Jian Liu‡
ab,
Lixia Liu‡a,
Wei Guoa,
Minglang Fua,
Minli Yanga,
Shengxiong Huangb,
Feng Zhang*a and
Yongsheng Liu*b
aInstitute of Food Safety, Chinese Academy of Inspection & Quarantine, Beijing 100176, China. E-mail: fengzhang@126.com; fengzhangchem@yahoo.com; Fax: +86-10-53898008; Tel: +86 13651290763
bSchool of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China
First published on 6th June 2019
This study has established a new method for the sensory quality determination of garlic and garlic products on the basis of metabolomics and an artificial neural network. A total of 89 quality indicators were obtained, mainly through the metabolomics analysis using gas chromatography/mass spectrometry (GC/MS) and high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The quality indicator data were standardized and fused at a low level, and then seven representative indicators including the a* (redness) value, and the contents of S-methyl-L-cysteine, 3-vinyl-1,2-dithiacyclohex-5-ene, glutamic acid, L-tyrosine, D-fructose and propene were screened by partial least squares discriminant analysis (PLS-DA), analysis of variance (ANOVA) and correlation analysis (CA). Subsequently, the seven representative indicators were employed as the input data, while the sensory scores for the garlic obtained by a traditional sensory evaluation were regarded as the output data. A back propagation artificial neural network (BPANN) model was constructed for predicting the sensory quality of garlic from four different areas in China. The R2 value of the linear regression equation between the predicted scores and the traditional sensory scores for the garlic was 0.9866, with a mean square error of 0.0034, indicating that the fitting degree was high and that the BPANN model built in this study could predict the sensory quality of garlic accurately. In general, the method developed in this study for the sensory quality determination of garlic and garlic products is rapid, simple and efficient, and can be considered as a potential method for application in quality control in the food industry.
Recently, various garlic products have become available, such as garlic paste, garlic slices, salted garlic, picked garlic and fermented garlic, so as to suit the growing demands of consumers. As one of the important aspects of garlic quality, the sensory quality of garlic and garlic products is easily influenced by the cultivars, geographical origin, and processing methods of the garlic.6,7 In addition, researchers have proved that the sensory quality of garlic and garlic products is not only associated with its content of organosulfur compounds, but also closely related to the contents of sugars, amino acids, and phenolics. For example, Pardo et al.7 analyzed relationships between the physicochemical and sensory parameters of 14 garlic varieties, and showed that the total sugar content was positively connected with the color parameters a* and h* (indicating red-green component and hue angle, respectively). Zhang et al.8 found that the temperature had a significant influence on the basic indictors of sensory quality of black garlic, including the browning intensity, reducing sugar, total acids and allicin. Li et al.9 identified several phenolic compounds in garlic which could inhibit the decomposition of alliin and alliinase. Allicin can be produced when alliin reacts with alliinase, and allicin is easily decomposed into a series of S-containing volatiles, which are the main source of the flavor of garlic. However, the literature on garlic sensory quality reported so far does not fully reflect the sensory quality of garlic and garlic products by considering both physical and chemical traits. Moreover, the relationship between physical and chemical traits and sensory quality needs a further study, which may promote the sensory evaluation of garlic and the development of garlic breeding for special flavor.
Currently, the most commonly used method for evaluating the sensory quality of garlic and garlic products depends on a descriptive sensory analysis and uses a trained panel,10–12 but this has the disadvantages of being time-consuming and easily affected by subjective factors. Although some new instruments have already been used in sensory analysis of food, such as an electronic tongue and nose, they are still too expensive to be routinely used for quality control.
Since metabolomics offers a powerful means of monitoring all component concentrations that may be related to the sensory quality of a food sample,13 it has been widely applied in the food industry and it makes the sensory quality assessment of garlic more accurate and comprehensive. Besides, a combination of multiple instrumental techniques can be more effective in discovering broader types of metabolites, and has been widely applied in the field of food.14,15 In particular, multivariate statistical analysis is used to screen the most important quality indicators of a food, and includes analysis of variance (ANOVA), principal component analysis (PCA) and correlation analysis (CA).16,17 These screened sensory quality indicators offer more accurate information about food quality and simplify the progression of the food quality assessment. However, the sensory evaluation of garlic based on metabolomics has not been reported.
