The Influence of Ecological Factors on the Contents of Nutritional Components and Minerals in Laver Based on Open Sea Culture System

Abstract

Laver is a popular food for its high nutritional value, which can change among culture areas and along with the progression of harvest. Neopyropia yezoensis and Neoporphyra haitanensis were cultured in succession in Taoluo and Muping, north China. The chemical composition of laver samples together with some ecological factors in the farms were investigated. From September to December, salinity increased while water temperature decreased in both areas. Dissolved inorganic nitrogen (DIN) and N:P decreased in Taoluo while increasing in Muping. Both N. yezoensis and N. haitanensis contained high levels of protein (26.90–41.38% DW) and low contents of fat (0.36–0.74% DW). High levels of minerals were detected in both species. The contents of protein, total amino acids, and total minerals in N. haitanensis increased significantly, while sugar content decreased significantly from September to December. The gray correlation analysis result implied that the typical ecological factors (DIN, dissolved inorganic phosphorus, N:P, pH, salinity, temperature, and transparency) have a great influence on accumulation of the crude nutrient, amino acid, fatty acid components, and mineral components in laver. The coefficient of variation analysis result also showed that environmental heterogeneity obviously enhanced differences in the contents of protein, amino acid, and trace elements in N. yezoensis. In addition, the principal component analysis result showed that the N. yezoensis strain ‘Huangyou No. 1’ had the highest comprehensive evaluation score in the four tested N. yezoensis strains, indicating that it has the best comprehensive quality and greatest exploitable value. We hope these findings will help to improve future laver breeding and farming.

1. Introduction

Laver, one of the dominant cultivated seaweeds, is popular for its high nutritional value, being the most cultivated seaweed species in Japan, Korea, and China. Neopyropia yezoensis (Ueda) L.-E.Yang & J.Brodie and Neoporphyra haitanensis (T.J. Chang & B.F. Zheng) J. Brodie & L.-E. Yang, the industrially cultivated species in China, have become the most widely farmed seaweed species since 2010. Sea farming of laver has also attracted much attention in other countries, due to the increasing market for this food source and its capacity for bioremediation in sustainable aquaculture systems [1,2,3,4,5].

Cultivation of laver is generally limited to shallow water based on the traditional semi-floating and standing-pole culture systems [6]. In China, N. yezoensis is traditionally cultivated in Jiangsu and south Shandong province, and N. haitanensis is traditionally cultivated in Zhejiang, Fujian and the northeast of Guangdong province. Long and intensive cultivation of N. yezoensis and N. haitanensis in the main production areas leads to problems such as decreasing production and quality, as well as increasing occurrence of diseases, especially under the global climate and environmental changes. Recruitment of new laver species and geographically distinct wild strains of the dominant cultivated species has been recommended and implemented to diversify genetic resources and in turn to strengthen tolerance to climate change and improve the product quality [6,7,8,9,10,11]. In recent years, a process to extend the dominant laver cultivars to offshore deep-sea areas and to develop northward industrial cultivation of laver in China is ongoing [6,10]. N. haitanensis has been cultivated in Jiangsu, Shandong and even Liaoning province, with culture area of more than 1000 ha. N. yezoensis has been cultivated in Liaoning province and north Shandong province, with culture area of 3000–4000 ha. However, it has been found in practice that the processed products of the northward cultivated laver are sometimes characterized by poorer quality in the light of appearance and taste, compared to the products from the main production areas.

The quality of laver, in terms of the composition of nutrients (proteins, amino acids (AAs), free amino acids (FAAs), minerals and trace elements, flavor [the ratio of delicious amino acids (DAAs) to total amino acids (TAAs)], thickness, color (pigment composition), etc., may differ among species [12], strains [13], harvesting times [7,14], culture areas [15], between the male and female parts of a blade [16], and between the periodically drying and non-drying culture systems [17]. It has been testified that the levels of dissolved inorganic nitrogen (DIN) and phosphorus (DIP) are closely related to the growth and metabolism of laver based on laboratory experiments [18,19,20,21,22]. Addition of N or P may have different effects on the nutrient components in N. yezoensis [22]. The reports regarding the influence of environment conditions on the chemical composition of laver were based on laboratory experiments, i.e., under controlled nutrient levels. The nutrients in the open sea change temporally and spatially. Although it has been reported that the laver quality may change among different culture areas [13,23] and along with the progression of harvesting [7,14,24], the corresponding changes of environmental conditions in the laver farm have been seldom reported.

It is necessary to study the relationship between laver quality and the culture environment, so as to better understand the key factors regulating the accumulation of certain nutrient substances and provide a more intuitive and practical support for farming practice in addition to the laboratory results. In this study, the chemical composition of N. yezoensis and N. haitanensis that were cultured at two laver farms with distinct nutrient levels were investigated; meanwhile, the corresponding changes of the following ecological factors were monitored: water temperature, salinity, pH, water transparency, and dissolved inorganic nutrients (nitrate NO₃⁻-N, ammonium NH₄⁺-N, nitrite NO₂⁻-N, phosphate PO₄³⁻-P). Correlations between the accumulation of the chemical compositions and the change of the environmental factors were identified. We hope these findings will help to improve laver farming in the future.

2. Materials and Methods

2.1. Water Sampling and Detection of Ecological Factors

Two locations in north China were investigated, with distinct environmental conditions and laver cultivation areas. One was in Muping, Yantai city (121°47′ E, 37°28′ N), and the other was in Taoluo, Rizhao city (119°24′–26′ E, 35°14′–16′ N) (Figure 1). The Muping study region was characterized by lower rainfall, less terrestrial input, and lower nutrient levels of DIN and DIP than the Taoluo study region. The laver farm in Muping was 180 m² in area and that in Taoluo was ~40 ha. N. haitanensis was cultured at the end of August and ended in late October (Oct) in Muping (MpPh) and Taoluo (TlPh). N. yezoensis were cultured from mid-Oct to the end of December (Dec) in Muping and from the end of Oct to the next March in Taoluo. N. haitanensis was the cultivar from south China (Fujian and Zhejiang province). N. yezoensis strain A was the dominant cultivated strain in northern Jiangsu province (Lianyungang city) and southern Shandong province (Rizhao city). It was cultivated both in Muping (MpPyA) and Taoluo (TlPyA). N. yezoensis strains B, C, D came from the wild populations in Muping, Changdao (120°56′ E, 37°42′ N) and Qingdao (120°30′ E, 36°35′ N), respectively. They were cultivated in Muping (MpPyB, MpPyC, MpPyD). MpPyC is a new strain that has been selected and cultivated for several years and named as Huangyou No. 1.

Sampling was carried out at the end of September (Sep), the end of Oct, and early Dec in 2019. Six sampling sites were set at the Muping station, with two at the near-shore area, two within the laver farm and two at the offshore area. The sampling sites were ~100 m away from each other. Nine sampling sites were set at the Taoluo station, with three at the near-shore area, three within the laver farm and three at the offshore area. The sampling sites were ~1000 m away from each other.

The ecological factors detected in this study included water temperature, salinity, pH, transparency, and dissolved inorganic nutrients (nitrate NO₃⁻-N, ammonium NH₄⁺-N, nitrite NO₂⁻-N, phosphate PO₄³⁻-P). Temperature, pH, and salinity were measured in situ (3 replicates) at 15–20 cm below water surface using the YSI environmental monitoring system (600 QS-M-O, Yellow Springs, OH, USA). Surface water samples were collected at a 15–20 cm depth at each sampling site by using rosette samplers. Every 50 mL sample water was filtered through a precombusted Whatman GF/F filter membrane with pore diameter of 0.22 μm. After filtering, each water sample was put in a plastic bottle and kept in dry ice for determination of DIN and DIP. After 2–3 h, the samples were taken back to the laboratory in Qingdao and preserved at −80 °C until analysis. Concentrations of DIN and DIP were determined using continuous flow colorimetric analysis (Auto Analyzer 3, Bran Luebbe, Norderstedt, Germany).

