Combinatorial Effect of Multiple Abiotic Factors on Up-Regulation of Carotenoids and Lipids in Monoraphidium sp. for Pharmacological and Nutraceutical Applications

Abstract

Carotenoids have attracted significant interest due to their potential use in human health and nutrition, and their global demand increases by 3.7% annually. Currently, synthetic carotenoids dominate the market, but possess challenges such as low antioxidant ability, issues with health benefits, and safety concerns. Microalgae are potential producers of natural carotenoids with extraordinary antioxidant properties, although the yield is often low in their natural cycle. The present investigation aimed to analyze the effect of multiple abiotic factors on enhancing algal carotenoids synthesis and other metabolites without affecting growth. The potential microalgae Monoraphidium sp. were grown under altered nutrient and light conditions employing RSM-CCD. The optimized conditions, such as Nitrogen (5 g·L⁻¹), Phosphorus +Potassium (250 mg·L⁻¹), Sulphur (70 mg·L⁻¹), and light (137.5 µmol·m⁻²·S⁻¹), resulted in increased biomass (1357.36 mg·L⁻¹), lipid accumulation (40.28% of dry biomass), and carotenoids (16.26 µg·mL⁻¹) as compared to the control conditions. The total carotenoids fraction consisted of astaxanthin (14.8%), violaxanthin (3.61%), lutein, (45.12%), 9-cis-β carotene (7.62%), and β-carotene-5,6-epoxide (24.21%). Among them, violaxanthin (1.32-fold), astaxanthin (1.19-fold), 9-cis- β carotene (1.07-fold), and β-carotene-5,6-epoxide (1.08-fold) content increased while lutein (1.32-fold) content decreased significantly. The improvement in algal carotenoids under novel culture conditions provides a significant advantage to pharmaceutical and nutraceutical industries.

1. Introduction

Microalgae are gaining prominence in the food, feed, and healthcare industry owing to their capacity to produce distinctive value-added products and biologically active compounds. As a result, the United Nations’ “Sustainable Development Goals” (SDGs) advocate microalgae biotechnology platforms for the synthesis of industrially significant bioproducts [1]. Microalgae produce a myriad of naturally occurring bioactive compounds, prominently carotenoids and other lipids [2]. Likewise, microalgal carotenoids can exhibit a plethora of health benefits against viral, microbial, and cancer activities, in addition to the advantages of antioxidants and vitamin A precursors [3]. Microalgal carotenoids serve as natural pigments in addition to their bioactive molecular attributes in nutraceuticals, food, feed, and cosmetics. This has led to a rise in consumption that has driven the pursuit for novel natural origins for carotenoids [4]. In recent years, the demand for natural and health protective compounds has grown tremendously. The US Food and Drug Administration has approved the application of numerous microalgae species in food and health [5]. Internationally, the carotenoids demand is, however, predicted to thrive with an average acquire profit of 3.9% [6]. Nonetheless, the industrialization of microalgae biotechnology remains difficult due to high operating and processing costs [7]. Numerous studies have been carried out in recent years to use stressors to reduce the expense of algal biotechnology. Nevertheless, commercialization is yet to be achieved because of the lower yield of value-added compounds and high cultivation costs [8]. Despite the lower production levels of intercellular compounds, a combination of abiotic factors can be optimized to obtain enhanced microalgal bioactive compounds.

For maximal biomass production, microalgae require nutrient-sufficient conditions, although the basal amount of lipids can be adjusted by altering nutritional factors and cultivation conditions [9]. Previous research has identified light, nutrients, temperature, and pH as abiotic stressors that have a direct impact on microalgae development. Intercellular carotenoid synthesis increases during light stress to protect cells from photoinhibition [10]. Consequently, different microalgal species require different levels of light for growth and biomolecule synthesis [11]. Since the nutrients in the growth medium have a major influence on the development of microalgae, optimizing an appropriate growth media is critical for industrial scale-up. Nitrogen is an essential nutrient for biomolecule synthesis (protein, lipids, chlorophyll, and nucleic acids), metabolism, and cell growth [12]. Nitrogen and phosphorus insufficiency alters the carbon flux protein, triggering higher lipid deposition in microalgae [13]. Moreover, a lack of phosphorus increases lipid content, which causes triacylglycerols (TAG) to build up during microalgal growth. The high production of biomass, lipid, and carotenoid necessitates the nutritional composition to be optimized [14]. Response surface methodology (RSM) is a computational and statistical design targeted at evaluating challenges associated with multiple independent factors with the intent to achieve the maximum response [15]. RSM was applied to optimize the variables predicated on the intricate dynamic interplay of multiple variables on biomass, lipids, and carotenoid production. It is widely known that stressful conditions could improve lipid content [16]. Nonetheless, little is known about the physiological and molecular components of the underlying processes.

Recently, the ability of Monoraphidium sp. FXY-10 to produce oil was reported by employing fulvic acid coupled with a two-stage culture, although its carotenoid-producing capability was not studied [17]. Haematococcus sp. is a popular microalga for the commercial production of astaxanthin. Numerous studies have shown that Monoraphidium GK12 possesses the ability to substitute Haematococcus sp. as a source of astaxanthin at the commercial level [18,19]. The potential for producing astaxanthin in Monoraphidium sp. is currently being investigated, although the complete profiling remains to be explored [20]. To the best of our knowledge, no empirical research is available on the concurrent augmentation of biomass, lipids, and carotenoids in Monoraphidium sp. through the optimization of culture conditions. In the present study, microalgae were cultivated under varying abiotic factors, including N, P + K, S, LI, and PP, that aimed to increase yield of carotenoids and lipids. Growth performance and photosynthetic efficiency were recorded on alternate days during the culture process, which was designed by employing the RSM-Central composite design (RSM-CCD). Carotenoid content was determined after analyzing the net biomass containing other bioproducts. The current study is intended to ascertain the optimal multimodal conditions for boosting biomass, carotenoids, and other lipids in Monoraphidium sp., along with the comprehensive profiling of carotenoids. Carotenoid variation under optimized cultivation conditions suggested that it could be useful for the nutraceutical and pharmaceutical sectors.