The back propagation artificial neural network (BPANN) model is an artificial intelligence information processing system and is recognized as one of the mostly extensively used artificial neural network models. Since the BPANN model mainly imitates the human brain in processing complicated issues, a BPANN model generally includes three layers: an input layer, a hidden layer, and an output layer.18 Central to the construction of a BPANN model is the continual training of the input data and the target output data until the accuracy of the predicted results is satisfactory, which is very convenient and does not require any mathematical formula, or weighting of input data.19 Therefore, a BPANN model not only has the capability to solve linear problems, but can also be applied to the handling of nonlinear problems. Nevertheless, the BPANN model does have several drawbacks: for instance, the model-related parameters (which are the sensitivity factors for applying the BPANN model) are difficult for learners to confirm, and non-ideal reproducibility is often encountered. However, a number of investigations have reported satisfactory results for food quality assessment using a BPANN model. For example, Wang et al.20 established a BPANN model based on the data of hyperspectral imaging and successfully discriminated rice variety and quality. Lu et al.21 developed an efficient approach based on a grade classification model and a BPANN model, and the accuracy of this new approach for predicting the eating quality of rice reached 90%. In order to accurately assess the storage quality of fresh-cut green peppers, Meng et al.22 built a BPANN model using oxygen, carbon dioxide, temperature and humidity as the input data, and b value, water loss, firmness and vitamin C content as the output data, and the established BPANN model gave good predicted results.
The present study aims to establish a new methodology for sensory quality assessment of garlic and garlic products based on metabolomics and an artificial neural network. Firstly, the whole chemical constituents of garlic were acquired by a non-targeted metabolomics method, mainly using gas chromatography/mass spectrometry (GC/MS) and high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS) for detecting different classes of compounds. Secondly, the obtained data on the garlic constituents was combined through low-level fusion, and filtered by partial least squares discriminant analysis (PLS-DA), ANOVA and CA, in order to identify representative sensory quality indicators for garlic. Thirdly, a BPANN model was established for predicting the sensory quality of garlic; the representative sensory quality indicators of garlic were used as the input data of the BPANN model, and the sensory scores of garlic obtained by traditional sensory evaluation were regarded as the output data. A correlation coefficient between the predicted scores (from the developed BPANN model) and the sensory scores of garlic (obtained by traditional sensory evaluation) using a linear regression model was adopted to validate the BPANN model. This method is valuable not only for evaluating the quality of garlic and garlic products comprehensively and objectively, so determining the quality characteristics of different garlic cultivars, but also for developing garlic products and promoting the development of the garlic industry.
Sample | Texture | Taste | Appearance | Flavor | Overall impression |
---|---|---|---|---|---|
a The results are indicated as mean ± std. error; different lowercase letters in the same column indicate significant differences (p < 0.05). | |||||
SD1 | 6.3 ± 0.5abcde | 6.3 ± 1.0abcde | 5.4 ± 0.9abc | 5.1 ± 0.7abcd | 5.8 ± 0.4abcd |
SD2 | 6.7 ± 0.6abcde | 5.6 ± 0.7abc | 5.7 ± 1.0abc | 5.0 ± 0.5abc | 5.6 ± 0.2a |
SD3 | 7.0 ± 1.0abcde | 5.6 ± 0.9abc | 5.7 ± 1.0abc | 4.9 ± 0.4ab | 5.6 ± 0.2a |
SD4 | 6.8 ± 0.7bcde | 5.5 ± 1.0ab | 5.7 ± 2.0abc | 5.3 ± 0.7abcdef | 5.7 ± 0.3ab |