2.2. Laver Sampling

Every 100 g (dozens to hundreds of individuals) of blades of each strain was sampled from Taoluo and Muping, respectively, at the end of Sep and Oct, and early Dec in 2019. The blades were washed with clean sterilized seawater and ddH₂O to remove attachments and then preserved at −80 °C until analysis.

2.3. Determination of Nutrient Components in Laver

The ash content of each laver sample was determined by using the burning method [25]. The samples were dried at 105 °C for 4 h, then ground into powder and filtered through 60 mesh sieve (0.28 mm). About 1 g of sample powder was put in a pre-weighed crucible and completely carbonized to smokeless by an electric furnace in the fume hood. After carbonization, the sample was burned at 550 ± 25 °C for 4 h by using a muffle furnace. The sample was taken out from the muffle furnace and then cooled in a desiccator. If there were still carbon particles in the burned residues, they were loosed by mixture with a small amount of dH₂O. Then the samples were burned in muffle furnace again until there were no carbon particles. The burning was conducted at least twice, until the differences between the weights were less than 0.5 mg.

The protein content of each laver sample was determined by the Kjeldahl method according to the China National Standard [26]. The samples were dried at 80 °C for 8 h, then ground into powder and filtered through 60 mesh sieve (0.28 mm). About 0.3 g sample was digested with 0.4 g CuSO₄, 6 g K₂SO₄, and 20 mL H₂SO₄ in a digester. When the temperature reached 420 °C and the sample liquid turned blue-green and transparent, the samples were digested for one more hour and then taken out. After cooling, each of the digested samples was added with 50 mL dH₂O and then subjected to analysis in a Kjeldahl nitrogen analyzer K9860 (Hanon, Jinan, China).

The crude fat content of each laver sample was determined by the Soxhlet extractor method [27]. The samples were dried at 80 °C for 8 h, then ground into powder and filtered through 60 mesh sieve (0.28 mm). About 1 g sample was put into a filter paper cylinder, which was placed in the extraction cylinder of the Soxhlet extractor SZF-06A (Xinjia, Shanghai, China) and connected with a receiving bottle (pre-dried to constant weight). Anhydrous ether or petroleum ether was added to 2/3 of the bottle volume through the upper end of the condenser tube. The sample was heated in a water bath for continuous reflux extraction (6–8 cycles h⁻¹) for 6–10 h. After extraction, the anhydrous ether or petroleum ether was recovered. When there was 1–2 mL of anhydrous ether or petroleum ether left in the receiving bottle, it was evaporated in a water bath and then dried at 100 ± 5 °C for 1 h. The sample was cooled in a dryer for 30 min and then weighed.

The sugar content of each laver sample was determined by the phenol–sulfuric acid method using D-glucose as standard [28]. The samples were dried at 80 °C for 8 h, then ground into powder and filtered through 90 mesh sieve (0.16 mm). About 0.3 g sample was put into 250 mL conical flask, then 50 mL dH₂O and 15 mL HCl were added. The conical flask was installed with the condensation reflux device and hydrolyzed in 100 °C water bath for 3 h. After cooling to room temperature, the sample was filtered with 0.45 μm filter paper. The residue was washed with dH₂O. The filtrate and washing solution were mixed and diluted to 250 mL with dH₂O. Accurate 0.2 mL of the testing solution was fixed to 1.0 mL with dH₂O and added with 1.0 mL of 5% phenol solution and 5.0 mL concentrated H₂SO₄ (quickly). After standing still for 10 min, the reaction solution was vortexed and mixed completely and then water-bathed at 30 °C for 20 min. The solution was subjected to measurement of absorbance at 490 nm by the Agilent 8453 UV-vis spectrophotometer (Agilent, Santa Clara, CA, USA). The content of sugar was calculated as X = (G × A × V × 10)/M, where X was the sugar content of each laver sample (%), G was the concentration of D-glucose in the standard solution (mg L⁻¹), A was the diluted ratio of the tested sample, V was the volume of the tested sample (L), and M was the amount of tested sample (mg).

The contents of TAAs and fatty acids were determined according to Wang et al. [17].

2.4. Determination of Mineral Elements in Laver

The mineral content of each laver sample was determined by the inductively coupled plasma method [29]. About 0.3 g of sample powder was put into a polytetrafluoroethylene digester, then added with 5 mL HNO₃. After reaction (>1 h), the sample solution was digested in the microwave digester Ethos 1 (Milestone, Italy) with the digestion parameter of 100 °C for 3 min, followed by 140 °C for 3 min, 160 °C for 3 min, 180 °C for 3 min, and a final of 190 °C for 15 min. After digestion, the sample was taken out from the digester when it had cooled to below 50 °C. The sample was washed with ddH₂O 3–4 times. The washing solutions were mixed and subjected to the inductively coupled plasma optical emission spectrometer (ICP-OES) Optima 8000 (Perkin Elmer, Norwalk, CT, USA) for determination of the Na, K, Ca, and Mg contents and the inductively coupled plasma mass spectrometer (ICP-MS) iCAPQ (Thermo Fisher Scientific, Waltham, MA, USA) for determination of the contents of Zn, Mn, Se, Cu, Cd, Pb, and As, respectively.

2.5. Statistics

All data are presented as means ± standard deviations (SD). Coefficient of variation (CV) (%) = (SD/Mean) × 100. For water samples, each of the three independent measurements at the near-shore, laver farm, and offshore areas were taken as parallels to calculate the mean and SD; i.e., 18 and 27 replicates were used for the statistical analysis in Muping and Taoluo at every sampling time, respectively. For laver samples, three independent replicates of each sample (mixture of dozens to hundreds of laver individuals) were carried out for nutrient components and minerals analysis. The data were submitted for one-way analysis of variance (ANOVA) and two-way ANOVA analysis with SPSS (Version 22.0, IBM Corp., Armonk, NY, USA). The principal components analysis (PCA) was carried out with SPSS software. Duncan’s multiple comparison was used to determine the difference between groups. The difference was considered significant when p < 0.05.

Gray correlation analysis (GCA) is usually applied in indefinite systems, including small sampling and poor data information systems, in which limited information is available and others are not known. Therefore, it is suitable to be used for the analysis of the data obtained in this study. GCA between the contents of crude nutrient, amino acid, fatty acid, and minerals and the environmental factors were conducted by Spsspro (an online data analysis platform, https://www.spsspro.com/, accessed on 27 March 2022) to screen primary factors from principal components of environmental factors.

3. Results

3.1. Water Temperature, Salinity, Transparency, and pH in Taoluo and Muping Laver Farm

Water temperature decreased linearly from late Sep to early Dec and there was significant difference among different months (p < 0.05) in both regions, and water temperature was slightly lower in Muping than in Taoluo at the same time (p > 0.05) (Figure 2A). Contrarily, salinity increased linearly from late Sep to early Dec with the significance p < 0.05 among almost all the water samples (Figure 2B). At the same sampling time, salinity was significantly higher in Muping than in Taoluo (p < 0.05). The pH level increased in Taoluo while it decreased in Muping from late Sep to early Dec with a significant difference being present in late Sep (Figure 2C). It was higher in Muping than in Taoluo in late Sep (p < 0.05), while it was higher in Taoluo than in Muping in late Oct and early Dec (p < 0.05). The transparency was much higher in Muping than in Taoluo at all sampling times (p < 0.01, Figure 2D). It increased in early Dec in Taoluo (p< 0.05) and decreased linearly from late Sep to early Dec in Muping (p < 0.05).

