Next Article in Journal
Systematic Study of Paeonol/Madecassoside Co-Delivery Nanoemulsion Transdermal Delivery System for Enhancing Barrier Repair and Anti-Inflammatory Efficacy
Previous Article in Journal
Comparison of Gas–Particle Partitioning of Glyoxal and Methylglyoxal in the Summertime Atmosphere at the Foot and Top of Mount Hua
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of Steaming and Drying of Black Sesame Seeds for Nine Cycles Using Grey-Correlation Analysis Based on Variation-Coefficient Weight

Key Laboratory of Biomarker—Based Rapid Detection Technology for Food Safety of Henan Province, Xuchang University, Xuchang 461000, China
*
Author to whom correspondence should be addressed.
Molecules 2023, 28(13), 5266; https://doi.org/10.3390/molecules28135266
Submission received: 9 June 2023 / Revised: 29 June 2023 / Accepted: 5 July 2023 / Published: 7 July 2023

Abstract

:
This study aimed to improve the steaming process of black sesame seeds. A comprehensive evaluation was conducted using the grey-correlation method based on the variation-coefficient weight to observe the treatments of normal-pressure (NPS) and high-pressure (HPS) steaming (with/without soaking in water) for nine cycles. Their effects on the contents of water, protein, fat, ash, melanin, sesamin, and sesamolin of black sesame seeds, as well as the sensory score of the black sesame pill, were determined. We found that with varied steaming methods and increased steaming cycles, the contents of the nutritional and functional components of black sesame seeds and the sensory score of the black sesame pill differed. The results of the variation-coefficient method showed that water, protein, fat, ash, melanin, sesamin, sesamolin, and sensory score had different effects on the quality of black sesame seeds with weighting factors of 34.4%, 5.3%, 12.5%, 11.3%, 13.9%, 11.3%, 7.8%, and 3.5%, respectively. The results of two-factor analysis of variance without repeated observations indicated that the grey-correlation degree of HPS was the largest among the different steaming treatments, and the following sequence was HPS after soaking in water (SNPS), NPS, and SNPS. There was no significant difference between NPS and SNPS (p < 0.05). Moreover, with increased cycles, the value of the grey-correlation degree increased. The comprehensive score of the procedure repeated nine times was significantly higher than other cycles (p < 0.05). The results of the grey-correlation degree and grade analysis showed that the best steaming process of black sesame seeds was HPS for nine cycles, followed by HPS for eight cycles and NPS after soaking in water (SNPS) for nine cycles. These findings could provide a scientific basis for replacing SNPS with HPS to simplify steaming and realize the parametric steaming of black sesame seeds, and thus, ensure the quality of black-sesame products.

Graphical Abstract

1. Introduction

Sesame (Sesamum indicum L.) of the Pedaliaceae family is an important and ancient oilseed crop with more than 50% oil [1] and 20% proteins [2]. Sesame seeds are also rich in carbohydrate, minerals, fat-soluble vitamins, and some secondary metabolites, such as flavonoids, saponins, and phenolic compounds [3]. Sesame oil also contains more than 80% linoleic and linolenic acids and is more resistant to oxidative changes than other vegetable oils due to lignans, such as sesamin, sesamolin, and sesamol [4]. These bioactive compounds confer sesame and sesame products with special health benefits such as protection against inflammation, cancer, hypocholesterol, coronary artery disease, and others chronic diseases [1]. Due to the high nutritional value, unique flavor, and good medical practice of sesame and sesame products, they are eaten as a kind of healthy food in Eastern and Western worlds [5,6,7]. However, some antinutrients (e.g., phytate and oxalate) also exist in sesame. The antinutrients and hard shells of sesame seeds cause raw sesame seeds to be poorly absorbed and digested in humans and also reduce the bioavailability of minerals [8]. Consequently, sesame seeds are commonly processed before consumption through various processing methods, such as roasting, steaming, drying, and stir frying [9].
Nine steaming–nine drying (NSND) is an ancient and traditional processing method that involves nine cycles of steaming and drying. It is commonly utilized to process food and medicinal ingredients in China [10]. NSND reportedly changes the overall carbohydrates, polysaccharide, and secondary metabolites of Rehmanniae radix significantly [11]. NSND further turns ginsenosides into low-polarity constituents that benefit the antiviral potency of ginsenosides [12,13]. Nevertheless, the ancient and traditional processing technology of NSND is so complex and long that it is not used anymore in some processing enterprises. To easily operate, some manufacturers even give up the special NSNS and instead choose other simple processes (e.g., stir frying). However, different processing methods have different effects [14]. The results of a comparison and analysis of antioxidant activity of black soybean soaked in vinegar have shown that the order of antioxidant activity decreases as roasting, stir-frying, raw black beans without treatment, high pressure, and boiling [15]. Accordingly, the quality of product prepared with the different processing methods differed on the market. To ensure processing effect and product quality, improving the processing technology of NSND is urgent.
High-pressure steaming (HPS) is a process for rapidly ripening grain by using saturated steam at high pressure [16]. The processing effects of some traditional Chinese medicinal herbs (e.g., Cistanches Herba and Radix Aconiti) have proven that HPS technology could meet the requirement of corresponding products, save time, and be simple to operate [17]. HPS is also a good hydrothermal treatment for lipid recovery and does not significantly change the fatty acid profile in lipid extraction from Chlorella vulgaris [14]. However, the application of HPS in processing black sesame seeds has not been reported.
Soaking seeds is a process in which seeds absorb water and swell simultaneously, thereby softening the seed coat to facilitate processing. After soaking, grain hardness decreases and other physical properties of minor millets (e.g., bulk density, true density, porosity, and coefficient of static friction) change [18]. Soaking can also remove some antinutritional factors of food materials to improve the nutritional value [19]. Before NSND, soaking is utilized in the actual production process of black sesame seeds by a factory named Yuzhou Hou Sheng Tang Chinese Medicine Co., Ltd. (Yuzhou, China). Actually, no work on the necessity of soaking treatment has been reported, and the processors primarily rely on trial to achieve the desired level of steaming black sesame seeds.
As a traditional Chinese health food, nine steamed–nine sun-dried black sesame seeds are regarded as a top-grade tonic [20]. A processing method is the basis for black sesame seeds to realize the modernization of technology and the standardization of quality. To provide a scientific basis for wisely selecting the effective processing method, determining the potential factors that affect the quality of black sesame seeds is necessary. The large amount of data obtained from various processing technologies is cumbersome and complex. The grey-correlation method can be used to determine the uncertain relations among factors or their contribution to a given system by calculating the absolute value of the data difference between sequences [21]. This method has been utilized to optimize multi-objectively the Q355C steel gas metal arc-welding process [22] and the process parameters for plastic injection molding [23]. The correlation degrees between the frost-penetration depths and different parameters, including thermal characteristics, average annual temperatures, and so on have also been presented by grey-correlation analysis [24]. The present study focused on determining the main nutritional and functional components and product sensory score of black sesame seeds processed for nine cycles by normal-pressure steaming (NPS) or HPS (with/without soaking treatment at water for 24 h). The experimental data were analyzed and compared. Then, the importance of different steaming technologies affecting the quality indicators of black sesame seeds was sorted by the grey-correlation method based on the variation-coefficient weight. Our findings may provide information and a comprehensive-evaluation approach to improving the steaming process of black sesame seeds and ensuring product quality.

