Rapid Determination of Water and Oil Content in Instant Noodles by Fourier

Transcription

1 Food Sci. Technol. Res., 19 (3), , 2013 Technical paper Rapid Determination of Water and Oil Content in Instant Noodles by Fourier Transform Near-Infrared Reflectance Spectroscopy Ying-guo Lü, Jie Chen *, Xue-qin Li, Chun Wang and Li-li Wei College of Grain, Oil and Food, Henan University of Technology, Zhengzhou, China Received September 29, 2012; Accepted January 7, 2013 A rapid, non-destructive method, based on Fourier transform near-infrared (FT-NIR) spectroscopy for water and oil content determination of instant noodles was presented. A calibration set of 80 samples, a validation set of 40 samples and a prediction set of 10 samples of instant noodles were used. The diffuse reflection spectra of samples were measured by FT-NIR analyzer in the nm spectral range. The optimal models for water and oil content were constructed by comparing different preprocessing method and partial least squares (PLS) factor number. Prediction results indicated good predictive ability of the developed models we set for water and oil content in instant noodles. The correlations between FT-NIR s and chemical s of the two indexes were and , respectively. The study provided a rapid method for instant noodles quality control using FT-NIR spectra combine with the method developed in this paper. Keywords: instant noodles, near-infrared, water content, oil content, partial least squares Introduction The first instant noodle, called chicken ramen, was produced by Nissin Foods of Japan in Instant noodles became a mainstream food instantly and their consumers are not only in Asia but worldwide (Fu, 2008). Most instant noodles are steamed and deep-fried. Noodles are partially cooked by steaming and further cooked and dehydrated by a deep-frying process. Health concerns about the fat in fried noodles have led to the production of steamed and hotair dried instant noodles. But fried instant noodles were still popular for its superior taste and sensory properties. Steamed-and-fried instant noodles represent a fast growing product and have a fast growing market in Asian countries (Shin and Kim, 2003; Yu and Ngadi, 2004). The moisture and oil contents were important indexes for instant noodles. The moisture content influences the number of microorganism. Oil content concerns with people s health directly, and it also account for half of the product cost. There are some standards for these two indexes (China National Standard, 2003a; China Industrial Standard, 1995). *To whom correspondence should be addressed. nlgg2010@yeah.net Testing the water and oil content is essential for the product quality control. Instant noodles are manufactured on a production line, so the quality must be controlled by in-line inspection. However, all of these chemical detection methods were time-consuming, laborious, and costly procedures (China National Standard, 2003b, 2003c ). Near infrared spectroscopy (NIR) is an ideal analytical method for in-line detection with the characteristics of high speed, low cost, nondestructive and reliable detection method for quantitative and qualitative analysis (Willams and Norris, 1987). In addition, several components can be determined simultaneously from a single spectrum with the help of the multivariate calibration process. During calibration, a correlation of the NIR spectra and the chemical/ physical parameters in question (determined by reference method) is searched for by means of chemometric evaluation techniques. Multivariate, linear and non-linear calibration methods, most often multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS) or BP neural network (BPNN) are applied. Compared to the MLR calibration, PCR and PLS calibration technique display higher accuracy as addressing not only one spectral data points for the calibration, but the whole spectral