3.2. The Dissolved Inorganic Nitrogen (DIN) and Phosphorus (DIP) in Taoluo and Muping Laver Farm

The concentrations of DIN were significantly higher at Taoluo than at Muping at the same sampling time (Figure 3A). In Taoluo, DIN decreased from Sep to Oct (p < 0.05). Among the DIN, nitrate was the dominant nitrogen at all study times and nitrite was the lowest. Nitrate decreased seasonally, ammonium decreased from late Sep to Oct and then increased in early Dec, and nitrite was the lowest in Sep. In Muping, both DIN and nitrite increased linearly from late Sep to early Dec (p < 0.05). In late Sep, ammonium was the dominant DIN and the composition of nitrite was the lowest. In late Oct, all three kinds of DIN increased and the concentration of ammonium-N became similar to that of nitrate-N. In early Dec, the concentrations of nitrate and nitrite increased and the concentrations of ammonium decreased to the level of late Sep. Nitrate became the dominant DIN and the levels of nitrite-N and ammonium were similar.

The differences of DIP (phosphate-P) concentrations between the two study regions were much less than the differences of DIN. DIP levels were higher in Taoluo than in Muping in late Sep (p < 0.05) and they were similar in both regions in late Oct and early Dec (p > 0.05). The DIP concentrations in Taoluo were significantly lower in late Oct than in late Sep and early Dec. The DIP levels in the water samples were significantly higher in early Dec than in late Sep and Oct in Muping (Figure 3B).

The mean N:P (ratio of DIN to DIP) was 15.05–24.63 in Taoluo and it was 3.07–7.44 in Muping. N:P decreased significantly in early Dec in Taoluo. N:P was significantly lower in late Sep than in late Oct and early Dec in Muping (Figure 3C).

3.3. The Content of the Nutrient Composition in the Varying Laver Species/Strains That Were Cultured in Taoluo and Muping

The mean contents of sugar accounted for 17.32–28.47% of the dry weight (

Table 1. The content of total sugar, protein, fat, amino acid and fatty acid in the N. haitanensis and N. yezoensis samples (n = 3). Data were shown in dry weight (DW). The different letters (a/b/c/d/e) in each row shows that the values are significantly different (p < 0.05). TlPh—N. haitanensis cultured in Taoluo, MpPh—N. haitanensis cultured in Muping, TlPyA—N. yezoensis strain A cultured in Taoluo, MpPyA, B, C, D—N. yezoensis strain A, B, C, D cultured in Muping. N. yezoensis strain A was the dominant cultivar that has been long cultured in Lianyungang, northern Jiangsu province, and in Rizhao, southern Shandong province. Taoluo is located in Rizhao. N. yezoensis strain B was from the local wild population in Muping, Yantai, northeastern Shandong province. N. yezoensis strain C was from the local wild population in Changdao, Yantai, northernmost Shandong province. N. yezoensis strain C was from the local wild population in Qingdao, Shandong province, locating between Rizhao and Yantai. TAA—total amino acids, EAA—essential amino acids (=Lys + Phe + Met + Thr + Ile + Leu + Val), DAA—delicious amino acids (=Glu + Asp + Gly + Phe + Ala + Tyr). TFA—total fatty acids, UFA—unsaturated fatty acids, EPA—eicosapentaenoic acid, C20:5n3.