2. Materials and Methods

2.1. Microalgae Growth

The microalgae Monoraphidium sp. (NCIM no. 5585) was procured from the National Chemical Laboratory (NCL), Pune, India. To initiate the culture, pure microalgae inoculum was added to a 100 mL Erlenmeyer flask containing 50 mL of Bold’s Basal Medium (BBM) to achieve an optical density (OD) of 0.1 at a wavelength of 680 nm. The cultures were then placed in an incubation room with a stable temperature of 28 ± 2 °C, and continuous white light illumination of 100 μmol.m⁻² s ⁻¹ for 24 h with intermittent agitation.

2.2. Biochemical Analysis

2.2.1. Gravimetric Estimation of Lipids

The microalgal cells were centrifuged for 15 min at 5000 rpm using a fixed angle centrifuge (Centrifuge 5810R, Eppendorf, Germany). The pellet was then washed twice with distilled water to remove contaminants before being dried at 55 °C in a hot air oven (RDHO 50, REMI, Maharashtra, India) to obtain dry microalgae biomass. Total lipid was extracted from 1 g of dry microalgal biomass using the Bligh and Dyer method with a combination of 2:1 chloroform and methanol (v/v), respectively. Total lipid production, productivity, and content were determined using Equations (1)–(3), respectively [21].

$$\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{l}\mathrm{i}\mathrm{p}\mathrm{i}\mathrm{d} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} (\mathrm{m}\mathrm{g}{·\mathrm{L}}^{-1})=\frac{\mathrm{M}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{l}\mathrm{i}\mathrm{p}\mathrm{i}\mathrm{d} \left(\mathrm{m}\mathrm{g}\right)}{\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{e} \left(\mathrm{L}\right)}$$

(1)

$$\mathrm{L}\mathrm{i}\mathrm{p}\mathrm{i}\mathrm{d} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y} (\mathrm{m}\mathrm{g}·{\mathrm{L}}^{-1}{\mathrm{d}}^{-1})=\frac{\mathrm{M}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{l}\mathrm{i}\mathrm{p}\mathrm{i}\mathrm{d} \left(\mathrm{m}\mathrm{g}\right)}{\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{e}\left(\mathrm{L}\right)\times \mathrm{C}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{d}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \left(\mathrm{d}\right)}$$

(2)

$$\mathrm{L}\mathrm{i}\mathrm{p}\mathrm{i}\mathrm{d} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} (\mathrm{\%})=\frac{\mathrm{M}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{l}\mathrm{i}\mathrm{p}\mathrm{i}\mathrm{d} \left(\mathrm{g}\right)}{\mathrm{M}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{c}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e}\left(\mathrm{g}\right)}\times 100$$

(3)

2.2.2. Estimation of Protein Content

Lowry’s method was used to estimate the protein content, with Bovine Serum Albumin (BSA) used as a standard [22]. The protein concentration was determined by taking absorbance at 600 nm using the Microplate reader Synergy H1 (BioTek, Winooski, VT, USA). By virtue of Equation (4), protein content was estimated as:

$$\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{t}\mathrm{e}\mathrm{i}\mathrm{n} (\mathrm{\%})=\frac{\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{t}\mathrm{e}\mathrm{i}\mathrm{n} (\mathrm{B}\mathrm{S}\mathrm{A} \mathrm{c}\mathrm{u}\mathrm{r}\mathrm{v}\mathrm{e})}{\mathrm{D}\mathrm{r}\mathrm{y} \mathrm{b}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \left(\mathrm{g}\right)}\times 100$$

(4)

2.2.3. Estimation of Carbohydrate Content

The 3,5-Dinitrosalicylic acid (DNS) method was used to determine the carbohydrate content [23]. DNS reagent was utilized to determine carbohydrate content, and the optical density was measured using the Microplate reader Synergy H1 (BioTek, Winooski, VT, USA) at 540 nm. The total carbohydrate content was estimated by Equation (5):

$$\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{h}\mathrm{y}\mathrm{d}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} (\mathrm{\%})=\frac{\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{h}\mathrm{y}\mathrm{d}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e} (\mathrm{g}\mathrm{l}\mathrm{u}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{e} \mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{d} \mathrm{c}\mathrm{u}\mathrm{r}\mathrm{v}\mathrm{e})}{\mathrm{D}\mathrm{r}\mathrm{y} \mathrm{b}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \left(\mathrm{g}\right)}\times 100$$

(5)

2.2.4. Estimation of Moisture and Ash Content

To determine the moisture content, the weight of the algal biomass was measured before and after drying for 1 h at 105 °C, followed by desiccation for 30 min. For the ash content, the dried biomass was heated at 550 °C for 30 min, followed by 30 min of desiccation and weighing, as per [24]. The total moisture and ash content were estimated by Equations (6) and (7), respectively:

$$\mathrm{M}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} (\mathrm{\%})=\frac{\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{w}\mathrm{e}\mathrm{t} \mathrm{b}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s}-\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{d}\mathrm{r}\mathrm{y} \mathrm{b}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s}}{\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{w}\mathrm{e}\mathrm{t} \mathrm{b}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s}}\times 100$$

(6)

$$\mathrm{A}\mathrm{s}\mathrm{h} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} (\mathrm{\%})=\frac{\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{a}\mathrm{s}\mathrm{h}}{\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{d}\mathrm{r}\mathrm{y} \mathrm{b}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s}}\times 100$$

(7)

2.3. Optimizing Nutrients, Light Intensity, and Photoperiod Using the Response Surface Methodology (RSM) Based Central Composite Design (CCD)

This investigation examined five abiotic factors, which included three nutrient factors and two illumination periods. The variables chosen were nitrogen (N) concentration ranging from 0 to 5000 mg·L⁻¹ (NaNO₃), combined potassium and phosphorus (P + K) concentration ranging from 0 to 500 mg·L⁻¹ (K₂HPO₄:KH₂PO₄) in a 1:1.5 ratio, Sulphur (S) concentration ranging from 0 to 140 mg L⁻¹ (MgSO₄·7H₂O), light intensity (LI) ranging from 75 to 200 µmol m⁻²s⁻¹, and photoperiod (PP) ranging from 0 to 24 h. The concentrations of the five factors were determined using the RSM-CCD design expert 13 (Design Expert 13.0.0, Stat-Ease, Minneapolis, MN, USA). Forty-three experimental sets were coded in duplicate, and the response was measured for biomass productivity, total lipid content, chlorophyll-a yield, chlorophyll-b yield, and carotenoids yield. All experiments were conducted using 250 mL Erlenmeyer flasks with a working volume of 100 mL. The flasks were fitted with cotton plugs and placed in a growth chamber that provided culture conditions according to RSM-CCD. Intermittent orbital shaking was applied at 100 rpm using a rotary orbital shaker (REMI CIS 24BL, Mumbai, India).