2 394 structure (Adams, 1995). Fourier transform near-infrared spectroscopy (FT-NIR) is widely applied in industries, such as agriculture, pharmaceuticals, food, textiles, cosmetics, and polymer production industry (Cen and He, 2007; Yan et al., 2005). Some studies have also applied NIR for instant noodles quality testing. The wavelength of oil in instant noodles were determined, and wavelet transforms in combination with NIR were applied for the improvement of predicting precision of oil content in instant noodles (Xiong et al.,1999; Chen et al., 2000; Chen et al., 2002). The calibration methods involved were MLR and BPNN. Liu and He (2008) applied visible and near infrared (Vis/NIR) spectroscopy combined with chemometric methods for the classification of brands of instant noodles using PLS and BPNN calibration method. However, there were few reports of the application of FT-NIR in water and oil content measuring in instant noodles by whole spectral calibration by PLS or PCR. The objective of this study was to develop a rapid determine method of water and oil content in instant noodles by FT-NIR. As the authors found the model calibrated by PLS is more stable and precise than PCR method in the previous work (Wei, 2010), PLS regression method was taken in this study. Preprocess method and PLS factor numbers were studied for the modeling for water and oil content measuring in instant noodles. Materials and Methods Samples Accordingly to the ISO standard (International Standard, 2006) requirements for at least 120 samples, a total of 130 instant noodle samples were taken into account the development of the calibration model. The samples were purchased from the supermarket. They all belong to popular brands in China, with acceptable water and oil contents. These samples were divided into three groups: 80 samples for calibration set, 40 samples for validation set and the remaining 10 samples for prediction. All samples were ground to pass a 40-mesh sieve after collecting and separated into two parts: one group for standard chemical analysis, the other for FT-NIR analysis. Chemistry measurements method The water and oil content of the samples were measured by chemistry method previously under the guidelines of the national inspection standard of GB/T and GB/T respectively (China National Standard, 2003b, 2003c). Spectral measurements The samples were subsequently measured on a Buchi NIRFlexN-500 FT-NIR analyzer (Buchi, Swiss) equipped with a TeO 2 wedge and polarization interferometer; a tungsten halogen light source; a peltier temperature control InGaAs detector working in the 1000 Y.-G. Lü et al. 2500nm wavelength range ( cm -1 wave number) combined with a NIRWare Operator (Buchi) software for Windows. Absorption spectra were collected in diffuse reflectance mode. The resolution was 8 cm -1. The scanner speed was 4 times/s and each spectra was the average spectrum of 16 subsequent scans. In order to lessen the inspecting error, the size, density, measurement position and smoothness of samples must be controlled strictly (Wu, 1994). Every sample was scanned for 3 times. All spectral data were stored for further studies. Spectral preprocessing and calibration NIRCal 5.2 Chemometric Software (Buchi) was used for the spectral preprocessing and calibration. Preprocessing for the spectral was carried out by three steps: (1) Spectral homogeneity of samples was tested before calibration. Samples with a signal strength higher than 2.5 times of their spectra residual or property residual (calculated by NIRCal 5.2) were regarded as spectral outliers (Zhu, 2004). (2) The following spectral preprocessing method was applied: no preprocessing (NONE), approach normalized (NCL), standard deviation normalized (NSD), multiplicative scatter correction (MSC), standard normal variable variations (SNV) and 9 convolution smooth processing(sg9). (3) Wavelength range selecting. The tests were carried out between the wavelength of nm. Then, the preprocessed spectra data was calibrated by NIRCal 5.2 uses PLS regression. The optimal number of PLS factors was auto-selected using minimum RMSE (root mean square error) by NIRCal 5.2 (Geladi, 2003). The following statistical parameters were used for characterizing the prediction models. The coefficient of determination R 2 (R 2 c for calibration set, R 2 v for validation set) reflects the relationship between predicted and chemical. The closer R 2 to 1, the more precise the model is. SEC (standard error of calibration) and SEP (standard error of prediction) were very important parameters for judging the model. The smaller of the two parameters, the closer of the predict result to the chemical measurement. Q reflects the quality of the model directly. This is more close to 1, the calibration model is better. The s of R 2, SEC, SEP and Q calculated by NIRCal 5.2, respectively, were used to characterize the calibration and validation models. Results and Discussion Sample analysis Water and oil content of the 120 modeling samples were measured by chemical method (result summarized in Table 1). The distribution of water and oil content of instant noodles used in calibration and validation set was showed in Fig. 1. The water content of the samples was lower than the limits of the national standard of instant noodles GB ( 8%) (China National Standard,

3 FT-NIR Method for Water and Oil Content Determination 395 Table 1. Water and oil content of samples in calibration and validation set. Composition Set Sample Minimum /% Maximum /% Mean /% Standard deviation Water content Oil content Calibration set Validation set Calibration set Validation set Sample number Validation set Calibration set Sample number Validation set Calibration set Water content (%) Oil content (%) (a) Water content distribution of samples (b) Fat content distribution of samples Fig. 1. Distribution of samples used in fat content calibration and validation set. Fig. 2. Original NIR spectra of instant noodle samples. As the number of sample is high, the blue color is too saturate and turns to be green. 2003a). The oil content of the samples was lower than the limits of the business standard of instant noodles LS/T ( 24%) (China Industrial Standard, 1995). The regularity of distribution in the sample sets fit the market regularity of instant noodle in china: most of the water contents were between 3 6%, and most of the oil contents were between 15 22% (Fu, 2008). This indicates that the samples we selected were quite typical. The method we developed has applicability for the instant noodles in this scope. FT-NIR spectrum Spectra of 120 instant noodle samples were scanned and investigated. 5 spectral outliers were de- leted from the set according to the spectra residual and property residual. Fig. 2 displays the sample spectrums without outliers. As can be seen in Fig. 2, the general absorption profiles of investigated samples are quite similar. The composition of the sample influences mainly the extent of reflectance; it has no significant effect on the shape of FT-NIR spectra. As described in the previous study, the spectral around nm related to the water content (Yan et al., 2005), and the characteristic band for palm oil used in instant noodles distributed in wavelength between

