm-1 formed high peak in the regression coefficient [21]. High regression coefficient value was observed at the region of 1000-1200 cm-1 related to C-C and C-O stretching [22]. Figure 8 represents the graphs of the measured and predicted values using the local PLSR model. The validation results for alcohol concentration, reducing sugar, and titratable acidity showed R2 value of 0.984 and SEP of 0.595%, R2 value of 0.983 and SEP of 0.579%, R2 value of 0.936 and SEP of 0.026%, respectively.
4. Conclusion A technique for rapid and non-destructive evaluation of the main quality factors using FT-MIR spectroscopy (4000-400 cm-1) during fermentation of Makgeolli was developed. MB-ATR was used to obtain the spectra, and quality factors affecting taste of Makgeolli were measured. The
monitoring model for Makgeolli quality was developed and evaluated during fermentation using PLSR analysis. The prediction model having the pretreatments, 1st and 2nd derivatives of Savitzky-Golay and Norris-Gap resulted in good performance, showing R2 value of over 0.9. The local PLSR model showed better results than the global PLSR model. It was found that the fingerprint region was suitable for developing the model for reducing sugar and titratable acid. The region of O-H functional group could be used for the prediction model for alcohol concentration. The results clearly showed FT-MIR spectroscopy could be a practical tool to rapidly and nondestructively the quality of Makgeolli during fermentation. Further studies on developing a real-time measurement system using FT-MIR will automatically control the entire fermentation process. If alcoholic beverage fermentation is monitored and controlled in real time, the high quality fermented alcoholic beverages can be produced in the brewing industry.
Acknowledgement This research was supported partially by High Value-added Food Technology Development Program, Ministry of Agriculture, Food and Rural Affairs (MAFRA) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2010-0006573), Republic of Korea.
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Biographies Dae-Yong Kim received his PhD degree in 2015 in agricultural machinery engineering from Chungnam national university, Korea. He is now working in CJ-Korea express as a researcher. His research areas are non-destructive biosensing for food quality and package engineering. Byoung-Kwan Cho is associate professor in the Department of Biosystems Machinery Engineering at the Chungnam National University Daejeon, South Korea. He received Ph.D. in Department of Agricultural and Biological Engineering from the Pennsylvania State University, University Park, USA in 2003. His research areas are non-destructive biosensing for quality and safety evaluation of agricultural and food materials. Seung Hyun Lee is assistant professor in the Department of Biosystems Machinery Engineering at the Chungnam National University Daejeon, South Korea. He received M.S and Ph.D. in Department of Molecular Biosciences and Bioengineering from University of Hawaii, Honolulu, USA in 2010 and in 2014, respectively. His research interests are the development of the combination methods for agro-food process engineering and food safety. Moon S. Kim is ARS, USDA research physicist working on development of optical sensing technologies for food safety research projects. He leads a multidisciplinary team of researchers to develop innovative sensing methodologies and technologies to address food safety concerns for food production and to aid in reducing food safety risks in food processing. Wang-Hee Lee is assistant professor in the Department of Biosystems Machinery Engineering at the Chungnam National University Daejeon, South Korea. He received M.S. and Ph.D. in Agricultural and Biological Engineering from the Purdue University, West Lafayette, in 2006 and 2011, respectively. His research interests are biosystem modeling and data analysis applicable for agricultural, biological and food systems including their products and process engineering.
Fig. 1. Schematic diagram of Makgeolli production.
Figure 2. Changes in ethanol concentration for 10 days during fermentation of Makgeolli (a: steamed non-glutinous rice, b: steamed glutinous rice).
(a)
(b)
Figure 3. Changes in reducing sugar concentration for 10 days during fermentation of Makgeolli (a: steamed non-glutinous rice, b: steamed glutinous rice).
(a)
(b)
Figure 4. Changes in titratable acidity for 10 days during fermentation of Makgeolli (a: steamed non-glutinous rice, b: steamed glutinous rice).
(a)
(b)
Figure 5. Total transmittance spectra of Makgeolli using the FT-IR spectroscopy
Figure 6. Fingerprint regions (a: alcohol, and b: reducing sugar and titiratable acidity) in the reflectance spectra of Makgeolli using FT-MIR spectroscopy.
(a)
(b)
Figure 78. Regression coefficients of alcohol (a), reducing sugar (b), and titratable acidity (C) in the local PLSR model.
Figure 89. Prediction results of alcohol (a), reducing sugar (b), and titratable acidity (c) using the best PLSR model.