2. Results

2.1. Effect of Various Steaming Processes on the Quality Indicators of Black Sesame Seeds

Black sesame seeds were treated for nine cycles with the process of SNPS, NPS, SHPS or HPS, respectively. Table 1, Table 2, Table 3 and Table 4 show that the quality indicators of black sesame seeds had a relatively high sensitivity to the changes in processing methods and processing cycles. With increased processing cycles, the contents of water, melanin, sesamin, and sesamolin decreased basically. When the procedure was repeated for nine cycles, the comparison between steaming methods affecting the quality of black sesame seeds is shown in Table 5 (p < 0.05). Except for ash content, other indicators of black sesame seeds showed significant differences among the four processes.

2.2. Weight Value of Each Influencing Factor

Using Equations (1) and (2), Vi and Wi were calculated and are shown in Table 6. Based on the different contributions of these indicators, the weights of water, protein, fat, ash, melanin, sesamin, sesamolin, and sensory score were 34.4%, 5.3%, 12.5%, 11.3%, 13.9%, 11.3%, 7.8%, and 3.5%, respectively. The highest weight of 34.4% indicated that water content exerted the greatest change in black sesame seeds throughout the four processes.

2.3. Nomalization of Raw Data

To meet quality targets, selecting the processing technologies with high contents of nutritional and functional components and excellent sensory properties for black-sesame products was desirable. Therefore, in the ideal process selected as the reference sequence (X0), the protein, fat, ash, melanin, sesamin, sesamolin, and sensory score were the highest in the corresponding indicators of SNPS, NPS, SHPS, and HPS. In terms of moisture, with increased processing cycles, the contents of water decreased (Table 1, Table 2, Table 3 and Table 4). And NSND that involves nine cycles of steaming and drying indicated that the lower the water content, the better the treatment effect. Thus, in the ideal process selected as the reference sequence (X0), the water was the lowest in the water contents of SNPS, NPS, SHPS, and HPS. All raw data were normalized with Equation (3). The standardized data of evaluation indices are listed in Table 7.

2.4. Absolute Difference between Comparison and Reference Sequence

The absolute differences between comparison and reference sequence were calculated with Equation (4) and are shown in Table 8.
According to Equations (5) and (6), the minimum absolute difference was 0.000, and the maximum absolute difference was 7.040.

2.5. Grey-Correlation Coefficient

Equation (7) was used to count the correlation coefficient between the steaming technologies (SNPS, NPS, SHPS, and HPS) and the quality indicators of black sesame seeds. Then, the calculated correlation coefficients are given in Table 9.

2.6. Grey-Correlation Degree and Grade

The grey-correlation degrees calculated with Equation (8) and the correlation grades (including the correlation grade within a group and correlation grade between groups) are given in Table 9. Two-factor analysis of variance without repeated observations was used to investigate the relation between grey-correlation degree and the processing technology (Figure 1 and Figure 2).
According to the correlation grade within a group, with increased cycles, the value of grey-correlation degree during HPS, SHPS, SNPS, and NPS generally increased. The procedure repeated nine times was the optimum, with the statistically highest comprehensive score of black sesame seeds among the four methods (Figure 2). This finding also indicated that the traditional processing method of NSND involving nine cycles of steaming and drying can reasonably process food and medicinal ingredients in China.
Based on the correlation grade between groups, the top three samples in terms of grey-correlation degree in decreasing order were X36, X35, and X9. Thus, the combined score of the sample during SNPS repeated steaming for nine cycles was lower than those during HPS repeated steaming for eight and nine cycles. Comparing the four processes (Table 9 and Figure 1), the comprehensive score of HPS with/without soaking in water was significantly higher than that of NPS or SNPS. Therefore, HPS could be used to replace SNPS and simplify the steaming process to realize the parametric steaming of black sesame seeds.

3. Discussion

Melanin is a photoprotective pigment which functions as an antioxidant, free-radical scavenger, and charge-transport mediator [25]. Sesamin and sesamolin are two lignans present in sesame seeds with some functions, such as antioxidant, decreasing blood lipids, and anti-inflammatory [26]. Considering the advantages of melanin, sesamin, and sesamolin on human health, selecting the processing technologies with high contents of melanin, sesamin, and sesamolin for black-sesame products was desirable. Our results indicated that fewer treatments were better for melanin, sesamin, and sesamolin during the steaming and drying of black sesame seeds (Table 1, Table 2, Table 3 and Table 4).
Table 5 shows that the processes with/without soaking in water led to different water contents of black sesame seeds (e.g., 5.09% of SNPS vs. 11.37% of NPS, 8.64% of SHPS vs. 4.92% of HPS). In other words, different effects were observed on the water contents of black sesame seeds by soaking before NPS and HPS. Soaking black sesame seeds could effectively reduce water content during NPS, but the opposite result was obtained during HPS. The reason for the promoted water reduction of black sesame seeds soaking during NPS may be that soaking makes seeds absorb water and swell simultaneously, and can soften the seed coat to facilitate transferring heat to reduce the water of black sesame seeds. Nevertheless, HPS can achieve steaming at high pressure to break the seed coat by providing sufficient pressure to vaporize water inside the seed, and the hard seed coat should be more sensitive to high pressure than the soft seed coat. Therefore, the coat-breaking effect of HPS on unsoftened black sesame seeds was better than that on softened ones such that water in the seeds was more effectively evaporated. This finding was confirmed by the significantly lower water content of HPS than SHPS (Table 5).
Meanwhile, water absorption in seeds’ soaking process increased the seed moisture content so that more water needed to be removed in the steaming and drying of black sesame seeds. If the processing time was insufficient, the increase in moisture content cannot be decreased. In the first 6 cycles, the water content of SNPS or SHPS was higher than that of NPS or HPS (Table 1, Table 2, Table 3 and Table 4). Steaming times must exceed six cycles for SNPS or SHPS to remove absorbed water in black sesame seeds. To reduce the adverse effects of the antinutrients and hard shells of sesame seeds and thus improve the bioavailability of minerals, processes such as roasting, steaming, drying, and stir-flying are commonly utilized before sesame-seed consumption [8,9]. Certainly, the moisture content of sesame seeds during these processes showed a downward trend. After processing, the water was less than 7.5%, which is commonly required for the long-term storage of sesame. Considering water content, the nine cycles of black sesame seeds treated with SNPS in actual production by Yuzhou Hou Sheng Tang Chinese Medicine Co., Ltd. (Yuzhou, China) were reasonable, and SNPS could be replaced by HPS because the 4.92% water content of HPS was statistically equal to the 5.09% of NSND with soaking in water (SNPS).
Based on the determinations of the quality indicators, such as water, protein, fat, ash, melanin, sesamin, sesamolin, and sensory score, the results of comprehensive evaluation using grey-correlation degree and grade analysis showed that the best steaming process of black sesame seeds was HPS for nine cycles. The effect of HPS on the black sesame seeds in our study was identical with the role of the high-pressure cooking method in the extraction of corn germ protein. The extraction effect of protein digestion in corn germ meal treated with HPS, and the high-pressure steam treatment significantly reduces the processing time, better than the soaking treatment of raw materials [27]. These findings could provide a scientific basis for replacing SNPS with HPS to simplify steaming and realize the parametric steaming of black sesame seeds, and thus, ensure the quality of black-sesame products. Furthermore, with the development of drying technology, such as infrared radiation drying [28] and advanced osmotic dehydration techniques combined with emerging drying methods [29], the drying method used in the NSND should be studied further to promote the application of NSND.

4. Materials and Methods

4.1. Raw Materials

Black sesame seeds, honey, and sealwort juice were provided by Yuzhou Hou Sheng Tang Chinese Medicine Co., Ltd. (Yuzhou, China). Black rice, black soya bean, white glutinous rice, dry mulberry, dry wolfberry, and dry haw were purchased from a local market in Xuchang, Henan, China. The materials were cleaned by hand picking and winnowing to remove foreign matter.

