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Magnitude measurement of chlorophyllafluorescence (ChlF) involves challenges, and dynamic responses to variable excitations may offer an alternative. In this research, ChlF was measured during strong actinic light by using a pseudo-random binary sequence as a time-variant multiple-frequency illumination excitation. The responses were observed in the time domain but were primarily analyzed in the frequency domain in terms of amplitude gain variations. The excitation amplitude was varied, and moisture loss was used to induce changes in the plant samples for further analysis. The results show that when nonphotochemical quenching (NPQ) activities start, the amplitude of ChlF responses vary, making the ChlF responses to illumination excitations nonlinear and nonstationary. NPQ influences the ChlF responses in low frequencies, most notably below 0.03 rad/s. The low-frequency gain is linearly correlated with NPQ and can thus be used as a reference to compensate for the variations in ChlF measurements. The high-frequency amplitude gain showed a stronger correlation with moisture loss after correction with the low-frequency gain. This work demonstrates the usefulness of dynamic characteristics in broadening the applications of ChlF measurements in plant analysis and offers a way to mitigate variabilities in ChlF measurements during strong actinic illumination. 

Evaluation of photosynthetic quantum yield is important for analyzing the phenotype of plants. Chlorophyll a fluorescence (ChlF) has been widely used to estimate plant photosynthesis and its regulatory mechanisms. The ratio of variable to maximum fluorescence, F v / F m , obtained from a ChlF induction curve, is commonly used to reflect the maximum photochemical quantum yield of photosystem II (PSII), but it is measured after a sample is dark-adapted for a long time, which limits its practical use. In this research, a least-squares support vector machine (LSSVM) model was developed to explore whether F v / F m can be determined from ChlF induction curves measured without dark adaptation. A total of 7,231 samples of 8 different experiments, under diverse conditions, were used to train the LSSVM model. Model evaluation with different samples showed excellent performance in determining F v / F m from ChlF signals without dark adaptation. Computation time for each test sample was less than 4 ms. Further, the prediction performance of test dataset was found to be very desirable: a high correlation coefficient (0.762 to 0.974); a low root mean squared error (0.005 to 0.021); and a residual prediction deviation of 1.254 to 4.933. These results clearly demonstrate that F v / F m , the widely used ChlF induction feature, can be determined from measurements without dark adaptation of samples. This will not only save experiment time but also make F v / F m useful in real-time and field applications. This work provides a high-throughput method to determine the important photosynthetic feature through ChlF for phenotyping plants.