Table 1. Compositions of materials for Makgeolli production Ratio (Water : Ingredients) Water
1:1
1.5:1
2:1
2.5 L
3.75 L
5L
Ingredient (Rice flour + Steamed non-/glutinous rice)
2.5kg
※ R stands for non-glutinous rice and G stands for glutinous rice. The numbers in the letters mean the amount of water in the mixing ratio of rice to water. For example, R1 means that the mixing ratio of rice to water is at one to one (rice flour of 500g and steamed rice of 2 kg: water of 2.5L).
Table 2. Operating conditions of HPLC for the analysis of alcohol concentration in the Makgeolli sample Item
Condition
Instrument
Dionex Ultimate 3000 HPLC (Dionex Corp., CA, USA)
Column
Rezex ROA organic acid column (300 mm x 7.8 mm, Phenomenex, Torrence, CA, USA)
Column temperature
60℃
Mobile phase
0.005 N sulfuric acid
Flow rate
0.5 mL/min
Detector
Shodex RI-101 refractive index detector
Table 3. Conditions of reagents for CBC method Items
Condition 97.1 mg of 2.2’-bicinchoninic acid disodium salt
Reagent A
3.2 g of sodium carbonate monohydrate 1.2 g of sodium bicarbonate in 50 ml H2O
Reagent B
62 mg of copper sulfate pentahydrate 63 mg of L-serine in 50 ml H2O
Copper reagent
reagent A : reagent B = 1 : 1
Table 4. Data set i.e. alcohol, reducing sugar, and titratable acid for global PLSR using the FT-IR spectroscopy Item
Number
Alcohol Concentration
Calibration set
Reducing sugar Concentration
Calibration set
Titratable acidity
Calibration set
Prediction set
Prediction set
Prediction set
129
129
129
Mean
Range
Stdev.
96
10.74
0.15 ~ 17.73
4.64
33
10.70
0.19 ~ 17.94
4.84
96
4.25
0.30 ~ 17.67
4.50
33
4.59
0.33 ~ 20.63
5.04
97
0.54
0.14 ~ 0.71
0.12
32
0.54
0.18 ~ 0.71
0.12
Table 5. Global PLSR results of calibration, validation, and prediction of alcohol concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.986 0.539 0.988 0.5178 0.986 0.549 0.987 0.536 0.984 0.590
Validation R v2 SEV 0.984 0.588 0.985 0.578 0.984 0.597 0.983 0.610 0.982 0.626
LV 5 4 4 4 3
Prediction Rp 2 SEP 0.981 0.661 0.982 0.643 0.982 0.641 0.981 0.646 0.982 0.636
Table 6. Global PLSR results of calibration, validation, and prediction of reducing sugar concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.960 0.897 0.983 0.585 0.978 0.661 0.981 0.625 0.980 0.628
Validation R v2 SEV 0.950 1.020 0.974 0.728 0.969 0.799 0.972 0.758 0.973 0.745
LV 5 6 6 6 5
Prediction Rp 2 SEP 0.939 1.245 0.955 1.061 0.944 1.182 0.953 1.088 0.951 1.110
Table 7. Global PLSR results of calibration, validation, and prediction of titratable acidity with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.904 0.037 0.945 0.028 0.945 0.028 0.947 0.027 0.943 0.028
Validation R v2 SEV 0.895 0.039 0.929 0.032 0.925 0.033 0.934 0.031 0.909 0.036
LV 3 4 4 4 4
Prediction Rp 2 SEP 0.883 0.043 0.922 0.036 0.922 0.036 0.930 0.035 0.928 0.035
Table 8. Local PLSR results of calibration, validation, and prediction of ethanol concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.984 0.584 0.987 0.528 0.982 0.622 0.989 0.493 0.991 0.449
Validation R v2 SEV 0.983 0.622 0.985 0.5280 0.981 0.657 0.987 0.532 0.990 0.466
LV 4 3 2 3 2
Prediction Rp 2 SEP 0.975 0.738 0.983 0.610 0.978 0.707 0.983 0.606 0.984 0.595
Table 9. Local PLSR results of calibration, validation, and prediction of reducing sugar concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.984 0.596 0.983 0.617 0.974 0.760 0.979 0.685 0.980 0.665
Validation R v2 SEV 0.970 0.817 0.970 0.824 0.961 0.930 0.968 0.847 0.970 0.826
LV 7 7 6 6 6
Prediction Rp 2 SEP 0.982 0.584 0.983 0.579 0.970 0.777 0.979 0.635 0.970 0.764
Table 10. Local PLS results of calibration, validation, and prediction of titratable acidity with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.980 0.018 0.983 0.016 0.980 0.018 0.977 0.019 0.938 0.031
Validation R v2 SEV 0.966 0.023 0.972 0.021 0.970 0.022 0.965 0.023 0.921 0.035
LV 8 8 7 7 3
Prediction Rp 2 SEP 0.951 0.023 0.962 0.020 0.945 0.024 0.936 0.026 0.912 0.030
S0925-4005(16)30225-8 http://dx.doi.org/doi:10.1016/j.snb.2016.02.076 SNB 19736
To appear in:
Sensors and Actuators B
Received date: Revised date: Accepted date:
28-10-2015 15-2-2016 17-2-2016
Please cite this article as: Dae-Yong Kim, Byoung-Kwan Cho, Seung Hyun Lee, Kyungdo Kwon, Eun Soo Park, Wang-Hee Lee, Application of Fourier Transform-Mid Infrared Reflectance Spectroscopy for Monitoring Korean Traditional Rice Wine ‘Makgeolli’ Fermentation, Sensors and Actuators B: Chemical http://dx.doi.org/10.1016/j.snb.2016.02.076 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Application of Fourier Transform-Mid Infrared Reflectance Spectroscopy for Monitoring Korean Traditional Rice Wine ‘Makgeolli’ Fermentation
Dae-Yong Kim1, Byoung-Kwan Cho1*, Seung Hyun Lee1, Kyungdo Kwon1, Eun Soo Park1, and Wang-Hee Lee1
Article note: 1
Department of Biosystems Machinery Engineering, Chungnam National University, 99
Daehak-ro, Yuseong-gu, Daejeon, 306-764, Republic of Korea
Corresponding Author: Byoung-Kwan Cho, Professor, Department of Biosystems Machinery Engineering, Chungnam National University, 99 Daehak-Ro, Yuseong-Gu, Daejeon, 305-764, Republic of Korea, Phone) +82-42-821-6715, Fax) +82-42-823-6246, Email) [email protected]