4.2. Steaming Technologies

(1) NPS after soaking in water (SNPS). First, the cleaned black sesame seeds were soaked in tap water for 24 h and then the water was drained. Second, the seeds were steamed for 2 h over boiling water in an electric steamer (Huidang Home, 34 cm, Shandong Huidang Home Appliances Co., Ltd., Jinan, China) for which the power was set to 1800 w. Then, the seeds were dried for 2 h in an oven (101-2AB, Beijing Zhongxing Weiye Instrument Co., Ltd., Beijing, China) at 60 °C, and stirred once every 30 min. The steaming and drying procedure was repeated for nine cycles to collect nine samples of SNPS denoted as X1–X9, respectively.
(2) NPS. Without soaking, the seeds were steamed and dried for nine cycles, following the same steps as the above section of “NPS after soaking in water (SNPS)”, to collect nine samples of NPS denoted as X10–X18, respectively.
(3) HPS after soaking in water (SHPS). The SHPS processing also involved three steps with the same as the above section of “NPS after soaking in water (SNPS)”, but the second step differed. During SHPS, the black sesame seeds were steamed for 30 min at 121 °C in a high-pressure steam sterilizer (YXQ-SG46-280SA, Shanghai Boxun Industry Co., Ltd., Shanghai, China) with 0.21 Mpa pressure. The steaming and drying procedure was repeated for nine cycles to collect nine samples of SHPS denoted as X19–X27, respectively.
(4) HPS. Without soaking, the seeds were steamed and dried for nine cycles, following the same steps as the above section of “HPS after soaking in water (SHPS)”, to collect nine samples of HPS denoted as X28–X36, respectively.

4.3. Nutritional and Functional Analysis of Black Sesame Seeds

The moisture content was measured using the method of direct drying described by GB 5009.3-2016 (China). Crude protein was determined using the Kjeldahl method described by GB 5009.5-2016 (China). Fat was detected by the Soxhlet extraction method described by GB 5009.6-2016 (China). Total ash was determined by the first method described by GB 5009.4-2016 (China). Each sample was set to have three parallels.
Melanin was detected with an ultraviolet photometer (Bluestar, Beijing Labtek Instrument Co., Ltd., Beijing, China) at 360 nm. Alcohol was used to extract black sesame pigments, and the extraction technology conditions were as follows: 50% alcohol (v/v), 1:50 ratio of black sesame seeds to solvent, pH 1.0, and extraction for 30 min. Each sample was set to have three parallels.
Sesamin and sesamolin were measured using a high-performance liquid chromatography with UV detector (Agilent1260, Agilent Technology Co., Ltd., Palo Alto, CA, USA) described by NY/T 1595-2008 (China). The chromatographic conditions of HPLC were Agilent XDB-C18 column (4.6 mm × 250 mm, 5 μm); column temperature 30 °C; mobile phase: CH3OH/H2O = 80/20; flow rate 0.8 mL/min; and wavelength 290 nm. Each sample was tested twice. The curves of sesamin and sesamolin standards were shown in Supplementary File S1: Figures S1 and S2.

4.4. Sensory Evaluation of Black-Sesame Product

Black-sesame products include black-sesame paste, black-sesame pill, black-sesame cake, and so on. Among them, the black-sesame pill is a kind of honeyed pill using the steamed black sesame seeds as their main raw materials. To measure the impact of steaming and drying on the quality of the black-sesame product, the black-sesame pill was prepared with the black sesame seeds exposed to the different steaming technologies in the Section 4.2, and sensory evaluation was conducted.
(1) Blend formulation and preparation of the black-sesame pill
Raw material flours. First, black rice, black soya bean, white glutinous rice, and the steamed black sesame seeds were separately fried with incense. Second, the fried raw materials, mulberry, wolfberry, and haw were milled using a stainless universal crusher and sieved into fine flour of 40 mesh, respectively.
Refining honey. No-crystal honey was boiled for 4 s on an induction cooker at 100 °C, and the boiling procedure was repeated three times.
Blend formulation. The basic formulation of the black-sesame pill used was as follows: black sesame flour, 30 g; the refined honey, 16 g; sealwort juice, 2 g; black rice flour, 1 g; black soya bean flour, 1 g; mulberry flour, 1 g; wolfberry flour, 1 g; wolfberry flour, 1 g; white glutinous rice flour, 1 g; haw flour, 0.4 g; and sodium carboxymethyl cellulose, 0.3 g.
Black-sesame pill preparation. The dry ingredients were weighed and thoroughly mixed. Sealwort juice was added and mixed until uniformity was achieved. The refined honey was added and kneaded into a ball while it was hot, and hand beaten 60 times. Then, the ball was weighed in groups. A black-sesame pill was 8 g. The pill of 8 g was initially hand rubbed in 20 circles to a smooth surface without cracks. Then, the rubbed pill was dried for 30 min in an oven at 60 °C, and flipped once at 15 min. Finally, the pill taken out from the oven was cooled to room temperature and packed using a silver paper of 8 cm × 8 cm. This was the finished product.
(2) Sensory evaluation of black-sesame pill
Quality indicators of black-sesame pill samples were evaluated by a panel of 10 students majoring in food according to the sensory scoring criteria (Supplementary File S2: Table S1). Sensory attributes of food morphology, color, texture and flavor were graded by percentage points.
The sensory evaluation process was as follows. First, the shape and color of the prepared black-sesame pill which was placed on a white plate were observed; second, the pill was smelt; third, tasted; then, the average of each factor was calculated; finally, the sensory score was calculated using a weighted scoring method, in which the weights of morphology, color, texture and flavor were 20%, 22%, 25% and 33%, respectively.

4.5. Variation-Coefficient Weighting Method

The variation-coefficient method can avoid equal division of weight and make results more reasonable [30]. The steps are listed below.
(1) Calculating the variation-coefficient of indicator.
V i = δ i X i  
where V i is the variation-coefficient of the indicator Ci; δ i is the standard deviation of the indicator Ci; X i is the mean value of the indicator Ci; i = 1, 2, …, n; and n is the number of the indicators, and n = 1, 2, …, 8.
(2) Calculating objective weight.
W i = V i i = 1 n V i
where Wi is the object weight of the indicator Ci; Vi is the variation-coefficient of the indicator Ci; and n is the number of indicators (n = 1, 2, …, 8).