Crude Nutrient (%)	Late September		Late October	Early December
	TlPh	MpPh	MpPh	TlPyA	MpPyA	MpPyB	MpPyC	MpPyD
Sugar	28.47 ± 1.03 e	27.74 ± 0.30 e	25.01 ± 0.90 d	19.44 ± 0.13 b	19.50 ± 0.08 b	19.88 ± 0.16 b	17.32 ± 0.48 a	22.40 ± 0.65 c
Protein	27.00 ± 0.11 a	26.90 ± 0.60 a	30.50 ± 0.20 b	41.38 ± 0.04 d	30.90 ± 1.12 b	33.27 ± 0.41 c	33.72 ± 1.62 c	31.61 ± 0.35 b
Fat	0.66 ± 0.02 c	0.52 ± 0.01 b	0.55 ± 0.01 b	0.68 ± 0.03 c	0.74 ± 0.03 c	0.68 ± 0.02 c	0.36 ± 0.02 a	0.65 ± 0.01 c
Ash	13.60 ± 0.15 b	15.38 ± 0.21 c	14.59 ± 0.26 c	12.25 ± 0.21 a	15.64 ± 0.41 c	16.53 ± 0.66 d	16.84 ± 0.03 d	15.32 ± 0.65 c
Amino acid (%)
Asp	2.48 ± 0.06	2.56 ± 0.099	3.07 ± 0	4.48 ± 0.01	2.61 ± 0.10	3.00 ± 0.01	2.81 ± 0.04	2.75 ± 0.09
Thr	1.36 ± 0.03	1.38 ± 0.05	1.62 ± 0	2.355 ± 0.01	1.51 ± 0.06	1.75 ± 0.01	1.63 ± 0.01	1.56 ± 0.06
Ser	1.32 ± 0.02	1.35 ± 0.05	1.62 ± 0	2.28 ± 0.09	1.43 ± 0.06	1.65 ± 0	1.54 ± 0.02	1.52 ± 0.05
Glu	2.79 ± 0.09	2.88 ± 0.11	3.58 ± 0.02	4.39 ± 0.05	3.23 ± 0.12	3.64 ± 0.01	3.40 ± 0.04	3.51 ± 0.11
Gly	1.53 ± 0.04	1.51 ± 0.06	1.80 ± 0	2.51 ± 0.04	1.64 ± 0.07	1.88 ± 0	1.76 ± 0.01	1.76 ± 0.07
Ala	3.03 ± 0.09	2.91 ± 0.14	3.42 ± 0.01	5.20 ± 0.01	4.03 ± 0.15	4.65 ± 0.03	4.30 ± 0	4.74 ± 0.13
Cys	0.30 ± 0.01	0.29 ± 0.01	0.30 ± 0	0.46 ± 0.01	0.32 ± 0	0.37 ± 0.01	0.32 ± 0	0.38 ± 0.02
Val	1.44 ± 0.04	1.51 ± 0.06	1.78 ± 0.01	2.51 ± 0.023	1.61 ± 0.06	1.89 ± 0.01	1.79 ± 0.01	1.67 ± 0.08
Met	0.15 ± 0	0.19 ± 0	0.31 ± 0.01	0.56 ± 0.05	0.26 ± 0.02	0.30 ± 0.01	0.23 ± 0.01	0.24 ± 0.01
Ile	0.86 ± 0.03	0.95 ± 0.05	1.12 ± 0.01	1.59 ± 0.03	1.04 ± 0.042	1.21 ± 0	1.25 ± 0.01	1.05 ± 0.04
Leu	1.86 ± 0.042	1.98 ± 0.09	2.32 ± 0.01	3.31 ± 0.09	2.19 ± 0.11	2.54 ± 0.01	2.38 ± 0.01	2.23 ± 0.09
Tyr	0.75 ± 0.02	0.88 ± 0.04	1.05 ± 0.03	1.58 ± 0.08	0.94 ± 0.01	1.01 ± 0.05	0.92 ± 0.01	0.93 ± 0.01
Phe	0.96 ± 0.03	1.01 ± 0.06	1.27 ± 0.01	1.65 ± 0.04	1.10 ± 0.04	1.27 ± 0.01	1.19 ± 0	1.13 ± 0.042
Lys	1.27 ± 0.03	1.30 ± 0.05	1.56 ± 0.01	2.30 ± 0.05	1.42 ± 0.06	1.65 ± 0	1.55 ± 0.01	1.50 ± 0.04
His	0.34 ± 0.01	0.36 ± 0.02	0.44 ± 0.01	0.64 ± 0.01	0.43 ± 0.01	0.51 ± 0.01	0.46 ± 0	0.46 ± 0.01
Arg	1.48 ± 0.04	1.51 ± 0.06	1.82 ± 0.01	2.61 ± 0.01	1.67 ± 0.06	1.93 ± 0.01	1.79 ± 0.01	1.85 ± 0.06
Pro	0.96 ± 0.06	1.00 ± 0.07	1.20 ± 0.05	1.71 ± 0.01	1.03 ± 0.07	1.24 ± 0	1.17 ± 0.02	1.11 ± 0.042
TAAs	22.85 ± 0.62 a	23.53 ± 0.96 a	28.25 ± 0.06 b	40.10 ± 0.01 d	26.41 ± 1.02 b	30.45 ± 0.02 c	28.47 ± 0.23 bc	28.36 ± 0.91 bc
EAAs/TAAs	0.35 ± 0 a	0.35 ± 0 a	0.35 ± 0 a	0.36 ± 0 a	0.35 ± 0 a	0.35 ± 0 a	0.35 ± 0 a	0.33 ± 0 b
DAAs/TAAs	0.50 ± 0 a	0.50 ± 0 a	0.50 ± 0 a	0.50 ± 0 a	0.51 ± 0 a	0.51 ± 0 a	0.51 ± 0 a	0.52 ± 0 b
Fatty acid (mg 100 g−1)
C14:0	1.26 ± 0.01	0.89 ± 0.01	1.88 ± 0.01	0.87 ± 0.03	1.08 ± 0.01	0.94 ± 0.01	0.72 ± 0.01	0.80 ± 0
C15:0	0.41 ± 0	0.27 ± 0.01	0.36 ± 0	0.38 ± 0.02	0.31 ± 0.01	0.30 ± 0	0.17 ± 0	0.29 ± 0.01
C16:0	212.70 ± 4.84	157.51 ± 1.73	148.36 ± 0.43	180.62 ± 0.13	166.50 ± 0.47	154.305 ± 1.61	84.67 ± 0.52	154.23 ± 0.311
C16:1	0.70 ± 0.01	0.65 ± 0.01	1.45 ± 0	0.75 ± 0.04	0.56 ± 0.01	0.59 ± 0.01	0.53 ± 0.01	0.80 ± 0
C17:0	0.28 ± 0	0.16 ± 0	0.17 ± 0.01	0.21 ± 0.042	0.19 ± 0	0.16 ± 0	0.07 ± 0	0.14 ± 0.01
C18:0	8.30 ± 0.02	4.52 ± 0.06	2.27 ± 0	2.49 ± 0.141	4.02 ± 0.05	1.84 ± 0.02	1.04 ± 0.01	2.55 ± 0.01
C18:1n9c	20.77 ± 0.24	14.91 ± 0.14	12.22 ± 0.01	9.60 ± 0.05	9.90 ± 0.02	6.20 ± 0.106	3.32 ± 0.01	9.77 ± 0
C18:2n6c	19.56 ± 0.21	10.22 ± 0.12	9.90 ± 0.04	9.07 ± 0.19	8.09 ± 0	7.07 ± 0.06	4.06 ± 0.03	9.00 ± 0.01
C18:3n3	0.38 ± 0	0.14 ± 0	0.17 ± 0.01	0.44 ± 0.01	0.36 ± 0	0.39 ± 0.01	0.24 ± 0	0.33 ± 0
C18:3n6	1.61 ± 0.03	0.89 ± 0.01	0.90 ± 0.01	1.21 ± 0.03	1.10 ± 0.01	0.88 ± 0.01	0.47 ± 0	1.10 ± 0.01
C20:1	12.05 ± 0.16	6.76 ± 0.08	11.61 ± 0.03	21.50 ± 0.06	21.98 ± 0.06	14.41 ± 0.11	8.51 ± 0.05	22.99 ± 0.04
C20:2	4.34 ± 0.03	2.24 ± 0.03	3.24 ± 0.01	4.91 ± 0.07	4.94 ± 0.06	3.66 ± 0.07	2.31 ± 0.03	4.82 ± 0.01
C20:3n6	16.66 ± 0.22	7.65 ± 0.09	5.75 ± 0.02	10.44 ± 0.01	9.75 ± 0.01	7.42 ± 0.06	4.14 ± 0.03	10.54 ± 0.01
C20:4n6	8.28 ± 0.06	4.36 ± 0.04	6.50 ± 0.01	4.17 ± 0.08	2.61 ± 0	2.03 ± 0	1.42 ± 0.02	4.29 ± 0.01
C20:5n3	256.44 ± 2.48	213.57 ± 2.28	254.80 ± 0.35	385.67 ± 0.07	365.47 ± 1.82	412.32 ± 1.61	247.26 ± 0.23	360.41 ± 0.10
C22:1N9	70.28 ± 0.51	60.91 ± 0.88	42.82 ± 0.11	11.49 ± 0.06	12.66 ± 0.01	8.90 ± 0.06	5.63 ± 0.07	12.19 ± 0.03
TFAs	633.99 ± 0.90 de	485.62 ± 5.49 b	502.35 ± 0.28 b	643.79 ± 0.01 e	609.48 ± 2.51 c	621.39 ± 0.32 d	364.54 ± 1.01 a	594.22 ± 0.45 c
TUFAs	411.05 ± 3.96 c	322.28 ± 3.68 b	349.33 ± 0.16 b	459.22 ± 0.23 e	437.40 ± 1.97 d	463.85 ± 1.90 e	277.87 ± 0.47 a	436.22 ± 0.16 d
TUFAs/TFAs	0.65 ± 0.01 a	0.66 ± 0 a	0.70 ± 0 b	0.71 ± 0 b	0.72 ± 0 bc	0.75 ± 0 d	0.76 ± 0 d	0.73 ± 0 c
EPA/TUFAs	0.62 ± 0 a	0.66 ± 0 b	0.73 ± 0 c	0.84 ± 0 d	0.84 ± 0 d	0.89 ± 0 e	0.89 ± 0 e	0.83 ± 0 d

). Sugar content was the highest in TlPh in late Sep and the lowest in MpPyC in early Dec. For the same strains, there was no statistically significant difference between the two farming regions (TlPh vs. MpPh in late Sep, TlPyA vs. MpPyA in early Dec). The sugar contents of N. haitanensis decreased significantly from late Sep to Oct (p < 0.05). There were significant difference among the different N. yezoensis strains (MpPyB, MpPyC, MpPyD) cultivated in the same region.

The mean contents of protein in 100 g (DW) of laver blades ranged from 26.90 g to 41.38 g (Table 1). It was the highest in TlPyA in early Dec and the lowest in MpPh in late Sep. For the same species cultivated in different regions, the protein content in TlPh was similar to that in MpPh in late Sep (p > 0.05), while that in TlPyA was much higher than that in MpPyA and the other strains of N. yezoensis cultivatedin Muping in early Dec (p < 0.05). The protein contents in MpPh increased significantly from late Sep to late Oct. In early Dec in the Muping laver farm, the protein contents in MpPyB and MpPyC were both significantly higher than those in MpPyA and MpPyD (p < 0.05).

The mean contents of TAAs in 100 g (DW) of laver blades ranged from 22.85 g to 40.10 g (Table 1). The differences of TAAs contents among different laver samples were similar to the difference of proteins contents. There was no significant difference of TAAs contents between TlPh and MpPh in late Sep (p > 0.05), while the TAAs contents in MpPh increased significantly from late Sep to late Oct (p < 0.05). In early Dec, the TAAs content was much higher in TlPyA than that in other N. yezoensis strains in Muping. The content of EAAs accounted for 33–35.6% of TAAs, with the lowest EAAs/TAAs in MpPyD (early Dec). DAAs accounted for 49.4–52.2% of TAAs.