ABSTRACT This study was performed to examine the application of Fourier Transform-Mid Infrared (FTMIR) spectroscopy to rapidly and non-destructively measure the quality of Makgeolli during fermentation process. A model using the entire ranges of the spectra and a Partial Least Squares Regression (PLSR) model using specific wavelength ranges closely associated with Makgeolli quality were compared to investigate the appropriate non-destructive monitoring method for Makgeolli production. In the global PLSR model using the entire range of the spectra and the local PLSR model using two separate regions (functional group spectral region and fingerprint spectral region), the optimal prediction model for alcohol concentration was from 2nd derivative of Savitzky-Golay pretreatment. The coefficient of determination of the developed model was 0.984, and Standard Error of Prediction (SEP) was 0.595%. Reducing sugar was detected best with 1st derivative of Norris-Gap pretreatment, showing the determination coefficient of 0.983 and SEP of 0.579%. Titratable acidity was accurately predicted with 1st derivative of SavitzkyGolay pretreatment, showing the determination coefficient of 0.936 and SEP of 0.026%. This study showed that FT-MIR spectroscopy can be utilized to monitor the quality changes of Makgeolli. Keyword: Nondestructively measurement, Furrier Transform Infrared, Spectroscopy, Makgeolli quality, Fermentation monitoring
Highlights •
Nondestructive FT-MIR spectroscopy is efficient for monitoring the quality changes in Makgeolli during fermentation.
•
PSRL models were developed to predict Makgeolli quality.
•
Depending on spectral preprocessing methods, the quality index indicator of Makgeolli was differently predicted.
1. Introduction Makgeolli is rich in protein, carbohydrates, and lactic acid bacteria, and also has a small amount of organic compounds such as the physiologically active substance [1]. There has been a noticeable increase in the consumption of Makgeolli in Korea because of its nutritional and functional values [2]. Nuruk containing yeast and several types of fungi is essential for subsequent Makgeolli fermentation [3,4]. Although Makgeolli is fermented under identical condition, its consistent quality is rarely achieved due to the complex biochemical mechanism of alcohol fermentation. For overcoming this issue, process analytical technology (PAT) has been spotlighted in alcoholic liquor industry. In order to apply and develop the PAT method for the fermentation process of alcoholic liquors, several elements that should be concerned in the PAT method are exact experimental design, the selection of sensors and technology, and process sampling [5]. It is particularly crucial to select appropriate sensor type in accordance with the characteristics of experimental methods and materials. As an alternative to conventional sensor technique based on chemical analysis, the spectroscopic detection methods have been recently developed to analyze complex structural information associated with biochemical reaction occurred during alcoholic fermentation [6]. The spectroscopy using near infrared (NIR, reciprocal wavelength range between 12000 and 4000 cm-1) and mid infrared (MIR, 4000-400 cm-1) has been employed in agro-food industry. MIR can be classified into four regions: C-H, O-H, N-H stretching region (4000 to 2500 cm-1), the triple bond region (2500 to 2000 cm-1), the double bond region (2000 to 1500 cm-1), and the fingerprint region (1500 to 400 cm-1). Since spectral libraries for the identification of food major components i.e., carbohydrates and proteins, are well established depending upon the spectral information provided from those regions, MIR spectroscopy can be used to qualitatively analyze the components of unknown samples with the measured spectra. MIR
spectroscopic method using the concept of chemomtrics is technically feasible for quantitative analysis of unknown samples [7,8]. Since MIR and NIR spectral data encompass overlapping information derived from the organic components of agricultural products, chemometrics could be used to determine distinctive spectral features of complex and high-dimensional data measured during MIR and NIR spectroscopy [9,10]. Representative analytical methods of chemometrics include principle component analysis, principle component regression, Partial Least Squares Regression (PLSR). The qualitative and quantitative quality prediction model using spectral information can be established by the aforementioned statistical techniques. Therefore, MIR spectral information obtained from chemometrics can be utilized to measure the quality factors (alcohol content, reducing sugar, and titratable acidity) which are closely associated with the taste of Makgeolli. This study was conducted to (1) monitor the quality change in Makgeolli during fermentation process by using FT-MIR spectroscopy and (2) develop a rapid and non-destructive detection technique to evaluate quality factors for Makgeolli.