2.4. Biomass, Lipid, and Pigment Analysis

Microalgal biomass was determined using a microplate reader synergy H1 (BioTek, Winooski, VT, USA) spectra-fluorometer every alternate day. About 200 µL of culture was loaded onto 96-well plates, and the absorbance was recorded at 680 nm to calculate the biomass concentration using Equations (8)–(11) [25].

$$\mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\left(\mathrm{g}{·\mathrm{L}}^{-1}\right)=0.675\times {\mathrm{A}}_{680}-0.0841$$

(8)

where A₆₈₀ depicts the density of algae at 680 nm wavelength.

$$\mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y}\left(\mathrm{g}·{\mathrm{L}}^{-1}{\mathrm{d}}^{-1}\right)=\frac{\mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} (\mathrm{g}·{\mathrm{L}}^{-1})}{\mathrm{C}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{d}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \left(\mathrm{d}\right)}$$

(9)

$$\mathrm{S}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{c} \mathrm{g}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h} \mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e} (\mathrm{\mu })({\mathrm{d}}^{-1})=\frac{\mathrm{l}\mathrm{n} \left({\mathrm{X}}_2/{\mathrm{X}}_1\right)}{{\mathrm{T}}_2-{\mathrm{T}}_1}$$

(10)

$$\mathrm{D}\mathrm{o}\mathrm{u}\mathrm{b}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e} (\mathrm{d})=\frac{\mathrm{l}\mathrm{n}2}{\mathrm{\mu }}$$

(11)

where T represents time in days; X₂ and X₁ showed final and initial biomass concentration; and T₂ and T₁ represent the final and initial time, respectively.

The lipid-sensitive fluorescent dye Nile Red was used to visualize and estimate triacylglycerol (TAG) accumulation in microalgae at different stages [26]. The quantification of neutral lipid was performed in duplicates on a black 96-well plate (Nunc maxisorp, Waltham, MA, USA) at each time interval (on the 1st, 5th, 9th, 13th, 18th, 23rd, 25th, and 30th day). About 500 µL of the culture was mixed with 1 µL of stock Nile Red (stock concentration) containing 499 µL of dimethyl sulfoxide (DMSO) solution. The sample was loaded onto the 96-well plate, and the fluorescence was recorded using a Synergid H1 spectra-fluorometer at an excitation wavelength of 530 nm and emission wavelength of 575 nm.

The microalgae pigment was determined by loading 200 µL of the culture onto a 96-well plate using a microplate reader synergy H1 spectra-fluorometer on alternate days. Pigment content was determined as chlorophyll a, chlorophyll b, and total carotenoid by Equations (12), (13), and (14), respectively [27].

$$\mathrm{C}\mathrm{h}\mathrm{l} a (\mathrm{\mu }\mathrm{g}·{\mathrm{m}\mathrm{L}}^{-1})=13.36 {\mathrm{A}}_{664}-5.19{\mathrm{A}}_{648}$$

(12)

$$\mathrm{C}\mathrm{h}\mathrm{l} b (\mathrm{\mu }\mathrm{g}·{\mathrm{m}\mathrm{L}}^{-1})=27.43 {\mathrm{A}}_{648}-8.12{\mathrm{A}}_{664}$$

(13)

$$\mathrm{C}\mathrm{a}\mathrm{r} (\mathrm{\mu }\mathrm{g}·{\mathrm{m}\mathrm{L}}^{-1})=\frac{(1000 {\mathrm{A}}_{470}-2.13\mathrm{C}\mathrm{h}\mathrm{l}a-97.64 \mathrm{C}\mathrm{h}\mathrm{l}b)}{209}$$

(14)

where Chl a, Chl b, and Car are chlorophyll a, chlorophyll b, and total carotenoids, respectively. A₆₆₄, A₆₄₈, and A₄₇₀ are the absorbances recorded at wavelength 664, 648, and 470 nm, respectively.

2.5. Microscopic Visualization of Lipophilic Compounds

To determine the lipid fluorescence intensity, microalgae cells were stained with the fluorescent dye Nile Red (Sigma, Burlington, MA, USA, 552/636), and the fluorescence intensity was measured to determine the presence of lipophilic compounds. Briefly, 1 mL of microalgae cell culture was centrifuged (Centrifuge 5810R, Eppendorf, Germany) at 7000 rpm for 5 min, and the pellets obtained were rinsed three times with PBS. The pellet was then dissolved in 1 mL of 20% DMSO, followed by the addition of 1.5 g·mL⁻¹ of Nile Red dye. After 15 min of incubation in the dark, the mixture was examined for intracellular lipophilic compounds in algal cells using an inverted fluorescence microscope (Nikon A1, Tokyo, Japan) at 552 nm excitation and 636 nm emission wavelength. The measurement of fluorescence intensity in the microalgal cells was determined using Image J software version 1.53o [28].