The mean fat contents of N. yezoensis and N. haitanensis ranged from 0.36% to 0.74% of the dry weight (Table 1). The fat contents of TlPh in late Sep was significantly higher than those of MpPh in late Sep and Oct. From late Sep to late Oct, there was no significant change of the fat contents in MpPh. In early Dec, the fat contents in N. yezoensis were similar between Muping and Taoluo (p > 0.05). In Muping, there were significant differences among the different N. yezoensis strains, with the fat contents being the highest in PyA and the lowest in PyC (p < 0.05).

The mean contents of total fatty acids (TFAs) and total unsaturated fatty acids (TUFAs) in 100 g (DW) of N. yezoensis and N. haitanensis were 364.54–643.79 mg and 277.87–463.85 mg, respectively (Table 1). TFAs and TUFAs contents were both significantly higher in Taoluo than those in Muping at the same sampling times (TlPh > MpPh, TlPyA > MpPyA). There was no significant change in both TFAs and TUFAs contents of MpPh from late Sep to late Oct. Significant differences of TFAs and TUFAs contents were both found among the different N. yezoensis strains cultivated in the same region, with the lowest in MpPyC and the highest in MpPyB. Significant difference in TUFAs/TFAs was present among the N. yezoensis strains with the highest in MpPyC. The EPA/TFAs was significantly higher in MpPh than in TlPh and the EPA/TFAs in MpPh increased significantly from late Sep to late Oct. For the different N. yezoensis strains cultivated in Muping, the EPA/TFAs was much higher in PyB and PyC than in PyA and PyD (p < 0.05).

The mean ash contents in 100 g (DW) of N. yezoensis and N. haitanensis ranged from 12.25 to 16.84 g (Table 1). For the same species at the same sampling time, it was significantly lower in Taoluo than in Muping (TlPh vs. MpPh in late Sep, TlPyA vs. MpPyA in early Dec, Table 1). There was no significant change of the ash contents in MpPh from late Sep to Oct.

3.4. The Content of the Minerals in the Varying Laver Species/Strains That Were Cultured in Taoluo and Muping

Twelve mineral elements were detected in the laver samples (

Table 2. The content of minerals in the N. haitanensis and N. yezoensis samples (n = 3). The different letters (a/b/c/d/e/f) in each row shows that the values are significantly different (p < 0.05). The abbreviations were the same in Table 1.

Minerals (mg kg−1 DW)	Late September		Late October	Early December
	TlPh	MpPh	MpPh	TlPyA	MpPyA	MpPyB	MpPyC	MpPyD
Na	2035 ± 49 b	3045 ± 106 c	1190 ± 42 a	3190 ± 7 c	2775 ± 177 c	3435 ± 7 d	3857 ± 64 e	3055 ± 134 c
K	19,250 ± 212 b	32,250 ± 636 c	42,250 ± 1485 d	17,900 ± 71 a	34,800 ± 3394 c	41,950 ± 70 d	42,270 ± 71 d	32,000 ± 1980 c
Ca	1200 ± 56 a	1460 ± 14 b	1720 ± 28 cd	1680 ± 28 c	1710 ± 85 cd	1810 ± 14 d	2080 ± 42 e	1750 ± 99 cd
Mg	3155 ± 120 a	4550 ± 170 bc	3515 ± 64 a	5100 ± 70 c	4380 ± 184 b	5105 ± 7 c	5495 ± 7 d	4755 ± 205 bc
Fe	601 ± 63 c	499 ± 57 b	977 ± 75 e	1340 ± 56 f	316 ± 62 a	604 ± 35 c	848 ± 64 d	1039 ± 86 e
Zn	21.70 ± 0.28 b	14.55 ± 0.07 a	17.95 ± 0.21 c	36.70 ± 0.28 c	286.50 ± 3.54 d	706 ± 42 e	724 ± 22 e	36.20 ± 0.14 c
Mn	19.15 ± 0.636 b	21.30 ± 0.28 b	41.55 ± 0.35 e	36.50 ± 0.14 d	15.40 ± 1.13 a	22.85 ± 1.06 b	30.65 ± 3.04 c	16.65 ± 0.50 a
Cu	9.69 ± 0.17 d	7.47 ± 0.134 a	8.97 ± 0.071 c	10.10 ± 0.28 e	7.69 ± 0.06 a	8.23 ± 0.12 b	8.03 ± 0.14 b	9.18 ± 0.18 c
Se	0.26 ± 0.01 c	0.29 ± 0.015 c	0.29 ± 0.01 c	0.34 ± 0.02 d	0.12 ± 0.01 a	0.10 ± 0.01 a	0.18 ± 0.020 b	0.13 ± 0.01 a
Cd	1.71 ± 0.02 c	0.67 ± 0.011 a	0.94 ± 0.01 b	4.09 ± 0.127 e	3.47 ± 0.35 d	4.15 ± 0.01 e	4.14 ± 0.12 e	4.23 ± 0.09 e
Pb	0.39 ± 0.05 b	0.32 ± 0.02 ab	0.50 ± 0.03 c	0.60 ± 0.01 d	0.29 ± 0.03 a	0.29 ± 0.03 a	0.29 ± 0.02 a	0.36 ± 0.011 b
As	16.55 ± 0.50 d	20.35 ± 1.06 e	25.50 ± 0.14 f	11.90 ± 0.85 c	8.10 ± 0.50 a	9.54 ± 0.19 b	8.35 ± 0.27 a	19.30 ± 1.27 e
Total	26,309 ± 20 a	41,868 ± 743 b	49,747 ± 1693 c	29,310 ± 234 a	44,302 ± 3899 bc	53,655 ± 135 d	55,225 ± 130 e	42,685 ± 2502 b

). K was the most dominant mineral, with the mean contents ranging from 19.90–42.27 g kg⁻¹ and the second highest abundant mineral was Mg (mean 3.16–5.50 g kg⁻¹). The contents of Se (0.10–0.34 mg kg⁻¹) were the lowest and the second lowest was Pb (0.29–0.60 mg kg⁻¹). The total minerals contents in the same species were higher in Muping than in Taoluo (p < 0.05). In late Sep, Fe, Zn, Cu, Cd and Pb were higher in TlPh than in MpPh. The total minerals were higher in MpPh than in TlPh, with statistical significance of the most minerals. The total minerals in MpPh increased significantly from late Sep to late Oct (p < 0.05). In early Dec, the contents of total minerals was higher in MpPyA than that in TlPyA, while the contents of Mg, Fe, Mn, Se, Cu, Pb, and As were significantly higher in TlPyA than those in MpPyA (p < 0.05). Especially, the content of Fe was over 3 times higher in TlPyA as compared with MpPyA. The content of Zn in MpPyA was approximately 8 times of that in TlPyA.

Significant differences in the mineral contents were present in the varying N. yezoensis strains that were cultivated at the same region and sampled at the same time. The contents of total minerals were both higher in PyC and PyB when compared to PyA and PyD (Table 2). The contents of Na, Ca, Mg, and Mn were the highest in PyC (p < 0.05). Besides, the contents of Fe and Se were both significantly higher in PyC compared to PyA and PyB. The contents of Cu and As were highest in PyD.

3.5. Coefficient of Variation of the Nutritional Components in Different N. yezoensis Strains

Coefficient of variation (CV) is useful when comparing variation between samples, which indicates the dispersing characteristics of the character value. Figure 4 shows the CV treemap of crude nutrient, amino acid, fatty acid, and minerals among different N. yezoensis samples. In the top 20 CV among the four N. yezoensis strains cultivated in the same sea area (Figure 4A), those related to fatty acid components accounted for 70%, and those related to mineral components accounted for 25%. Of the fatty acid components, C18:0 had the highest CV among different N. yezoensis strains (53.58%), followed by C20:4n6 (47.73%), C18:1n9c (43.31%), C20:1 (40.18%), C17:0 (36.42%), C20:3n6 (36.08%), C18:3n6 (33.43%), and C22:1N9 (33.22%). Of the mineral components, Zn had the highest CV (76.59%) among the four N. yezoensis strains, followed by As (47.32%), Fe (44.61%), Mn (32.64%), and Se (26.25%). CV of fat was the highest (27.85%) in the crude nutrient components group. All the amino acid components had low CV (4.10% to 11.21%) among the four N. yezoensis strains.