2. Material and Methods 2. 1. Sample preparation and Makgeolli brewing procedure Non-glutinous rice of 20 kg and five packs of glutinous rice of 4 kg were purchased from a local market. Wheat Nuruk produced at a brewery (Gwangju, Korea) was used for further Makgeolli fermentation. The mixing ratios of rice to water for making Makgeolli used in this study were 1 (Rice flour of 500 g, steamed rice of 2 kg):1 (water of 2.5 L), 1:1.5 (3.75 L), and 1:2 (5 L) in the table 1. Two different type of rice (non-glutinous rice and glutinous rice) was used as steamed rice. In addition, Makgeolli was brewed and produced as following the protocol used in the study done by Kim and Cho (2015) (Fig. 1).
2. 2. Fourier Transform Infrared equipment FT-MIR spectroscopy (Nicolet 6700, Thermo Scientific Inc., Pittsburgh, PA, USA) equipped with MB-ATR (multi-bounce attenuated total reflectance; ZnSe) was used to obtain the spectra of Makgeolli samples. OMNIC software (OMNIC 9.2.41, Thermo Scientific Inc., Pittsburgh, PA, USA) was used for achieving the spectra of Makgeolli samples with the resolution of 4 cm-1. The spectra of Makgeolli samples were scanned for 32 times. The average value of 32 scans was used as mean spectrum, and the obtained spectra were converted to absorbance, and acquired spectra of Makgeolli were analyzed by multivariate analysis software (Unscrambler 9.7 Ver., Camo Co., Oslo, Norway).
2. 3. Wet chemistry 2.3.1. Alcohol concentration measurment High performance liquid chromatography (HPLC) that can measure a very small amount of samples was used in this study. For the measurement of alcohol concentration in Makgeolli, the sample was diluted in 0.005 N sulfuric acid and filtered using a 0.2 μm Syringe filter (25HP020AN, Advantech, Japan). The experimental condition and the information for column and device used in HPLC analysis are listed in Table 2.
2.3.2. Reducing sugar measurement Copper–bicinchoninate (CBC) method was used to measure reducing sugar in Makgeolli. After two stock solutions (reagent A and reagent B) were prepared as shown in Table 3, copper reagent was made by mixing the reagent A and B at the proportion of 1:1 in CBC method. The calibration curve was established with maltose (1–20 μg/mL at the rate of standard amount of 1 μg/mL) used as reducing sugar standard because maltose has the stable reducing sugar value. Reducing sugar concentration in Makgeolli sample was estimated from the calibration curve
and maltose concentration in Makgeolli sample was calculated as reducing sugar.
