The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.
Chinese liquor is a distilled spirit mainly made from grain and obtained using distiller’s yeast. This type of liquor also contains abundant micronutrients and active ingredients. Moderate drinking results in positive effects to various aspects. China is the leading country in liquor production and consumption. Unfortunately, many fake products are being sold in the market because liquor occupies sufficient market share and is highly profitable. These fake liquors are generally composed of the low-cost inferior liquors that counterfeit famous liquor brands and are prepared by simple dilution of industrial ethanol. They not only cause economic losses to producers of famous liquor brands but also pose serious threat to the health of consumers. As an important part of liquor quality inspection, the identification of liquor brands is increasingly attracting considerable attention.
Chinese liquor is a complex mixtures and composed mainly of water and ethanol; the remaining components contain hundreds of trace elements with various contents. Identification of liquor brands usually requires the determination of various feature components and their content recipes using traditional instrumental analysis methods (e.g., high-performance liquid chromatography). Such a detection method is complicated, costly, and cannot meet the needs of large-scale applications. Currently, identification of liquor products relies mainly on the sensory judgment of tasters. This method results in problems (e.g., great subjectivity and low precision) and is difficult to conduct in large-scale promotions. Thus, developing a simple and effective identification method of liquor brands is valuable.
Chemometric developments have demonstrated the significant potential of the near-infrared (NIR) spectroscopy analysis method in rapid and reagent-less measurement. This method is a powerful tool for quantitative analysis in various fields, such as agriculture [
Spectral discriminant analysis uses computer pattern recognition to identify and classify samples on the basis of collected spectral data. Instead of the quantitative analysis for some components of samples, its bases are the spectral overall features including the spectral similarities of the same type samples and the spectral differences among the different type samples. Spectral discriminant analysis mainly includes two main algorithms. The first one is the extraction of feature information; the known effective methods for extraction are principal component analysis (PCA) [
Chinese liquor brands use three main flavors, namely, strong fragrant, mild fragrant, and sauce fragrant flavors. Compared with the liquor samples with same flavor, the difference of the liquor components and the corresponding spectra with different flavors are more obvious [
The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. As far as we know, there is currently barely no effective discriminant method for liquor samples with the same flavor and alcohol content.
The present study focused on the identification method for liquor brands with the same flavor and ethanol content. Although difficult, such a method is important and effective. The partial least squares discrimimant analysis (PLS-DA) method combined with the NIR spectroscopy were employed for the spectral discriminant analysis of liquor brands, and the principal component linear discriminate analysis (PCA-LDA) method is also performed for comparison.
As a kind of popular liquor brand with strong fragrant flavor in China, Luzhou Laojiao was used as the identified liquor brand. Liquors of the 10 other kinds of brands with strong fragrant flavor and the same ethanol content were used as the interferential samples. The collected liquor samples totalled 360. The identified liquor samples (160 bottles, negative) were composed of 160 bottles of Luzhou Laojiao Danya Erqu liquor (Luzhou Laojiao Group Co., Ltd., 52 vol, 125 mL). The interfering liquor samples (200 bottles, positive) were composed of 20 bottles from 10 other liquor brands. The 10 liquor brands were (1) Bainian Hutu (Value Pack) (Hutu Alcohol Co., Ltd., 52 vol, 125 mL), (2) Dukang Taibai (Luoyang Dukang Holdings Co., Ltd., 52 vol, 125 mL), (3) Shixiantaibai Xiaojiu-xiaojiuxian (Chongqing Shixiantaibai Alcohol Co., Ltd., 52 vol, 125 mL), (4) Luzhou Laojiufang Xiaoluzhou (Luzhou Laojiufang Sales Co., Ltd., 52 vol, 125 mL), (5) Tangchao Laojiao Gujiuwang (Tangchao Laojiao Co., Ltd., 52 vol, 125 mL), (6) Wudang Xiaojiufan (Xianzunniangjiu Co., Ltd., 52 vol, 125 mL), (7) Jingjuyuan Pingjian (Luzhou Jingjuyuan Alcohol Co., Ltd., 52 vol, 125 mL), (8) Tianxiafu (Anhui Tianxiafu Alcohol Co., Ltd., 52 vol, 125 mL), (9) Guifeizuijiu Xiaozui (Sichuan Guifeizuijiu Alcohol Co., Ltd., 52 vol, 125 mL), and (10) Kongfujia Shengshidatao (Kongfujia Alcohol Co., Ltd., 52 vol, 125 mL). A sample was extracted from each bottle of liquor and used for spectral measurement.