4.6. Grey-Correlation Analyses

The grey-correlation analysis is a geometric approach used to evaluate the correlation between different sequences within a design [24]. To eliminate the dimensional differences of the various indicators, the influences of various steaming processes on the quality indicators of black sesame seeds were evaluated by grey-correlation analysis based on the contents of water, protein, fat, ash, melanin, sesamin, and sesamolin of black sesame seeds, and the sensory score of the black sesame pill. The following steps were included in grey-correlation analysis.
(1) Normalizing raw data. The ideal process was selected as the reference sequence (X0), and the other processing methods (SNPS, NPS, SHPS, and HPS) were taken as the comparison sequence (Xj). Except for the minimum water content, the other quality indicators of the ideal process were the highest in the corresponding indicators of SNPS, NPS, SHPS, and HPS. The equalization method was used to preprocess the raw data and convert them into dimensionless data [31].
  C i k = C i k c 0 k    
where i is the number of the samples (i = 0, 1, 2, …, 36); and k is the number of indicators (k = 1, 2, …, 8).
(2) Determining the absolute difference between the comparison and the reference sequence. The absolute difference was calculated using Equation (4) [32].
  Δ i k = | C 0 ( k ) C i ( k ) |  
The minimum absolute difference was as follows:
Δ m i n = m i n m i n | C 0 ( k ) C i ( k ) | i         k  
The maximum absolute difference was as follows:
Δ m a x = m a x m a x | C 0 ( k ) C i ( k ) | i         k  
where i is the number of samples (i = 0, 1, 2, …, 36); and k is the number of indicators (k = 1, 2, …, 8).
(3) Calculating the grey-correlation coefficient. The grey-correlation coefficient is defined as the correlation between the ideal and actual standardized value. It can be calculated using Equation (7) [32].
ζ i k = Δ m i n + ρ Δ m a x Δ i k + ρ Δ m a x
where i is the number of the samples (i = 0, 1, 2, …, 36); k is the number of indicators (k = 1, 2, …, 8); and   ρ is the resolution ratio, which serves to enlarge the difference between various coefficients, and   ρ   = 0.5 is generally used.
(4) Calculating grey-correlation degree and arranging grey-relational grade. To obtain the grey-relational degree, the following Equation (8) was used:
X i = i = 1 k ζ i k × W i   k
where ζ i   k is the grey-correlation coefficient of the indicator Ci; W i   k is the object weight of the indicator Ci; i is the number of the samples, and i = 0, 1, 2, …, and 36; k is the number of the indicators, and k = 1, 2, …, 8.
The grey-relational grade Ri is the sequence arranged by the value of grey-correlation degree of the samples.

4.7. Statistical Analyses

Experimental and sensory results were subjected to analysis of variance, and means were compared using the Student–Newman–Keuls test. Significance was accepted at p ≤ 0.05.

5. Conclusions

The four steaming processes of SNPS, NPS, SHPS, and HPS exerted different degrees of influence on the nutritional and functional ingredients of black sesame seeds and the sensory score of the black sesame pill. The influences of various steaming processes on the quality indicators of black sesame seeds were evaluated using grey-correlation analysis by analyzing the contents of water, protein, fat, ash, melanin, sesamin, and sesamolin of black sesame seeds and the sensory score of the black sesame pill. We found that nine cycles of black sesame seeds treated with steaming and drying was the optimum. Moreover, the traditional technology of NSND was reasonable. These findings indicated that HPS, as a new production process of black sesame seeds, was suitable for replacing SNPS, and the soaking before steaming process could be avoided. Overall, HPS could save time and simplify SNPS steps. The required equipment was also simple and desirable for the parametric steaming and large-scale production of black-sesame products. However, the effects of these processes on other components (e.g., amino acid and fat acid) in black sesame seeds were not observed. Therefore, further research is needed to assess the processing effect of black sesame seeds by examining other components, especially essential amino acids and essential fatty acids, to provide an idea for the study of traditional and modern processing technology. Overall, a method of comprehensive analysis was established to determine the effects of different processing technologies on black sesame seeds by analyzing multiple indicators. This study provided a scientific basis for the further development and utilization of new technologies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules28135266/s1. Supplementary File S1: Figure S1. Curve of sesamin standard. Figure S2. Curve of sesamolin standard. Supplementary File S2: Table S1. Sensory scoring criteria.

Author Contributions

Y.Z., J.W., H.T., X.L. and Q.W. performed the experiments, analyzed the data, and wrote the manuscript. D.W. reviewed, edited, and supervised. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Xuchang college crosswise tasks from Yuzhou Hou Sheng Tang Chinese Medicine Co., Ltd. (Yuzhou, China), grant number 2021HX057, and by Henan Science and Technology Plan Project, grant number 202102310468.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Sample Availability

Samples of the compounds are not available from the authors.