2.3.3. Titratable acidity measurement Organic acid concentration in Makgeolli samples was measured by using 5 different types of acids (acetic acid, citric acid, lactic acid, malic acid, and succinic acid) and was determined by the accessible literature concerning physicochemical content analysis of Makgeolli (Kim et al., 2008). Five different organic acid concentrations of the samples were measured from Makgeolli fermentation day 1, day 5, and day 10. The concentrations of 5 different acids were determined and compared. The organic acid with the highest acid concentration among 5 different acids was selected as the representative acid for titratable acidity measurement of Makgeolli. The titratable acidity was calculated from the following equation: Titratable acid %
N
100
(1)
where N is the volume (mL) of 1 N NaOH added to sample, f is 0.059 of the tiiter of organic acid using 1 N NaOH solution, and S is the sample amount (mL)
2. 4. Procedures of modeling global and local PLSRs The concentrations of alcohol, reducing sugar, and titratable acid that significantly affected the taste of Makgeolli were determined as quality factor. Therefore, partial least squares regression analysis (PLSR) was used to develop a monitoring method for prediction of quality factors using a spectrum variable. PLSR was regression analysis decreasing or solving the problems of multicollinearity among numerous variables such as spectra [11]. PLSR method exploits inner correlation of a dependent variable, score vector of the spectrum and an independent variable, score vector of Y, and created a regression model with the relationship between the
spectra and actual measured values [11]. Due to the recent active research on online monitoring technology, studies on Partial Least Squares Regression (PLSR) models using specific wavelength ranges related to the quality rather than models using the entire ranges of the spectra were conducted to develop the monitoring model. The information of the constituents in the models using the entire ranges of the spectrum was redundant; as a result, unnecessary information, noise, and background information were included which result in decreasing of prediction performance [12]. Thus, the developed a model using local PLSR was divided into two groups: fingerprint region in MIR region to trace the changes of reducing sugar and titratable acidity, and O-H region to detect the changes of alcohol concentration. The local PLSR model was compared to global PLSR model using the entire ranges of the spectrum.
2. 5. Data preprocessing When there was a significant difference between standard error of calibration (SEC) and standard error of prediction (SEP) in the developed model, a number of latent variables could be generated and the model could become unstable due to undesirable noise [13]. SEC, SEP, and bias are given by:
∑
∑
∑
(2)
(3)
(4)
where
is the measured value,
is the predicted value of each observation, and m is
observation number in the calibration and the validation set To establish a good prediction model, the pretreatment of spectra could be used to eliminate the environmental noise during spectra measurement and to amplify the minor spectra (Kim et al., 2010). In this study, the differential pretreatment of Savitzky-Golay and Norris-Gap was employed for increasing the effect of minor components of critical wavelength [14,15].
3. Results and Discussion 3.1. Quality changes The mixing ratios of rice to water for making Makgeolli were 1 (Rice flour of 500 g, steamed rice of 2 kg):1 (water of 2.5 L), 1:1.5 (3.75 L), and 1:2 (5 L). Since the fixed amounts of rice flour and steamed rice were used and the amount of water was varied depending on the mixing ratio, the generated alcohol, reducing sugar, and titratable acidity during fermentation were diluted with the water. Therefore, the amounts of alcohol, reducing sugar, and titratable acidity in the Makgeolli that was produced by using one to one ratio and has low amount of water were higher than those by using 1:1.5 and 1:2. The amounts of alcohol and titratable acidity were stable at the final stage of Makgeolli fermentation; however, the amount of reducing sugar was low regardless of material and water because of enzymatic hydrolysis.
3.1.1. The changes of alcohol concentration Figure 2 shows the changes in the concentration for ten days after adding the steamed rice (a: steamed non-glutinous rice, b: steamed glutinous rice). After fermentation day 3, there was a significant difference in alcohol concentrations depending on the concentrations of ingredients and water. Alcohol concentration was substantially increased from the day of adding the
steamed rice to day 2; however, after day 6 it was gradually increased. Alcohol concentration in Makgelli could be affected by the difference in the ingredients and brewing method. From initial fermentation to the completion of fermentation, alcohol concentration with steamed non-glutinous rice was in a range of 0.16 ~ 17.94% and the one with steamed glutinous rice was in a range of 0.15 ~ 17.73%. Among the brewed samples with steamed non-glutinous rice, alcohol concentration of the sample made with the mixing ratio of ingredient to water 1:1 was lowest at the initial stage of fermentation, but highest at the completion of fermentation. In addition, alcohol concentration of Makgeolli was increased with decrease in ratio of water.
3.1.2. The changes of reducing sugar Since yeast consumed sugar for the metabolism of alcohol, reducing sugar in Makgelli was decreased over the fermentation time as shown in Figure 3. The amount of reducing sugar is significantly changed at initial stage of Makgeolli fermentation due to high potential activity of microbial enzyme. Thus, the generated amount of reducing sugar at initial stage of Makgeolli fermentation was varied and has the highest variation. However, the variation of alcohol and titratable acidity were low because the amounts of alcohol and titratable acidity generated by the conversion of reducing sugar were low. The highest concentration of reducing sugar was observed on the second day after adding the steamed rice because the rice starch was actively converted to sugar at initial stage. Since sugar played a considerable part in alcohol fermentation, the samples (G1 and R1) with highest alcohol concentration also contained high sugar concentration [16].
3.1.3. The changes of titratable acidity Figure 4 represents the changes in titratable acidity for 10 days, showing significant differences based on mixing ratios of ingredient and water. The concentration of titratable acidity was
significantly increased by day 2 but remained constant until the completion of fermentation. The highest titratable acidity was observed Makgeolli sample having the ratio of 1:1, and depending on the mixing ratios there wave notable differences.