2.6. Pigments Sample Preparation

The microalgae carotenoid extract was prepared by mixing 1 g of finely powdered, dried microalgal biomass with 2.5 mL of 2 M ethanolic KOH and 10 mL of DCM. The sample was then sonicated for 1 h in a sonicator bath (Labman, LMUC-3), followed by 2 h of temperature-controlled orbital shaking at 300 rpm using a shaker (REMI, CIS 24BL). Then, 2.5 mL of DCM was added, vortexed briefly, and centrifuged (Centrifuge 5810R, Eppendorf, Germany) at 4000 rpm for 10 min to obtain the supernatant. The aforementioned procedure was repeated using the residue pellet and DCM until a colorless solvent was visible. The cumulative supernatant portion was dried using a rotary evaporator (BUCHI, R-210) operating at 40 °C. Subsequently, 7 mL of 50% aqueous ethanol (v/v) was added to the dried residue, followed by 10 min of centrifugation at 4000 rpm, and the supernatant was collected as the microalgal carotenoid extract [29].

2.7. Ultra-High Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry (UPLC/Q-ToF-MS) Analysis

The extracts containing microalgal carotenoids were analyzed using the Acquity H class series UPLC system (Waters, Synapt XS HDMS) coupled with a quadrupole time-of-flight mass spectrometry (QToF). A 5 µL sample was injected into the Acquity UPLC C18 BEH column (2.1 × 50 mm, 1.7 µm particle size, Waters) using electrospray ES⁺ with a 5 µL injection volume and a flow rate of 0.25 mL min⁻¹. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) solvent. The initial condition was 90% A—10% B (0–2 min), followed by 80% A—20% B (2–5 min), 70% A—30% B (5–10 min), 50% A—50% B (10–12 min), 90% A—10% B (12–14 min), and thereafter. The mobile phase and column were then allowed to return to their initial states of equilibrium. Electrospray ionization (ESI) acquisition was performed with the Auto MS/MS method in positive mode. For the mass spectrometer, the following parameters were used: desolvation gas at 950 L/h, cone gas at 30 L/h, desolvation temperature at 450 °C, source temperature at 120 °C, capillary voltage at 3.22 keV, cone voltage at 50 V, and collision energy at 4 ev. The gases used were N₂ and Argon, with a pressure of 6–7 bar and 5–6 bar, respectively.

3. Results and Discussion

Freshwater microalgae belonging to the genus Monoraphidium sp. are found all over the globe and are known for having a very extensive range of biotic communities [30]. Recently, Monoraphidium sp. has gained recognition as a possible biofuel feedstock because of its adaptability to a variety of environmental factors [31]. However, the potential of Monoraphidium sp. as a source of carotenoids has been less investigated; hence, in this investigation, different abiotic factors were studied for the improved production of biomass, lipids, and carotenoids.

3.1. Morphology and Biochemical Analysis

The microalgae Monoraphidium sp. were grown photo-autotrophically in BBM medium under controlled light and atmospheric CO₂ for 30 days. The morphometric analysis of Monoraphidium sp. using confocal microscopy is illustrated in Figure 1. The cells appeared comma-shaped, typically with a length of 2–3 µm and a diameter of 1–2 µm.

To determine the precise quantities of lipids, carbohydrates, and proteins in Monoraphidium sp., a biochemical examination was conducted. It was found that the percentage of lipids, carbohydrates, and proteins in Monoraphidium sp. on BBM medium were 18.5%, 42%, and 35.21%, respectively (Figure 2).

3.2. Regression Modeling and Statistical Analysis

The results on the improved production of biomass, lipids, and carotenoids under different abiotic factors were presented based on the design of RSM-CCD. Design Expert was used to determine the anticipated and actual values in

Table 1. Combined effect of nutrition (Nitrogen, Phosphorus, Potassium, and Sulphur) and light factors (Light intensity and Photoperiod) on biomass productivity (mg·L⁻¹d⁻¹), lipid content (%), and carotenoids production (µg·mL⁻¹), with experimental and predicted values using RSM-CCD (Each value represents mean ± SD; n = 3).

Run	Nitrogen (mg·L−1)	Potassium + Phosphorus (mg·L−1)	Sulphur (mg·L−1)	Light Intensity (µmol·m−2s−1)	Photoperiod (h)	Experimental Value			Anticipated Value
						Biomass Productivity (mg·L−1d−1)	Lipid Content (%)	Total Carotenoid Yield (µg·mL−1)	Biomass Productivity (mg·L−1d−1)	Lipid Content (%)	Total Carotenoid Yield (µg·mL−1)
1	2500	250	70	137.5	12	24.5	13.1	3.18	34.27	18.31	4.98
2	2500	0	70	137.5	24	27.8	14.8	4.94	44.9	23.99	6.55
3	2500	500	70	137.5	24	54.9	29.3	12.2	50.26	26.85	7.35
4	2500	0	140	137.5	12	41.09	21.9	4.5	32.09	17.14	2.87
5	5000	250	70	137.5	0	11.3	6.04	0.78	24.05	12.85	2.168
6	5000	250	70	75	12	24.01	12.8	1.1	28.66	15.31	3.52
7	2500	250	140	75	12	23.28	12.4	3.5	26.08	13.93	5.09
8	5000	0	70	137.5	12	39.7	21.2	3.5	34.67	18.52	4.05
9	2500	250	0	200	12	76.4	40.8	8.07	42.47	22.69	7.34
10	2500	250	0	137.5	24	52.5	42.09	6.12	47.09	25.16	6.2
11	2500	250	140	137.5	24	45.9	24.5	7.02	48.08	25.68	7.7
12	0	250	70	137.5	24	34.8	18.6	6.7	44.5	23.78	3.91
13	2500	500	70	137.5	0	25.3	13.5	3.69	23.65	12.63	3.75
14	0	0	70	137.5	12	25.03	13.3	4.96	28.52	15.23	4.77
15	2500	500	0	137.5	12	29.1	15.5	6.14	36.46	19.48	5.89
16	0	250	0	137.5	12	30.8	16.4	6.18	30.7	16.4	6.19
17	0	250	140	137.5	12	24.8	13.2	4.5	31.69	16.93	3.95
18	2500	0	70	75	12	49.85	30.6	8.14	22.91	12.24	5.99
19	2500	250	70	200	0	27.6	14.78	2.6	29.65	15.84	3.13
20	2500	250	70	200	24	33.8	18.09	4.09	56.27	30.06	7.2
21	2500	500	70	75	12	17.5	9.39	3.01	28.27	15.1	3.61
22	2500	250	70	75	24	34.01	18.17	4.4	38.89	20.78	6.71
23	2500	250	0	75	12	24.5	13.1	5.43	25.09	13.41	4.5
24	0	500	70	137.5	12	37.8	20.2	6.8	33.88	18.1	5.37
25	5000	250	70	137.5	24	75.4	40.28	16.2	50.66	27.06	10.7
26	0	250	70	137.5	0	30.1	16.08	3.79	17.89	9.56	6.23
27	2500	500	70	200	12	55.73	29.7	6.87	45.64	24.38	7.5
28	2500	250	70	137.5	12	34.8	18.6	5.7	34.27	18.31	4.98
29	2500	0	0	137.5	12	27.15	14.5	5.13	31.1	16.61	5.95
30	5000	500	70	137.5	12	41.28	22.05	6.48	40.03	21.39	5.74
31	2500	0	70	137.5	0	12.08	6.45	1.74	18.29	9.77	2.26
32	2500	250	70	137.5	12	44.5	23.8	5.94	34.27	18.31	4.98
33	2500	500	140	137.5	12	37.5	20.08	7.93	37.45	20.01	5.22
34	2500	250	70	75	0	23.83	12.7	3.3	12.28	6.56	2.89
35	2500	250	140	137.5	0	8.5	4.54	1.2	21.46	11.47	3.815
36	2500	250	0	137.5	0	7.81	4.17	7.06	20.47	10.94	5.64
37	5000	250	140	137.5	12	34.2	18.3	3.01	37.85	20.22	4.14
38	0	250	70	75	12	25.1	13.4	4.73	22.51	12.03	6.08
39	2500	0	70	200	12	33.6	17.9	4.9	40.28	21.52	2.83
40	0	250	70	200	12	30.5	16.3	3.7	39.88	21.31	4.06
41	5000	250	70	200	12	32.04	17.1	4.9	46.04	24.6	6.27
42	2500	250	140	200	12	71.3	38.1	1.3	43.46	23.22	2.99
43	5000	250	0	137.5	12	30.4	16.2	3.9	36.86	19.69	5.65