SD5 | 7.0 ± 1.0bcde | 5.8 ± 0.8abcd | 5.1 ± 2.0ab | 5.3 ± 0.7abcdef | 5.8 ± 0.3abc |
SD6 | 6.8 ± 0.4bcde | 5.8 ± 0.9abcd | 6.5 ± 0.9abcd | 4.7 ± 0.7a | 5.7 ± 0.2a |
SD7 | 6.8 ± 0.2bcde | 6.2 ± 0.9abcde | 5.7 ± 1.0abc | 5.2 ± 0.6abcde | 5.9 ± 0.3abcde |
JS1 | 6.8 ± 0.7bcde | 5.4 ± 0.6ab | 5.7 ± 1.0abc | 5.4 ± 0.7abcdefg | 5.7 ± 0.2ab |
JS2 | 6.7 ± 0.5abcde | 5.8 ± 1.0abcd | 4.6 ± 1.0a | 5.6 ± 0.7abcdefgh | 5.8 ± 0.4abc |
JS3 | 7.1 ± 0.9de | 6.2 ± 1.0abcde | 7.0 ± 1.0abcd | 5.6 ± 0.4abcdefgh | 6.3 ± 0.2efg |
JS4 | 7.0 ± 0.8cde | 6.3 ± 0.8abcde | 6.8 ± 2.0abcd | 5.4 ± 0.5abcdefg | 6.2 ± 0.1cdefg |
JS5 | 6.6 ± 0.7abcde | 6.5 ± 2.0abcde | 4.9 ± 1.0a | 5.9 ± 0.8bcdefghi | 6.1 ± 0.4bcdef |
JS6 | 6.7 ± 0.6abcde | 5.2 ± 1.0a | 6.3 ± 2.0abcd | 5.0 ± 0.2abcd | 5.6 ± 0.2a |
JS7 | 6.8 ± 0.9bcde | 6.6 ± 0.8abcde | 7.1 ± 1.0abcd | 5.1 ± 0.8abcd | 6.2 ± 0.4bcdef |
HN1 | 6.3 ± 0.7abcde | 6.7 ± 2.0abcde | 6.5 ± 2.0abcd | 5.7 ± 0.7abcdefgh | 6.2 ± 0.1defg |
HN2 | 7.1 ± 0.8e | 7.5 ± 0.4de | 6.2 ± 0.5abcd | 6.2 ± 0.6defghi | 6.8 ± 0.2ijk |
HN3 | 6.9 ± 0.6bcde | 6.2 ± 1.0abcde | 6.2 ± 1.0abcd | 6.2 ± 1.0cdefghi | 6.3 ± 0.4efgh |
HN4 | 6.5 ± 0.8abcde | 7.3 ± 0.6cde | 7.6 ± 0.9bcd | 6.1 ± 0.7bcdefghi | 6.7 ± 0.3hijk |
HN5 | 7.2 ± 0.4e | 6.2 ± 1.0abcde | 6.2 ± 0.5abcd | 6.2 ± 1.0cdefghi | 6.4 ± 0.3fghi |
HN6 | 7.0 ± 0.9bcde | 6.8 ± 2.0abcde | 5.9 ± 2.0abcd | 6.0 ± 0.3bcdefghi | 6.5 ± 0.1fghi |
HN7 | 6.5 ± 1.0abcde | 6.5 ± 0.4abcde | 6.5 ± 2.0abcd | 6.2 ± 1.0cdefghi | 6.4 ± 0.4fgh |
YN1 | 5.6 ± 0.3a | 7.8 ± 0.5e | 7.0 ± 1.0abcd | 5.9 ± 1.0bcdefghi | 6.6 ± 0.4ghijk |
YN2 | 6.1 ± 0.4abcde | 7.5 ± 1.0de | 5.9 ± 2.0abcd | 6.7 ± 0.9hi | 6.8 ± 0.4hijk |
YN3 | 5.6 ± 0.1a | 7.9 ± 0.4e | 6.2 ± 2.0abcd | 6.5 ± 0.9fghi | 6.8 ± 0.4hijk |
YN4 | 5.9 ± 0.2abc | 7.8 ± 0.9e | 6.8 ± 2.0abcd | 7.1 ± 0.6i | 7.1 ± 0.2k |
YN5 | 6.1 ± 0.4abcde | 7.7 ± 0.5e | 7.8 ± 1.0cd | 6.5 ± 0.5ghi | 7.0 ± 0.2jk |
YN6 | 5.9 ± 0.1abcd | 7.1 ± 0.5bcde | 6.5 ± 0.9abcd | 6.4 ± 0.7efghi | 6.6 ± 0.1fghij |
YN7 | 5.8 ± 0.2ab | 7.4 ± 0.6de | 8.1 ± 1.0d | 6.8 ± 0.8hi | 6.9 ± 0.2jk |
The next step was to combine the multiple data sets obtained from the different instrumental sources, to realize a complementarity between instruments and produce a more comprehensive and accurate sensory quality assessment of the garlic. Before the data fusion, the initial data were normalized to eliminate dimensional disturbances. Thus, zero-mean normalization of initial data was adapted in this study.38 In order to distinguish the garlic samples and investigate the differences in the sensory quality indicators, the normalized data was entered into the software SIMCA-P+ to perform PLS-DA. A PLS-DA model with 3 components was established (Fig. 1a) and the model quality was evaluated, with R2(X) = 0.583, R2(Y) = 0.969, and Q2=
0.948. A permutation test (n = 200) was performed to validate the PLS-DA model and the results are shown in Fig. 1b, indicating that the PLS-DA model based on 89 sensory quality indicators was not overfitted and was statistically acceptable. Each dot in Fig. 1a represents a garlic sample and the corresponding garlic name is marked nearby. The garlic samples that are placed together have similar levels of quality indicators and the garlic samples that are well separated have extremely different levels of quality indicators. From the PLS-DA score, it can obviously be seen that the garlic samples collected from the same province are closely placed, which implies that they have similar levels of sensory quality indicators. In particular, the garlic samples produced in Shandong and Yunnan are separated significantly from those from Jiangsu and Henan, suggesting that the sensory quality of garlics in Shandong and Yunnan is quite different from that in garlics from the other two provinces. In addition, the garlic samples collected from Henan and Jiangsu are mixed together in the score plot, indicating that there is little difference in the sensory quality of garlic samples from these two places.