In the top 20 CV between two N. yezoensis samples (same strain) cultivated in different sea areas (Figure 4B), those related to amino acid components accounted for 60%, and those related to mineral components accounted for 30%. Of the amino acid components, Met had the highest CV between the two N. yezoensis samples (52.38%), followed by Asp (37.3%), Tyr (36.06%), and Pro (35.10%). CV of Zn (109.30%), Fe (87.52%), Se (66.83%), and Mn (57.50%) were the highest in the mineral components group. CV of protein (20.50%) was the highest in the crude nutrient components group.

3.6. Principal Component Analysis (PCA) of Different N. yezoensis Strains

PCA is used to decompose the data into a smaller number of components and investigate the correlations among the different variables. In the present study, PCA was applied on the data set (using 20 variables: sugar, protein, fat, ash, TAAs, EAAs/TAAs, DAAs/TAAs, TFAs, TUFAs, TUFAs/TFAs, EPA/TUFAs, Na, K, Ca, Mg, Fe, Zn, Mn, Cu, and Se) to identify which nutritional components provided greatest ability to differentiate the N. yezoensis strains. The PCA result shows that the first three principal components have a cumulative reliability of 100% (

Table 3. Principal component analysis for nutritional components of N. yezoensis strains.

Component	Initial Characteristic Value
	Eigenvalue	Variance	Cumulative
1	13.086	65.43%	65.43%
2	4.142	20.71%	86.14%
3	2.772	13.86%	100.00%

). The first principal component accounted for 65.43% of the total variance in the data set. MpPyC got the highest score of the first principal component (

Table 4. Principal component value of nutritional components of N. yezoensis strains. The abbreviations were the same in Table 1.

N. yezoensis Strains	Component 1	Component 2	Component 3	Synthetic Component
MpPyC	1.286	0.466	−0.616	0.852
MpPyB	0.297	−0.789	1.240	0.203
MpPyD	−0.844	1.188	0.354	−0.257
MpPyA	−0.739	−0.865	−0.978	−0.798

) associated with protein, ash, TUFAs/TFAs, EPA/TUFAs, Na, K, Ca, Mg, Zn, and Mn contributing to the first component (

Table 5. Factor-loading matrix for the three principal components. The abbreviations were the same in Table 1.

Variables	Component 1	Component 2	Component 3
Sugar	−0.823	0.341	0.455
Protein	0.929	0.071	0.362
Fat	−0.848	−0.466	0.252
Ash	0.963	−0.243	0.116
TAAs	0.445	0.017	0.895
EAAs/TAAs	0.783	−0.588	−0.205
DAAs/TAAs	−0.641	0.764	−0.073
TFAs	−0.813	−0.386	0.436
TUFAs	−0.77	−0.379	0.513
TUFAs/TFAs	0.923	0.298	0.242
EPA/TUFAs	0.917	−0.28	0.283
Na	0.958	0.224	0.178
K	0.911	−0.355	0.212
Ca	0.943	0.282	−0.179
Mg	0.931	0.274	0.241
Fe	0.17	0.937	0.304
Zn	0.892	−0.422	0.161
Mn	0.992	0.129	0.014
Cu	−0.342	0.787	0.514
Se	0.55	0.577	−0.605

). The variance ratio contribution of the second and third principal component was 20.71% and 13.86%, respectively (Table 3). MpPyD and MpPyB got the highest scores of the second and third principal components, respectively (Table 4). The variables that contribute most to the second and third components are TAAs, EAAs/TAAs, DAAs/TAAs, Fe, Cu, and Se (Table 5). Principal component and factor analyses were used to calculate the comprehensive scores of nutritional components. The comprehensive scores of each N. yezoensis strain were in the order of MpPyC > MpPyB > MpPyD > MpPyA (Table 4).

3.7. Gray Correlation Analysis (GCA)

GCA was conducted between the contents of crude nutrient, amino acid, fatty acid, and minerals and the ecological factors. The results of GCA demonstrated that ecological factors both imposed different degrees of influences on different nutritional components of N. yezoensis (

Table 6. Gray correlation coefficients between the contents of crude nutrient, amino acid, fatty acid, and minerals in N. yezoensis and related ecological factors. The abbreviations were the same in Table 1.

Nutritional Components		Correlation Degree
		Ammonium-N	Nitrate-N	Nitrite-N	TN	P	N:P	pH	Sal	T	Tra
Crude nutrient	Sugar	0.816	0.549	0.951	0.421	0.992	0.979	0.960	0.963	0.964	0.926
	Protein	0.851	0.546	0.916	0.411	0.937	0.865	0.850	0.967	0.853	0.821
	Fat	0.741	0.518	0.849	0.405	0.881	0.946	0.963	0.859	0.959	0.998
	Ash	0.793	0.540	0.916	0.417	0.954	0.985	0.982	0.928	0.986	0.947
Amino acid	TAAs	0.836	0.541	0.908	0.408	0.954	0.880	0.864	0.958	0.868	0.835
	EAAs/TAAs	0.741	0.519	0.849	0.406	0.881	0.946	0.963	0.859	0.959	0.998
	DAAs/TAAs	0.741	0.518	0.849	0.406	0.881	0.946	0.963	0.859	0.959	0.998
Fatty acid	TFAs	0.689	0.789	0.664	0.914	0.657	0.648	0.645	0.661	0.646	0.641
	TUFAs	0.542	0.664	0.512	0.852	0.506	0.495	0.492	0.510	0.493	0.488
	TUFAs/TFAs	0.741	0.518	0.849	0.405	0.881	0.946	0.963	0.858	0.959	0.998
	EPA/TUFAs	0.740	0.518	0.849	0.405	0.881	0.946	0.963	0.858	0.959	0.998
Minerals	Na	0.927	0.949	0.920	0.971	0.918	0.916	0.915	0.920	0.915	0.914
	K	0.831	0.834	0.830	0.836	0.830	0.830	0.830	0.830	0.830	0.830
	Ca	0.877	0.918	0.865	0.960	0.862	0.857	0.856	0.864	0.856	0.854
	Mg	0.934	0.948	0.930	0.962	0.929	0.927	0.927	0.930	0.927	0.926
	Fe	0.621	0.677	0.616	0.738	0.609	0.600	0.599	0.611	0.600	0.596
	Zn	0.576	0.489	0.689	0.509	0.681	0.649	0.632	0.701	0.636	0.614
	Mn	0.835	0.549	0.927	0.417	0.968	0.954	0.938	0.974	0.942	0.905
	Cu	0.761	0.523	0.880	0.406	0.915	0.986	0.994	0.890	0.997	0.958
	Se	0.741	0.518	0.849	0.406	0.881	0.945	0.962	0.858	0.958	0.997
	Cd	0.755	0.525	0.868	0.410	0.902	0.970	0.988	0.878	0.983	0.996
	Pb	0.740	0.518	0.848	0.405	0.880	0.945	0.962	0.858	0.958	0.997
	As	0.763	0.524	0.885	0.406	0.920	0.990	0.989	0.895	0.993	0.953

) and N. haitanensis (

Table 7. Gray correlation coefficients between the contents of crude nutrient, amino acid, fatty acid, and minerals in N. haitanensis and related ecological factors. The abbreviations were the same in Table 1.