. The data on augmented biomass, lipids, and carotenoids production under distinct abiotic factor optimization scenarios are shown in Table 1, and the obtained yields of chlorophyll a and b are provided in Table S1.

The relationship between the growth and biochemical parameters (biomass, lipid, chlorophyll a, chlorophyll b, and total carotenoids) and the factor variables (nitrogen, potassium and phosphorus, sulphur, light intensity, and photoperiod) was obtained by performing regression analysis and curve fitting of the data using RSM-CCD. The relationships were expressed by the polynomial equations presented in

Table 2. Accurate models and associated adjusted R² (R_adj²) of response surface methodology- central composite design (RSM-CCD) for biomass productivity (mg·L⁻¹d⁻¹), lipid content (%), chlorophyll a (µg·mL⁻¹), chlorophyll b (µg·mL⁻¹), and total carotenoids (µg·mL⁻¹) in response to abiotic factors including A-Nitrogen (mg·L⁻¹), B-Potassium and phosphorus (mg·L⁻¹), C-Sulphur (mg·L⁻¹), D- Light intensity (µmol·m⁻²s⁻¹), and E-Photoperiod (h).

Response	Model	Radj2
Biomass productivity (mg·L−1 d−1)	=+38.71 + 3.08 × A + 8.69 × B + 2.68 × C + 17.74 × D + 0.4950 × E	0.33
Lipid content (%)	=+20.68 + 1.64 × A + 4.64 × B + 1.43 × C + 9.48 × D + 0.2645 × E	0.41
Chlorophyll a yield (µg·mL−1)	=+12.18 + 0.0744 × A + 0.8357 × B + 0.6287 × C + 2.44 × D − 0.4549 × E + 0.6908 × AB + 0.9296 × AC + 0.0032 × AD + 0.5578 × AE + 2.40 × BC + 0.4617 × BD − 0.9000 × BE + 0.4461 × CD + 0.4023 × CE − 0.2485 × DE + 0.1134 × A2 − 0.1134 × B2 + 0.6203 × C2 − 6.10 × D2 + 0.1341 × E2	0.82
Chlorophyll b yield (µg·mL−1)	= + 4.34 + 1.60×A + 0.5636×B−0.3356×C + 2.51×D + 0.0567×E	0.29
Total Carotenoid yield (µg·mL−1)	=+4.87 − 0.8053 × A − 0.4848 × B − 0.1982 × C + 0.2919 × D + 1.94 × E + 0.2719 × AB + 0.1836 × AC + 1.91 × AD + 3.14 × AE + 0.6001 × BC + 2.82 × BD − 0.1718 × BE − 1.97 × CD + 1.69 × CE + 0.0983 × DE	0.31

. The equations were developed by the Design Expert and their corresponding R_adj² values are also included in the table.

Figures S1–S5 depict 3D surface response plots of the association between factors and the response value achieved. As illustrated in the 3D surface response plots, the regression equation generated by the RSM Design Expert Software was subjected to analysis of variance (ANOVA) to assess the efficacy of the selected model and the accuracy of the anticipated results. The fitted model appeared to be accurate and could offer recommendations for enhancing productivity by employing suitable conditions.

Table 3. Analysis of variance (ANOVA) for the proposed most accurate regression model.