![]() | ||
Fig. 1 (a) PLS-DA score plot and (b) permutation test plots (200 permutation tests) for garlic samples. |
As one of the most frequently used criteria in variable selection methods, VIP clarifies the importance of variables in the projection and determines the variables that contribute the most to a PLS model.39 Therefore, the variables with VIP > 1 in the present study were thought to be important sensory quality indicators of garlics from the four different origins. According to the VIP values in the PLS-DA model (Table 2), 43 sensory quality indicators were identified initially from the original 89 indicators. Simultaneously, these 43 sensory quality indicators showed significant differences by Duncan's multiple-range test (p < 0.05), probably reflecting the regional differences in the garlic samples.
Indicator | VIP value | Indicator | VIP value |
---|---|---|---|
L* value | 1.04 | Ethanimidic acid | 1.12 |
a* value | 1.12 | Serine | 1.34 |
b* value | 1.11 | L-Threonine | 1.2 |
Ascorbic acid | 1.06 | L-Proline | 1.71 |
S-Methyl-L-cysteine | 1.08 | n-Octanoic acid | 1.39 |
Quercitrin | 1.61 | Xylulose | 1.51 |
Propene | 1.16 | Ornithine | 1.09 |
Propene sulfide | 1.15 | Glutamic acid | 1.24 |
2-Propen-1-ol | 1.01 | Arabinose | 1.28 |
Di(1-propenyl) sulfide | 1.12 | L-Asparagine | 1.24 |
2-Ethylthiophene | 1.23 | Lanthionine | 1.19 |
Methyl propenyl disulfide | 1.06 | L-Tyrosine | 1.22 |
Dimethyl trisulfide | 1.15 | Isocitric acid | 1.51 |
Diallyl disulfide | 1.19 | D-Fructose | 1.26 |
Diallyl tetrasulfide | 1.15 | D-Glucose | 1.13 |
(Z)-Benzaldoxime | 1.12 | L-Lysine | 1.38 |
3-Vinyl-1,2-dithiacyclohex-4-ene | 1.3 | Gluconic acid | 1.6 |
Diallyl trisulfide | 1.18 | L-Homoserine | 1.06 |
3-Vinyl-1,2-dithiacyclohex-5-ene | 1.08 | Myo-inositol | 1.09 |
Propionic acid | 1.16 | Succinylacetone | 1.04 |
1,3 Propanediol | 1.18 | Sucrose | 1.06 |
L-Alanine | 1.25 |
Different degrees of correlation of the 43 differential quality indicators are shown in Fig. 2 and Table S4.† For instance, high correlations were found between methyl propenyl disulfide, dimethyl trisulfide, diallyl disulfide and diallyl tetrasulfide (p < 0.05), implying that one of these four compounds can be chosen to represent the others. As a consequence, we could conclude that there is a certain degree of correlation between the sensory quality indicators that causes overlapping among these indicators. Thus, the 43 sensory quality indicators could reasonably be reduced to several independent indicators according to the CA.