Nutritional Components		Correlation Degree
		Ammonium-N	Nitrate-N	Nitrite-N	TN	P	N:P	pH	Sal	T	Tra
Crude nutrient	Sugar	0.889	0.749	0.954	0.660	0.980	0.946	0.985	0.994	0.940	0.923
	Protein	0.893	0.756	0.951	0.663	0.974	0.943	0.982	0.998	0.937	0.920
	Fat	0.860	0.749	0.986	0.645	0.975	0.975	0.978	0.956	0.981	0.963
	Ash	0.827	0.721	0.963	0.626	0.931	0.971	0.934	0.914	0.976	0.995
Amino acid	TAAs	0.884	0.757	0.960	0.658	0.975	0.953	0.987	0.988	0.947	0.930
	EAAs/TAAs	0.827	0.722	0.964	0.626	0.932	0.972	0.934	0.915	0.977	0.995
	DAAs/TAAs	0.827	0.722	0.964	0.626	0.932	0.972	0.934	0.915	0.977	0.995
Fatty acid	TFAs	0.435	0.537	0.408	0.653	0.413	0.406	0.413	0.417	0.406	0.403
	TUFAs	0.539	0.692	0.487	0.625	0.496	0.484	0.496	0.503	0.483	0.479
	TUFAs/TFAs	0.827	0.721	0.964	0.626	0.932	0.972	0.934	0.915	0.977	0.995
	EPA/TUFAs	0.827	0.722	0.964	0.626	0.932	0.972	0.934	0.915	0.977	0.995
Minerals	Na	0.762	0.792	0.750	0.815	0.752	0.749	0.751	0.753	0.748	0.747
	K	0.787	0.790	0.786	0.792	0.786	0.786	0.786	0.786	0.786	0.786
	Ca	0.652	0.708	0.634	0.745	0.638	0.634	0.637	0.639	0.633	0.632
	Mg	0.819	0.841	0.811	0.855	0.812	0.811	0.812	0.813	0.810	0.809
	Fe	0.460	0.557	0.435	0.634	0.440	0.435	0.440	0.443	0.434	0.432
	Zn	0.864	0.749	0.981	0.644	0.984	0.973	0.986	0.964	0.967	0.949
	Mn	0.893	0.771	0.953	0.665	0.959	0.946	0.966	0.969	0.940	0.923
	Cu	0.845	0.736	0.983	0.635	0.956	0.985	0.959	0.938	0.998	0.981
	Se	0.827	0.722	0.964	0.626	0.932	0.972	0.934	0.915	0.977	0.995
	Cd	0.830	0.724	0.968	0.628	0.936	0.976	0.938	0.919	0.981	0.997
	Pb	0.827	0.722	0.964	0.626	0.932	0.972	0.934	0.915	0.977	0.995
	As	0.880	0.762	0.979	0.658	0.980	0.966	0.985	0.980	0.965	0.948

). The primary ecological factors affecting crude nutrient contents, amino acid contents, TUFAs/TFAs, EPA/TUFAs, and most trace elements (e.g., Mn, Cu, Se, Cd, Pb, As) contents in both N. yezoensis and N. haitanensis were Ammonium-N, Nitrite-N, P, N:P, pH, Sal, T, and Tra (gray correlation degree > 0.7). TN greatly affected contents of TFAs and TUFAs in both N. yezoensis (gray correlation degree > 0.8) and N. haitanensis (gray correlation degree > 0.6). Furthermore, both Nitrate-N and TN had the greatest impact on the contents of major elements (i.e., Na, K, Ca, Mg) in N. yezoensis and N. haitanensis, with gray correlation degree of 0.834–0.971 and 0.708–0.855, respectively. In addition, the primary ecological factors affecting Zn content in N. haitanensis were Ammonium-N, Nitrite-N, P, N:P, pH, Sal, T, and Tra (gray correlation degree > 0.8), and those affecting Fe and Zn contents in N. yezoensis were TN and Sal, respectively (gray correlation degree > 0.7).

4. Discussion

4.1. Characteristics of Nutritional Components in N. yezoensis and N. haitanensis

The chemical compositions of N. yezoensis and N. haitanensis were generally in accordance with the previous studies regarding the same species or the other laver species, and these laver species were all characterized by high content of protein and low content of fat [7,12,13,14,15,16,17,22,23,30,31]. The mineral content in seaweeds is generally higher compared to that of terrestrial plants and animal products [32,33,34,35]. In seaweeds, calcium is available as calcium phosphate, and that is more bioavailable than the form of calcium in milk, which is calcium carbonate [36]. The present study revealed a relatively high level of minerals in both N. yezoensis and N. haitanensis, with the total amount of the twelve tested minerals accounting for 2.63–5.52% of the dry weight. In addition, the contents of the total minerals were significantly higher in the laver samples cultivated in Muping than in Tuoluo, exhibiting a clear regional variation. The amount of minerals in laver varied among different studies, which may be mostly related to different species, sampling sea areas, sampling seasons, or chemical detection techniques [12,37,38]. Marinho-Soriano et al. [39] investigated the relationship between the nutritive components of different seaweed species and the environment and found that the variation in chemical composition was generally related to environment. It was also reported that the elemental compositions showed variations among the same seaweed species obtained from different locations [40]. This variability may explain regional differences in the productivity of commercial fisheries [41].

The most abundant macronutrient in the tested laver samples was K, which was not only previously reported in varying laver species [12,37,38], but also in kelps [42,43,44]. High levels of K are generally found in raw materials of laver (16–42.27 g/kg DW) [12,37,38] and kelp (35–48.5 g/kg DW) [43]. It indicates that the K contents are commonly high in seaweed raw materials. However, the obtained K levels in the S. japonica hydrolysates were only 4.92–9.18 g/kg DW [42].

The content of Zn varied significantly among varying strains and with the progression of harvests. It was found in this study that Zn content was surprisingly high in some N. yezoensis strains. A much higher level has also been detected in the first crop of N. yezoensis [24], while the contents of Zn together with Mn and Cu are lower in kelp [43]. The inorganic As was 0.14–0.16 mg/kg DW in the N. yezoensis cultivated in north Shandong province [24], which is near the laver farm in Muping in this study.

4.2. Comparison of Different N. yezoensis Strains

We carried out comparisons for the nutritional components of different N. yezoensis strains cultivated in the same sea area. When compared to MpPyA and MpPyD, MpPyC and/or MpPyB contained relatively higher contents of proteins and TAAs, higher ratios of TUFAs and EPA, and higher levels of the minerals (K, Na, Ca, Mg, Zn, Mn, and Se), but contained lower or similar contents of Cu, Cd, Pb, and As. Moreover, we obtained a rank (MpPyC > MpPyB > MpPyD > MpPyA) according to the scores of the three principal components based on PCA analysis. These results indicated that PyC and PyB are promising cultivars for developing the laver culture industry in north Shandong province.

The genetic variation coefficient reflects the potential for a particular trait to respond to selection pressures on a particular population; the larger the coefficient, the greater the evolvability of the trait [45]. We found that there were significant coefficient of variation (CV) of fat, fatty acid, and mineral components among different N. yezoensis (Figure 4A), indicating that N. yezoensis strains have rich inherent genetic variations. The protein and amino acid components had low CV among the four N. yezoensis strains cultivated in the same sea area, but had high CV between two N. yezoensis samples (same strain) cultivated in different sea areas (Figure 4), suggesting that environmental heterogeneity enhanced differences of protein and amino acids content in N. yezoensis. Moreover, environmental heterogeneity also significantly enhanced differences of trace elements (e.g., Zn, Fe, Se, and Mn) in N. yezoensis (Figure 4).