3.2. Total absorbance using FT-MIR Reflectance spectra using FT-MIR spectroscopy were obtained once a day for 10 days from the day of adding steamed rice to the completion of fermentation. Six samples based on the ingredients and mixing ratio of Makgeolli to water were used, and 11 spectra of each sample measured at the same time of the day were extracted (total 66 spectra). The measurements were conducted in a duplicate, and total 132 spectra were obtained. Among the total spectra, 129 spectra excluding three outliers were converted into absorbance and used for the analysis. The developed model was randomly divided. 75% of them (96–97 spectra) were used for developing a model to predict the concentrations of alcohol, reducing sugar, and titratable acidity and 25% of them (32–33 spectra) were utilized for validation. Table 4 shows the basic statistical values of the divided data for model development and validation. Three major peaks were observed in the entire spectra of Makgeolli (Fig. 5). The high peak related to water was observed at 3500-3200 cm-1, and two peaks at the fingerprint region, 18001500 cm-1 and 1100-900 cm-1 were observed [8]. The main absorbance of wines in MIR spectra appeared prominently by alcohol and water, and the fingerprint region depending on water, ethanol, sugar, and organic acid was observed at 1800-900 cm-1 [17]. Absorbance of Makgeolli spectra showed similar pattern with the spectra obtained from wine. Concentration changes in alcohol, sugar, and organic acid during fermentation showed two peaks: fingerprint region and functional group (O-H stretch) region. As shown in Figure 6, the O-H region, 3650-2600 cm-1 in the spectra of Makgeolli during fermentation was used to develop a prediction model for alcohol concentration, and the
fingerprint region, 1800-950 cm-1 was used for prediction of reducing sugar and titratable acidity (Schindler et al., 1998). That is, local PLSR model to predict the quality of Makgeolli was developed with the spectral data, dividing functional group region (2600 ~ 3650 cm-1) and fingerprint region [18].
3.3. Global PLSR results Table 5 represents the PLSR analysis results of alcohol concentration. The spectra at 4000-650 cm-1 were only measured because material of cell window was ZnSe. By eliminating front end noise and section without absorption spectra, only region from 750 to 3950 cm-1 was used for PLSR analysis. In both spectra with/without pretreatment, the coefficient of determination was over 0.9. The model having 2nd derivatives of Savitzky-Golay showed excellent performance. The validation results obtained from the model with 1st derivatives of Norris-Gap showed R2 value of 0.955 and SEP of 1.061% and the developed model had R2 value of 0.983 and SEC of 0.585% (Table 6). The model having 1st derivatives of Savitzky-Golay was practical to analyze titratable acidity by using PLSR (Table 7). The coefficient of determination was 0.930, and SEP was 0.035%. The developed model had R2 value of 0.947 and SEC of 0.027.
3.4. Local PLSR results Local PLSR analysis results of alcohol concentration are presented on Table 8. The model having 2nd derivatives of Savitzky-Golay resulted in good performance. The developed model provided R2 value of 0.991 and SEC of 0.449%. Table 9 represents the PLSR results of reducing sugar, and the model having 1st derivatives of Norris-Gap showed excellent performance. The developed model had R2 value of 0.983 and SEC of 0.617%. Table 10 shows the PLSR results of titratable acidity, and the model having 1st derivatives of Savitzky-Golay showed excellent performance.
R2 value slightly increased when using the specific wavelength ranges in the development of the model for alcohol concentration and titratable acidity prediction. R2 value increased more when using local PLSR model in the development of the model for reducing sugar. Local PLSR model showed better results than global PLSR model in the same pretreatment. Figure 7 shows the regression coefficient of alcohol, reducing sugar, and titratable acidity used in prediction. C-H stretching peak related to CH2 and CH3 was observed at the range between 2985 and 2912 cm-1 in the regression coefficient of alcohol [19]. The large coefficient value from 2800 to 3100 cm-1 was caused by the influence of the amplified small peak on the alcohol prediction model due to the derivative pretreatment. The regression coefficient of reducing sugar was shown in Figure 7 (b). Region for polysaccharides was 950-1200 cm-1 which was associated with C-O-C and C-O-H link positions, and the region for pseudo polysaccharide component was 1045 cm-1 [20]. In Figure 7 (c), results of regression coefficient showed many peaks at these regions. Peak in the C=O stretching involving acid from the region for organic acid, 1700-1725 cm-1 formed high peak in the regression coefficient [21]. High regression coefficient value was observed at the region of 1000-1200 cm-1 related to C-C and C-O stretching [22]. Figure 8 represents the graphs of the measured and predicted values using the local PLSR model. The validation results for alcohol concentration, reducing sugar, and titratable acidity showed R2 value of 0.984 and SEP of 0.595%, R2 value of 0.983 and SEP of 0.579%, R2 value of 0.936 and SEP of 0.026%, respectively.
