Hua Xiao

This study assessed the effects of different intensities of broad-spectrum white LED light (PAR range: 415–748 nm) on growth, fucoxanthin accumulation, and fatty acid composition of Isochrysis galbana. This study classified light intensity into three categories based on the white LED light source: high (HL, 150 μmol·m⁻²·s⁻¹), medium (ML, 80 μmol·m⁻²·s⁻¹), and low (LL, 30 μmol·m⁻²·s⁻¹). The results showed that biomass concentration was optimized under high light intensity (HL, 150 μmol·m⁻²·s⁻¹), whereas low light (LL, 30 μmol·m⁻²·s⁻¹) yielded the highest fucoxanthin concentration (71.15 mg/L on day 12) and the only positive volumetric fucoxanthin productivity (3.14 mg/L/d) among the three treatments tested. The results further showed that low light (LL, 30 μmol·m⁻²·s⁻¹) produced maximum cell density (10.08 × 10⁶ cells/mL) and the most polyunsaturated fatty acids (PUFAs), particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), which constituted 1.93% and 22.47% of total fatty acids, respectively. This study demonstrates that low-intensity (LL, 30 μmol·m⁻²·s⁻¹) white LED light supports the maximum co-production of valuable metabolites in I. galbana, establishing a scientific basis for scaling up I. galbana cultivation for nutraceutical and aquafeed applications.

Real-time microalgae biomass monitoring is essential for industrial biofuel production, yet image-based density assessment remains limited by background interference under variable cultivation conditions. Here, a color-space modeling approach based on a polynomial regression framework is proposed. It overcomes a key limitation of traditional Euclidean-distance metrics, namely their inability to distinguish color metamers, which are distinct color configurations that share identical scalar distances yet represent different biomass densities. By preserving directional information within multi-dimensional color vectors and capturing subtler chromatic transitions, our model enables physically consistent nonlinear fitting and eliminates systematic errors arising from pigment-specific absorption characteristics. The methodology is realized through a cost-effective integrated system (<$200) featuring remote-controlled LED illumination, portable colorimetry, and adaptive background tuning, enhanced by three correction algorithms (ratio, offset, linear) that ensure robustness across diverse measurement environments. Validation on two industrially relevant microalgal species (Nannochloropsis sp. and Isochrysis galbana) under comprehensive experimental matrices (6 hues × 6 saturations × 6 densities) achieves average accuracies of 0.90 and 0.94, respectively, significantly outperforming traditional image-based methods. Our approach fundamentally transforms background from a nuisance variable into a tunable experimental parameter, thereby enabling cost-effective, scalable, and robust microalgae biomass monitoring for industrial biofuel production.

Image-based microalgae density measurement is widely adopted but suffers from inaccuracy due to background interference with elusive underlying mechanisms. This bottleneck necessitates decoupling the relationship between background color and density-dependent microalgal coloration. We investigate background color dynamics, including image hue, saturation, texture roughness, light intensity, and microalgal species, by proposing (1) a polynomial model enabling more accurate and flexible nonlinear curve fitting, and (2) a cost-effective, integrated, remotely controlled system composed of a position- and intensity-adjustable LED panel, a portable colorimeter, and an adaptive color-tunable backdrop. Results show that background colors similar to microalgal cells disrupt stable correlations between RGB color differences and cell density, whereas strong optical absorption leads to rapid attenuation of the blue component toward zero with increasing density, particularly under orange backgrounds. Using a gray background, the proposed system achieves measurement accuracies and R² values of 0.92 and 0.94 for Nannochloropsis sp., and 0.87 and 0.98 for I. galbana, demonstrating significantly enhanced robustness. This work presents a comprehensive analysis of background interference mechanisms in image-based microalgal density estimation. The proposed measurement framework, integrating mathematical modeling with tailored instrumentation, provides a validated solution for improving accuracy and reliability in environmental and bioprocess monitoring.

Commercial light-emitting diodes (LEDs) offer fixed lighting patterns with limited canopy coverage, creating a mismatch with dragon fruit’s dynamic photosynthetic and phenological needs. To mitigate the inherent limitations of conventional unidirectional irradiance distributions, a bi-directional emission LED system incorporating adjustable focusing mechanisms is engineered. Monte Carlo ray-tracing and numerical integration are employed to quantitatively analyze the influence of plant architectural complexity, LED positioning strategies, and angular emission profiles on canopy light distribution. Concurrently, three optimization algorithms—Simulated Annealing (SA), Genetic Algorithm (GA), and Ant Colony Optimization (ACO)—are comparatively evaluated for their efficacy in maximizing radiation efficiency (RE) and utilization efficiency (UE). The proposed method (1) achieves targeted photon delivery while minimizing light spillage between canopies, resulting in 39.39% and 206.67% higher UE compared with the other two schemes under the same static fixture at a height of 1.7 m and an emission angle of 50°, and (2) achieves high prediction accuracy, with RE and UE determination coefficients (R²) of 96% and 97%, respectively.

Assessing microalgae density is essential for microalgae cultivation and marine environmental surveillance. This study introduces a quantum dot (QD)-based visible light sensing and communication (VLSC) system that employs emissions spanning the blue, green, yellow, orange, and red spectra to evaluate the density of microalgae across different species and varying densities. The bit error rate (BER) of the VLSC system serves as an intermediate to calculate microalgae density under various data rates and light colors by fitting the linear area of BERs. Orange light demonstrates superior linearity, high optical power, rapid response, and high accuracy in assessing microalgae species of Euchlorocystis marina and Isochrysis galbana. The highest accuracy attained for I. galbana samples is 0.98, averaging at 0.79, when utilizing orange light. These values surpass those obtained through direct measurement of optical power. The proposed method offers high feasibility and adjustable color options for assessing microalgae density, showing potential for integration into smart illumination–sensor–communication systems aimed at microalgae cultivation and marine environmental monitoring.