Colors represent the Pearson's correlation coefficient values: the red color indicates a positive (0 < r < 1) correlation and the blue color represents a negative (−1 < r < 0) correlation. X1-X43: L* value, a* value, b* value, ascorbic acid, S-methyl-L-cysteine, quercitrin, propene, propene sulfide, 2-propen-1-ol, di(1-propenyl) sulfide, 2-ethylthiophene, methyl propenyl disulfide, dimethyl trisulfide, diallyl disulfide, diallyl tetrasulfide, (Z)-benzaldoxime, 3-vinyl-1,2-dithiacyclohex-4-ene, diallyl trisulfide, 3-vinyl-1,2-dithiacyclohex-5-ene, propionic acid, 1,3-propanediol, L-alanine, ethanimidic acid, serine, L-threonine, L-proline, n-octanoic acid, xylulose, ornithine, glutamic acid, arabinose, L-asparagine, lanthionine, L-tyrosine, isocitric acid, D-fructose, D-glucose, L-lysine, gluconic acid, L-homoserine, myo-inositol, succinylacetone, sucrose.
Regarding the color parameters, the a* value was highly positively associated with the b* value and negatively related with the L* value (p < 0.05), suggesting that the a* value can be chosen to represent the L* value and b* value. The organosulfur compounds among the 43 differential sensory quality indicators included S-methyl-L-cysteine, propene sulfide, di(1-propenyl) sulfide, 2-ethylthiophene, methyl propenyl disulfide, dimethyl trisulfide, diallyl disulfide, diallyl tetrasulfide, 3-vinyl-1,2-dithiacyclohex-4-ene, diallyl trisulfide, and 3-vinyl-1,2-dithiacyclohex-5-ene. These organosulfur compounds showed different degrees of correlation with each other. In particular, S-methyl-L-cysteine was positively connected with di(1-propenyl) sulfide, methyl propenyl disulfide, dimethyl trisulfide, diallyl disulfide, and diallyl tetrasulfide (p < 0.05), and negatively correlated with propene sulfide, 2-ethylthiophene and 3-vinyl-1,2-dithiacyclohex-4-ene (p < 0.05). This might be because S-methyl-L-cysteine is one of the key garlic flavor precursors, and a number of volatile organosulfur compounds could be released by the action of alliinase on S-alk(en)yl-L-cysteine sulfoxides when garlic is crushed and disrupted.33,40 Therefore, S-methyl-L-cysteine can be used to stand for di(1-propenyl) sulfide, methyl propenyl disulfide, dimethyl trisulfide, diallyl disulfide, diallyl tetrasulfide, propene sulfide, 2-ethylthiophene and 3-vinyl-1,2-dithiacyclohex-4-ene in our study. Moreover, a notable positive correlation was discovered between diallyl trisulfide and 3-vinyl-1,2-dithiacyclohex-5-ene (CV (Coefficient of Variance) values were 21.65% and 25.98%, respectively); herein, 3-vinyl-1,2-dithiacyclohex-5-ene was chosen to represent diallyl trisulfide.
Organic acids and amino acids are important constituents that contribute to the taste, flavor and color of garlic and garlic products. Based on Fig. 2, glutamic acid was positively associated with L-alanine, serine, L-threonine, L-asparagine and L-lysine (p < 0.05), whereas negatively connected with lanthionine, L-homoserine, ascorbic acid, propionic acid, n-octanoic acid and isocitric acid (p < 0.05), indicating that glutamic acid can be selected to represent the variations of L-alanine, serine, L-threonine, L-asparagine, L-lysine, lanthionine, L-homoserine, ascorbic acid, propionic acid, n-octanoic acid and isocitric acid. L-Tyrosine was positively linked to ornithine and negatively correlated with L-proline (p < 0.05) and thus, L-tyrosine can indicate the changes in ornithine and L-proline.
Considering the sugars and other compounds in the garlic, D-fructose was positively correlated with D-glucose and sucrose, and negatively associated with myo-inositol (p < 0.05), suggesting that D-fructose has the ability to predict the variations in D-glucose, sucrose and myo-inositol. In addition, as an odorless compound in garlic, propene displayed significant positive associations with 2-propen-1-ol and (Z)-benzaldoxime (p < 0.05), and thus, propene was adopted to represent 2-propen-1-ol and (Z)-benzaldoxime.
In summary, CA of differential quality indicators in our study proved that the 43 sensory quality indicators can be reduced to several representative indicators. Furthermore, the association between differential sensory quality indicators and sensory attributes should be a major consideration when screening the representative sensory indicators of garlic.