References

  1. Dutta, D.; Harper, A.; Gangopadhyay, G. Transcriptomic analysis of high oil-yielding cultivated white sesame and low oil-yielding wild black sesame seeds reveal differentially expressed genes for oil and seed coat colour. Nucleus 2022, 65, 151–164. [Google Scholar] [CrossRef]
  2. Kanu, P.J. Biochemical analysis of black and white sesame seeds from China. Am. J. Biochem. Mol. Biol. 2011, 1, 145–157. [Google Scholar] [CrossRef] [Green Version]
  3. Khademian, R.; Asghari, B.; Sedaghati, B.; Yaghoubian, Y. Plant beneficial rhizospheric microorganisms (PBRMs) mitigate deleterious effects of salinity in sesame (Sesamum indicum L.): Physio-biochemical properties, fatty acids composition and secondary metabolites content. Ind. Crops Prod. 2019, 136, 129–139. [Google Scholar] [CrossRef]
  4. Shi, L.K.; Liu, R.J.; Jin, Q.Z.; Wang, X.G. The contents of lignans in sesame seeds and commercial sesame oils of China. J. Am. Oil Chem. Soc. 2017, 94, 1035–1044. [Google Scholar] [CrossRef]
  5. Hou, L.X.; Zhang, Y.J.; Wang, X.D. Characterization of the volatile compounds and taste attributes of sesame pastes processed at different temperatures. J. Oleo Sci. 2019, 68, 551–558. [Google Scholar] [CrossRef] [Green Version]
  6. Zambelli, R.A.; Pinto, L.Í.F.; dos Santos Junior, E.C.; de Lima, A.C.V.; Goiana, M.L.; de Mendonça, L.G.; Rodrigues, B.G. Effect of black sesame (Sesamum indicum) and flaxseed flour (Linum usitatissimum) on bread quality using surface response methodology. J. Eng. Process. Manag. 2018, 10, 32–40. [Google Scholar] [CrossRef]
  7. El-Roby, A.M.; Hammad, K.S.M.; Galal, S.M. Enhancing oxidative stability of sunflower oil with sesame (Sesamum indicum) coat ultrasonic extract rich in polyphenols. J. Food Process. Preserv. 2020, 44, e14564. [Google Scholar] [CrossRef]
  8. Lawal, S.O.; Idowu, A.O.; Malomo, S.A.; Badejo, A.A.; Fegbemi, T.N. Effect of toasting on the chemical composition, functional and antioxidative properties of full fat and defatted sesame (Sesamum indicum L.) seed flours. J. Culin. Sci. Technol. 2019, 19, 18–34. [Google Scholar] [CrossRef]
  9. Cheng, R.; Liao, X.; Addou, A.M.; Qian, J.; Wang, S.; Cheng, Z.; Wang, L.; Huang, J. Effects of “nine steaming nine sun-drying” on proximate composition, oil properties and volatile compounds of black sesame seeds. Food Chem. 2021, 344, 128577. [Google Scholar] [CrossRef]
  10. Meng, X.L.; He, M.J.; Guo, R.; Duan, R.; Hou, F.X.; Lv, C.Z.; Wang, B.; Zhang, S. Investigation of the effect of the degree of processing of radix rehmanniae preparata (Shu Dihuang) on Shu Dihuangtan carbonization preparation technology. Molecules 2017, 22, 1193. [Google Scholar] [CrossRef] [Green Version]
  11. Zhou, L.; Xu, J.D.; Zhou, S.S.; Mao, Q.; Kong, M.; Shen, H.; Li, X.Y.; Duan, S.M.; Xu, J.; Li, S.L. Integrating targeted glycomics and untargeted metabolomics to investigate the processing chemistry of herbal medicines, a case study on Rehmanniae Radix. J. Chromatogr. A 2016, 1472, 74–87. [Google Scholar] [CrossRef]
  12. Kim, E.H.; Kim, S.W.; Park, S.J.; Kim, S.; Yu, K.M.; Kim, S.G.; Lee, S.H.; Seo, Y.K.; Cho, N.H.; Kang, K.; et al. Greater efficacy of black ginseng (CJ EnerG) over red ginseng against lethal Influenza a virus infection. Nutrients 2019, 11, 1879. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Guo, N.; Zhu, L.L.; Song, J.; Dou, D.Q. A new simple and fast approach to analyze chemical composition on white, red, and black ginseng. Ind. Crops Prod. 2019, 134, 185–194. [Google Scholar] [CrossRef]
  14. Gong, P.Y.; Guo, Y.J.; Tian, Y.S.; Gu, L.F.; Qi, J.; Yu, B.Y. Reverse tracing anti-thrombotic active ingredients from dried Rehmannia Radix based on multidimensional spectrum-effect relationship analysis of steaming and drying for nine cycles. J. Ethnopharmacol. 2021, 276, 114177. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, J.; Liu, D.; Li, Y.; Xie, T.; Cui, M.; Zhao, W.; Zhang, X. Comparison and analysis of the special flavor and antioxidant activity of black soybean soaked in vinegar by five kinds processing techniques. Mod. Food Sci. Technol. 2022, 38, 211–219. [Google Scholar] [CrossRef]
  16. Yang, S.; Sun, B.; Li, Z.; Wang, X. Effect of high-pressure steaming and roasting on the flavor compounds of Buckwheat. Food Ferment. Ind. 2023, 41, 263–269. [Google Scholar] [CrossRef]
  17. Liu, B.N.; Shi, J.; Jia, T.Z.; Lv, T.T.; Li, Z. Optimization of high-pressure steaming process for Cistanches Herba. Chin. Tradit. Pat. Med. 2019, 41, 2576–2580. [Google Scholar] [CrossRef]
  18. Balasubramanian, S.; Visvanathan, R. Influence of moisture content on physical properties of minor millets. J. Food Sci. Technol. 2010, 47, 1–6. [Google Scholar] [CrossRef] [Green Version]
  19. Mazı, B.G.; Yıldız, D.; Barutçu Mazı, I. Influence of different soaking and drying treatments on anti-nutritional composition and technological characteristics of red and green lentil (Lens culinaris Medik.) flour. J. Food Meas. Charact. 2023. [Google Scholar] [CrossRef]
  20. Zhang, J.S.; Zhang, Z.L.; Lin, X.M.; Yan, M.Z.; Chen, Y.T. Discussion on historical evolution and modern research progress of Sesami Semen Nigrum processing based on visual thinking. China J. Chin. Mater. Med. 2020, 45, 4528–4536. [Google Scholar] [CrossRef]
  21. Zhou, P.; Yao, Y.J.; Ai, Y.F.; Liu, A.M.; Xu, Z.L.; Xie, J.C. Grey correlation analysis of factors influencing maldistribution in feeding device of copper flash smelting. J. Cent. South Univ. 2012, 19, 1938–1945. [Google Scholar] [CrossRef]
  22. Weng, H.; Jiang, J.; Feng, M.; Yao, M.; Chen, C.; Lian, G. Multi-objective optimizations of the Q355C steel gas metal arc welding process based on the grey correlation analysis. Int. J. Adv. Manuf. Technol. 2022, 121, 3567–3582. [Google Scholar] [CrossRef]
  23. Xu, G.; Yang, Z. Multiobjective optimization of process parameters for plastic injectionmolding via soft computing and grey correlation analysis. Int. J. Adv. Manuf. Technol. 2015, 78, 525–536. [Google Scholar] [CrossRef]