Response	Model Term	Coefficient Estimated	df	Standard Error	Mean Square	F-Value	p-Value
Biomass productivity (mg·L−1 d−1)	Intercepts	29.06	1	2.75	862.17	5.20	0.0010
	A	3.08	1	3.22	151.53	0.9135	0.3454
	B	2.68	1	3.22	114.96	0.6931	0.4105
	C	0.4950	1	3.22	3.92	0.0236	0.8786
	D	13.90	1	5.15	1207.42	7.28	0.0104
	E	13.31	1	3.22	2833.02	17.08	0.0002
Lipid content (%)	Intercepts	15.53	1	1.47	246.07	5.20	0.0010
	A	1.64	1	1.72	43.25	0.9135	0.3454
	B	1.43	1	1.72	32.81	0.6931	0.4105
	C	0.2645	1	1.72	1.12	0.0236	0.8786
	D	7.43	1	2.75	344.61	7.28	0.0104
	E	7.11	1	1.72	808.58	17.08	0.0002
Chlorophyll a yield (µg·mL−1)	Intercepts	10.72	1	0.7372	22.69	10.69	<0.0001
	A	−0.3409	1	0.5689	0.7621	0.3591	0.5551
	B	−0.9199	1	0.5689	5.55	2.61	0.1201
	C	0.1472	1	0.5689	0.1421	0.0670	0.7982
	D	1.37	1	1.25	2.55	1.20	0.2848
	E	3.91	1	0.5689	100.41	47.32	<0.0001
	AB	0.9296	1	0.7284	3.46	1.63	0.2152
	AC	0.5578	1	0.7284	1.24	0.5865	0.4519
	AD	1.11	1	1.17	1.91	0.8995	0.3532
	AE	0.0024	1	0.7284	0.0000	0.0000	0.9974
	BC	0.4023	1	0.7284	0.6472	0.3050	0.5863
	BD	3.83	1	1.17	22.95	10.81	0.0034
	BE	0.3345	1	0.7284	0.4477	0.2109	0.6505
	CD	−1.44	1	1.17	3.24	1.53	0.2296
	CE	−0.1864	1	0.7284	0.1389	0.0655	0.8004
	DE	0.5540	1	1.17	0.4796	0.2260	0.6392
	A²	0.1134	1	0.5758	0.0823	0.0388	0.8457
	B²	0.6203	1	0.5758	2.46	1.16	0.2930
	C²	0.1341	1	0.5758	0.1151	0.0542	0.8180
	D²	−0.2904	1	1.47	0.0823	0.0388	0.8457
	E²	−3.43	1	0.5758	75.40	35.53	<0.0001
Chlorophyll b yield (µg·mL−1)	Intercepts	3.38	1	0.4599	20.92	4.53	0.0026
	A	1.60	1	0.5375	41.11	8.89	0.0050
	C	−0.3356	1	0.5375	1.80	0.3898	0.5362
	E	0.0567	1	0.5375	0.0514	0.0111	0.9166
	B	0.9018	1	0.8601	5.08	1.10	0.3012
	D	1.88	1	0.5375	56.56	12.23	0.0012
Total Carotenoid yield (µg·mL−1)	Intercept	4.87	1	0.4612	10.62	2.28	0.0299
	A	−0.8053	1	0.8419	4.25	0.9148	0.3473
	B	−0.4848	1	0.8419	1.54	0.3316	0.5695
	C	−0.1982	1	0.8419	0.2575	0.0554	0.8157
	D	0.2919	1	0.8624	0.5324	0.1145	0.7376
	E	1.94	1	0.8419	24.57	5.29	0.0295
	AB	0.2719	1	1.08	0.2957	0.0636	0.8028
	AC	0.1836	1	1.08	0.1349	0.0290	0.8660
	AD	1.91	1	1.72	5.70	1.23	0.2779
	AE	3.14	1	1.08	39.36	8.47	0.0072
	BC	0.6001	1	1.08	1.44	0.3100	0.5823
	BD	2.82	1	1.72	12.42	2.67	0.1138
	BE	−0.1718	1	1.08	0.1180	0.0254	0.8746
	CD	−1.97	1	1.72	6.09	1.31	0.2624
	CE	1.69	1	1.08	11.41	2.46	0.1288
	DE	0.0983	1	1.72	0.0151	0.0032	0.9550

presents the F value and p-value of the model for the responses’ correspondence to the influence of factors. The combined effect of five distinct abiotic factors showed the greatest impact. The reliability of the model was indicated by the obtained R_adj² coefficient values. The regression model and actual values showed a strong correlation, as evidenced by the R_adj² value, and the model had a high probability of accurately predicting the outcome of the experimental setup. The test demonstrated great reliability and precision, as indicated by the low coefficient of variation, which reflected the repeatability of the test. The overall consistency was significant, indicating that the experimental design was effectively executed and the regression model was significant.

3.2.1. Biomass Model

The Model F-value of 5.2 indicated that the model was statistically significant. There was only a 0.10% probability that such a large F-value could arise due to noise. Model terms are considered significant when their p-values are below 0.0500. Table 1 shows the possible combinations of experiments based on the CCD paradigm, with biomass being the primary response. After performing the RSM combinations and recording the experimental data, the biomass ranged from 7.8 to 75.4 g·L⁻¹d⁻¹, depending on the nutrients in the medium and the illuminance conditions. A high-order polynomial regression equation was used to fit the linear model, as shown in Figure S1. The model was well-synchronized with the data and showed no significant lack of fit, accurately representing the interaction between factors and response. The relevance of the linear model for biomass was evaluated using ANOVA, and the results are reported in Table 2 and Table 3. Biomass was significantly affected by N (5000 mg·L⁻¹), P + K (250 mg·L⁻¹), S (70 mg·L⁻¹), LI (137.5 µmol m⁻²s⁻¹), and PP (24 h). The correlation between the predicted and actual values was remarkable, with a coefficient of 29.06. Furthermore, a 3D response surface plot illustrates the effects of N, P + K, S, LI, and PP individually or in combination on biomass concentration, and adequately optimizes the verified variables. This study is one of the few to explore CCD using multiple variables, including N, P + K, S, PP, and LI, to increase biomass productivity.

3.2.2. Lipids Model

The second response was the lipid content, and the polynomial fitted model was presented in Table 2, with a distinctive linear regression equation suitable for the data and no significant lack of fit. R_adj² of 0.3 indicates that the experimental data fit the linear model very adequately. Only statistically significant terms with p-values less than 0.05 were selected for model calibration. The influence of N, P + K, S, PP, and LI on lipids was empirically characterized using regression coefficients. The lipid content was governed by the combination of multiple abiotic factors estimates, each of which has a favorable overall impact on the lipid content. The 3D response surface plot with the contour map shown in Figure S2 provides significant information that confirms whether an optimum abiotic condition boosts the lipids and enables them to attain their highest prevalence.