S-Methyl-L-cysteine is one of the garlic flavor precursors, and is closely related to the release of organosulphur compounds in garlic, which are the main source of garlic aroma.2 According to our results (Table 3), a strong positive correlation was discovered between S-methyl-L-cysteine and garlic texture (p < 0.05), suggesting that a high content of S-methyl-L-cysteine may be associated with a softer garlic texture, while significant negative associations were shown between S-methyl-L-cysteine, and garlic taste, flavor and overall impression (p < 0.05). This may be because a higher retained S-methyl-L-cysteine content in a garlic extract means that lower levels of S-methyl-L-cysteine are converted to organosulfur compounds, leading to a weak spicy taste, a lighter garlic flavor and a lower overall impression. Volatile organosulfur compounds are recognized as the major contributors to the characteristic taste and flavor of fresh garlic. Furthermore, the different types and concentrations of volatile organosulfur compounds are the main factors in the sensory differences among garlic varieties.42,43 On the basis of our results (Tables 2 and 3), a number of volatile organosulfur compounds including propene sulfide, di(1-propenyl)sulfide, methyl propenyl disulfide, dimethyl trisulfide, diallyl disulfide, diallyl tetrasulfide, 3-vinyl-1,2-dithiacyclohex-4-ene, diallyl trisulfide, and 3-vinyl-1,2-dithiacyclohex-5-ene were actually found to be differential sensory quality indicators, and exhibited various degrees of correlations with sensory attributes of garlic. In particular, propene sulfide presented notable correlations with garlic texture, taste, appearance, flavor and overall impression (p < 0.01). Di(1-propenyl)sulfide and 2-ethylthiophene showed notable associations with garlic texture, taste, flavor and overall impression (p < 0.01). Methyl propenyl disulfide, dimethyl trisulfide and diallyl disulfide were significantly correlated only with garlic texture (p < 0.01), while diallyl tetrasulfide correlated with garlic texture and taste (p < 0.01); the relationship between these four compounds and the overall impression of garlic was not significant (p < 0.05), indicating that methyl propenyl disulfide, dimethyl trisulfide, diallyl disulfide, and diallyl tetrasulfide may not be key indicators contributing to the sensory quality of garlic. A previous literature study has proved that 3-vinyl-1,2-dithiacyclohex-4-ene and 3-vinyl-1,2-dithiacyclohex-5-ene are isomers, and originate from diallyl disulfide and allyl 1-propenyl disulfide, respectively.44 In our study, they were both positively associated with garlic taste, appearance, flavor and overall impression (p < 0.05), implying that they have an important role in garlic sensory quality. According to our CA of these organosulfur compounds (Fig. 2), S-methyl-L-cysteine and 3-vinyl-1,2-dithiacyclohex-5-ene were finally chosen as the representative organosulfur compounds.
Indicator | Texture | Taste | Appearance | Flavor | Overall impression |
---|---|---|---|---|---|
a *Represents significance levels of p < 0.05. **Represents significance levels of p < 0.01. | |||||
L* value | 0.66** | −0.66** | −0.41* | −0.51** | −0.58** |
a* value | −0.83** | 0.72** | 0.47** | 0.63** | 0.65** |
b* value | −0.78** | 0.67** | 0.38* | 0.65** | 0.62** |
Ascorbic acid | −0.71** | 0.76** | 0.42* | 0.73** | 0.73** |
S-Methyl-L-cysteine | 0.73** | −0.52** | −0.26 | −0.42* | −0.41* |
Quercitrin | −0.36* | 0.10 | 0.18 | 0.14 | 0.08 |
Propene | −0.05 | 0.32* | 0.27 | 0.50** | 0.46** |
Propene sulfide | −0.54** | 0.66** | 0.46** | 0.69** | 0.69** |
2-Propen-1-ol | −0.33* | 0.65** | 0.53** | 0.59** | 0.69** |
Di(1-propenyl) sulfide | 0.72** | −0.53** | −0.29 | −0.48** | −0.45** |
2-Ethylthiophene | −0.32* | 0.46** | 0.31 | 0.65** | 0.57** |
Methyl propenyl disulfide | 0.53** | −0.22 | 0.09 | −0.04 | −0.04 |
Dimethyl trisulfide | 0.51** | −0.13 | 0.03 | 0.08 | 0.06 |
Diallyl disulfide | 0.55** | −0.16 | 0.01 | 0.03 | 0.03 |