  24. Firat, M.E.O. Experimental investigation on the thermal characteristics and grey correlation analysis of frost penetration depths for different subgrade Soils. Environ. Earth Sci. 2021, 80, 394. [Google Scholar] [CrossRef]
  25. Solano, F. Photoprotection and Skin Pigmentation: Melanin-Related Molecules and Some Other New Agents Obtained from Natural Sources. Molecules 2020, 25, 1537. [Google Scholar] [CrossRef] [Green Version]
  26. Senouwa, S.K.D.; Xu, F.; Komivi, D.; Zhou, R.; Zhao, Y.; Wang, L. Antioxidant lignans sesamin and sesamolin in sesame (Sesamum indicum L.): A comprehensive review and future prospects. J. Integr. Agric. 2023, 22, 14–30. [Google Scholar] [CrossRef]
  27. Yang, L.; Wang, L.J.; Zheng, Y.W. The role of high-pressure cooking method in the extraction of corn germ protein. Food Sci. Technol. 2011, 36, 177–179. [Google Scholar] [CrossRef]
  28. Delfiya, D.S.A.; Prashob, K.; Murali, S.; Alfiya, P.V.; Samuel, M.P.; Pandiselvam, R. Drying kinetics of food materials in infrared radiation drying: A review. J. Food Process Eng. 2022, 45, e13810. [Google Scholar] [CrossRef]
  29. Pandiselvam, R.; Tak, Y.; Olum, E.; Sujayasree, O.J.; Tekgül, Y.; Koç, G.Ç.; Kaur, M.; Nayi, P.; Kothakota, A.; Kumar, M. Advanced osmotic dehydration techniques combined with emerging drying methods for sustainable food production: Impact on bioactive components, texture, color, and sensory properties of food. J. Texture Stud. 2021, 53, 737–762. [Google Scholar] [CrossRef]
  30. Li, J.; Hu, Y.; Wang, X.; Diao, M.; Diao, M. Study on the operation safety evaluation system of shiplock combined with variation coefficient method and matter-element extension method. Proc. PIANC Smart Rivers 2022, 264, 656–667. [Google Scholar] [CrossRef]
  31. Gao, Q.; Li, J.; Han, H.; Liu, Z.; Zhang, J.; Liu, C.; Liu, C.; Xue, Y. Effects of different drying methods on turnip chips as evaluated based on grey relational analysis. Food Sci. 2019, 40, 95–101. [Google Scholar] [CrossRef]
  32. Wang, H.; Gao, Y.; Han, Y. Determining the main controlling factors of nitrogen diffusion fluxes at sediment-water interface by grey correlation analysis. Water Resour. Manag. 2022, 36, 4951–4964. [Google Scholar] [CrossRef]
Figure 1. Results of analysis of variance between treatments and grey-correlation degree. Spots marked by a different letter are significantly different (p < 0.05).
Figure 1. Results of analysis of variance between treatments and grey-correlation degree. Spots marked by a different letter are significantly different (p < 0.05).
Molecules 28 05266 g001
Figure 2. Results of analysis of variance between cycles and grey-correlation degree. Spots marked by a different letter are significantly different (p < 0.05).
Figure 2. Results of analysis of variance between cycles and grey-correlation degree. Spots marked by a different letter are significantly different (p < 0.05).
Molecules 28 05266 g002
Table 1. Effect of processing cycles on the quality indicators of black sesame seeds treated with SNPS.
Table 1. Effect of processing cycles on the quality indicators of black sesame seeds treated with SNPS.
SampleCycleWater
(g/100 g)
Protein
(g/100 g)
Fat
(g/100 g)
Ash
(g/100 g)
Melanin
(Abs)
Sesamin
(mg/g)
Sesamolin
(mg/g)
Sensory Score
(Point)
X1139.54 ± 0.31 a21.92 ± 0.25 b33.96 ± 0.46 e2.93 ± 0.02 g0.775 ± 0.003 a4.49 ± 0.01 a2.29 ± 0.00 a78.36 ± 0.88 f
X2236.23 ± 0.61 b21.17 ± 0.44 c34.15 ± 0.66 e3.48 ± 0.06 f0.704 ± 0.002 b4.25 ± 0.00 d2.16 ± 0.00 d79.26 ± 0.83 e
X3335.36 ± 0.48 c20.31 ± 0.17 d34.91 ± 0.66 e3.74 ± 0.03 e0.695 ± 0.002 c4.45 ± 0.00 b2.25 ± 0.00 b80.64 ± 0.84 d
X4430.00 ± 0.20 d19.71 ± 0.07 d36.55 ± 0.40 d3.55 ± 0.01 f0.576 ± 0.003 d4.23 ± 0.00 e2.14 ± 0.00 e83.68 ± 0.66 c
X5529.60 ± 0.11 d19.81 ± 0.11 d42.01 ± 0.61 c3.70 ± 0.02 e0.544 ± 0.003 e4.28 ± 0.00 c2.18 ± 0.00 c84.12 ± 0.95 c
X6623.22 ± 0.14 e19.64 ± 0.16 d43.67 ± 0.19 b3.94 ± 0.05 d0.535 ± 0.003 f3.71 ± 0.00 g1.91 ± 0.00 g85.33 ± 0.73 b
X7722.56 ± 0.71 e24.33 ± 0.54 a44.77 ± 0.47 a4.52 ± 0.10 c0.516 ± 0.004 g3.49 ± 0.00 i1.85 ± 0.00 h87.06 ± 0.61 a
X8811.19 ± 0.29 f22.27 ± 0.47 b43.48 ± 0.11 b5.09 ± 0.13 b0.505 ± 0.001 h3.72 ± 0.00 f1.95 ± 0.00 f86.95 ± 0.44 a
X995.09 ± 0.44 g21.25 ± 0.20 c42.10 ± 0.26 c5.24 ± 0.05 a0.505 ± 0.002 h3.58 ± 0.00 h1.91 ± 0.00 g85.14 ± 0.90 b
Values (mean ± SD) in each column followed by a different letter are significantly different (p < 0.05); SNPS: normal-pressure steaming after soaking in water.
Table 2. Effect of processing cycles on the quality indicators of black sesame seeds treated with NPS.
Table 2. Effect of processing cycles on the quality indicators of black sesame seeds treated with NPS.
SampleCycleWater
(g/100 g)
Protein
(g/100 g)
Fat
(g/100 g)
Ash
(g/100 g)
Melanin
(Abs)
Sesamin
(mg/g)
Sesamolin
(mg/g)
Sensory Score
(Point)
X10132.79 ± 0.31 a22.87 ± 0.17 a33.81 ± 0.41 h3.49 ± 0.01 g0.786 ± 0.003 a5.03 ± 0.00 a2.53 ± 0.00 a80.33 ± 0.66 h
X11226.23 ± 0.04 b21.05 ± 0.43 bc33.42 ± 0.26 h3.84 ± 0.05 e0.739 ± 0.002 b4.67 ± 0.00 b2.41 ± 0.00 b81.03 ± 0.60 g
X12326.19 ± 0.41 b20.41 ± 0.20 c35.65 ± 0.10 g3.77 ± 0.02 e0.687 ± 0.002 c4.43 ± 0.00 c2.29 ± 0.00 c82.95 ± 0.77 f
X13425.67 ± 0.68 b21.33 ± 0.22 bc36.91 ± 0.53 f4.31 ± 0.06 d0.616 ± 0.003 d4.26 ± 0.00 d2.20 ± 0.00 d85.64 ± 0.75 e
X14524.79 ± 0.66 c20.82 ± 0.49 bc40.39 ± 0.67 d4.78 ± 0.06 c0.556 ± 0.003 e4.05 ± 0.00 e2.15 ± 0.00 e86.95 ± 0.56 d
X15620.49 ± 0.10 d21.04 ± 0.18 bc39.65 ± 0.33 e4.74 ± 0.03 c0.525 ± 0.002 f3.40 ± 0.00 i1.88 ± 0.00 i87.81 ± 1.08 c
X16719.75 ± 0.43 e23.03 ± 0.44 a43.22 ± 0.44 b4.92 ± 0.02 b0.506 ± 0.002 g3.56 ± 0.00 h1.91 ± 0.00 h91.28 ± 0.94 a
X17814.35 ± 0.34 f21.69 ± 0.66 b42.27 ± 0.20 c5.03 ± 0.04 a0.505 ± 0.002 g3.71 ± 0.00 f1.93 ± 0.00 f90.33 ± 0.55 b
X18911.37 ± 0.25 g22.61 ± 0.70 a44.02 ± 0.11 a5.04 ± 0.04 a0.504 ± 0.002 g3.67 ± 0.00 g1.94 ± 0.00 g88.15 ± 0.66 c