4. Conclusion A technique for rapid and non-destructive evaluation of the main quality factors using FT-MIR spectroscopy (4000-400 cm-1) during fermentation of Makgeolli was developed. MB-ATR was used to obtain the spectra, and quality factors affecting taste of Makgeolli were measured. The
monitoring model for Makgeolli quality was developed and evaluated during fermentation using PLSR analysis. The prediction model having the pretreatments, 1st and 2nd derivatives of Savitzky-Golay and Norris-Gap resulted in good performance, showing R2 value of over 0.9. The local PLSR model showed better results than the global PLSR model. It was found that the fingerprint region was suitable for developing the model for reducing sugar and titratable acid. The region of O-H functional group could be used for the prediction model for alcohol concentration. The results clearly showed FT-MIR spectroscopy could be a practical tool to rapidly and nondestructively the quality of Makgeolli during fermentation. Further studies on developing a real-time measurement system using FT-MIR will automatically control the entire fermentation process. If alcoholic beverage fermentation is monitored and controlled in real time, the high quality fermented alcoholic beverages can be produced in the brewing industry.
Acknowledgement This research was supported partially by High Value-added Food Technology Development Program, Ministry of Agriculture, Food and Rural Affairs (MAFRA) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2010-0006573), Republic of Korea.
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Biographies Dae-Yong Kim received his PhD degree in 2015 in agricultural machinery engineering from Chungnam national university, Korea. He is now working in CJ-Korea express as a researcher. His research areas are non-destructive biosensing for food quality and package engineering. Byoung-Kwan Cho is associate professor in the Department of Biosystems Machinery Engineering at the Chungnam National University Daejeon, South Korea. He received Ph.D. in Department of Agricultural and Biological Engineering from the Pennsylvania State University, University Park, USA in 2003. His research areas are non-destructive biosensing for quality and safety evaluation of agricultural and food materials. Seung Hyun Lee is assistant professor in the Department of Biosystems Machinery Engineering at the Chungnam National University Daejeon, South Korea. He received M.S and Ph.D. in Department of Molecular Biosciences and Bioengineering from University of Hawaii, Honolulu, USA in 2010 and in 2014, respectively. His research interests are the development of the combination methods for agro-food process engineering and food safety. Moon S. Kim is ARS, USDA research physicist working on development of optical sensing technologies for food safety research projects. He leads a multidisciplinary team of researchers to develop innovative sensing methodologies and technologies to address food safety concerns for food production and to aid in reducing food safety risks in food processing. Wang-Hee Lee is assistant professor in the Department of Biosystems Machinery Engineering at the Chungnam National University Daejeon, South Korea. He received M.S. and Ph.D. in Agricultural and Biological Engineering from the Purdue University, West Lafayette, in 2006 and 2011, respectively. His research interests are biosystem modeling and data analysis applicable for agricultural, biological and food systems including their products and process engineering.
Fig. 1. Schematic diagram of Makgeolli production.
Figure 2. Changes in ethanol concentration for 10 days during fermentation of Makgeolli (a: steamed non-glutinous rice, b: steamed glutinous rice).
(a)
(b)
Figure 3. Changes in reducing sugar concentration for 10 days during fermentation of Makgeolli (a: steamed non-glutinous rice, b: steamed glutinous rice).
(a)
(b)
Figure 4. Changes in titratable acidity for 10 days during fermentation of Makgeolli (a: steamed non-glutinous rice, b: steamed glutinous rice).
(a)
(b)
Figure 5. Total transmittance spectra of Makgeolli using the FT-IR spectroscopy
Figure 6. Fingerprint regions (a: alcohol, and b: reducing sugar and titiratable acidity) in the reflectance spectra of Makgeolli using FT-MIR spectroscopy.
(a)
(b)
Figure 78. Regression coefficients of alcohol (a), reducing sugar (b), and titratable acidity (C) in the local PLSR model.
Figure 89. Prediction results of alcohol (a), reducing sugar (b), and titratable acidity (c) using the best PLSR model.
Table 1. Compositions of materials for Makgeolli production Ratio (Water : Ingredients) Water
1:1
1.5:1
2:1
2.5 L
3.75 L
5L
Ingredient (Rice flour + Steamed non-/glutinous rice)
2.5kg
※ R stands for non-glutinous rice and G stands for glutinous rice. The numbers in the letters mean the amount of water in the mixing ratio of rice to water. For example, R1 means that the mixing ratio of rice to water is at one to one (rice flour of 500g and steamed rice of 2 kg: water of 2.5L).