Diallyl tetrasulfide | 0.66** | −0.39* | −0.14 | −0.25 | −0.25 |
(Z)-Benzaldoxime | −0.02 | 0.36* | 0.15 | 0.53** | 0.48** |
3-Vinyl-1,2-dithiacyclohex-4-ene | −0.37* | 0.48** | 0.40* | 0.55** | 0.54** |
Diallyl trisulfide | 0.27 | 0.10 | 0.18 | 0.25 | 0.26 |
3-Vinyl-1,2-dithiacyclohex-5-ene | −0.28* | 0.61** | 0.44** | 0.62** | 0.68** |
Propionic acid | −0.52** | 0.72** | 0.60** | 0.84** | 0.83** |
1,3-Propanediol | −0.58** | 0.32 | 0.37* | 0.29 | 0.28 |
L-Alanine | 0.17 | −0.52** | −0.44* | −0.72** | −0.69** |
Ethanimidic acid | 0.46** | −0.01 | 0.19 | −0.04 | 0.10 |
Serine | 0.07 | −0.42* | −0.21 | −0.62** | −0.56** |
L-Threonine | 0.28 | −0.50** | −0.28 | −0.60** | −0.57** |
L-Proline | 0.15 | −0.23 | 0.00 | −0.27 | −0.24 |
n-Octanoic acid | −0.65** | 0.58** | 0.34* | 0.53** | 0.53** |
Xylulose | 0.07 | 0.06 | 0.21 | 0.21 | 0.18 |
Ornithine | −0.69** | 0.46** | 0.27 | 0.41* | 0.38* |
Glutamic acid | 0.59** | −0.79** | −0.46** | −0.86** | −0.84** |
Arabinose | 0.62** | −0.31 | −0.07 | −0.19 | −0.16 |
L-Asparagine | 0.04 | −0.30 | −0.32* | −0.48** | −0.45** |
Lanthionine | −0.75** | 0.61** | 0.39* | 0.52** | 0.53** |
L-Tyrosine | −0.49** | 0.44** | 0.05 | 0.30 | 0.32* |
Isocitric acid | 0.02 | 0.42* | 0.14 | 0.46** | 0.49** |
D-Fructose | 0.70** | −0.88** | −0.54** | −0.91** | −0.91** |
D-Glucose | 0.82** | −0.85** | −0.50** | −0.86** | −0.84** |
L-Lysine | 0.45** | −0.39* | −0.17 | −0.38* | −0.36* |
Glucuronic acid | 0.17 | 0.02 | 0.09 | −0.17 | −0.02 |
L-Homoserine | −0.66** | 0.50** | 0.23 | 0.45** | 0.42* |
Myo-inositol | −0.66** | 0.76** | 0.29 | 0.72** | 0.72** |
Succinylacetone | 0.01 | −0.02 | 0.07 | −0.09 | −0.04 |
Sucrose | −0.73** | 0.48** | 0.21 | 0.45** | 0.39* |
Previous literature reports have suggested that garlic is an excellent source of free amino acids, and that amino acid composition has an important influence on the taste attributes of garlic and garlic products.34,45 L-Alanine, L-proline and L-tyrosine may be responsible for the sweet taste, while glutamic acid is likely related with the umami taste.46 Free amino acids also play an important role in the color formation of garlic and garlic products. For example, the research of Zhang et al.8 indicated that racemization of amino acids is probably correlated with the color change of black garlic during its processing. Cho et al.47 identified candidate amino acids that may be connected with the color formation in crushed garlic; the results showed that amino acids other than glycine participated in the generation of blue pigments. Among the 43 differential sensory quality indicators of the garlic samples collected from four areas, a number of amino acids and organic acids showed various associations with sensory attributes of garlic (Table 3). Specifically, the garlic texture was not significantly correlated with L-alanine, serine, L-threonine, L-proline, L-asparagine or isocitric acid (p > 0.05), but it was tightly linked to ornithine, glutamic acid, lanthionine, L-tyrosine, L-lysine, L-homoserine, ascorbic acid, propionic acid, ethanimidic acid and n-octanoic acid (p < 0.05). Moreover, lanthionine, ascorbic acid and propionic acid were positively correlated with the garlic appearance, and in contrast, L-alanine, glutamic acid and L-asparagine presented negative correlations with the garlic appearance. Furthermore, lanthionine, L-tyrosine, L-homoserine, ornithine, ascorbic acid, propionic acid, n-octanoic acid and isocitric acid were obviously positively associated with the garlic taste, flavor and overall impression (p < 0.05), implying that they make notable contributions to the sensory quality differences between garlic samples. Negative relationships were observed between L-alanine, serine, L-threonine, glutamic acid, L-asparagine and L-lysine and the garlic taste, flavor, and overall impression (p < 0.05). Combining the correlations among these amino acids and organic acids, glutamic acid and L-tyrosine were selected as the representative indicators.