4 396 Y.-G. Lü et al. Table 2. Results of water content calibration based on different pretreatments. Q Value Preprocessing Number of factors R 2 c SEC R 2 v SEP NONE MSC NSD SNV NCL nm (Xiong et al.,1999). The finally selected wavelength region ( nm) involved the characteristic region of water and palm oil. Modeling Spectral preprocessing and PLS regression were carried out to construct the models for water and oil content determination. 1. Modeling for water content measurement Table 2 indicates PLS results of prediction for water content using FT- NIR. The purpose of pretreatment is to purify spectra, eliminate fully or partly the systematic and random error brought by all kinds of factors. The five preprocessing methods were performed to determine the most appropriate model. We can see from the results that preprocess method affect the model quality very obviously. The optimal factor number was also different when different preprocess method was used. For water content in instant noodles measurement, the best model with maximum Q is: preprocessing by SNV, and calibration by 12 factor PLS. In this model, R 2 c and R 2 v were closer to 1, SEC and SEP were closer to 0. Linear regression plot of chemical s against FT-NIR in the case of calibration for water content determination is shown in Fig. 3 (a) (using the optimal model suggested). 2. Modeling for oil content measurement For oil content modeling, statistical parameters of calibration and validation are presented in Table 3. Five optimal preprocessing methods were performed to the original FT-NIR spectra. The best model to measure oil content in instant noodles including the procedures of: preprocessing by MSC, and calibration by 13 factor PLS, with the maximum Q of In this model, R 2 c and R 2 v were and , respectively. SEC and SEP was quite small at the mean time. Fig. 3 (b) shows measured oil content using chemical method versus predicted oil content based on optimal model. Model prediction Predictive abilities of the developed models were tested by the use of a prediction set composed by 10 instant noodle samples. The samples of the prediction set were not used in the calibration procedures. Results of the verification were showed in Table 4. Fig. 4 illustrated the correlation of chemical and FT-NIR predicted for prediction set. Fig. 3. Comparison of chemical and FT-NIR predicted for calibration set and validation set. Table 3. Results of oil content calibration based on different pretreatments. Q Value Preprocessing Number of factors R 2 c SEC R 2 v SEP NONE NCL MSC SNV SG

5 FT-NIR Method for Water and Oil Content Determination 397 Table 4. Comparison of chemical s and FT-NIR predicted s for prediction set. Water content/% Oil content /% Sample Chemical FT-NIR Deviation Relative deviation (%) Chemical FT-NIR Deviation Relative deviation (%) FT-NIR predicted /% y = x R 2 = Chemical /% (a) Water content model FT-NIR predicted /% y = x R 2 = Chemical /% (b) Oil content model Fig. 4. Correlation of chemical and FT-NIR predicted for prediction set. The result indicated good predictive ability of the developed models we set for water and oil content in instant noodles. The correlations between FT-NIR s and chemical s of the two indexes were and , respectively. The relative deviations of water and oil content model were all under the limits of chemical method GB/T (5%) and GB/T (10%) (China National Standard, 2003b, 2003c). The models we set were qualified to test the water and oil content in instant noodles with higher precision than the method reported before (Xiong et al., 1999; Chen et al., 2000; Chen et al., 2002). An advantage of PLS calibration is the amount of spectral information used, so that even minor differences in the sample spectra can be accounted for. Another advantage is full cross-validation used in PLS calibration model to validate the quality and to prevent over-fitting. In conclusion, FT-NIR spectra combine with the method developed in this paper, is useful for rapid determination of water and oil content in instant noodles. Acknowledgements This work was financially supported by the National Natural Science Foundation of China (Project number ), and the Doctoral Foundation of Henan University of Technology (2010BS012). References Adams, M. (1995). Chemometrics in Analytical Spectroscopy. The Royal Society of Chemistry, Thomas Graham House, Science Park, Cambridge CB4 4WF, Cambridge. Cen, H.Y. and He, Y. (2007). Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci. Tech., 18, Chen, B., Fang, R.M. and Zhao, J.W. (2000). Fast detection of oil content in instant noodles by near-infrared reflection spectroscopy. Trans. Chin. So. Agric. Mach., 31(5), Chen, B., Fu, X.G., and Lu, D.L. (2002). Improvement of predicting precision of oil content in instant noodles by using wavelet transforms to treat near-infrared spectroscopy. J. Food Eng., 53,

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