Values (mean ± SD) in each column followed by a different letter are significantly different (p < 0.05); NPS: normal-pressure steaming.
Table 3. Effect of processing cycles on the quality indicators of black sesame seeds treated with SHPS.
Table 3. Effect of processing cycles on the quality indicators of black sesame seeds treated with SHPS.
SampleCycleWater
(g/100 g)
Protein
(g/100 g)
Fat
(g/100 g)
Ash
(g/100 g)
Melanin
(Abs)
Sesamin
(mg/g)
Sesamolin
(mg/g)
Sensory Score
(Point)
X19133.50 ± 0.41 a21.34 ± 0.41 b29.22 ± 0.59 f3.74 ± 0.06 c0.796 ± 0.002 a5.75 ± 0.00 a2.39 ± 0.00 a80.76 ± 1.07 e
X20230.20 ± 0.94 b21.66 ± 0.28 b31.73 ± 0.47 e4.27 ± 0.05 b0.777 ± 0.002 b5.12 ± 0.00 f2.24 ± 0.00 b81.28 ± 0.57 e
X21324.14 ± 0.45 c23.01 ± 0.20 a36.07± 0.49 d4.95 ± 0.06 a0.774 ± 0.002 c5.01 ± 0.00 h2.12 ± 0.00 d83.11 ± 0.92 d
X22422.58 ± 0.04 d23.44 ± 0.14 a35.35 ± 0.49 d4.85 ± 0.04 a0.773 ± 0.003 c5.10 ± 0.00 g2.09 ± 0.00 e84.02 ± 0.67 c
X23517.34 ± 0.07 e23.43 ± 0.14 a37.07 ± 0.48 c4.93 ± 0.01 a0.773 ± 0.001 c5.31 ± 0.00 b2.22 ± 0.00 c86.33 ± 0.53 b
X24615.87 ± 0.71 f22.68 ± 0.16 a40.29 ± 0.25 b5.07 ± 0.11 a0.765 ± 0.002 d4.84 ± 0.00 i1.98 ± 0.00 f86.58 ± 0.77 b
X25713.20 ± 0.34 g23.20 ± 0.45 a41.03 ± 0.67 b4.93 ± 0.01 a0.765 ± 0.002 d5.21 ± 0.00 c2.09 ± 0.00 e88.64 ± 0.71 a
X26810.82 ± 0.32 h22.00 ± 0.68 b40.65 ± 0.72 b5.03 ± 0.10 a0.764 ± 0.002 d5.14 ± 0.00 d1.95 ± 0.00 g87.19 ± 1.02 b
X2798.64 ± 0.30 i22.92 ± 0.48 a42.14 ± 0.19 a5.01 ± 0.18 a0.764 ± 0.002 d5.13 ± 0.00 e1.94 ± 0.00 h86.62 ± 0.65 b
Values (mean ± SD) in each column followed by a different letter are significantly different (p < 0.05); SHPS: high-pressure steaming after soaking in water.
Table 4. Effect of processing cycles on the quality indicators of black sesame seeds treated with HPS.
Table 4. Effect of processing cycles on the quality indicators of black sesame seeds treated with HPS.
SampleCycleWater
(g/100 g)
Protein
(g/100 g)
Fat
(g/100 g)
Ash
(g/100 g)
Melanin
(Abs)
Sesamin
(mg/g)
Sesamolin
(mg/g)
Sensory Score
(Point)
X28127.64 ± 0.52 a23.74 ± 0.17 b31.09 ± 0.10 e4.94 ± 0.00 a0.814 ± 0.002 a5.66 ± 0.00 a2.76 ± 0.00 a81.38 ± 0.75 h
X29224.29 ± 0.68 b23.66 ± 0.21 b30.86 ± 0.38 e4.99 ± 0.04 a0.806 ± 0.002 b4.70 ± 0.00 f2.36 ± 0.00 c82.19 ± 1.14 g
X30323.72 ± 0.22 b23.62 ± 0.36 b40.82 ± 0.64 d5.03 ± 0.04 a0.800 ± 0.002 c5.04 ± 0.00 b2.40 ± 0.00 b84.03 ± 1.09 f
X31422.30 ± 0.24 c24.77 ± 0.13 a40.32 ± 0.48 d5.02 ± 0.04 a0.793 ± 0.002 d4.20 ± 0.00 i2.04 ± 0.00 f84.98 ± 0.79 e
X32519.90 ± 0.01 d24.77 ± 0.11 a47.27 ± 0.27 c4.99 ± 0.07 a0.791 ± 0.002 e4.50 ± 0.00 h2.10 ± 0.00 e87.96 ± 0.70 d
X33614.64 ± 0.12 e23.53 ± 0.13 b52.13 ± 0.33 b5.07 ± 0.03 a0.784 ± 0.002 f4.89 ± 0.00 d2.14 ± 0.00 d89.97 ± 1.09 bc
X34713.92 ± 0.86 e24.80 ± 0.11 a53.04 ± 0.45 a5.05 ± 0.03 a0.774 ± 0.001 g4.92 ± 0.00 c2.04 ± 0.00 f92.44 ± 0.73 a
X3585.53 ± 0.08 f22.33 ± 0.22 c51.56 ± 0.39 b5.09 ± 0.02 a0.774 ± 0.002 g4.79 ± 0.00 e1.99 ± 0.00 g90.37 ± 0.66 b
X3694.92 ± 0.28 f22.37 ± 0.43 c51.94 ± 0.11 b5.10 ± 0.14 a0.773 ± 0.002 g4.69 ± 0.00 g1.88 ± 0.00 h89.35 ± 0.90 c
Values (mean ± SD) in each column followed by a different letter are significantly different (p < 0.05); HPS: high-pressure steaming.
Table 5. Effect of processing methods on the quality indicators of black sesame seeds treated for nine cycles.
Table 5. Effect of processing methods on the quality indicators of black sesame seeds treated for nine cycles.
IndicatorsWater
(g/100 g)
Protein
(g/100 g)
Fat
(g/100 g)
Ash
(g/100 g)
Melanin
(Abs)
Sesamin
(mg/g)
Sesamolin
(mg/g)
Sensory Score
(Point)
SNPS5.09 ± 0.44 c21.25 ± 0.20 b42.10 ± 0.26 c5.24 ± 0.05 a0.505 ± 0.002 c3.58 ± 0.00 d1.91 ± 0.00 c85.14 ± 0.90 d
NPS11.37 ± 0.25 a22.61 ± 0.70 a44.02 ± 0.11 b5.04 ± 0.04 a0.504 ± 0.002 c3.67 ± 0.00 c1.94 ± 0.00 b88.15 ± 0.66 b
SHPS8.64 ± 0.30 b22.92 ± 0.48 a42.14 ± 0.19 c5.01 ± 0.18 a0.764 ± 0.002 b5.13 ± 0.00 a1.94 ± 0.00 a86.62 ± 0.65 c
HPS4.92 ± 0.28 c22.37 ± 0.43 a51.94 ± 0.11 a5.10 ± 0.14 a0.773 ± 0.002 a4.69 ± 0.00 b1.88 ± 0.00 d89.35 ± 0.90 a
Values (mean ± SD) in each column followed by a different letter are significantly different (p < 0.05).
Table 6. Weights of various indicators used in the comprehensive evaluation of black sesame seeds.
Table 6. Weights of various indicators used in the comprehensive evaluation of black sesame seeds.
IndicatorsWaterProteinFatAshMelaninSesaminSesamolinSensory Score
Mean value21.30422.2939.9404.5610.6904.5282.12885.339
Standard deviation9.0141.4546.1310.6340.1180.6260.2053.632
Vi0.4230.0650.1540.1390.1700.1380.0960.043
Wi0.3440.0530.1250.1130.1390.1130.0780.035
Table 7. Standardized data of evaluation indices.
Table 7. Standardized data of evaluation indices.
SampleTreatmentCycleWaterProteinFatAshMelaninSesaminSesamolinSensory Score
X0Ideal process 1.0001.0001.0001.0001.0001.0001.0001.000
X1SNPS18.0400.8840.6400.5600.9520.7810.8300.848
X2 27.3650.8530.6440.6650.8650.7390.7820.857
X3 37.1890.8190.6580.7140.8540.7740.8130.872
X4 46.0990.7950.6890.6770.7070.7360.7740.905
X5 56.0180.7990.7920.7060.6680.7450.7880.910
X6 64.7220.7920.8230.7520.6560.6450.6910.923
X7 74.5870.9810.8440.8630.6330.6070.6710.942
X8 82.2750.8980.8200.9720.6200.6470.7060.941
X9 91.0340.8570.7941.0000.6200.6230.6910.921
X10NPS16.6670.9220.6370.6650.9650.8750.9170.869
X11 25.3330.8490.6300.7320.9080.8130.8720.877
X12 35.3240.8230.6720.7200.8430.7700.8300.897
X13 45.2180.8600.6960.8230.7560.7410.7950.926
X14 55.0400.8390.7620.9120.6820.7040.7790.941
X15 64.1650.8480.7480.9040.6450.5910.6810.950
X16 74.0150.9290.8150.9400.6210.6190.6900.987
X17 82.9170.8750.7970.9600.6200.6460.7010.977
X18 92.3110.9120.8300.9620.6190.6390.7020.954
X19SHPS16.8110.8610.5510.7130.9771.0000.8660.874
X20 26.1390.8730.5980.8150.9540.8900.8110.879
X21 34.9080.9280.6800.9450.9500.8710.7670.899
X22 44.5910.9450.6660.9250.9490.8870.7570.909
X23 53.5240.9450.6990.9410.9490.9240.8060.934
X24 63.2260.9150.7600.9670.9390.8420.7150.937
X25 72.6840.9350.7740.9410.9390.9060.7570.959
X26 82.2000.8870.7660.9600.9380.8930.7080.943