The main constituents of raw garlic are carbohydrates, of which the majority are known as fructans, a kind of fructose polymer with water solubility that provides a significant source of soluble dietary fiber.48,49 Furthermore, carbohydrates account for not only the major constituents of the cell wall but also the soluble sugars in the cytoplasm, and so carbohydrates (i.e. fructose, glucose and sucrose) are also closely related to the garlic texture.50 As seen in Table 3, arabinose, D-fructose and D-glucose were positively connected with the garlic texture, which suggests that high contents of arabinose, D-fructose and D-glucose may cause a soft garlic texture, whereas, sucrose presented a negative correlation with the garlic texture, indicating that a high level of sucrose probably leads to a hard garlic texture. However, negative associations between D-fructose, D-glucose and the garlic taste, appearance, flavor and overall impression were observed. This could be explained according to previous research:51 with an increasing content of fructans, the water-retentive performance of garlic cells may be reduced and the content of organosulfur compounds responsible for pungency may be relatively increased. Moreover, fructans can decompose into fructose, glucose and sucrose, which are responsible for the sweet taste of garlic. Thus, we can conclude that high concentrations of D-fructose and D-glucose are likely to lead to a decrease in garlic taste, appearance, flavor and overall impression, while sucrose displays the opposite results. After correlations among these sugars and other compounds were analyzed, D-fructose and propene were ultimately determined to be representative indicators.
Taken together, a total of seven representative indicators of garlic sensory quality were identified according to the multivariate statistical analysis. These indicators were the a* value, and the contents of S-methyl-L-cysteine, 3-vinyl-1,2-dithiacyclohex-5-ene, glutamic acid, L-tyrosine, D-fructose and propene, all of which were tightly correlated with the garlic sensory attributes and had the ability to reflect the sensory quality of garlic samples based on both physical and chemical traits.
In order to establish a BPANN model for the sensory quality assessment of garlic and garlic products, the seven representative indicators (a* value, S-methyl-L-cysteine, 3-vinyl-1,2-dithiacyclohex-5-ene, glutamic acid, L-tyrosine, D-fructose and propene) were standardized (such that they ranged from 0 to 1) and set as the input data of the BPANN model, while the sensory scores obtained by the traditional sensory evaluation were treated as the output data of the BPANN model. Because the measurement of garlic quality indicators and the traditional sensory evaluation were both repeated three times, altogether 84 groups of data were generated. 60 groups of data were randomly selected and regarded as training samples for the BPANN model. After the training process using the 60 groups of data was completed, the remaining 24 groups of data were used for predictions to test the accuracy of the model.
The hidden layers are the most important layers in the architecture of the BPANN model; the number of hidden layers and the number of neurons in the hidden layers influence the prediction accuracy of the BPANN model.52 A lower number of neurons in the hidden layers is not competent in obtaining information from a complicated data set, while an excessive number of neurons in the hidden layers may increase the time required for data training and cause “overfitting”. Finally, one hidden layer with six neurons was determined in this study to offer the best prediction of the sensory quality of garlic. A continuous training process was completed until the mean square error (MSE) between the training data and test data was lower than 0.006, and the other parameters were set as follows: maximum number of cycles, 500000; learning rate, 0.7; and momentum factor, 0.5. Fig. 3 shows the predicted results by the developed BPANN model. Test data (24 groups) was used to validate the prediction accuracy of the BPANN model, and a linear regression equation between the predicted score and the traditional sensory score for the garlic was y = 1.0538x − 0.3421. The R2 value and MSE were 0.9866 and 0.0034, respectively. This result indicates that the BPANN model matches well to the sensory evaluation and that the prediction precision is high. Thus, the sensory quality of garlic can be precisely predicted by the developed BPANN model. Furthermore, a BPANN model with a more reliable training set calibrated from a bigger group set and a larger amount of data is expected to be developed in the future. This could be used to predict the garlic sensory quality of garlic not only from China but also from elsewhere in the world.
Footnotes |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c9ra01978b |
‡ These authors contributed equally to this work. |
This journal is © The Royal Society of Chemistry 2019 |