4.3. The Influence of Ecological Factors on the Contents of Nutritional Components in Laver

Climate change has been predicted to result in elevated seawater temperatures, declining salinity (caused by increases in rainfall), coastal eutrophication and ocean acidification. The correlations between large-scale seaweed cultivation and the change of some environmental factors have been discussed [46,47,48], and previous studies have reported the relationships between wild macroalgal communities and abiotic factors [49,50,51,52]. They found that macroalgal abundance, distribution, and diversity responded to variability in salinity, light availability, nutrient availability, depth, bottom type, and wave exposure, and that the tissue-nutrient content in macroalgae was variable in space and time. The environmental changes may also have great influences on the nutritional components of cultivated macroalgae either directly or through interacting effects. The result of GCA reflects the close degree of the relationship between principal environmental factors and nutritional component factors for deducing primary factors based on the gray correlation degrees. In the present study, high gray correlation degree was found between the typical ecological factors (DIN, DIP, N:P, pH, Sal, T, and Tra) and the contents of crude nutrient, amino acid, fatty acid, and mineral components in laver, demonstrating that distinct nutritional components of laver are related to specific water quality conditions.

4.3.1. The Influence of DIN and DIP on the Contents of Nutritional Components in Laver

Laver is characterized by high protein contents (may be over 50% DW) [23,30] and high ratios of DAAs to TAAs [17]. Regulation of protein accumulation and AA composition is important for improving laver product quality. The contents of protein and TAAs were significantly influenced by DIN (nitrate and ammonium) and N:P. It has been testified that the levels of DIN are closely related to the growth and metabolism of laver based on laboratory experiments [18,19,20,21]. If not used immediately for growth, N can be sequestered as photosynthetic pigments, amino acids, proteins, etc. Addition of N increases accumulation of tissue N, soluble protein, FAAs, MAAs, and phycoerythrin [18,19,21,22]. Low nitrate has a significant negative effect on the growth and phycoerythrin concentration of Porphyra torta [20]. The seasonal cycle of the AA pool of Porphyra umbilicalis increased in response to the increase of environmental [NO₃] and the TAAs content reached the maximum simultaneously with the peak of environmental [NO₃] based on field study [53]. All the above indicate that accumulation of protein and TAAs in laver is closely dependent on the DIN concentrations of the culture system. It has been proven that some laver species have the capacity to store N while lacking the capacity to store P [19,54]. Thus, they are less efficient in assimilating DIP than DIN from the environment [3]. The contents of tissue P in N. yezoensis ranged 0.88–1.07% DW, being 11.5–22.1% of the tissue N content [47]. Moreover, the content of P in N. yezoensis was the lowest among the macrominerals, even lower than the amount of Fe [24].

Fe was the most abundant in the tested laver samples, in accordance with the previous studies on laver and kelp [12,24,37,38,43]. In the present study, we found TN had the greatest impact on the Fe content in N. yezoensis. It was reported that N nutrition is critical in both the uptake and translocation of Fe to plant [55]. N supply reduced the molar ratios of phytic acid to Fe in plants, thus enhancing Fe bioavailability [56].

4.3.2. The Influence of Temperature, Light, and Salinity on the Contents of Nutritional Components in Laver

It has been recognized that temperature plays an important role in regulating the production of PUFAs [57,58,59,60]. An inverse relationship has been recognized between temperature and fatty acid unsaturation in microalgae [61,62]. For example, it was reported that lowering culture temperature of Phaeodactylum tricornutum could significantly raise its yields of EPA and PUFAs [60]. PUFAs in Chlamydomonas were significantly increased at 0 and 5 °C [63]. Our study confirmed the same pattern in laver; that is, EPA and PUFAs as percentages of TFAs increased significantly in MpPh in late Oct compared to late Sep (Table 1). In addition, the highest TUFAs/TFAs and EPA/TUFAs was 0.76 and 0.89, respectively, in the strain that contained the lowest TFAs and TUFAs contents (Table 1). These results were in accordance with the previous finding in Bangia fuscopurpurea that the EPA content increased 29.8% in the samples grown at 4 °C, while the TFAs content remained unchanged [64]. The accumulation of PUFAs is regulated through gene expression. The mRNA levels of the genes regarding PUFAs including EPA biosynthesis have been found to be up-regulated in B. fuscopurpurea, Synechocystis sp. PCC 6803, Chlorella minutissima, and Nitzschia closterium under cold stress [57,64,65]. Unsaturation of FAs and redistribution of FA species in membrane lipids is one of the sophisticated mechanisms to maintain the membrane integrity in response to cold stress [66,67].

Generally, interactive effects between ecological factors on the FAs concentration and composition are present, e.g., temperature and P supply, light, and P supply [68]. The FAs are also influenced by the function of the photosynthetic apparatus in autotrophic organisms [69]. Generally, increasing light intensities (below photo-inhibition) results in increased synthesis of carbon compounds. The activity of acetyl CoA carboxylase, catalyzing the first step of FA synthesis, is regulated by light [70]. Thus, excess carbon would be stored in the form of lipids, that is generally dominated by saturated FAs and mono-unsaturated FAs [69]. Carbohydrates are also sophisticated carbon storage molecules apart from lipids [71]. As shown in this study and all the previous studies, the content of sugar in Bangiales seaweeds is much higher than that of fat and FAs, indicating that the dominant carbon storage is carbohydrates other than lipids.

Low light intensities have been reported to stimulate the synthesis of galactolipids, which are important components of photosynthetic membranes (Sukenik et al., 1989) and contain high amounts of PUFAs [72]. Thus, increasing TUFAs/TFAs and EPA/TUFAs under low light may promote light utilization through regulating the fluidity and plasticity of the photosynthetic membrane. Nutrient limitation increases the FAs concentration of phytoplankton [72,73,74], which is generally determined as a consequence of a limited growth rate and the consequent storage of excess carbon in the form of lipids [68].

Salinity can affect the quantity and quality of TAAs in seaweeds indirectly by altering growth rates and thereby diluting or concentrating the amino acid content of the biomass, or directly by altering the synthesis of specific amino acids as osmolytes [75]. The TAAs content is most strongly related to the growth rate of Ulva ohnoi and is the highest in the slowest growing blades. Increases in salinity are positively correlated with the proportion of proline, tyrosine, and histidine, whereas they are negatively correlated with alanine [75]. The salinity was within the range of 28–31, above the threshold of producing a stress effect [6].

Salinity is a complex abiotic factor which affects the physicochemical characteristics of trace metals, resulting in changes in the metal uptake [76]. Fe, Zn, Mn, and Cu are the active centers of many enzymes and play key roles in cell metabolism. Among the microelements, Zn was the second most abundant microelement in the tested laver samples. Zn is a precursor of essential vegetable hormones like indoleacetic acid [77]. Especially, it was found that Zn content in N. yezoensis was affected greatly by salinity in the present study.

5. Conclusions

CV and GCA analysis together showed that the typical environmental factors (e.g., DIN, DIP, N:P, pH, Sal, T, and Tra) have remarkable influence on the contents of nutritional components and minerals in laver. The contents of the total minerals exhibited a clear regional variation. Fe and Zn were the most abundant microelements in the tested laver samples, and the contents of Fe and Zn in N. yezoensis were influenced obviously by TN and salinity, respectively. The contents of TFAs and TUFAs in laver were also greatly affected by TN, and the contents of protein and TAAs were significantly influenced by DIN and N:P. Moreover, the CV result suggested that environmental heterogeneity obviously enhanced differences of contents of protein, amino acids, and trace elements in N. yezoensis. In addition, we comprehensively evaluated the nutritional quality of different laver samples based on PCA analysis and found that the N. yezoensis strains that came from Yantai city (MpPyB and MpPyC) had higher comprehensive evaluation scores, indicating that they have the greatest exploitable value and are promising cultivars for developing commercial laver cultivation in northern China.