3.2.3. Chlorophyll a and b Model

In subsequent research on chlorophyll, the quadratic and linear models for chlorophyll a and b, respectively, were constructed using RSM-CCD design experts. The p-values for each model term were less than 0.05, indicating that the model was appropriate. The significance of the model was increased by a lower p-value and a larger magnitude of the factors. The best-fitting model, based on the data, was quadratic and linear. The surface model showed that some actual data values were lower than predicted values, and some were higher than projected values, as indicated in Table S1. Table 3 shows a coefficient of 10.72 for chlorophyll a and 3.38 for chlorophyll b. Moreover, the highest-order polynomial equations indicate that the models are significant.

3.2.4. Carotenoids Model

A comprehensive investigation was conducted on total carotenoids, and a 2FI model was developed through statistical analysis of the data. According to this paradigm, each abiotic factor had a unique and distinct effect on the response. The model’s robustness is indicated by a R_adj² of 0.31 and a p-value of 0.029. The Model F-value of 2.28 was impressive, with only a 2.9% chance that such a large F-value could occur due to noise. The 3D surface plot and contour lines demonstrated that different conditions had varying effects on the responses (see Figure S3). Furthermore, the 3D model illustrates the best model-fitted ranges of N, P + K, S, LI, and PP.

3.2.5. Validation of RSM-CDD Based Models

The results obtained from various models were validated, and the corresponding values were shown in Table 1, Table 2 and Table 3. The quantification of biomass, lipids, and carotenoids was performed using spectrophotometric and gravimetric methods. Based on model analysis and the prediction of optimal culture conditions, the optimum cultivation conditions were found to be N (5000 mg·L⁻¹), P + K (250 mg·L⁻¹), S (70 mg·L⁻¹), PP (24 h), and LI (137.5 µmol m⁻²s⁻¹). To confirm the accuracy of the regression model used in the response surface approach, the maximum development efficiency under optimal conditions was examined. The actual values of biomass, lipid, and carotene development efficiencies were 75.4 mg·L⁻¹d⁻¹, 40.28%, and 16.2 µg·mL⁻¹, respectively. These findings confirmed the applicability of the obtained models, and the experimental values corresponds to the predicted values. Based on the aforementioned experimental findings, it was demonstrated that the model was reliable.

3.3. Effect of Distinctive Abiotic Factors on the Yield of Biomass, Lipids, Chlorophylls, and Carotenoids

Under the photoautotrophic (BBM) conditions, which served as the control, Monoraphidium sp. exhibited a dynamic growth pattern, with a maximal biomass production of 576.6 mg·L⁻¹, biomass productivity of 28.3 mg·L⁻¹d⁻¹, lipid production of 106 mg·L⁻¹, and total carotenoid production of 3.76 µg·mL⁻¹ during the stationary phase. In contrast, under optimized cultivation conditions, the highest biomass production was 1357.36 mg·L⁻¹, the highest biomass productivity was 75.4 mg·L⁻¹d⁻¹, the highest lipid production was 542.8 mg·L⁻¹, and the highest total carotenoid production was 16.25 µg·mL⁻¹, all of which were achieved during the stationary phase. Both the control and optimized conditions were used for harvest on the 18th day of culture, as this day resulted in the highest biomass, lipid, and carotenoid yields under the optimized conditions. These results supported our hypothesis that abiotic factors, such as nutrients and light, play a crucial role in the accumulation of value-added metabolites in Monoraphidium sp.

Previous studies have shown that single abiotic factors can affect the synthesis of metabolites and microalgal growth [32]. However, not much research has been conducted on the effects of multiple abiotic factor combinations on microalgal development [33,34]. In this study, a wide range of abiotic factors were studied with an emphasis on boosting carotenoid synthesis while enhancing the ability to produce biomass and lipid levels. Carotenoid and lipid production were directly correlated with biomass production, irrespective of various external factors, including light, nutrition, temperature, etc. As shown in

Table 4. Biomass, lipid, chlorophyll, and carotenoid analysis of Monoraphidium sp. under control (autotrophic cultivation under BBM medium) and optimized multiple abiotic variables (Each value represents mean ± SD; n = 3).

Cultivation Condition	Biomass Production (mg·L−1)	Biomass Productivity (mg·L−1d−1)	Doubling Time (d)	Specific Growth rate (µ) (d−1)	Lipid Production (mg·L−1)	Lipid Content (%)	Chlorophyll a Production (µg·mL−1)	Chlorophyll b Production (µg·mL−1)	Total Carotenoid Production (µg·mL−1)
Control	567.644	28.3822	3.46	0.05	106.02	18.5	12.76	8.65	3.76
Optimized	1357.36	75.4	2.56	0.1	542.8	40.28	10.87	5.81	16.26

, N, P + K, S, LI, and PP promoted the accretion of biomass, carotenoids, and lipids. Several abiotic factors are combined to trigger biomass, fatty acid, and carotenoid production compared to the control (autotrophic cultivation under BBM medium). In D. salina cultivated under light and nitrogen deprivation stress conditions, a concomitant rise in β-carotene and TAG accumulation has been reported [35]. Kaha et al. (2021) reported that Monoraphidium sp. SP03 increased astaxanthin concentration under UV-A (0.476 µg·mL⁻¹ biomass) compared to the control (0.363 µg·mL⁻¹ biomass) [36]. Nitrogen stress has been widely used to improve lipid formation in microalgae such as Chlorella vulgaris and Scenedesmus sp. [37]. These studies suggest that nutrient and illumination stress constitute a feasible paradigm that can be employed in Monoraphidium sp. for industrial applications, as it triggers the synthesis of significant quantities of carotenoids and lipids. Recent investigations have focused on the ability of Monoraphidium sp. in the biofuel area, although the immense potential in the pharmacological and nutraceutical industries also need intense investigation [38]. The present work reports on the improvement of carotenoids content in Monoraphidium sp. for its proven applications in the nutraceutical and pharmaceutical industries.