Spectral measurement was performed using a VERTEX 70 FT -NIR Spectrometer (Bruker Co., Germany) equipped with a transmission accessory and a 1 mm cuvette. Twelve scans of symmetrical interferograms at an 8 cm−1 resolution were added to each spectrum. The scanning scope of the spectrum ranged from 14994 cm −1 to 3996 cm −1 at a 3.857 cm −1 wavenumber interval, with 2852 wavenumbers. An InGaAs detector was used for the entire scanning region. Each liquor sample was measured twice, and the mean value of the measurements was used for modeling and validation. The spectra were obtained at 25˚C ± 1˚C and 45% ± 1% RH. The time of acquisition of an NIR spectrum was about 0.5 min.
The Kennard-Stone (K-S) algorithm [
A framework of calibration, prediction, and validation was performed to produce objective models. To ensure modeling representativeness and integrity, the calibration, prediction, and validation sets must all contain negative and positive samples. First, 60 negative and 80 positive samples were randomly selected for validation. The remaining samples (100 negative and 120 positive) were used for modeling. Using the K-S algorithm, the modeling samples were further divided into calibration (50 negative and 60 positive) and prediction (50 negative and 60 positive) sets to achieve uniformity and representativeness. Then, all models were established for the calibration and prediction sets, and the modeling parameters were optimized on the basis of the prediction recognition rate. Finally, the selected model was revalidated against the validation samples excluded from the modeling process.
PCA-LDA is the commonly well-performed method for spectral discriminant analysis [
Based on principal component analysis, the PCA-LDA method uses the principal component vector of the spectral matrix to achieve qualitative discrimination of samples.
Unlike the PCA-LDA method, the PLS-DA method classified the results of PLS quantitative analysis based on the assignment method, and then achieved qualitative discrimination of samples [
The validation samples excluded from the modeling optimization process were used to validate the optimal model of PLS-DA method. According to the genuine brand type of each validation sample and the number of correctly recognized validation samples, the validation recognition rate can be calculated easily and was denoted as V_REC. Moreover, the corresponding validation recognition rates of negative and positive samples can be calculated and were denoted as V_REC− and V_REC+, respectively.
The NIR spectra in the entire scanning region (14994 - 3996 cm − 1) of 200 Luzhou Laojiao (negative) and 160 non-Luzhou Laojiao (positive) liquor samples are shown in
Using the method in Section 2.3, the PCA-LDA model was first established. The optimal principal component combination was PC1 - PC2, and the corresponding P_REC was 92.4%.The corresponding modeling parameters and effects were summarized in
Methods | F | PCC | P_REC |
---|---|---|---|
PCA-LDA | - | 1, 2 | 92.4% |
PLS-DA | 7 | - | 99.1% |
Note: PCC: principal component combination; F: number of PLS factors; P_REC: prediction recognition rate.
The PLS-DA model was established according to the method in Section 2.4. The optimal number of PLS factors (F) was 7, and the corresponding P_REC was 99.1%. The corresponding modeling parameters and effects were also summarized in
The randomly selected validation samples excluded from the modeling
optimization process were used to validate the optimal model of PLS-DA method (F = 7). The corresponding validation recognition rates V_REC−, V_REC+, and V_REC achieved 96.7%, 100% and 98.6%, respectively. As shown in
Chinese liquor is a popular alcoholic beverage, and occupies huge market share in China. The identification of liquor brands is of great significance for liquor food safety. There is currently barely no effective discriminant method for liquor samples with the same flavor and alcohol content because their chemical components are very similar.
In the present study, the PLS-DA method was successfully applied to the NIR spectral discriminant analysis of liquor brands with the same flavor and ethanol content. The experimental results indicate that the optimal PLS-DA model achieved high prediction recognition rate for the identification of liquor brands, and were obviously better than the results obtained from the PCA-LDA method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the identification of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.
Further wavelength selection can usually improve the spectral prediction effect, and reduce the scope of waveband, which will be the direction of the future researches.
This work was supported by the Science and Technology Project of Guangdong Province of China (No. 2014A 020213016, No. 2014A 020212445).
Yang, B., Yao, L.J. and Pan, T. (2017) Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands. Engineering, 9, 181-189. https://doi.org/10.4236/eng.2017.92009