Table 2. Operating conditions of HPLC for the analysis of alcohol concentration in the Makgeolli sample Item
Condition
Instrument
Dionex Ultimate 3000 HPLC (Dionex Corp., CA, USA)
Column
Rezex ROA organic acid column (300 mm x 7.8 mm, Phenomenex, Torrence, CA, USA)
Column temperature
60℃
Mobile phase
0.005 N sulfuric acid
Flow rate
0.5 mL/min
Detector
Shodex RI-101 refractive index detector
Table 3. Conditions of reagents for CBC method Items
Condition 97.1 mg of 2.2’-bicinchoninic acid disodium salt
Reagent A
3.2 g of sodium carbonate monohydrate 1.2 g of sodium bicarbonate in 50 ml H2O
Reagent B
62 mg of copper sulfate pentahydrate 63 mg of L-serine in 50 ml H2O
Copper reagent
reagent A : reagent B = 1 : 1
Table 4. Data set i.e. alcohol, reducing sugar, and titratable acid for global PLSR using the FT-IR spectroscopy Item
Number
Alcohol Concentration
Calibration set
Reducing sugar Concentration
Calibration set
Titratable acidity
Calibration set
Prediction set
Prediction set
Prediction set
129
129
129
Mean
Range
Stdev.
96
10.74
0.15 ~ 17.73
4.64
33
10.70
0.19 ~ 17.94
4.84
96
4.25
0.30 ~ 17.67
4.50
33
4.59
0.33 ~ 20.63
5.04
97
0.54
0.14 ~ 0.71
0.12
32
0.54
0.18 ~ 0.71
0.12
Table 5. Global PLSR results of calibration, validation, and prediction of alcohol concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.986 0.539 0.988 0.5178 0.986 0.549 0.987 0.536 0.984 0.590
Validation R v2 SEV 0.984 0.588 0.985 0.578 0.984 0.597 0.983 0.610 0.982 0.626
LV 5 4 4 4 3
Prediction Rp 2 SEP 0.981 0.661 0.982 0.643 0.982 0.641 0.981 0.646 0.982 0.636
Table 6. Global PLSR results of calibration, validation, and prediction of reducing sugar concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.960 0.897 0.983 0.585 0.978 0.661 0.981 0.625 0.980 0.628
Validation R v2 SEV 0.950 1.020 0.974 0.728 0.969 0.799 0.972 0.758 0.973 0.745
LV 5 6 6 6 5
Prediction Rp 2 SEP 0.939 1.245 0.955 1.061 0.944 1.182 0.953 1.088 0.951 1.110
Table 7. Global PLSR results of calibration, validation, and prediction of titratable acidity with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.904 0.037 0.945 0.028 0.945 0.028 0.947 0.027 0.943 0.028
Validation R v2 SEV 0.895 0.039 0.929 0.032 0.925 0.033 0.934 0.031 0.909 0.036
LV 3 4 4 4 4
Prediction Rp 2 SEP 0.883 0.043 0.922 0.036 0.922 0.036 0.930 0.035 0.928 0.035
Table 8. Local PLSR results of calibration, validation, and prediction of ethanol concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.984 0.584 0.987 0.528 0.982 0.622 0.989 0.493 0.991 0.449
Validation R v2 SEV 0.983 0.622 0.985 0.5280 0.981 0.657 0.987 0.532 0.990 0.466
LV 4 3 2 3 2
Prediction Rp 2 SEP 0.975 0.738 0.983 0.610 0.978 0.707 0.983 0.606 0.984 0.595
Table 9. Local PLSR results of calibration, validation, and prediction of reducing sugar concentration with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.984 0.596 0.983 0.617 0.974 0.760 0.979 0.685 0.980 0.665
Validation R v2 SEV 0.970 0.817 0.970 0.824 0.961 0.930 0.968 0.847 0.970 0.826
LV 7 7 6 6 6
Prediction Rp 2 SEP 0.982 0.584 0.983 0.579 0.970 0.777 0.979 0.635 0.970 0.764
Table 10. Local PLS results of calibration, validation, and prediction of titratable acidity with raw and pretreated data using FT-MIR Preprocessing Raw NorrisGap S. Golay
st
1 deri. 2nd deri 1st deri. 2nd deri.
Calibration R c2 SEC 0.980 0.018 0.983 0.016 0.980 0.018 0.977 0.019 0.938 0.031
Validation R v2 SEV 0.966 0.023 0.972 0.021 0.970 0.022 0.965 0.023 0.921 0.035
LV 8 8 7 7 3
Prediction Rp 2 SEP 0.951 0.023 0.962 0.020 0.945 0.024 0.936 0.026 0.912 0.030