3.4. Visualization of Lipophilic Compounds via Confocal Imaging

Microalgal lipophilic compounds were visualized using Nile Red under fluorescence microscopy, as shown in Figure 3. The lipophilic compounds of microalgal cells under BBM control conditions are represented in Figure 3A (1,2,3), while the visualization of cells under abiotic-optimized conditions is shown in Figure 3B (4,5,6). The cells appeared larger, and the accumulation of intracellular carotenoids and other lipids inside the Monoraphidium sp. cells was found to be higher under optimized conditions (abiotic factors) compared to the control (autotrophic cultivation under BBM medium).

The above findings reinforce our notion that the proportion of light and nutrients has a substantial impact on the accumulation of metabolites in Monoraphidium sp. Generally, carotenoids are classified into two types: primary and secondary carotenoids. Primary carotenoids are photosynthetic carotenoids that are physically and functionally associated with photosynthetic machinery. In contrast, secondary carotenoids have no association with the photosynthetic machinery and are not constrained by stoichiometry with other cellular molecules in terms of content [18]. Nutrient modulation improves carotenoid production for microalgal cells under optimized abiotic conditions, such as light intensity, salinity, and temperature [39]. Hence, the balancing of abiotic factors can lead to the accumulation of substantial quantities of chlorophyll and lipophilic compounds.

3.5. Carotenoid Profiling by UPLC-Q-TOF-MS

To achieve the precise identification and quantification of the carotenoids present in Monoraphidium sp., complete carotenoid profiling was performed. The major carotenoids identified are presented in Figure 4 and

Table 5. Carotenoids retention time, percent fractions acquired in both control (autotrophic cultivation under BBM medium) and optimized cultivation conditions, molecular mass of identified carotenoids, their abducts, and obtained mass are displayed by UPLC-Q-TOF-MS carotenoids profiling.

S. No.	Retention Time (min)		Compound	% Fraction		Molecular Mass (Da)	Molecular Formula	Abducts	Obtained Mass (Da)
	Control	Optimized		Control	Optimized
1	28.07	28.07	7,8-Didehydroastaxanthin	14.8	17.7	594.823	C40H50O4	M + K−2H	631.31
2	30.41	30.41	Violaxanthin	3.61	4.85	600.870	C40H56O4	M−H	599.413
3	31.76	31.76	Lutein 5,6-epoxide	45.12	33.93	584.871	C40H56O3	M−H	583.41
4	33.67	33.67	9-cis-β carotene	7.62	8.19	536.873	C40H56	M−H	599.411
5	33.92	33.92	β-carotene-5,6-epoxide	24.41	26.46	600.8	C40H56O4	M−H	599.411

. In the extracted fraction of carotenoids, astaxanthin (Peak A), violaxanthin (Peak B), lutein (Peak C), 9-cis- β carotene (Peak D), and β-carotene-5,6-epoxide (Peak E) were detected with retention times of 28.07, 30.41, 31.76, 33.67, and 33.92 min, respectively. The existence of astaxanthin, violaxanthin, lutein, 9-cis-β carotene, and β-carotene-5,6-epoxide was confirmed by mass spectrometry and their presence was estimated to be 14.8%, 3.61%, 45.12%, 7.62%, and 24.21%, respectively, in the cells grown under the control conditions.

Under optimized conditions, the type of carotenoids detected was the same as those in the control cells, but with variation in their quantity. Peaks A and B of the chromatogram showed that astaxanthin and violaxanthin had increased by 1.19- and 1.34-fold. Similarly, 9-cis-β carotene (Peak D) and β-carotene-5,6-epoxide (Peak E) showed a modest increase of 1.07-fold and 1.08-fold, respectively. However, lutein (Peak C) showed a decline of 1.32-fold under optimized conditions compared to the control. Similar to our findings, studies have shown that the concentrations of violaxanthin, β-carotene, and lutein were enhanced in Tetraselmis striata CTP4 grown under intense heat and light stress [40]. Asterarcys quadricellulare PUMCC 5.1.1 also yielded 47.0, 28.7, 15.5, and 14.0 μg β-carotene, lutein, astaxanthin, and canthaxanthin mg⁻¹ dry biomass, respectively, under optimized culture conditions [41]. Many studies have shown that astaxanthin is a valuable secondary carotenoid produced under stress situations [42]. Ankistrodesmus sp. has also shown an increased production of violaxanthin and astaxanthin when grown under nitrogen and light stress [43]. Additionally, the Bracteacoccus aggregatus BM5/15 strain produced astaxanthin and beta-carotene when grown in glass bubble-column photobioreactors [44]. The synthesis of carotenoids in microalgae is greatly influenced by the optimization of both nutrients and light [45]. As stated by several recent publications, further research on optimizing the culture conditions of inadequately characterized microalgae is necessary [18]. This study demonstrates that Monoraphidium sp. carotenoids have potential applications in the health sector when grown under highly specialized, optimized culture conditions.

4. Conclusions

A novel method was developed for the improved production of carotenoids and other lipids in Monoraphidium sp. by combining multiple abiotic factors. The optimum conditions were identified as 5000 mg·L⁻¹ N, 250 mg·L⁻¹ P + K, 70 mg·L⁻¹ S, 137.5 µmol m⁻² s⁻¹ LI, and 24 h PP, resulting in a maximum biomass productivity of 75.4 mg·L⁻¹d⁻¹, lipid production of 542.8 mg·L⁻¹, and total carotenoid production of 16.25 µg·mL⁻¹. The results suggest that the modulation of abiotic factors has a significant influence on carotenogenesis and lipogenesis, yielding elevated amounts of carotenoids and other lipids, including the five economically significant carotenoids, astaxanthin, violaxanthin, lutein, 9-cis-β-carotene, and β-carotene-5,6-epoxide. These bioactive compounds are valuable to the pharmaceutical and nutraceutical industries. The results showed that lutein content was slightly reduced under optimal conditions while a substantial increase was noted in astaxanthin, violaxanthin, and β-carotene. This study reveals that Monoraphidium sp. is a feasible option for commercial application in the food, feed, and healthcare industries due to its enormous ability to yield carotenoids and other lipids in considerable amounts under optimal conditions.