X27 91.7560.9240.7950.9560.9380.8930.7030.937
X28HPS15.6190.9570.5860.9421.0000.9851.0000.880
X29 24.9390.9540.5820.9520.9890.8170.8550.889
X30 34.8230.9520.7700.9600.9830.8770.8690.909
X31 44.5340.9990.7600.9580.9740.7300.7390.919
X32 54.0460.9990.8910.9510.9710.7830.7620.952
X33 62.9760.9490.9830.9680.9620.8510.7750.973
X34 72.8301.0001.0000.9640.9500.8550.7391.000
X35 81.1250.9000.9720.9720.9500.8340.7190.978
X36 91.0000.9020.9790.9730.9490.8160.6800.967
Table 8. Absolute value between reference sequence and comparative sequence.
Table 8. Absolute value between reference sequence and comparative sequence.
SampleTreatmentCycleWaterProteinFatAshMelaninSesaminSesamolinSensory Score
X1SNPS17.0400.1160.3600.4400.0480.2190.1700.152
X2 26.3650.1470.3560.3350.1350.2610.2180.143
X3 36.1890.1810.3420.2860.1460.2260.1870.128
X4 45.0990.2050.3110.3230.2930.2640.2260.095
X5 55.0180.2010.2080.2940.3320.2550.2120.090
X6 63.7220.2080.1770.2480.3440.3550.3090.077
X7 73.5870.0190.1560.1370.3670.3930.3290.058
X8 81.2750.1020.1800.0280.3800.3530.2940.059
X9 90.0340.1430.2060.0000.3800.3770.3090.079
X10NPS15.6670.0780.3630.3350.0350.1250.0830.131
X11 24.3330.1510.3700.2680.0920.1870.1280.123
X12 34.3240.1770.3280.2800.1570.2300.1700.103
X13 44.2180.1400.3040.1770.2440.2590.2050.074
X14 54.0400.1610.2380.0880.3180.2960.2210.059
X15 63.1650.1520.2520.0960.3550.4090.3190.050
X16 73.0150.0710.1850.0600.3790.3810.3100.013
X17 81.9170.1250.2030.0400.3800.3540.2990.023
X18 91.3110.0880.1700.0380.3810.3610.2980.046
X19SHPS15.8110.1390.4490.2870.0230.0000.1340.126
X20 25.1390.1270.4020.1850.0460.1100.1890.121
X21 33.9080.0720.3200.0550.0500.1290.2330.101
X22 43.5910.0550.3340.0750.0510.1130.2430.091
X23 52.5240.0550.3010.0590.0510.0760.1940.066
X24 62.2260.0850.2400.0330.0610.1580.2850.063
X25 71.6840.0650.2260.0590.0610.0940.2430.041
X26 81.2000.1130.2340.0400.0620.1070.2920.057
X27 90.7560.0760.2050.0440.0620.1070.2970.063
X28HPS14.6190.0430.4140.0580.0000.0150.0000.120
X29 23.9390.0460.4180.0480.0110.1830.1450.111
X30 33.8230.0480.2300.0400.0170.1230.1310.091
X31 43.5340.0010.2400.0420.0260.2700.2610.081
X32 53.0460.0010.1090.0490.0290.2170.2380.048
X33 61.9760.0510.0170.0320.0380.1490.2250.027
X34 71.8300.0000.0000.0360.0500.1450.2610.000
X35 80.1250.1000.0280.0280.0500.1660.2810.022
X36 90.0000.0980.0210.0270.0510.1840.3200.033
Table 9. Grey-relational coefficient and grey-correlation degree between the reference sequence and comparative sequence.
Table 9. Grey-relational coefficient and grey-correlation degree between the reference sequence and comparative sequence.
SampleTreatmentCycleWaterProteinFatAshMelaninSesaminSesamolinSensory ScoreGCDGWGB
X1SNPS10.3330.9680.9070.8890.9860.9420.9540.9590.731936
X2 20.3560.9600.9080.9130.9630.9310.9420.9610.736835
X3 30.3630.9510.9120.9250.9600.9400.9500.9650.741734
X4 40.4080.9450.9190.9160.9230.9300.9400.9740.750633
X5 50.4120.9460.9440.9230.9140.9320.9430.9750.754532
X6 60.4860.9440.9520.9340.9110.9080.9190.9790.777425
X7 70.4950.9950.9580.9630.9060.9000.9150.9840.785323
X8 80.7340.9720.9510.9920.9030.9090.9230.9830.87026
X9 90.9900.9610.9451.0000.9030.9030.9190.9780.95613
X10NPS10.3830.9780.9070.9130.9900.9660.9770.9640.757930
X11 20.4480.9590.9050.9290.9740.9490.9650.9660.775626
X12 30.4490.9520.9150.9260.9570.9390.9540.9720.771828
X13 40.4550.9620.9200.9520.9350.9310.9450.9800.773727
X14 50.4660.9560.9370.9760.9170.9220.9410.9830.778524
X15 60.5270.9590.9330.9740.9080.8960.9170.9860.792421
X16 70.5390.9800.9500.9830.9030.9020.9190.9960.801317
X17 80.6470.9660.9450.9890.9030.9090.9220.9940.838212
X18 90.7290.9750.9540.9890.9020.9070.9220.9870.86818
X19SHPS10.3770.9620.8870.9250.9941.0000.9630.9650.756931
X20 20.4060.9650.8980.9500.9870.9700.9490.9670.765829
X21 30.4740.9800.9170.9850.9860.9650.9380.9720.794719
X22 40.4950.9850.9130.9790.9860.9690.9350.9750.801618
X23 50.5820.9840.9210.9830.9860.9790.9480.9820.835513
X24 60.6130.9760.9360.9910.9830.9570.9250.9820.843411
X25 70.6760.9820.9400.9830.9830.9740.9350.9880.86739
X26 80.7460.9690.9380.9890.9830.9710.9230.9840.88925
X27 90.8230.9790.9450.9880.9830.9700.9220.9820.91714
X28HPS10.4320.9880.8950.9841.0000.9961.0000.9670.787922
X29 20.4720.9870.8940.9870.9970.9510.9600.9690.793820
X30 30.4790.9870.9390.9890.9950.9660.9640.9750.803716
X31 40.4991.0000.9360.9880.9930.9290.9310.9780.803615
X32 50.5361.0000.9700.9860.9920.9420.9370.9860.822514
X33 60.6400.9860.9950.9910.9890.9590.9400.9920.863410
X34 70.6581.0001.0000.9900.9860.9600.9311.0000.86937
X35 80.9660.9730.9920.9920.9860.9550.9260.9940.97222
X36 91.0000.9730.9940.9920.9860.9500.9170.9910.98311
GCD: grey-correlation degree; GW: correlation grade within a group; GB: correlation grade between groups.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, Y.; Wang, J.; Tan, H.; Lu, X.; Wang, D.; Wei, Q. Evaluation of Steaming and Drying of Black Sesame Seeds for Nine Cycles Using Grey-Correlation Analysis Based on Variation-Coefficient Weight. Molecules 2023, 28, 5266. https://doi.org/10.3390/molecules28135266

AMA Style

Zhang Y, Wang J, Tan H, Lu X, Wang D, Wei Q. Evaluation of Steaming and Drying of Black Sesame Seeds for Nine Cycles Using Grey-Correlation Analysis Based on Variation-Coefficient Weight. Molecules. 2023; 28(13):5266. https://doi.org/10.3390/molecules28135266

Chicago/Turabian Style

Zhang, Yongqing, Jiaojiao Wang, Huihui Tan, Xinyue Lu, Deguo Wang, and Quanzeng Wei. 2023. "Evaluation of Steaming and Drying of Black Sesame Seeds for Nine Cycles Using Grey-Correlation Analysis Based on Variation-Coefficient Weight" Molecules 28, no. 13: 5266. https://doi.org/10.3390/molecules28135266

APA Style

Zhang, Y., Wang, J., Tan, H., Lu, X., Wang, D., & Wei, Q. (2023). Evaluation of Steaming and Drying of Black Sesame Seeds for Nine Cycles Using Grey-Correlation Analysis Based on Variation-Coefficient Weight. Molecules, 28(13), 5266. https://doi.org/10.3390/molecules28135266

Article Metrics

Back to TopTop